Journal of Accounting and Finance - Northern … Wilson The central goal of this article is to provide nonprofessional investors with an understanding of the impact of accrual accounting

Journal of Accounting and Finance

North American Business Press Atlanta – Seattle – South Florida - Toronto

Journal of Accounting and Finance

Dr. Samanthala Hettihewa Co-Editor

Dr. Christopher Wright

Co-Editor

Dr. David Smith Editor-In-Chief

NABP EDITORIAL ADVISORY BOARD

Dr. Nusrate Aziz - MULTIMEDIA UNIVERSITY, MALAYSIA Dr. Andy Bertsch - MINOT STATE UNIVERSITY Dr. Jacob Bikker - UTRECHT UNIVERSITY, NETHERLANDS Dr. Bill Bommer - CALIFORNIA STATE UNIVERSITY, FRESNO Dr. Michael Bond - UNIVERSITY OF ARIZONA Dr. Charles Butler - COLORADO STATE UNIVERSITY Dr. Jon Carrick - STETSON UNIVERSITY Dr. Min Carter – TROY UNIVERSITY Dr. Mondher Cherif - REIMS, FRANCE Dr. Daniel Condon - DOMINICAN UNIVERSITY, CHICAGO Dr. Bahram Dadgostar - LAKEHEAD UNIVERSITY, CANADA Dr. Anant Deshpande – SUNY, EMPIRE STATE Dr. Bruce Forster - UNIVERSITY OF NEBRASKA, KEARNEY Dr. Nancy Furlow - MARYMOUNT UNIVERSITY Dr. Mark Gershon - TEMPLE UNIVERSITY Dr. Philippe Gregoire - UNIVERSITY OF LAVAL, CANADA Dr. Donald Grunewald - IONA COLLEGE Dr. Russell Kashian - UNIVERSITY OF WISCONSIN, WHITEWATER Dr. Jeffrey Kennedy - PALM BEACH ATLANTIC UNIVERSITY Dr. Dean Koutramanis - UNIVERSITY OF TAMPA Dr. Malek Lashgari - UNIVERSITY OF HARTFORD Dr. Priscilla Liang - CALIFORNIA STATE UNIVERSITY, CHANNEL ISLANDS Dr. Tony Matias - MATIAS AND ASSOCIATES Dr. Patti Meglich - UNIVERSITY OF NEBRASKA, OMAHA Dr. Robert Metts - UNIVERSITY OF NEVADA, RENO Dr. Adil Mouhammed - UNIVERSITY OF ILLINOIS, SPRINGFIELD Dr. Shiva Nadavulakere – SAGINAW VALLEY STATE UNIVERSITY Dr. Roy Pearson - COLLEGE OF WILLIAM AND MARY Dr. Veena Prabhu - CALIFORNIA STATE UNIVERSITY, LOS ANGELES Dr. Sergiy Rakhmayil - RYERSON UNIVERSITY, CANADA Dr. Fabrizio Rossi - UNIVERSITY OF CASSINO, ITALY Dr. Ira Sohn - MONTCLAIR STATE UNIVERSITY Dr. Reginal Sheppard - UNIVERSITY OF NEW BRUNSWICK, CANADA Dr. Carlos Spaht - LOUISIANA STATE UNIVERSITY, SHREVEPORT Dr. Ken Thorpe - EMORY UNIVERSITY Dr. Calin Valsan - BISHOP'S UNIVERSITY, CANADA Dr. Anne Walsh - LA SALLE UNIVERSITY Dr. Thomas Verney - SHIPPENSBURG STATE UNIVERSITY

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This Issue

Necessary Attributes, Preparations, and Skills for the Selection and Promotion of Accounting Professionals .................................................................................................. 11 Nas Ahadiat, Rose M. Martin In a recent report the Pathways Commission expressed its concern by stating that accounting students often lack the preparations and skills that are necessary for a thorough comprehension of the multifaceted and complex environment of the accounting profession (The Pathways Commission, 2012). A questionnaire was distributed among a random sample of accounting professionals representing two different groups: the American Institute of Certified Public Accountants and the Institute of Management Accountants. The results indicate that personal attributes are more important than traditional educational preparations. The significance of personal attributes as being more important than educational preparations is also found in promotion decisions. Straight from the Horse’s mouth: Auditors’ on Fraud Detection and Prevention, Roles of Technology, and White-Collars Getting Splattered with Red! .............................................. 26 Aditya Simha, Sandhya Satyanarayan Fraud is increasingly an enormous problem for organizations all over the world today – the losses occurring due to fraud are over several billion dollars every year alone (Smith, 2008). In this article, we provide a thorough review of the extant literature on fraud, and conduct a qualitative interview (N=18) based study on forensic auditors’ perceptions of fraud detection and fraud prevention methods, as well as their impression of the role of technology in fraud. We then highlight a rarely addressed problem – that of physical safety concerns that forensic auditors have to face while investigating fraud investigations. We finally suggest some directions for further research. The Use of Accounting Screens for Separating Winners from Losers Among the S&P 500 Stocks ...................................................................................................................... 45 Victoria Geyfman, Hayden Wimmer, Roy Rada This study uses accounting screens based on the Piotroski’s (2000) F-score and the derived MagicP formulae and finds that it is an effective investment strategy, which results in risk-adjusted outperformance of stocks with high book-to-market (BM) ratios over a market weighted benchmark portfolio and its subset of growth stocks. Unlike other studies that utilized similar tests on smaller firms, we examine the performance of large value stocks within the S&P 500 between 2007 and 2014 and find evidence of the value premium. The results were robust to the time period; in fact, the highest-ranked value stocks suffered less severely during the period of market correction.

The Necessity for a New Kind of Accounting: Conscious Accounting .......................................................................... 61 Miriam Gerstein, Hershey H. Friedman Conscious capitalism is about creating businesses that are concerned with all stakeholders and do not simply focus on maximizing profits for shareholders. It is also about compassion and making the United States and the entire world a better place. Conscious capitalists want to provide employees with meaningful work at fair wages. Organization leaders who follow its principles have to be people of integrity and motivated by a higher purpose than greed and have a desire to serve the public. This paper posits that accountants and auditors must become the conscience of the organization and therefore have an obligation to ensure an ethical tone at the top and have to practice conscious accounting. This paper shows how accountants can provide firms with a competitive edge and create sustainable, flourishing businesses with a higher purpose. Capital Structure, Firm Performance and the Recent Financial Crisis .............................................. 76 Ashrafee Tanvir Hossain, Dao Xuan Nguyen We examine the impact of financial leverage on firm performance. By analyzing stock and operating performances of top ten Canadian oil & gas companies for a ten year period (2004-2013), we find that leverage has a strong negative relationship with performance, for all three periods in concern, that is the pre-crisis (2004-2006), crisis (2007-2009), and post-crisis recovery (2010-2013) periods. These results hold both in univariate and cross-sectional set up even after controlling for firm specific variables. Behind the Scenes of Mutual Fund Alpha .................................................................................90 Qiang Bu This study examines whether fund alpha exists and whether it comes from manager skill. We found that the probability and the value of fund alpha vary depending on market states and fund styles. Overall, the funds with earned alpha do not exhibit a market-timing ability, though some of them show an ability to select stocks. We also used a sample of bootstrapped funds as the benchmark for funds without skill to explore the topic. Our test results suggest that fund alpha is resulted from pure luck instead of manager skill. Stocks, Bonds, Bills and Long-Run Returns for Retirement Portfolios ............................................. 104 Charles Rayhorn A USA Today article, “Investors Look Back on a Decade of Grim Stock Returns,” summed up returns for the first decade of the 21st century—grim. This paper shows the wealth relative, or future value interest factor, for this decade was worse than for the depression years. The other periods of this study are positive. These unpleasant results were nothing that most investors didn’t already ‘feel’. The purpose of this study is to examine various retirement horizons to see how they fared since 1926 with the ups and downs of the stock market.

Nonprofessional Investors’ Framework for Understanding Earnings Quality ................................. 113 Reginald Wilson The central goal of this article is to provide nonprofessional investors with an understanding of the impact of accrual accounting on earnings quality. Improving nonprofessional investors’ understanding of earnings quality is essential to the capital market, as they have been found to own nearly one-third of all outstanding stock (Bogle, 2005), a number which anecdotally has increased with the advent of employer-directed compensation plans and online stock trading platforms. Following a presentation of the importance of accruals in the earnings process, the framework discusses various earnings management practices that nonprofessional investors should consider when evaluating earnings quality. The Effects of Capital Infusions after IPO on Diversification and Cash Holdings ........................... 124 Soohyung Kim, Hoontaek Seo, Daniel L. Tompkins This paper examines how the number and the timing of capital infusions after the IPO affect the firm’s diversification decision and the firm’s level of cash-holdings. We find that the frequent capital infusions ultimately affect the firm’s liquidity management policy, resulting in holding less cash consistent with the behavior life-cycle hypothesis. At the same time, the hurried external financings after the IPO influence managers to be more conservative in management, resulting in a high propensity for the firm’s diversification and a relatively high level of firm’s cash holdings. Low Volume and Future Changes in the Stock Market ...................................................................... 134 Paul Bursik This paper provides an empirical investigation of whether low volume days produce different subsequent results than other days. Daily S&P 500 returns are groups classified by volume as well as market direction. Subsequent returns are tied both to previous market direction and volume. High volume is associated with higher subsequent daily return variability, and low volume is associated with lower subsequent daily return variability. Of particular note, down market days with low volume have the highest next-day average returns and the lowest next-day return standard deviation. Integrated Reporting & the Future of Auditing................................................................................... 140 Sean Stein Smith Disruptive technologies and innovations have caused dramatic changes throughout numerous industries, most notably in the expectations and requirements of businesses operating on a global scale. Additionally, requirements from both financial and non-financial stakeholders continue to increase in both complexity and time sensitivity; users of organizational data require that it be produced and distributed in a timely and cost-efficient manner. In order to produce and report the information required by increasingly important non-traditional stakeholders, accounting and financial professionals must evolve and adapt to a rapidly changing and evolving marketplace. Systems and processes associated with auditing and forensics must be improved upon and integrated into the real time requirements of the market. In essence, accounting and finance must evolve into a more strategic function, and embrace a role as a strategic business partner.

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Necessary Attributes, Preparations, and Skills for the Selection and Promotion of Accounting Professionals

Nas Ahadiat

California State Polytechnic University

Rose M. Martin California State Polytechnic University

In a recent report the Pathways Commission expressed its concern by stating that accounting students often lack the preparations and skills that are necessary for a thorough comprehension of the multifaceted and complex environment of the accounting profession (The Pathways Commission, 2012). A questionnaire was distributed among a random sample of accounting professionals representing two different groups: the American Institute of Certified Public Accountants and the Institute of Management Accountants. The results indicate that personal attributes are more important than traditional educational preparations. The significance of personal attributes as being more important than educational preparations is also found in promotion decisions. INTRODUCTION

Students in accounting programs too often are only exposed to technical accounting skills. They often lack, and fail to develop, a thorough comprehension of the multifaceted setting and complex environment of the accounting profession (The Pathways Commission, 2012). The Pathways Commission encourages faculty to develop new standards of excellence through several recommendations. First, “accountants are to build a “learned profession” through purposeful integration of accounting research, education, and practice for students, accounting practitioners, and educators.” (The Pathways Commission, p 27) Faculty are to focus more academic research on practice issues, and enhance the value of practitioner-educator exchanges. (The Pathways Commission, p 30). Faculty are to facilitate and create mechanisms for collecting, analyzing, and disseminating information about the current and future markets for accounting faculty and practitioners. (The Pathways Commission, p 42) These objectives provide the impetus for this study.

The need for change in the expected skills of future accountants has placed quite a bit of pressure on educators and accounting students alike. “The consensus opinion is that university courses can no longer be entirely content-driven and limited to specific technical skills. If students are to succeed as knowledge professionals in the highly changeable business environment, they must exhibit a range of technical and generic skills. Generic skills, in this context, encapsulate transferrable qualities to suit the industry in which graduates work; these include but are not limited to communication, team skills, leadership, problem solving, analytical and interpersonal skills. Therefore, it has been suggested that the actual content of accounting courses should reflect a greater focus on the development of generic skills to ensure

Journal of Accounting and Finance Vol. 16(1) 2016 11

the accounting profession gains access to proficient graduates.” (De Lange, Jackling, and Gut, 2006, p. 384).

As the number of students at degree-granting institutions grows, enrollment continues to increase in the accounting programs across the country (NCES, 2011). The AICPA’s latest report on supply of accounting graduates indicates that college enrollments in the Accounting programs reached the 240,000 level for the first time in 2012. In addition, the report shows that the total graduates with both bachelors and masters’ degrees topped 82,177 in 2012, nearly 84% higher than the number in 2002. During the same period, demand for new accounting graduates by the CPA firms reached a total of 40,350 still less than half of the supply. According to this report, the highest rate of growth in demand occurred between 2004 and 2007 when it changed from 19,705 to 36,112, an increase of more than 83%. In fact, since 2007 when the U.S. economy dropped into recession, demand for accounting graduates has increased by only 4,238 or less than 12%, while the supply jumped by 17,956 or 28%. (AICPA, 2013).

On the demand side, accounting and auditing jobs are projected to be among the top 30 occupations with the most job growth from 2012 through 2022 (BLS, 2013). Based on this projection, it is expected that employment will grow at a rate of 15.7% by the year 2020. This indicates that a total of 166,800 additional Accounting jobs will become available during the ten-year period or an average annual job growth of 16,680. Thus, it is apparent that there is a sizable disparity between the supply of and demand for accounting graduates. The current job market is capable of absorbing only 27% (or less than one-third of 61,334) of those that graduated from all Accounting programs in 2011-12.

Thus, the Accounting programs are faced with the challenge of training students that are more marketable than ever before. Teaching traditional technical skills may no longer be adequate to make students qualified to compete for the inadequate number of positions that are available for accountants. Our students’ education should take a much broader approach and go beyond the conceptual and technical accounting knowledge. By involving practitioners, academics can learn how to improve students’ abilities so as to benefit the entire profession and making the job market most efficient. The purpose of this research is to facilitate this goal by investigating what additional attributes, preparations, and skills are necessary to improve qualifications of Accounting graduates. LITERATURE REVIEW

Several earlier studies draw attention to the fact that in today’s environment accounting students need to demonstrate more qualifications than just the technical skills they learn in traditional accounting classes. In one study, 322 students from three Australian universities and 28 practitioners working in various organizations across the country were surveyed. Researchers discovered that there were considerable differences between the two groups in prioritizing the skill sets that students need (Kavanagh and Drennan, 2008). While students included decision-making, continuous learning, and oral communication among the most important skills, the employers considered analytical/problem-solving skills, business awareness, and basic accounting skills more important for graduates entering the profession.

In another study, Jackling and De Lange (2009) surveyed 650 accounting graduates as well as 28 professionals. The results showed that both groups agreed on the importance of technical accounting skills. However, the professionals found it necessary that the universities teach a broad set of additional skills including team skills, leadership, oral communication, and interpersonal skills. Along this line of research, Ahadiat (2010) in a survey of 250 accounting professionals identified five attributes that were rated highly important for entry-level employment in public accounting. These included accounting technical skills, analytical skills, critical thinking, leadership skills, and team orientation. Accounting recruiters from the private industry viewed many of the skills significantly more important than public accountants. These professionals’ preferences included analytical skills, creative ability, critical thinking, leadership skills, people skills, team orientation, and personal demeanor.

12 Journal of Accounting and Finance Vol. 16(1) 2016

Stovall and Stovall in a survey of local employers discovered that generic or soft skills were ranked more important for success in the field of public accounting than technical skills (Stovall and Stovall, 2009).

With these and similar findings, it is evident that universities have no choice but to redesign their curriculum to help students develop the skills needed in order for them to succeed in a rapidly evolving and globally expanding business environment. Students who lack these skills may find it more and more difficult to find employment and if employed, may find it harder to move up in their firm. Furthermore, the accounting programs that fail to incorporate these skills in their curriculum may see their enrollments drop or their reputation jeopardized (Stovall and Stovall, 2009, p. 104).

While the majority of previous studies have focused their investigations on a limited set of skills such as technical skills, leadership potential, interpersonal communication skills, enthusiasm, and motivation toward the profession, few have examined the importance of such traits and attributes as, manners, attitudes, and self-confidence. In a recent paper, Violette and Chene (2010) looked at a campus recruiting process by four accounting firms and identified several social and personal etiquettes extremely essential. These etiquettes include students’ attire, handshake, and eye contact when students meet with the interviewers.

Furthermore, with the increasing emphasis on globalization of business, accounting programs are now expected to go beyond teaching students the technical country-specific accounting knowledge and should include trainings that prepare them as global citizens, as life-long learners, and as agents for social and personal development (Kavanagh, Drennan, 2008). These graduates should be expected to develop a sense of cultural diversity and sensitivity, make efforts to further develop a different level of communication skills by becoming multilingual, and learn to become frequent travelers. They should also have an understanding of or the training to apply the international accounting standards (IFRS). Just as the business environment is undergoing a more globalized change, the traditional role of an accountant has evolved to more of a professional advisor or an information specialist (Jackling, De Lange, 2009). In an effort to move their ranks from a staff or assistant position to a managerial or executive level, accountants are expected to need a much broader set of skills and competencies than those of traditional accountants (Bui, Porter, 25). As Howieson (2003) pointed out “It seems likely that the traditional approaches will be inadequate and that new philosophies, materials, and technologies will be needed. The end objectives of innovations in accounting education will be to develop students’ skills in abstraction, systems thinking, experimentation, and the capacity for communication and collaboration”.

In line with the above recommendations, the purpose of this study is to survey practitioners to investigate the skills, preparations, and attributes that are considered relevant for a successful employment in accounting. This study is unique in that unlike previous research it examines the following research questions:

Research Question 1: What skills, preparations, and attributes do practitioners seek in entry-level accounting graduates? Research Question 2: What skills, preparations, and attributes do practitioners seek for promoting accounting employees?

Finally, the study compares responses from public accounting practitioners with non-public

accountants. Some studies suggest that public accountants are more interested in traditional accounting skills such as generally accepted accounting principles used for financial reporting, auditing techniques, and taxation rules while non-public accountants seek more contemporary skills such as organizational behavior, group skills, and personal attributes (Jones, et, al, 2009; Howieson, 2003).

Research Question 3: Are there any differences in skills, preparations, and attributes that professionals in public accounting expect of accounting employees at entry-level than non-public accounting professionals?


Research Question 4: Are there any differences in skills, preparations, and attributes that professionals in public accounting expect of accounting employees for promotion than non-public accounting professionals?

STUDY DESIGN

To investigate the skills, preparations, and attributes accounting professionals need, the survey research method was found most appropriate. The details of data collection, sampling, and mailing of the survey instrument are described below.

Data Collection The data required for this study were collected via a questionnaire containing two general sections.

Electronic survey and E-mail were used as the primary data collection tools for this research. The instrument included a list of skills, preparations, and attributes that were either noted in the previous studies (De Lange, Jackling, and Gut, 2006; Kavanagh and Drennan, 2008; Ahadiat, 2010) or discussed in the relevant literature (Howieson, 2003; Jackling and De Lange, 2009; Bui and Porter, 2010). It also contained demographic questions to be used for classification purposes. Participants were asked to indicate the level of importance of each item by choosing one of seven possible responses ranging from, “Most Important” to “Most Unimportant”.1

Sampling

The sampling frame contained the accounting professionals from two different groups. The first group consisted of the American Institute of Certified Public Accountants (AICPA) membership available through the 2013 Nextmark.com. The second group included membership of the Institute of Management Accountants (IMA) available through infocusmarketing.com. Using systematic random design, a sample of 250 subjects was drawn from each of the above groups. The sample included subjects only residing in the United States of America.

The survey link was sent to each participant by E-mail. The E-mail explained the purpose of the study and why the results were important to the profession as well as the accounting education. It also assured anonymity of the responses. Two subsequent reminders were sent to encourage participation.2

RESULTS Background Information

The two mailings resulted in a total of 198 useable responses, producing a response rate of 39.6 percent.3 The majority of the respondents indicated that they were CPAs (66%), 22 percent were CMAs, only 3 percent were CIAs, 8 percent were CFEs, and the remainder had other or no certifications. Most of the participants worked for the state-wide and regional firms (52%), with nearly one quarter working for the small firms (26%), and the other 22 percent were employed with the national and multinational firms.

Nearly one-third of the practitioners surveyed (32%), were in the ages of 45-54 years, 18 percent were between 35-44 years, 31 percent were less than 35 years old, and the other 19 percent were 55 years or older. Participants’ primary employment was in public accounting (72%), with only 6 % working for the Big-4 firms. One quarter of the respondents worked for public or private industries (25%), and only 3% worked for the government. Table 1 contains a summary of all demographic information.


TABLE 1 DEMOGRAPHICS OF RESPONDENTS

Certification: Total CPA CMA CIA CFE Others None Number 260 131 44 5 16 42 22 Percentage 131% 66 22 3 8 21 11 Firm Size: Total Large Medium Small Number 198 43 103 52 Percentage 100% 22 52 26 Firm Ownership:

Total

Private

Public

Number 198 169 29 Percentage 100% 85 15

Role in Recruiting:

Total

Involved

Not Involved

No Experience

Number 198 156 26 16 Percentage 100% 79 13 8

Ethnicity: Total Asian Hispanic White Others Number 198 39 4 141 14 Percentage 100% 20 2 71 7

Age: Total 18-24 25-34 35-44 45-54 55-64 ≥65 Number 198 15 46 36 64 23 14 Percentage 100% 8 23 18 32 12 7

Education:

Total

BA/BS

MA/MS

Post Graduate/Law


Marital Status:

Total

Single

Married

Others


Gender: Total Female Male Number 198 75 123 Percentage 100% 38 62

Employment:

Total

Big 4

Other CPA

Public Industry

Private Industry

Government

Number 198 12 131 8 42 5 Percentage 100% 6 66 4 21 3

Employment Position:

Total

Top Management

Middle Management

Staff



Analysis of Research Questions Research Question 1

The survey results indicated that although many of the skills, preparations, and attributes were rated important (the scale ranged from Most Important to Somewhat Important), only thirty were among the most notable. Table 2 contains a list of these items along with their means and standard deviation. A closer look at this list reveals that the items can be divided into two distinct groups, personal attributes and educational preparations. Interestingly, the top five items on the list were the personal attributes of trustworthiness, dependability, oral communication skills, cleanliness, and punctuality. These results are consistent with previous research that pointed out that personal trait and communication skills are among

TABLE 2

TOP 30 ATTRIBUTES, PREPARATIONS, AND SKILLS ACCOUNTING PROFESSIONALS SEEK IN ENTRY-LEVEL COLLEGE GRADUATES

Rank Attributes, Preparations, and Skills Mean S.D.

1. Trustworthiness 1.87 1.503 2. Dependability 1.94 1.208 3. Oral communication skills 1.99 1.385 4. Cleanliness 2.09 1.318 5. Punctuality 2.11 1.056 6. B.S. or B.A in Accounting 2.15 1.759 7. Positive attitudes 2.19 1.432 8. Desire to learn new skills 2.21 1.21 9. Ability to get along with others 2.22 1.305 10. Ability to work overtime 2.30 1.244 11. Business attire 2.34 1.143 12. Manners 2.45 1.207 13. Sense of responsibility 2.45 1.269 14. Technical skills 2.46 1.207 15. Enthusiasm and Energy Level 2.50 1.351 16. Sincerity 2.52 1.253 17. Friendliness 2.54 1.431 18. Interpersonal Skills 2.55 1.335 19. Accounting Grade Point Average 2.56 1.376 20. Poise and Composure 2.59 1.166 21. Written Communication Skills 2.60 1.449 22. Maturity 2.61 0.980 23. Self-control when provoked 2.61 1.206 24. Conceptual accounting knowledge 2.63 1.081 25. Overall grade point average 2.69 1.389 26. Self-confidence 2.72 1.282 27. Tactfulness 2.73 1.256 28. Ability to work in pressure situations 2.86 2.533 29. Plan to become a Certified Public Accountant 2.89 2.291 30. Reputation of the university attended 2.96 1.258


the most important criteria for selecting accounting employees (De Villiers, 2010; Kavanagh and Drennan, 2008). The results also indicated that while many states have passed legislations requiring 150 semester hours of education for the CPA licensure, accounting professionals still look for college graduates who hold a bachelor’s degree for the entry-level employment. Other highly important college level preparations included knowledge of both technical and conceptual accounting as well as written communication skills. In addition, professionals have rated both students’ overall and accounting GPA among the important factors.

TABLE 3 TOP 30 ATTRIBUTES, PREPARATIONS, AND SKILLS ACCOUNTING PROFESSIONALS

SEEK IN PROMOTING ACCOUNTING EMPLOYEES

Rank Attributes, Preparations, and Skills Mean S.D. 1. Dependability 1.65 1.405 2. Trustworthiness 1.76 1.488 3. Ability to work in pressure situations 1.83 1.467 4. Sense of responsibility 1.86 1.424 5. Positive attitudes 1.87 1.404 6. Written Communication Skills 1.89 1.103 7. Ability to get along with others 1.93 1.346 8. Knowledge of a firm’s expectations 1.94 1.367 9. Interpersonal skills 2.01 1.465 10. Technical skills 2.02 1.398 11. Leadership skills 2.02 1.472 12. Conceptual Accounting Knowledge 2.02 1.489 13. Business attire 2.06 1.297 14. Enthusiasm 2.11 1.426 15. Ability to work overtime 2.17 1.402 16. Business Awareness 2.18 1.102 17. Conscientiousness 2.20 1.316 18. Self-control when provoked 2.20 1.410 19. Ability to represent the firm 2.22 1.258 20. Maturity 2.22 1.447 21. Self confidence 2.25 1.516 22. Cleanliness 2.25 1.281 23. Poise and Composure 2.29 1.327 24. Punctuality 2.29 4.137 25. Sincerity 2.32 1.277 26. Tactfulness 2.33 1.139 27. B.S. or B.A. degree in Accounting 2.38 1.642 28. Desire to learn new skills 2.39 1.633 29. Manners 2.45 1.035 30. Stability 2.49 1.269


Research Question 2 Mean ratings for the top 30 skills, preparations, and attributes relevant to promotion are presented in

Table 3. Accounting practitioners in both public and corporate accounting felt that personal attributes such as dependability, trustworthiness, ability to work in pressure situations, sense of responsibility, and positive attitudes were more important than written communication skills. In addition, we found that the ability to get along with others, knowledge of firm’s expectations, and interpersonal skills are considered significantly superior for promoting employees than their technical or conceptual accounting skills. These findings are extremely significant as they are somewhat in contrast with criteria for employee selection outlined in this research and by earlier studies suggesting that oral and written communication are the two most important skills (Morgan, 1997; De Lange et al., 2006; Kavanagh and Drennan, 2008).

It is interesting to note that accounting professionals believe that for promotion to a higher rank, employees’ personal attributes are much more significant than holding a bachelor’s degree in accounting. Twelve other personal traits that are found important in promoting accounting staff included leadership skills, enthusiasm, conscientiousness, self-control, maturity, self-confidence, sincerity, poise and composure, punctuality, tactfulness, desire to learn new skills, and stability. It was also noted that wearing business attire and knowledge of business are highly important when employees are being considered for promotion. In addition, ability to work overtime, cleanliness, and manners play an important role in the professionals’ decision process.

Although significant differences exist between the rankings of skills, preparations, and attributes necessary for the selection and promotion of accounting employees (Tables 2 and 3), commonalities also abound. It is interesting to note that twenty of the personal attributes and four of educational preparations are shared in both tables. Research Question 3

Review of Tables 4 and 5 indicate significant differences in the rankings of top skills, preparations, and attributes expected in new hires by public accountants vs. corporate accountants. Public accountants (CPAs) rank trustworthiness as most important (corporate accountants rated this as 7th in importance), while corporate accountants selected B.S. or B.A. in Accounting as most important (public accountants rated the degree at the 13th position). Looking at the next 9 mean ratings for public accountants brings the following attributes into focus: plan to become a CPA (not ranked by corporate accountants in the Top 30), dependability (13th), positive attitude (26th), cleanliness (6th), oral communication skills (4th), desire to learn new skills (14th), written communication skills (not ranked by corporate accountants), friendliness (25th), and ability to get along with others (15th)4. Corporate accountants ranked their next 9 selections as follows: accounting grade point average (not ranked by public accountants in the Top 30), punctuality (12th), oral communication skills (6th), overall grade point average (not ranked by public accountants), cleanliness (5th), trustworthiness (1st), class standing (not ranked by public accountants), AACSB accreditation of business school attended (not ranked by public accountants), and business attire (15th)5.

Looking at the top 10 mean rankings for both corporate and public accountants only brings oral communication skills, cleanliness, and trustworthiness into this set of common attributes. Interestingly, corporate accountants included three items that are not included in the Top 30 by public accountants: accounting grade point average, overall grade point average, and AACSB accreditation of school. It could be that public accountants included these 3 preparations within their assessment of plan to become a Certified Public Accountant (CPA), which they ranked 2nd in importance.

Although in general the top 30 rankings of both public and corporate accountants contain similar attributes, the individual rankings vary substantially. These differences could be driven by the different nature of the work environments in which each type of accountant is expected to perform and the different types of demands they must satisfy. As the public accountant deals directly with the public in the communication and presentation of financial information, the corporate accountant only communicates with the public in an indirect fashion. New hires in public accounting could be expected to do more written communication, and work in teams with tight deadlines. Every assignment a public accountant


receives would be subject to time pressure, whereas a corporate accountant at entry level would have time pressure expectations at month end close.

TABLE 4 TOP 30 MOST IMPORTANT ATTRIBUTES, PREPARATIONS, AND SKILLS USED BY

PUBLIC ACCOUNTANTS IN ENTRY-LEVEL SELECTION DECISIONS

No. Attributes, Preparations, and Skills Mean S.D. 1 Trustworthiness 1.91 1.683 2 Plan to become a Certified Public Accountant (CPA) 2.04 1.541 3 Dependability 2.05 1.436 4 Positive attitudes 2.21 1.478 5 Cleanliness 2.21 1.438 6 Oral communication skills 2.27 1.546 7 Desire to learn new skills 2.36 1.323 8 Written communication skills 2.38 1.277 9 Friendliness 2.43 1.477

10 Ability to get along with others 2.43 1.382 11 Business awareness 2.43 1.405 12 Punctuality 2.46 1.001 13 B.S. or B.A. degree in accounting 2.52 1.946 14 Ability to work overtime 2.54 1.360 15 Business attire 2.56 1.217 16 Tactfulness 2.57 1.031 17 Interpersonal skills 2.62 1.326 18 Sense of responsibility 2.62 1.356 19 Ability to work in pressure situations 2.65 1.286 20 Manners 2.66 1.260 21 Poise and composure 2.67 1.187 22 Maturity 2.67 1.110 23 Sincerity 2.68 1.343 24 Technical accounting skills 2.70 1.273 25 Enthusiasm and energy level 2.70 1.404 26 Conceptual accounting knowledge 2.71 1.179 27 Conscientiousness 2.73 1.411 28 Self-confidence 2.81 1.269 29 Body language 2.85 1.321 30 Self-control when provoked 2.85 1.284


TABLE 5 TOP 30 MOST IMPORTANT ATTRIBUTES, PREPARATIONS AND SKILLS USED BY

CORPORATE ACCOUNTANTS IN ENTRY-LEVEL SELECTION DECISIONS

No. Attributes, Preparations, and Skills Mean S.D. 1 B.S. or B.A. degree in accounting 1.27 0.660 2 Accounting grade point average 1.43 0.728 3 Punctuality 1.52 0.792 4 Oral communication skills 1.64 0.718 5 Overall grade point average 1.66 0.834 6 Cleanliness 1.70 0.509 7 Trustworthiness 1.70 0.594 8 Class standing 1.93 1.189

9 AACSB accreditation of business school attended 2.00 0.835 10 Business attire 2.02 0.263 11 Ability to work overtime 2.05 0.645 12 Technical accounting skills 2.09 0.936 13 Dependability 2.11 0.538 14 Desire to learn new skills 2.14 0.554 15 Ability to get along with others 2.18 0.870 16 Reputation of university attended 2.23 0.937 17 Leadership role in student clubs or professional organizations 2.30 0.553 18 Sincerity 2.36 0.718 19 Recommendations letters from faculty 2.39 0.841 20 Poise and composure 2.43 0.501 21 Conceptual accounting knowledge 2.43 0.789 22 Manners 2.52 0.792 23 Self-confidence 2.55 0.761 24 Enthusiasm and energy level 2.59 0.583 25 Friendliness 2.61 0.970 26 Positive attitudes 2.70 0.765 27 Body piercing 2.70 2.348 28 Membership in student clubs or professional organizations 2.73 0.872 29 Ability or desire to specialize 2.82 0.947 30 Self-control when provoked 2.89 0.813

Research Question 4

Tables 6 and 7 indicate the Top 30 skills, preparations, and attributes ranked as important by public and corporate accountants as to employee promotion decisions. Again, the rankings of each table show significant differences in 7 practitioner assessments. The next 3 are held in common by both corporate and public accountants in the top 10. Public accountants (CPAs) ranked dependability as most important (corporate accountants rated this as 22nd in importance), and corporate accountants selected ability to get along with others as most important (public accountants rated this attribute at the 10th position). The next 9 mean ratings for public accountants are the following: trustworthiness (ranked 20th by corporate accountants), plan to become a Certified Public Accountant (not ranked by corporate accountants), plan to become a Certified Internal Auditor (not ranked by corporate accountants), positive attitude (16th), written communication skills (18th), ability to work in pressure situations (2nd), knowledge of firm’s expectations (7th), and ability to get along with others (1st) 4. Corporate accountants ranked their next 9 selections as:


ability to work in pressure situations (ranked by public accountants as 8th), punctuality (19th), self-confidence (25th), sense of responsibility (11th), conceptual accounting knowledge (not ranked by public accountants), knowledge of firm’s expectations (9th), enthusiasm and energy level (23rd), business attire (15th), and leadership skills (17th) 5.

Three of these attributes are held in the top 10 mean rankings for both public and corporate accountants: ability to work in pressure situations, ability to get along with others, and knowledge of firm’s expectations. These selections make sense when we consider that an accountant would be promoted to a position that would require team leadership responsibilities. The promoted accountant would also be expected to carry out their job responsibilities in line with the firm’s goals and expectations. We would also expect a public accountant to require their employees to become licensed, whereas the license may be optional for corporate accountants. It is interesting that the top 20 attributes on Table 7 (containing corporate accountant assessments) are rated as more important than all attributes on Table 6 (public accountant assessments).


PUBLIC ACCOUNTANTS IN PROMOTION DECISIONS

No. Attributes, Preparations, and Skills Mean S.D. 1 Dependability 1.65 1.536 2 Trustworthiness 1.68 1.543 3 Plan to become a Certified Public Accountant (CPA) 1.70 1.244 4 Plan to become a Certified Internal Auditor (CIA) 1.85 1.699 5 Positive attitudes 1.96 1.334 6 Written communication skills 1.99 1.089 7 Others 2.04 1.450 8 Ability to work in pressure situations 2.06 1.387

9 Knowledge of firm’s expectations 2.08 1.235 10 Ability to get along with others 2.10 1.355 11 Sense of responsibility 2.12 1.446 12 Technical accounting skills 2.15 1.302 13 Oral communication skills 2.15 1.487 14 Conceptual accounting knowledge 2.15 1.432 15 Business attire 2.17 1.348 16 Desire to learn new skills 2.19 1.474 17 Leadership skills 2.20 1.420 18 Conscientiousness 2.25 1.242 19 Punctuality 2.26 1.158 20 Interpersonal skills 2.27 1.364 21 Ability to represent the firm (e.g., recruiting, meetings) 2.28 1.280 22 Ability to work overtime 2.29 1.436 23 Enthusiasm and energy level 2.30 1.350 24 Poise and composure 2.34 1.092 25 Self-confidence 2.34 1.289 26 Cleanliness 2.34 1.277 27 Sincerity 2.38 1.155 28 Maturity 2.42 1.509 29 Tactfulness 2.45 1.260 30 Self-control when provoked 2.50 1.318



CORPORATE ACCOUNTANTS IN PROMOTION DECISIONS

No. Attributes, Preparations, and Skills Mean S.D. 1 Ability to get along with others 1.14 0.347 2 Ability to work in pressure situations 1.14 0.462 3 Punctuality 1.16 0.370 4 Self-confidence 1.20 0.408 5 Sense of responsibility 1.23 0.522 6 Conceptual accounting knowledge 1.25 0.438 7 Knowledge of firm’s expectations 1.32 0.639 8 Enthusiasm and energy level 1.32 0.561

9 Business attire 1.32 0.561 10 Leadership skills 1.36 0.487 11 Technical accounting skills 1.39 0.895 12 Maturity 1.41 0.726 13 Self-control when provoked 1.43 0.625 14 B.S. or B.A. degree in accounting 1.45 0.697 15 Interpersonal skills 1.45 0.504 16 Positive attitudes 1.48 0.505 17 Business awareness 1.52 0.664 18 Written communication skills 1.59 0.757 19 Poise and composure 1.64 0.780 20 Trustworthiness 1.64 0.487 21 Cleanliness 1.66 0.479 22 Dependability 1.66 0.479 23 Body piercing 1.75 1.123 24 Oral communication skills 1.75 0.438 25 Prior work experience 1.75 1.203 26 Ability to work overtime 1.89 0.618 27 Ability to travel 1.91 1.178 28 Ability to represent the firm (e.g., recruiting, meetings) 1.91 0.603 29 Manners 1.98 0.151 30 Sincerity 2.02 0.821

The data do not suggest, as reported by previous studies, that public accountants are more interested in traditional accounting skills such as generally accepted accounting principles used for financial reporting, auditing techniques, and taxation rules and that non-public accountants seek more contemporary skills such as organizational behavior, group skills, and personal attributes. Both public and corporate accountants rank personal attributes as highly important. A review of Tables 4-7 shows many commonalities in the Top 30 rankings of preparations and attributes. In fact, when reviewing the rankings for promotion we see more common rankings between corporate and public accountants than in entry level hiring decisions.


CONCLUSION

The purpose of this research was to investigate what skills, knowledge, and characteristics are necessary to prepare students for successful employment. The focus of this investigation was on soft skills, to go beyond traditionally required technical skills, to ascertain what generic skills would enable the knowledge professional to succeed in the highly changeable business environment. Generic skills were operationalized as including but not limited to communication, team skills, leadership, problem solving, analytical and interpersonal skills.

Given the first recommendation of The Pathways Commission, “Build a learned profession for the future by purposeful integration of accounting research, education, and practice for students, accounting practitioners, and educators”, a survey was designed and administered to accounting professionals from two different groups: the American Institute of Certified Public Accountants (AICPA) and the Institute of Management Accountants (IMA). The overall results indicate that the top five most important items for hiring entry-level accountants are personal attributes such as trustworthiness, dependability, oral communication skills, cleanliness, and punctuality. Based on these findings, it is evident that accounting programs should prepare graduates so that their qualities go beyond technical accounting skills and include attributes that prepare students as ethical citizens. Accordingly, accounting curriculum is in need of reform to produce graduates that have a broader set of skills, preparations, and attributes than taught in a traditional accounting program.

We found that ability to get along with others, knowledge of firm’s expectations, and interpersonal skills are considered significantly superior for promoting employees than their technical or conceptual accounting knowledge. Personal attributes such as dependability, trustworthiness, ability to work in pressure, sense of responsibility, and positive attitudes are all too often more important than theoretical and technical accounting skills or even business awareness. For promotion to a higher rank, employees’ personal attributes are much more significant than holding a bachelor’s degree in accounting. Accounting professionals are seeking a diverse set of skills, preparations, and attributes in college graduates, which signals the need for a change in accounting curriculum.

The results indicate that it is not possible for accounting students to acquire all that is necessary for a successful career by taking university courses which are focused primarily on a rule based curriculum. Instead, the accounting curriculum must include a set of well-planned activities designed to teach students a wide range of generic skills and attributes that employers pursue for the selection and/or promotion of accounting employees. The findings herein support earlier research suggesting that many of the non-technical skills necessary for one’s professional development are not adequately taught in accounting curricula. Thus, all indications are that accounting curricula must be revised to produce graduates with a mix of skills, preparations, and attributes that seem essential to solve the challenges faced by business organizations.

As technology continues to modernize accounting, and as the various technical functions and processes become computerized, the accountant’s role is changed from the provider of information to information analyst. The Pathways Commission notes, “As in the past, in the face of challenges of the changing environment for the profession, the Committee believes that the educational system should thoughtfully consider the feasibility of a visionary educational model. The commission should consider developments in accounting standards and their application, auditing needs, regulatory framework, globalization, the international pool of candidates, and technology” (The Pathways Commission, pp. 26-27).

While the technical skills and preparations are still required, it is evident that students and prospective accountants are to take their abilities a step further and be able to serve as business experts. To achieve this objective, accounting programs should revise their faculty merit and reward systems in a way that place more emphasis on teaching excellence. Presently the faculty merit and compensation schemes at top-ranked universities are entrenched with publication ranking which is highly skewed to non-education-related research (Bonner, et al, 2006). Improvement in teaching may only be possible by bringing more real-world discussions into the classroom and enhance teaching by incorporating the expectations and


feedback from the accounting profession. As stated by The Pathways Commission “A robust partnership between the accounting academic community and the accounting practice community is critical to the relevance of accounting education and, ultimately, the accounting profession’s future” (The Pathways Commission, 2012, p. 55). In addition, university administrators should takes steps to encourage faculty to undertake more research directed towards improved teaching, learning, and pedagogy if they are interested to promote the Commission’s recommendations.

As with other survey research this study is marred by several limitations. 1. The number of respondents in the CPA and non-CPA groups was different, which can

potentially result in a certain degree of bias. A total of 66% of the respondents possessed a CPA certificate while only half as many had a certificate other than CPA (e.g. CMA, CIA, etc.). The remaining 11% possessed no certificates.

2. A non-response bias can inhibit the results’ generalization. After several attempts, we were able to improve the response rate to nearly 40%. However, the attitudes of the other 60% of the professionals who did not participate in the study are unknown.

3. Finally, the results are biased by self-selection. Undoubtedly, some individuals are more likely to respond to a questionnaire than others. Given the lack of information about the entire sample, it is impossible to know how the non-respondents would have reacted in completing the survey.

ENDNOTES

1. To ensure validity and reliability of the questions, the instrument was pre-tested by using a small group of accounting practitioners in Southern California. As the result of this pre-testing, several questions were added or modified prior to mass distribution.

2. In order to measure the probability of non-response bias, statistical tests were conducted on the early and late responses. The results showed no significant differences between the two groups, leading to the conclusion that the chance of non-response bias was statistically non-existent (P = 0.05).

3. The first E-mail produced 101 useable responses. Ninety-seven additional useable responses were received after the second and third E-mail bringing the total to 198 responses.

4. The numbers in the parentheses indicate the rankings by the corporate accountants for the same attribute. 5. The numbers in the parentheses indicate the rankings by the public accountants for the same attribute.

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Straight from the Horse’s mouth: Auditors’ on Fraud Detection and Prevention, Roles of Technology, and White-Collars

Getting Splattered with Red!

Aditya Simha University of Wisconsin – Whitewater

Sandhya Satyanarayan Independent Scholar

Fraud is increasingly an enormous problem for organizations all over the world today – the losses occurring due to fraud are over several billion dollars every year alone (Smith, 2008). In this article, we provide a thorough review of the extant literature on fraud, and conduct a qualitative interview (N=18) based study on forensic auditors’ perceptions of fraud detection and fraud prevention methods, as well as their impression of the role of technology in fraud. We then highlight a rarely addressed problem – that of physical safety concerns that forensic auditors have to face while investigating fraud investigations. We finally suggest some directions for further research. INTRODUCTION

Honesty for the most part is less profitable than dishonesty – Plato

The above mentioned quote by Plato has certainly been taken to heart by a lot of individuals, if one goes by the enormity and scale of fraud in organizational settings. Recent estimates have placed the loss occurring due to fraud as being as high as several billion dollars every year in developed countries alone - in Australia and the United Kingdom for instance; the cost of fraud is estimated as being about A$10 billion and £20 billion respectively (Smith, 2008). Similarly, Rossouw (2000) also mentions that fraud is a serious problem facing organizations in developing nations, and he asserts that the problem is further amplified as they face a serious and urgent need to engage in economic growth and development, and fraud prevents and hampers their growth and competitiveness. Fraud also tends to erode investor confidence in financial markets thereby compounding the problem (Peterson and Buckhoff, 2004; Rezaee et al., 2004).

The Association of Certified Fraud Examiners (ACFE) too estimated that annual fraud losses are approximately 5% of the annual revenues of organizations which translates to about $3.7 trillion (ACFE, 2014). This number has certainly risen in magnitude, if one just considers that the amount was $652 billion in 2006, which then shot up to $994 billion in 2008, and then to 2.9 trillion in 2010 (ACFE, 2006, 2008, 2010; Kaplan et al., 2010; Murphy and Dacin, 2011), and then to 3.5 trillion in 2012 (ACFE, 2012). A survey found that about one third of organizations operating worldwide are victims of fraud (PricewaterhouseCoopers, 2009). Similarly, fraud tends to adversely affect a very broad range of


stakeholders including audit committee and board members, top managers, employees, auditors, creditors, shareholders, and pensioners (Dyck et al., 2010; Kaplan et al., 2010).

This high rate of fraud and the resultant damage has certainly not escaped scrutiny or study by researchers – several studies have been devoted towards studying and examining fraud, in several settings (Alleyne and Howard, 2005; Archambeault, Webber, and Greenlee, 2014; Ashforth and Anand, 2003; Anand et al., 2004; Boolaky, 2012; Collins et al., 2009; Marcel and Cowen, 2014; Murphy and Dacin, 2011; Owusu-Ansah, Moyes, Oyelere, and Hay, 2002; Palmer, 2008; Rabl, 2011; Rabl and Kuhlmann, 2008; Sarens and Abdolmohammadi, 2011; Smith, 2008; Vanasco, 1998; Zahra, Priem, and Rasheed, 2005). Similarly, exposés of white-collar crime and situations of fraud are seemingly never-ending: the sordid details of grand schemes of fraud in organizations such as Enron, Credit Suisse First Boston, Global Crossing, Tyco, Waste Management, WorldCom, for instance have been widely documented (Brickey, 2006; Healy and Palepu, 2003; Ivancevich et al., 2003; Kulik et al., 2008; Rezaee, 2005; Sidak, 2003).

However, despite the presence of these different studies, there still is a lot of research and work that needs to and can be conducted on fraud, its prevention and detection, and other facets of fraud. Several calls for more research on how to understand fraud and prevent and detect it have been issued by different agencies and individuals (ACAP, 2008; AICPA, 2002; Carcello et al., 2009; Smith, 2008; Wells, 2004). Our paper is a response to those calls for more research.

In our survey of the literature on the topic of fraud, we happened to notice that there was a paucity of articles with rich qualitative data, especially articles that happened to utilize actual forensic auditors/investigators as participants in them. Our study aims to attempt to correct that imbalance. We conducted interviews with current forensic investigators who are actively investigating fraud situations in the field, as we felt that one of the best ways by which we could better understand fraud was by talking directly with the folks that dealt with it on a frequent basis. Another facet of our study sample is that these forensic investigators have had experience investigating fraud in several different countries, both developing and developed.

One of the chief purposes of this study was to add some depth and richness to the current reservoir of knowledge that exists on fraud. Our study aims to discover from currently active forensic investigators, their thoughts on fraud detection and prevention methods, as well as their thoughts on technology as it relates to fraud, and finally, their thoughts about the need for physical security in the work environment.

This paper is organized in the following way - our next section provides a detailed literature review of fraud in a forensic auditing and accounting context. Next we touch upon the different facets of fraud detection and fraud prevention methods, fraud and its relationship with technology, and then finally touch upon the physical security angle to forensic investigations. After doing so, we then introduce our study objectives, and then present our methodology section, followed by our results, and then finally present our discussion section. Our discussion section also provides suggestions for future research.

LITERATURE REVIEW OF FRAUD IN AN ACCOUNTING CONTEXT

Before embarking on a detailed literature synopsis on fraud, defining fraud may be beneficial. For the most part, fraud and corruption seem to have been used interchangeably in the literature base (for e.g., Ashforth and Anand, 2003; Anand et al., 2004; Collins et al., 2009; Marcel and Cowen, 2014); however, Rossouw (2000) tries and differentiates between the two concepts. He argues that this differentiation is essential because corruption tends to be mostly associated with the public sector and public officials, whereas fraud can occur in both the public as well as the private sector. Anyone in short, can be a perpetrator of fraud! Also, corruption inherently entails third-party involvement, whereas fraud can be a solitary act as well as a group act. The absence of third-party involvement in situations of fraud makes fraud much more difficult to detect, or to prevent even.

Rossouw (2000) defines fraud as “intentional deception by concealing or misrepresenting information that harms the financial interest of another person(s) and benefits the financial interests of the perpetrator” (p. 887). A more technical definition of fraud is provided by AICPA (2002, paragraph 5) and is as


follows, “fraud is an intentional act that results in a material misstatement in financial statements that are the subject of an audit.” Corruption on the other hand is defined by Rabl (2011, p. 85) as “a deviant behavior that manifests itself in an abuse of a function in favor of another person or institution.” In this paper, we chose to utilize Rossouw’s and the AICPA’s definitions of fraud, and therefore in our recruitment of study participants, we chose to interview forensic investigators who were dealing with cases of fraud in the private sector as opposed to investigators who were dealing with and investigating cases of governmental corruption.

One major reason that has been suggested as being the cause of fraud in many a case is the high pressure that corporate management has to face in order to achieve earnings targets. If those targets are missed, significant declines in the stock price ensue, and that in turn drives down and reduces executive compensation, as that is frequently tied in to stock price and earnings targets (Carpenter and Reimers, 2005). The Public Oversight Board (2000) too has identified this as being a frequent harbinger of fraud. Several empirical studies too have corroborated the finding that an excessive emphasis on earnings projections and aggressive management attitudes towards financial reporting were primary indicators of managerial motivation for fraudulent financial reporting (Albrecht and Romney, 1986; Bell and Carcello, 2000; Loebbecke et al., 1989).

This is very much linked to the fraud triangle (PCAOB, 2005), which suggests that three factors (opportunity, incentive/pressure, and attitude/rationalization) if all are present, predict the presence of fraud within an organization (Cohen et al., 2010; Murphy and Dacin, 2011). These elements in the triangle were first identified by Sutherland (1949) and then developed by Cressey (1953, p. 30, as cited in Cohen et al., 2010). Albrecht et al. (1982) started off the process of adapting the concept from criminology onto an accounting context, and identified 82 different fraud related variables, which they combined into three categories: situational pressures, opportunities to commit fraud and personal integrity (p. 37).

Auditing regulation through the ages has outlined numerous fraud-risk factors – the most recent standard, SAS No. 99 (AICPA, 2002, Para. 7), has organized risk factors by referring them to the three conditions generally present when fraud occurs. These definitions and risk-factors are directly aligned with and tied in to the fraud triangle (Cohen et al., 2010). A lot of empirical research has been carried out to demonstrate the importance of both the “incentives” and “opportunities” corners of the fraud triangle (for e.g., Albrecht and Romney, 1986; Loebbecke et al., 1989)

The first two corners of the fraud triangle have certainly been borne out as being associated with fraud (AICPA, 2002; Erickson et al., 2004; Graham et al., 2005; Murphy, 1999; Murphy and Dacin, 2011), however not as much attention seems to have been showered on the third corner (i.e. attitude/rationalization) of the fraud triangle (Carcello and Hermanson, 2008; Hogan et al., 2008; Murphy and Dacin, 2011; Wells, 2004), which led Murphy and Dacin (2011) to propose a framework that describes an individual’s decision-making process when confronted with both opportunity and motivation to commit fraud. However, there still can be work attempted that would complement the current existing research on the third corner of the fraud triangle. As Cohen et al. (2010) attest, there is an increasing integration of the attitudes/rationalization factor into auditing regulation; however, despite that, there still needs to be a lot more research attempted to better understand the third corner.

Cohen et al. (2010) have also stressed that the third corner of the fraud triangle is also one of the most difficult one for auditors to assess, therefore it makes sense to try and see how and what auditors think about efforts to make it easier to assess this third corner of the fraud triangle. One of our aims in this study is to discover what forensic investigators think about criminal profiling and behavioral finance, as one way by which the third corner of the fraud triangle can be better understood is by bringing in concepts and strategies from criminal profiling and behavioral finance.

Rezaee (2005) found that five interactive factors explained several high-profile financial frauds – he categorized them into an interesting acronym (Cooks, Recipes, Incentives, Monitoring, and end results (CRIME)). Murphy and Dacin( 2011) proposed a model wherein they described the psychological pathways to fraud for individuals, who believe that committing fraud is wrong. They propose three distinct pathways to fraud: (i) lack of awareness, (ii) intuition coupled with rationalization, and (iii)


reasoning. They also identify certain situational factors by which individuals commit fraud without even recognizing it. Their model is a useful one in proposing different interventions which can then be used to deter fraud.

We will now touch upon the different facets of fraud detection and fraud prevention methods, fraud and its relationship with technology, and then finally touch upon the physical security angle.

Fraud Detection Methods

Fraud detection involves identifying fraud as quickly as possible once it has been perpetrated (Bolton and Hand, 2002, p. 236), and these methods need to evolve in order to better detect fraud that perpetrators may have disguised better. Matsumura and Tucker (1992) were pivotal in developing an early theoretical foundation for fraud detection. Their empirical results supported a direct relationship between testing, fraud detection, and fraud prevention. Fraud detection methods have curiously enough not changed much for a very long time (Bishop, 2004) – this would perhaps explain why a lot of times fraud is “accidently discovered”, although when one examines the difference between the percentages reported by the ACFE in 2004, and compares it to the percentage reported by the ACFE in 2014, one can notice that it’s reduced a bit, and that today detection seems to rely more on tips from employees and other individuals (23.8% in 2004, and 40.2% in 2010). Johnson and Rudesill (2001) compile a list of guidelines that can be useful in fraud detection, and they also suggest that organizations need to make each and every employee responsible for detecting fraud incidences. Hassink et al. (2010) also examine the extent to which auditors comply with standards once fraud has been detected, and they found that auditors do not seem to comply with all standards properly.

One very interesting facet of fraud is that the median time span of reported frauds from their beginnings until detection is about 18 months, and there are quite a few frauds that last significantly longer than two years (ACFE, 2014; Lord, 2010), some types of frauds, such as check tampering and fraudulent financial statement fraud appear to go on for a much longer time and have a median time till discovery of about 27 months (ACFE, 2010).

It transpires that a lot of fraud cases are initially discovered by a tip or a complaint from an employee or from someone outside of the organization (ACFE, 2014). This pattern has been observed in prior ACFE studies as well (ACFE, 2012, 2010, 2008, 2006; Lord, 2010). One heartening conclusion can be reached though, which suggests that the Sarbanes-Oxley Act may be having a positive effect. It transpires that the percentage of frauds detected by internal controls reported by the most recent ACFE study (2014) appears to be higher than the percentage reported in the ACFE study in 2008 and previous years.

Some of the most commonly utilized fraud detection methods appear to be as follows – tips by insiders or outsiders, management review, by internal audit, by accident, by account reconciliation, document examination, external audit, surveillance/monitoring, notified by police, confession, and IT controls (ACFE, 2014, p. 19). This list is virtually identical to the one released by the ACFE in 2010, and suggests that fraud detection methods are mostly reactive measures and not proactive measures. Another compiled list of fraud detection methods was suggested by Bierstaker et al. (2006), however the methods on their compiled list fall into the ACFE categorization. Therefore, it makes sense to find out what active forensic investigators think about these fraud detection methods, and also see how they think these methods can be improved.

While some previous studies have looked into the question of the effectiveness of some of the fraud detection methods (e.g. Bierstaker et al., 2006; Blocher, 1992; Hylas and Ashton, 1982; Loebbecke et al., 1989; Wright and Ashton, 1989), they have mostly either been statistical analyses or survey based studies. While those studies are certainly important, and have added considerably to the existing literature base, there is definitely a need to supplement the knowledge base with some rich and interesting qualitative research on the topic. This is one of our purposes with this study, to be able to add some richness via our findings.


Fraud Prevention Methods Fraud prevention on the other hand describes measures that are taken to stop fraud from occurring in

the first place (Bolton and Hand, 2002, p. 235). Adams et al. (2006) argue that fraud prevention is the most cost-effective way to deal with financial loss through fraud, especially in light of the fact that companies that are defrauded are extremely unlikely to ever recover their losses. Bishop (2004) strongly advocates that preventing fraud needs to be made into a responsibility of each and every employee. Similarly, Ivancevich et al. (2003) delineate several responses that could be utilized to prevent and deter fraud in organizations.

Johnson and Rudesill (2001) assert that there are no sure-shot ways to completely eliminate fraud, but that with proper attention being bestowed on fraud prevention methods, organizations can hope to minimize the occurrences and losses. They provide a list of proactive tips that organizational managers can utilize in order to prevent occurrences of fraud from happening, many of which are similar to the list suggested by the ACFE (2010). Krummeck (2000) also asserted that fraud prevention methods need to be integrated completely with an ethics base, as not doing so, will render the process futile and useless.

The ACFE study in 2014 essentially found that anti-fraud controls or fraud prevention methods seemed to be quite effective; however it appears that smaller organizations are disproportionately victimized by occupational fraud than are their larger counterparts, although the variation between size categories is not much (ACFE, 2014). This disproportionate result may be in part due to the fact that smaller organizations have fewer anti-fraud control schemes in place.

Some of the most commonly utilized fraud prevention methods are as follows – surprise audits, job rotation/mandatory vacation, hotlines, employee support program, fraud training for employees as well as managers, internal audits, anti-fraud policy, external audit, code of conduct, management review, independent audit committee, management certification, and rewards to whistleblowers (ACFE, 2010, 2012). Here again, it makes sense to find out what active forensic investigators think about these fraud prevention methods, and also see how they think these methods can be improved. We again aim to add to the extant knowledge base on the topic with our study. Fraud and Technology

The advent of technology and development has certainly brought in a lot of benefit to human society, however at the same time perpetrators have learnt to use technology in a negative way, and fraud has not been behind. Several financial fraudulent schemes have originated in a digital environment, and that typically involves using an IT-based solution to detect and handle cases of digital fraud (John, Jabber, and Sudarshan, 2012; Pearson and Singleton, 2008). This rise in sophisticated technology being (mis)utilized to perpetrate and commit fraud was predicted by Albanese (1988), where he predicts that fraud could become the crime of choice in the computer age. He also predicted that law enforcement would often lag behind the innovative techniques of criminals (p. 25).

The now presently ubiquitous presence of information systems in organizations has also contributed to the growth in technology related frauds. Schwartz and Wallin (2002) found that information systems tended to place a distance between the fraud and participants such that participants then chose to maximize their earnings. Essentially, information systems tend to depersonalize the situation, and make it easier for fraud to be committed. Similarly, Oates (2001) too suggested that technology makes it a lot easier for fraud to be committed via means of cyber crime incidences. Oates also cites the fact that technology tends to make it easier for individuals to maintain anonymity, which in turn makes them much more likely and liable to commit and engage in fraud (p. 93). The propensity of IT to be utilized in computer fraud is also elaborately described by Dippel (2000).

Some evidence does exist that suggests that while technology can be utilized negatively to commit crime and fraud, technology can also be utilized to combat crime and even detect and perhaps prevent fraud from happening (Fox, 2000; Flegel et al., 2010; Kakis, 1992; Pathria, 1999; Weatherford, 2002). Pearson and Singleton (2008) also point out that IT is being used to a great degree in fraud detection, and sometimes in fraud prevention. They mention that software such as data analytics and data-mining are


now being increasingly relied on during fraud detection processes. Some automated macros have also been developed by organizations to detect fraud (Albrecht et al., 2008; Pearson and Singleton, 2008).

However, in the end, technology and IT seem to having a rather “Dr. Jekyll and Mr. Hyde’ish” relationship with fraud. On the one hand, technology appears to be facilitating fraud, but then on the other hand it appears to be immensely useful in combating, detecting, and even preventing fraud from occurring. Therefore, asking forensic investigators their thoughts and opinions on this dichotomous relationship may be useful in better understanding the relationship between technology and fraud.

Fraud and Physical Security Risks

Most of the literature dealing with risks associated with fraud appears to be concerned with either the risks of fraud itself (Graham and Bedard, 2003; Knapp and Knapp, 2001; Wilks and Zimbleman, 2004; Zimbelman, 1997), or else appears to be concerned with the risks of auditors getting hit with lawsuits (Bonner et al., 1998; Kaplan, 1987; Lowe et al., 2002; Narayanan, 1994; Patterson and Wright, 2003). However, there have been some recent cases that suggest that not all risks are of a litigious nature, and that some risks pose serious harm to the auditors’ physical well-being (Perri and Lichtenwald, 2007; Perri and Brody, 2011).

Perri and Lichtenwald (2007) proposed an addition to the FBI Criminal Classification Manual, and they suggested that a new homicide classification termed as fraud-detection homicide be included in the FBI’s Crime Classification Manual (Douglas et al., 1992; Perri and Lichtenwald, 2007). In fact, Perri and Lichtenwald (2007) list about 27 known cases where homicide resulted as a result of fraud-detection, and the homicide was directed at the person(s) responsible for the fraud detection. They concluded that the homicides were not spur-of-the moment homicides, but that they were coldly calculated homicides (p. 29). Another study by Perri and Brody (2011) documented the case of Sallie Rohrbach who was an insurance auditor and was murdered during an investigation of fraud at an insurance agency. Both these studies seem to suggest that threats of physical violence are much more frequent than commonly thought. Perhaps, as is suggested by both studies, forensic auditors need to start becoming more cautious about their safety.

There does not seem to be much literature out there that delineates the safety risks that forensic auditors face – most research appears to be focused on litigious risks. However, there is reason to believe that forensic auditors (especially those in developing countries) have a valid reason to fear threats of physical reprisal. Since, there really isn’t much literature on the topic, we believe that our study is one of the first few ones that examine the likelihood of physical reprisal against forensic investigators, and also take into account the perceptions of forensic auditors to determine the same. The next section outlines our study objectives. STUDY OBJECTIVES

Our study attempts to add to the current knowledge base on fraud, fraud detection methods, fraud prevention methods, the relationship between fraud and technology, and physical safety issues that pertain to fraud investigators investigating fraud. As we mentioned earlier, qualitative studies will add richness to the extant literature base on fraud, and that is one of our primary purposes here with this qualitative research based study of ours. We also intend to shed some light on certain facets of fraud investigations that do not seem to have gotten as much attention as they ought to be getting.

We have chosen to undertake this study keeping four objectives in mind – (a) to discover forensic investigators’ thoughts and perceptions on the effectiveness of current fraud detection and fraud prevention methods; (b) to discover forensic investigators’ thoughts and perceptions on how to improve current fraud detection and fraud prevention methods; (c) to discover forensic investigators’ thoughts and perceptions on the relationship between technology and fraud; and (d) to discover forensic investigators’ thoughts and perceptions on the physical safety risks posed to them in the line of duty. Our specific research questions are as follows:


(i) What are forensic investigators’ thoughts and perceptions on the effectiveness of current fraud detection and fraud prevention methods?

(ii) What are forensic investigators’ thoughts and perceptions on how to improve current fraud detection and fraud prevention methods?

(iii) What are forensic investigators’ thoughts and perceptions on the relationship between technology and fraud?

(iv) What are forensic investigators’ thoughts and perceptions on the physical safety risks posed to them in the line of duty?

Our methodology section outlines the approach we took in order to achieve our four study objectives. METHODOLOGY Paradigm Guiding the Research

Our key interest in this study is to understand the phenomenon of fraud, fraud detection and prevention methods, the relationship between fraud and technology, and the physical safety risks associated with fraud investigations, from the perspective of forensic investigators. Since forensic investigators deal with fraud and the other mentioned facets of fraud on a very continual basis, we believe that our study’s paradigm may be best classified as a phenomenological approach. Phenomenology is a perspective that advocates that knowledge can only be gained by understanding the direct, lived experience of others (McMillan and Wergin, 2006; Polkinghorne, 1989), and since we are essentially studying and trying to understand from the experiences of forensic investigators, we believe that the paradigm most appropriate for our research is that of phenomenology.

Research Design

Our study used a qualitative interview based method. Our interview based method was an in-depth, phenomenological based interview method (Kvale and Brinkman, 2009; Seidman, 2006). We used open-ended questions in our interviews, and also built upon and explored our participants’ responses to pursue further exploration of their answers (Seidman, 2006). We followed Myers’ (2009) advice about utilizing a semi-structured interview format as that would allow us to capture and obtain rich data. In addition to following a semi-structured interview format, we also followed the technique of mirroring which Myers and Newman (2007) suggest is a technique to ensure that our participants’ language and perceptual vision of the world would be reflected in our analysis. We followed mirroring by using the words and phrases that our participants used to construct our comments to them through the interview process. We also ensured that we demonstrated triangulation of participants (Rubin and Rubin, 2005; Myers, 2009) by seeking out participants who were from different organizations and had different sorts of professional backgrounds and experiences.

Researcher as Instrument

Reflexivity is a very important mark of rigor in qualitative research, as it follows that researchers are pretty much the primary instrument in a qualitative study. Researchers shape not just the data but also affect the research process. The acknowledgment of prior assumptions and experiences, as well as personal and intellectual biases, is a very important way by which to address issues of validity and the trustworthiness of a study (Marshall and Rossman, 1999). We now provide a small discussion on how our assumptions and experiences support increased understanding of the forensic auditor perspective that is studied.

Both researchers involved in this study have had experience studying and researching fraud. One of the researchers has had over 10 years of experience in the field investigating fraud cases for organizations, and is also an active forensic auditor. The other researcher has had some experience studying and designing fraud detection software.


This experience on our parts has had a positive effect on our research, because when we study the perceptions and attitudes of our forensic auditor participants, we can certainly identify with them better and that helps us avoid errors of misunderstanding that can sometimes happen while analyzing the transcribed notes. At the same time though, since neither of us was directly involved in any of the cases or situations that our participants talked about, we were able to retain our independent objectivity while studying our participants.

During the duration of this study we took extensive field notes and also kept track of analytical insights through memos that were included in the data to be analyzed. We then engaged in researcher triangulation, which involved each researcher working individually with the raw data to perform open coding. We then came together as a team and engaged in a co-analysis and shaping of the final themes resultant from our data.

Participants, Recruitment, and Selection Procedures

The participants in this study were currently active forensic auditors, working at different organizations (including the big 4 accounting firms). A total of 18 auditors participated in the study. These auditors were predominantly from India; however they also had experience working in several countries besides India, including the United States, United Kingdom and South Africa. Their average experience in the field of forensic accounting and fraud investigation was about 16.7 years. 12 of our participants were male and the remaining 6 were female. Our participants had a varied range of professional backgrounds ranging from engineering backgrounds to police backgrounds to military backgrounds among others, however at present all our participants were actively working in the capacity of forensic auditors for accounting firms.

The sample size in qualitative research is typically determined by the researcher’s estimation of the richness of data that participants generate, and the sample size also depends on the specific method that is used (Patton, 1990). Conventional knowledge posits that the number of required participants becomes obvious as studies progress, when new categories, themes or explanations stop emerging from the data, and data saturation occurs (Marshall, 1996; Seidman, 2009). In our case, we obtained data saturation at about 11 participants; however since we had already obtained consent from and fixed interview slots with the remaining participants, we continued to collect more data and finally obtained a total of 18 participants.

After obtaining approval from the university Institutional Review Board (IRB), we set about recruiting participants for the study. We obtained a list of potential candidates to interview from three senior partners that one of the researchers knew, and set about emailing those potential candidates. We emailed 21 different forensic auditors, and obtained 18 suitable participants from the lot. 2 auditors did not respond to our emails, and 1 refused to participate in the study. However, the remaining 18 agreed and were consequently interviewed by both of us. Our selection criterion was that our participants needed to have experience in forensic auditing and investigating fraud, and also needed to be currently active in the field.

Sources of Data

Data was collected through our in-depth interviews, which were obtained through telephonic interviews. The interviews were audio recorded and we took process notes during the interviews. The interviews were semi-structured interviews; and the interview protocol consisted of five questions. Each researcher also kept analytical memos and field notes which were included in the data to be analyzed. Data Analysis and Management

The final data for analysis consisted of audio recordings and transcripts of the interviews, as well as the field notes and analytical memos. Two graduate students initially transcribed all the audio recordings, after which both the researchers then independently reviewed the text and recordings, and checked for omissions and errors. The field notes of both researchers were also used to address inconsistencies in the transcripts. The analysis of this qualitative data was handled by utilizing the method that Marshall and


Rossman (1999) and Myers (2009) suggest. The first step in the analysis of qualitative data requires that researchers immerse themselves in the data, and therefore, both the researchers independently read all transcripts and process notes and then performed open coding (Marshall and Rossman, 1999). The next step in the process of interpretation involved identifying the essence of the data. After both of us had completed this process of finding meaningful units in the data, we got together and discussed emerging themes in the data. At this point the analytical memos were also considered, and structural synthesis occurred (Marshall and Rossman, 1999).

RESULTS

The following five themes emerged from our data. In response to our first two research questions we obtained the following two themes – (i) Fraud Detection methods are inadequate and need to be urgently improved, and (ii) Fraud Prevention methods need to be made much more pronounced and ubiquitous. In response to our third research question we obtained the following theme – (iii) The dual role of technology in fraud, and in response to our fourth research question we obtained the following theme – (iv) Physical safety concerns are a valid threat, and need to be addressed by organizations. We also obtained an additional theme from our data, which was – (v) Training forensic auditors in behavioral finance and criminal profiling may be advantageous in detecting fraud and preventing physical safety concerns.

Inadequacy of Current Fraud Detection Methods and a Need for Their Improvement

Our participants were asked to tell us their perceptions of fraud detection methods. Almost all of our participants seemed to suggest that current fraud detection methods were not perfect and indeed in the words of one of our participants, “…. the problem is that [these] fraud detection methods are very reactive…. We only find the tip of the iceberg when using one of these detection methods… very inefficient methods….” (P11). Included below are some of the numerous statements made by our participants in this regard:

“……most companies have these old detection methods that are not sufficient or adequate. Because of lack of active monitoring from station’s perspective… so there’s always a window of opportunity there for the perpetrator….” (P1) “…… a lot of fraud detection methods are much too old fashioned…. they include basic fundamentals such as document analysis and background checks, but they are not having any in-house facilities to detect fraud……. Understanding the network and network flow is still lacking a lot…. in my own experience we have had to start from scratch and learn the process and then start the investigation which is not helpful in handling a rigorous investigation” (P4) “….. but my feeling is that there are no real up-to-date methods of detecting fraud… in a few instances, I have seen some in the current environment….but still I think most of the fraud detection methods are based on reacting to the fraud and a bit too late…..” (P5) “…. you know, the problem with fraud detection methods are that they are just too late…by the time, one gets to discover or detect the fraud, it’s already too late….fraud detection methods will always have a lag time….. as long as the lag time is small, it should be fine, but that’s not the case….right now, the lag times are increasingly long….” (P12)

We also obtained consensus among our participants that current fraud detection methods needed to be

improved, and that the improvement needed to be effected on an important scale if fraud is to be detected


early enough. Included below are some of the numerous statements made by our participants in this regard:

“….handwriting analysis is something that can be extremely useful…..especially in rural areas where there isn’t much influence of computers…. If fraud detection methods could somehow incorporate handwriting analysis, it may be very useful….” (P4) “…. companies just don’t have enough in-house capabilities to detect fraud…and they are not able to proactively monitor pressing issues and see if, “can there be fraud”… becoming proactive and changing the mindset of top-management is a must if companies need to improve their fraud detection methods…” (P7) “…. what we need is a method that has an element of surprise built into it….also, now detecting fraud is not happening because we wait for signs that randomly happen…... Rather than having to put certain things off, we need to have potential audits for everything to be able to better detect fraud…” (P8) “….some of the current methods in the US might not be here in India, because in the US it is more technology driven, India is more dividends driven…... we need to integrate and incorporate the latest software into fraud detection methods in India….” (P9) “….most fraud detection methods are like cancer detection kits, by the time you get to know that there is cancer (or) fraud in a person or organization, it’s too late already… fraud detection methods must be added to and with fraud prevention methods…make it an integrated system, where both prevention and detection are handled together…if we make it impossible for people to commit fraud, it will be so much easier to catch them when they commit fraud…” (P14) “…a much more cohesive and formalized thought process needs to go in for improving fraud detection methods…. The lack of rigor of methodology is a prime reason for this….” (P18)

For the most part, all our participants seemed to suggest that in their opinion fraud detection methods

were much too reactive, and therefore susceptible to serious time lags. After all, if fraud detection methods could somehow be improved such that a reduction could be brought about in the amount of time taken to detect fraud, the severity of fraud would be reduced significantly. Similarly, several of our participants also suggested that fraud detection methods could be improved if the latest technology and software were to be incorporated into their design. Augmentation in the Scale and Role of Fraud Prevention Methods Essential

Our participants were asked to tell us their perceptions of current fraud prevention methods. Most of our participants seemed to think that current methods of fraud prevention were fairly effective; however they did seem to suggest that these methods of fraud prevention and control needed to be augmented and utilized in a more amplified and increased fashion. The general consensus seemed to be that fraud prevention methods were not being used to the extent that they should be. Some examples that are indicative of this trend are as follows:

“… fraud prevention and control is really important….where you highlight things on real time basis are those that can prevent the environment for fraud… more mechanisms are out there that can be used to prevent fraud…but they need to be implemented more…” (P3)


“… There needs to be a better system for communication by which employees can report to their superiors or others if something fishy is going on…they should be able to report it to some third party. Also companies need to be able to track activity, and their controls need to incorporate that... right now, not all companies have that sort of system built in….” (P6) “…. one aspect of fraud control is that it needs to be both overt as well as hidden… overt control mechanisms will avoid most simple cases of fraud, while the hidden mechanisms will help prevent complicated fraud…. if all companies could get some sort of increased role of prevention methods in their functioning, fraud could be wiped out…” (P12) “…. fraud control is a really fine way to prevent fraud from occurring…. if people and employees know that there are mechanisms in place to monitor them if they screw up, they won’t screw up… I just wish that more organizations would implement fraud control in the first place and not rely on detection….” (P16)

Part of the process of qualitative analysis also involves looking for disconfirming evidence. Most

participants in the study had a positive opinion of fraud prevention and control methods and also felt that it was a proactive method as opposed to fraud detection methods. However, one participant seemed to disagree with the rest of the participants about the nature of fraud prevention methods. That participant seemed to suggest that prevention methods were also reactionary; however there was consensus with the other participants in that augmentation of fraud prevention methods

“….both methods are very reactionary methods. It is often a reactionary measure to any kind of fraud, especially in India…. Also most companies do not have in house capabilities to solve problems, so they have to rely on other external agencies to assist them in investigations…. Maybe instead of reactive measures we should take a more proactive method of prevention or stopping the fraud…. the top management mindset has to change, they have to promote fraud prevention methods enthusiastically….” (P7)

The Dual Role of Technology in Fraud

We asked our participants to tell us their perceptions of the role that technology plays when it comes to fraud related situations. We found that on the whole, the general consensus from all our participants was that technology played a very dual role when it came to fraud. On the one hand, technology made it very possible and easy for perpetrators to commit fraud, but on the other hand, it also assisted auditors and investigators in their efforts to detect and prevent fraud. All our participants though seemed to recognize the potential benefits of harnessing technology to combat fraud. Some statements from them are provided below to better emphasize this theme:

“… technology is definitely being used to commit fraud…Similarly, I think if you look at an anomaly I think most organizations monitor transactions, do analysis on transactions, but they don’t do analysis on computer activity….companies definitely need to monitor computer activity of their employees more rigorously..” (P4) “…technology has its own set of pros and cons, we have to be aware of the situation and we also need to be more tech-savvy….I feel a forensic accountant needs to be a tech savvy person; he has to be a person who combines forensics with technology.” (P6) “…now fraud you already know what happens, it is a technology loophole and those loopholes are used to hide accounts…for example, if there is some money lying in the pool, you can move it elsewhere and then bring it back to balance the sheet later on…


these kinds of things can be exploited using technology…there definitely needs to be supervision for technology….” (P7) “….I would not want to consider technology a friend, nor as an enemy. It is just a tool at the end of the day. It depends, like an atom bomb or nuclear energy, it depends on the user. If you want to use it for good, you can, or if you want to use it destructively, you can. But I realize that as the day goes by a lot of investigators use technology. So it is important for the fraud investigators to be equally equipped with the latest technology… but it is one of the most difficult factors for investigators to keep themselves updated on the latest technology…” (P9) “…. So, on one hand technology helps us to gather data, which is deleted over a period of time; on the other hand, technology is now helping subjects delete that data permanently. So that is one aspect, but I still feel that technology helps me in catching fraud…” (P11) “…..technology can be a resource for you to counter fraud….but also, if you are not going to rely on anything but technology you are going to fail…” (P17)

One of our participants made a particularly emphatic point, which seems to tally very well with what

most of the participants seemed to feel. That point is exemplified with the following statement:

“…. I definitely would prefer technology, with anything there is good and bad. With technology there are too many good things. I’d definitely go for technology, but we have to make sure that the technology we are implementing, we understand properly…” (P1)

Physical Safety Concerns are a Valid Threat and Need to be Addressed

Our participants were asked by us to relate whether they ever felt as if their physical safety and well-being was an issue during a forensic investigation, and whether they felt the need for physical security during an investigation. Most of our participants seemed to suggest that physical safety concerns were a very serious issue, and needed to be addressed by organizations. They especially felt that the need for physical safety was heightened when investigating fraud in remote locations. Some of their narratives are provided below to better contextualize this theme:

“….when I am in big cities I never felt any need for security but when I go to remote places, small towns, I am a bit concerned about my security… in one small town, the people we were investigating arranged a strike so they gathered outside the office and we had to leave right away….” (P1) “….when the contract was allotted, it turned out that the beneficiary was a brother of a politically strong person… our client wants us to go and analyze the data at the physical location of that other place, even though it was located in a very unsafe location… it was infested with naxalite elements….” (P2) “…. my team has had to say no twice to a client…the reason being that the client wanted us to investigate matters which when we did some digging into, we found that it was tied in with the underworld….we had to refuse and tell the client that they needed to contact the governmental authorities to do that job…” (P5) “….what happens is when we start investigations there are sensitive times and there can be threats to your life that way….so sensitive crime is a hard bargain because you also


need to be worried about your family and your personal security is also very important…” (P7) “…I have experienced this once….I was required to conduct certain interviews and during the interviews, they [interviewees] perceived it as a threat. It was in a remote area in [place deleted for confidentiality] and people were making enquiries about where we were staying and what we were detecting and things like that….” (P9) “…a colleague of mine once continuously received threatening calls, at all times of the day and night….the police also was unable to do anything because the callers kept changing their SMS cards to avoid detection….but they felt that the calls were tied in with the investigation at the time, as the party concerned was a very powerful and influential person…” (P13)

Many participants also seemed to indicate that organizations needed to do something to ensure the

safety of their forensic investigators. Some narratives to that effect are provided below:

“… I think organizations need to ensure that their forensic teams are provided with proper security arrangements… it may cost them a little bit more, but that amount is negligible in the long term…” (P12) “… especially in some regions of the world, one has to be very careful about physical safety concerns…. I remember when I was sent to [name of place deleted], there was no real law structure in place there… my company sent us there, but they thoughtfully provided us with security personnel to accompany us there… I think more organizations need to do that…it’s almost like they do not anticipate anything happening to threaten the auditors….” (P15)

Training in Behavioral Finance, Criminal Profiling, and Technology May be Beneficial in Combating Fraud

Another interesting theme emerged from our data – we had asked our participants to tell us about what kind of training methodologies would be most beneficial in training forensic auditors to combat fraud. We found that most of our participants seemed to think that criminal profiling and behavioral finance training would greatly benefit forensic investigators. We also found that our participants seemed to think that training in the latest technology and software would also help improve auditors’ abilities to combat fraud. Some of the narratives around that theme follow:

“…. it can be helpful…sometimes we have a hunch that something is wrong, so having training in criminal profiling may help pick up on the behavior of the employee…to find that something is wrong…..” (P1) “…based on my initial experience as a police officer, I can definitely state that criminal profiling training will help…I will give you one example: one senior vice-president of a company had become a contact, he wanted us to investigate a case wherein he accused a few other top management guys of stealing money….we started investigating, this senior VP kept calling us and asking us for updates… and you know then we said this is not the right behavior of an innocent person…we found something very funny about this man…. Then during the final interview, I established an interface with the suspect and then we realized that the suspect was a witness…. And the VP was the actual guilty party… in an ironic way; he hired us to bust himself…” (P2)


“… it would be a great advantage…. If a guy has already done criminal profiling properly it will help in investigations…when I was in the Army, we did criminal profiling on people especially the high level criminals for being involved in terrorist factories….. I think that organizations need to latch onto existing organizations to facilitate this sort of training….” (P6) “… training in criminal profiling and behavioral finance both help with fraud investigation teams….. with specific investigations, where you need to go beyond the documents, the things you can’t see with your eyes, you need to understand the person you are investigating, their psyche and personality..” (P9) “… I feel they should because there are lots of instances when the behavior assessment is required. For example, when an open interview is undertaken it is more meaningful as the body language, the answers, expressions all convey a meaning and also help is modifying the interviewing approach to get the maximum information….” (P10) “… if auditors end up getting trained in diverse fields such as behavioral finance and criminal profiling, it will only add to their arsenal…I am confident that it will increase the success rate of forensic auditors’….” (P13)

However, as we mentioned earlier, searching for disconfirming evidence is part of the qualitative

research process. One of our participants had a more cautious attitude towards training auditors in criminal profiling:

“…. Yes and no for criminal profiling….we have to be careful about that training not coloring one’s judgment…also, auditors need to make sure that they avoid stereotypes that may be generated due to criminal profiling….but then, criminal profiling training can facilitate out-of-the-box thinking, which is a positive aspect…” (P18).

DISCUSSION

Our study intended to better understand the phenomenon of fraud, and also glean from forensic investigators their thoughts and perceptions on four different matters – fraud detection and fraud prevention methods, the link between technology and fraud, and the physical safety issues arising from fraud investigation. We were interested in adding some rich depth to the work already conducted on fraud, as it appeared that rich qualitative data was not abundant in previous research on fraud.

Some limitations of our study are typical qualitative research issues – it is rather hard to generalize our findings to the entire population of forensic auditors in the world. However, we tried to make our findings as generalizable as we could by choosing several participants who had work experience in a few different countries (besides India). Also, while our qualitative data provided rich information about the auditors’ perceptions on the different matters pertaining to fraud investigations, our sample was small. However, as we mentioned earlier, in qualitative research the sample size depends on when saturation of data occurs (Marshall, 1996; Seidman, 2009), and since we obtained data saturation at about 11 participants, our final sample of 18 is more than adequate.

Our findings indicate that forensic auditors seem to think that current fraud detection methods are not infallible, and indeed are quite prone to serious time lags. There is also the perception that these methods are too reactive, and need to somehow be improved such that the time lag between when the fraud is committed and when it is detected reduces. Similarly, our forensic auditors also seem to think that while current methods of fraud prevention are fairly effective, they need to be utilized more, and more organizations need to start utilizing them. This finding corroborates the findings of Bierstaker et al. (2006) when they found that small organizations in particular were not utilizing fraud prevention and


fraud control mechanisms, possibly due to the setting-up cost of doing so, even though fraud affects small organizations tremendously (ACFE, 2006, 2008, 2010).

Another finding of ours is with regard to the link between technology and fraud. Many of our participants identified technology as being a way for savvy fraudsters to commit fraud, and also identified technology loops as being very exploitable. However, they also recognized the positive role that technology plays in their being able to better detect and combat fraud. This suggests that the role of technology in fraud is a rather dichotomous one, but that on the whole, technology offers a lot more positive benefits and outcomes than negative ones.

Our findings have helped us understand the necessity for highlighting the physical safety risks that forensic auditors can very possibly face during the course of an investigation. While, one could argue perhaps that this may be a phenomenon unique to a developing country context, the number of cases in the US (Perri and Lichtenwald, 2007; Perri and Brodi, 2011) is testimony to the fact that this phenomenon is perhaps a lot more pervasive. Further research in this direction would be a welcome step as emphasizing and recognizing the possible dangers of fraud investigation is something that would benefit forensic investigators as well as their organizations.

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The Use of Accounting Screens for Separating Winners from Losers Among the S&P 500 Stocks

Victoria Geyfman

Bloomsburg University of Pennsylvania

Hayden Wimmer Georgia Southern University

Roy Rada

University of Maryland Baltimore County

This study uses accounting screens based on the Piotroski’s (2000) F-score and the derived MagicP formulae and finds that it is an effective investment strategy, which results in risk-adjusted outperformance of stocks with high book-to-market (BM) ratios over a market weighted benchmark portfolio and its subset of growth stocks. Unlike other studies that utilized similar tests on smaller firms, we examine the performance of large value stocks within the S&P 500 between 2007 and 2014 and find evidence of the value premium. The results were robust to the time period; in fact, the highest-ranked value stocks suffered less severely during the period of market correction. INTRODUCTION

Substantial research has been conducted on testing a well-known concept of efficient market

hypothesis (EMH) that focuses on the extent to which markets incorporate information into stock prices (Fama, 1970, 1998; Malkiel, 1987, 2003, 2005, among others). Theoretically, the more efficient is the market, the more information is incorporated into stock prices and the more rapidly that information is reflected in price changes. While most studies support some degree of market efficiency (ranging from strong form or “fully reflected” information in stock prices to semi-strong and weak forms), researchers have uncovered some market anomalies that reveal patterns of trading strategies that earned higher ex-post returns than would be expected in efficient markets.

Anomalies which stray from the efficient market hypothesis may be used to garner positive abnormal returns in the market. However, supporting the EMH, is the observation that once anomalies are published, investors tend to exploit these anomalies until they disappear (Green, Hand, & Soliman, 2011). One of such anomalies is related to how stock prices react to earnings announcements. Specifically, several studies have documented a tendency for stocks to “drift” after earnings announcements in the same directions as the initial reactions. Thus, when companies report better-than-expected earnings, their stock prices jump immediately, earning positive abnormal returns. This result is consistent with investors who trade using the momentum strategies causing stock prices to overreact and deviate temporarily from their fundamental values (Chan, Hamao, & Lakonishok, 1993; Jegadeesh & Titman, 1993, 2001). The


existence of profitable momentum trading is perpetuated by the belief that prices of past winners (known as glamour or growth stocks) will continue to rise while the prices of past losers will decline beyond their fundamental values (Jegadeesh & Titman, 2001).

A contrary to the momentum strategy, the value approach suggests that the best way to make money in the market is to invest in undervalued stocks as determined by fundamental values (low P/E ratio and high book-to-market, BM, ratio) and short-sell the overvalued growth stocks (high P/E and low BM ratios). Unlike the momentum strategy, an investor who follows the contrarian strategy would sell the stock that moved up and buy the stock that moved down. Extensive research has been conducted on the value investing strategy, and findings suggest that it tends to outperform investing in growth stocks (Fama & French, 1992, 1995; Lakonishok, Shleifer, & Vishny, 1994), and it holds true for diverse asset classes, markets, as well as internationally (Asness, Moskowitz, & Pedersen, 2013; Athanassakos, 2013). While it is possible that value firms have high book-to-market ratios because of high financial distress and a greater degree of risk, Piotroski (2000) proposed to screen high BM firms based on a set of financial statement criteria to separate truly financially struggling firms from those value firms that are fundamentally financially sound.

This study applies accounting screens – Piotroski’s F-score and a derived variant of the Greenblatt (2006) MagicP formula – to S&P 500 firms between 2007 and 2014 in order to examine performance differences based on investment strategy (value versus growth), firm size, risk, and industry classification. The contribution of this study is the test of accounting screens on S&P 500 companies to examine whether these strategies can be applied to larger firms. Secondly, we introduce and test a new method, MagicP, to locate positive abnormal returns in the S&P 500. Thirdly, we test the robustness of these strategies using the fixed effects regression analysis to account for differences between firms and over time as the period under study includes the recent financial crisis and subsequent market recovery. Finally, we discuss the findings and implications as they pertain to individual and institutional investors who are seeking to optimize their investment strategies. The remainder of this paper is structured as follows: Section 2 provides a literature review, Section 3 describes the data collection and methodology, results appear in Section 4, finally, Section 5 concludes with the summary of findings and recommendations. LITERATURE REVIEW

As discussed above, while growth investment is based on past performance of stocks or growth momentum (Bauman, Conover, & Miller, 1998), value investing targets investing in undervalued stocks as determined by fundamental financial analysis (Graham, Dodd, & Cottle, 1934). There is considerable evidence that value stocks earn higher long-term returns than growth stocks. Basu (1977) found abnormal returns for U.S. stocks with low price-earnings ratios. Lo and MacKinlay (1990) showed that while the momentum strategy may work for individual stocks, it will not work in a portfolio of stocks because of positive cross-autocorrelations among these stocks, which would render a contrarian strategy a success. Lakonishok et al. (1994) also provided evidence that the contrarian strategy outperform the market. They contend that the strategy is successful because investors consistently overweigh announcement, short-term information, and thus overestimate the value of glamour stocks relative to value stocks, resulting in “suboptimal” investor behavior. This theory has also been tested internationally and results confirmed the efficacy of the contrarian strategy (Chan et al., 1993; Dhatt, Kim, & Mukherji, 2004).

What are some explanations offered for better performance of value stocks? One possible explanation may be that value firms have high BM ratios because of high financial distress and increased risk; thus, higher returns may be a mere reflection of the risk premium (Chen & Zhang, 1998; Fama & French, 1992, 1995). Another explanation for market anomalies is market mispricing. In particular, investors may have too pessimistic of a view about past performance of high BM firms and have negative expectations about these firms’ future performance (Lakonishok et al., 1994). Stock prices of such firms are bid down by pessimistic biases, which may be reversed in the future periods when positive earnings announcements are made (Porta, Lakonishok, Shleifer, & Vishny, 1995). There is also assertions that investors are


susceptible to cognitive failures and psychological biases that include loss aversion, overconfidence, and overreaction that may result in a suboptimal investment behavior (Lo, 2004, 2009) and in stock mispricing (De Bondt & Thaler, 1985; Hirschey & Nofsinger, 2008). Others provided an alternative explanation for the returns to value investing based on data-selection bias (Kothari, Shanken, & Sloan, 1995), but this suggestion was rejected by Chan, Jegadeesh, and Lakonishok (1995), who asserted that no such bias can explain the differential performance of value and growth investing.

Piotroski (2000) proposed to use accounting signals of financial soundness for high BM value firms to differentiate truly distressed firms from out-of-favor but financially strong firms. This is consistent with findings that show that while the return on growth or glamour stocks are mainly momentum driven (Asness, 1997), the assessment of value stocks should focus on firm fundamentals based on company’s financial statements. Investing based on momentum variables paired with fundamental variables have been shown to be successful (Guerard, Xu, & Gültekin, 2012). According to Piotroski (2000), financial reports are likely to provide the best and most relevant information that can be used to forecast future performance of high BM companies. We supplement the Piotroski’s F-score with the derived MagicP formula to compare the performance of value stocks with the market benchmark. Greenblatt’s (2006) principles suggest to buy a portfolio of 20-30 good stocks at bargain prices based on return on capital and earnings yield, and hold winners for at least one year (Lee, 2014). Unlike other studies that test accounting screens on small firms with high BM ratios (Piotroski, 2000; Woodley, Jones, & Reburn, 2011), this study tests this short-term buy-and-hold investment strategy to examine the performance of large value stocks within the S&P 500.

DATA AND METHODOLOGY

Data collection began with extracting of historical listings for the S&P 500. Authors regularly use the

S&P 500 as a benchmark for investment style/strategy comparison (Chan, Chen, & Lakonishok, 2002; Chen & De Bondt, 2004). First, we obtained the S&P 500 constituent list from S&P Capital IQ database. Each year, the S&P 500 may change slightly with the removal and addition of new companies; thus, we retrieved a listing of the S&P firms at the beginning of each year in the study period between 2007 and 2014. The financial statement components required for the derivation of the MagicP and Piotroski’s F-score were obtained from Capital IQ. We captured the share price at the beginning and the end of the trading year after the year-end financial reporting to calculate the holding period return for each stock included in our dataset. Adjusted close prices were employed to account for dividends and stock splits.

In preparing an investment strategy, stocks were screened for incorrect or missing data. Stocks were removed from the set if there was an incorrect ticker information, such as for international parent companies, a ticker that changed and was no longer on the S&P 500, or due to the M&A activity.1 Stocks were only removed from the set for missing data in year t; however, if data for the company were missing for year t+1, these stocks would remain in the set for the current period t (as the hypothetical investor would not have a prior knowledge of such an event). This would be reflected in the selling of the stock in year t+1.

Once the data were filtered, we applied the screens and performed sorting based on each investment strategy to identify top 50 stocks as our investment targets. F-Score is sorted in descending order as our work hypothesizes that a higher F-Score indicates a strong stock; conversely, the Total Rank is sorted in ascending order as the lower the rank the stronger the stock (i.e. #1 is the strongest). It is important to note that Piotroski’s screen has to be used in conjunction with the book-to-market rankings. Thus, we ranked companies by BM and utilized our screening strategies in order to choose the top 50 companies from the pool of value firms (similarly, we chose bottom 50 firms with low BM ratio, i.e., the growth stocks). Exhibit 1 presents a summary flowchart that describes the data extraction, filtering, and analysis processes.


EXHIBIT 1 DATA EXTRACTION, FILTERING, AND INVESTING FLOWCHART DIAGRAM

Flowchart A: Procedure for Data Extraction and Analysis

Piotroski F-Score Methodology

Piotroski (2000) demonstrated that by taking stocks with high BM ratios (value stocks) and then using a nine-point scale to test the financial strength of the companies, an investor can significantly outperform the market. The composite signal, denoted as F-score was calculated by summing the individual values of the binary performance scores as described in Exhibit 2.

EXHIBIT 2 PIOTROSKI’S METHODOLOGY

Variable Description Explanation Profitability F_ROA if ROA > 0 then F_ROA = 1, else 0 Reward positive net income in t0 F_CFO if CFO > 0 then F_CFO = 1, else 0 Reward positive cash flow from

operations in t0 F_ΔROA if ΔROA > 0 then F_ΔROA = 1, else 0 Reward higher ROA in t0 vs. t-1 EARN_QUALITY if CFO > ROA then EARN_QUALITY = 1, else 0 Reward if the cash flow from

operations exceeds net income Leverage, Liquidity, and Source of Funds

F_ΔLEVER if ΔLEVER < 0 then F_ΔLEVER = 1, else 0 Reward decrease in leverage in t0 compared to t-1

F_ΔLIQUID if ΔLIQUID > 0 then F_ΔLIQUID = 1, else 0 Reward increase in liquidity in t0 compared to t-1

EQ_OFFER If no equity issued EQ_OFFER=1, else 0 Reward absence of dilution in t0 Operating Efficiency F_ΔMARGIN if ΔMARGIN > 0 then F_ΔMARGIN = 1, else 0 Reward higher gross margin in

t0 compared to t-1 F_ΔTURN if ΔTURN > 0 then F_ΔTURN = 1, else 0 Reward higher asset turnover

(efficiency) in t0 compared to t-1 * where t0 and t-1 refer to the current and previous years, respectively as adopted from Van Der Merwe (2013).


Piotroski awarded up to four points for profitability: one for positive return on assets, one for positive cash flow from operations, one for an improvement in return on assets over the last year, and one if cash flow from operations exceeds income. He awarded one point if the company had positive operating cash flow and up to three points for capital structure and the company’s ability to meet future debt obligations. Ideally, the company would earn the highest score of nine. However, the time period investigated in this study includes a severe downturn when credit markets impaired balance sheets of many firms. According to Exhibit 3, the average F-score for our sample was 5.8 (out of 9) during the 2007-2014 period. The frequency distribution is bell shaped, relatively symmetrical and unimodal. The median was 6, which is only slightly greater than the average. The higher the F-score, the fewer are the red flags about the firm’s financial health. Therefore, we hypothesize that financially strong firms with high BM ratio (value firms) will have high F-scores, which will be positively associated with their future performance and stock returns.

EXHIBIT 3 DISTRIBUTION OF F-SCORES FOR S&P 500 FIRMS, 2007-2014

S&P 2007 2008 2009 2010 2011 2012 2013 2014 AVG

F-Score 5.8 5.5 5.3 6.3 6.1 5.7 5.8 6.0 5.8 MagicP Formula

Enhanced value strategies can realize higher returns than whole market strategies (Elze, 2010). This research proposes an enhanced investment strategy titled the MagicP formula. The MagicP formula utilized in our scenarios is an adaptation of Greenblatt’s Magic Formula (Greenblatt, 2006), where rankings become incorporated into the formula, market indicators, and Piotroski’s F-Score. We also employ the well-tested Piotroski’s F-score. This combination of F-score, market indicators, and rankings has not been explored in the financial investing literature, thereby demonstrating the novelty of this method.

0.2

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MagicP employs eight variables described in Exhibit 4. The first variable is a market indicator, and it is a ratio of closing price to earnings per share. The second variable is the return on equity. Each of the aforementioned variables is assigned a rank. The two ranks are added to produce a total indicator rank. The Piotroski F-Score is retrieved and subsequently ranked according to methodology listed above. A Final Total Rank is found by adding the total indicator rank and the Piotroski rank. It is then sorted in the ascending order and used as the key variable in making our investment selection decisions.

EXHIBIT 4

MAGIC_P METHODOLOGY

Variable Description Indicator 1 (Close Price)/(Earnings Per Share) Indicator 2 Return on Equity Rank Indicator 1 Ranking of Indicator 1 Rank Indicator 2 Ranking of Indicator 2 Total Indicator Rank Rank Indicator 1 + Rank Indicator 2 Piotroski Piotroski’s F-Score Piotroski Rank Rank of Piotroski F-Score Final Total Rank Total Indicator Rank + Piotroski Rank

The average MagicP ranking in Exhibit 5 is 484. The total ranking ranges between a minimum of 2 and a maximum of 1378, with the median of 457. The distribution is bell shaped and relatively symmetric, with both skewedness and kurtosis minimally above the values expected for a normal distribution. Since MagicP rankings are sorted in the ascending order, we hypothesize that financially strong value firms will have a lower Total Rank measure compared to growth firms with low BM ratios.

EXHIBIT 5 DISTRIBUTION OF MAGIC_P RANK FOR S&P 500 FIRMS, 2007-2014

S&P 2007 2008 2009 2010 2011 2012 2013 2014 AVG Total Rank 441.1 478.4 460.4 478.6 487.4 495.3 504.9 523.0 484

05.

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015

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0 500 1000 1500total rank

MagicP Total Rank S&P 500 firms

Mean = 484 StDev = 236 Skewness = 0.54 Kurtosis = 3.03 Median = 457


RESULTS

Although Piotroski’s screen was originally implemented to separate winners from losers among financially distressed stocks, this study tests whether the tool can be applicable for the purposes of picking winners among the S&P 500 stocks to improve short-term portfolio returns. Table 1 reports the average market returns for S&P 500 stocks, value stocks, and growth stocks. The results indicate that the selection of financially strong firms with high book-to-market ratios (top 50 companies based on the MagicP formula and the F-score) yielded impressive results, earning average returns of 15.53% and 17.21%, respectively, compared to the overall universe of S&P 500 firms with the return of 11.46%, and growth stocks (bottom 50 companies) with 12.59% and 3.5% returns based on the same rankings. The standard deviation of returns based on MagicP is slightly greater than that of the average market, 25.12% versus 22.54%, but risk differences are statistically insignificant according to the systematic risk measures reported in Table 2. The bottom 50 firms based on the MagicP ranking performed roughly the same as the market, while the bottom firms based on the F-score metric significantly underperformed the market and top 50 F-score firms in the sample. This affirms the F-score’s ability to separate “winners” from “losers”.

TABLE 1 AVERAGE MARKET RETURNS FOR S&P 500, VALUE, AND GROWTH STOCKS

This table reports the average market returns and standard deviations for S&P 500, top 50 and bottom 50 firms based on MagicP and Piotroski’s F-score measures over the period between 2007 and 2014. MagicP employs eight financial statement variables as described in Exhibit 4. The measures are added and ranked in ascending order resulting in the Total Rank measure. Piotroski’s F-score uses a nine-point scale to test the financial strength of the companies. The composite signal is calculated by summing the individual values of the binary performance scores as described in Exhibit 2.

Market Return

S&P 500* Top 50 MagicP

Bottom 50 MagicP

Top 50 F-score

Bottom 50 F-score

2007 1.84% 9.50% -4.44% 9.44% -8.21% 2008 -32.65% -31.92% -29.81% -24.99% -50.47% 2009 42.42% 50.35% 34.76% 50.89% 57.22% 2010 18.61% 17.75% 22.62% 23.86% 5.49% 2011 0.59% 0.09% 6.50% 5.22% -11.95% 2012 18.47% 31.81% 20.40% 27.05% 7.99% 2013 29.97% 35.28% 36.54% 30.86% 25.93% 2014 11.65% 11.41% 14.17% 15.35% 1.96% Mean 11.46% 15.53% 12.59% 17.21% 3.50% STDEV 22.54% 25.12% 21.89% 22.20% 30.95%

*Note: The discrepancies between returns derived from this dataset and the reported S&P 500, such as Morningstar, may arise due to the fact that we excluded securities of companies that had missing or insufficient inputs for our screening formulae (see the description of this in Data Description and Methodology section).

The possibility exists that higher returns on value stocks are driven by higher risk and are only a reflection of risk premium. Table 2 reports market betas for top value stocks based on F-score and MagicP formula rankings and compares the results to the overall S&P 500 portfolio. The table shows that while value portfolio generated higher returns for top performing stocks, the market betas of the portfolios are statistically the same (1.12 for the market, 1.17 for MagicP, and 0.96 for F-score firms) based on the two-tail paired t-test. Thus, market risk is not an obvious explanation for the differences in returns. Similar findings were reported in Chan and Lakonishok (2004).


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The S&P panel in Table 2 reports the levels and statistical significance of various financial indicators from S&P firms and top 50 firms (value) based on MagicP and F-score rankings (panels A and C). We note that rankings, annualized market return, and market capitalization are statistically different, but beta and total assets are not. Table 2 also reports the levels and statistical significance of these measures between top 50 and bottom 50 firms based on MagicP (panels A and B) and F-score (panels C and D). All measures for top 50 MagicP firms are statistically different from their bottom 50 counterparts. Top 50 MagicP firms report better Total Rank and market returns, higher beta, and lower market capitalization. According to F-score rankings, top 50 F-score stocks have a significantly higher market return, similar betas, but insignificant differences between the size of top and bottom firms.

There is also a possibility that the value strategy is fundamentally riskier and should underperform relative to the growth strategy during market downturns. Both tables 1 and 2 indicate that when the market return was negative, value stocks outperformed the average market. Top 50 value stocks based on the F-score measure outperformed the market and growth stocks in each year between 2007 and 2014. The outperformance was more pronounced during the worst market environment; the bottom 50 F-score (growth) stocks performed significantly worse than other firms. When the market earned a positive return, the top 50 firms based on the MagicP ranking at least matched the market and F-score top ranking firms strongly outperformed the market in each year. This confirms that superior performance of top 50 firms does not seem to reflect their higher fundamental risk (as in Chan and Lakonishok, 2004) or the market cycle risks.

A competing explanation for the possibility of higher return on the top 50 value stocks can be drawn from the nature of industries that these stocks represent. For example, we know that a significant portion of growth-oriented stocks come from the technology industry. However, the sharp rise and decline in recent years of the technology sector call into question the argument that these stocks are less risky investments than value stocks. We decided to examine the composition of the S&P portfolio and portfolios of our top 50 firms based on MagicP and F-score strategies. Exhibit 4 shows that the composition of S&P remains quite static over time, with four largest sectors – Financials, Consumer Staples, Information Technology, and Industrials – accounting for approximately 60% of the portfolio in terms of the number of firms and their capitalization. The largest constituents of the top 50 portfolio based on MagicP are Financials, Energy, Consumer Staples, and Information Technology, while of top 50 firms based on F-score are Consumer Staples, Financials, Industrials, and Information Technology. More importantly, there is a lot of movement within these sectors as reflected in panels b and c in Exhibit 4, suggesting that the composition of the value portfolio is adjusted to reflect the changes in macroeconomic and financial markets conditions. Thus, we see the movement within the Financials sector in 2009 following the credit crisis reflecting the very nature of the value strategy of acquiring firms that become a good value (low P/E or high BM ratios) and short-sell the stocks with the high ratios.


EXHIBIT 4 COMPOSITION OF S&P 500 VS. VALUE PORTFOLIOS

This exhibit presents the composition of the S&P 500 (Panel A) and value portfolios based on MagicP (Panel B) and F-score (Panel C) by industry sector between 2007 and 2014. The data are derived from S&P Capital IQ.

Panel A: S&P 500 Portfolio

Panel B: Value Firms by MagicP

Panel C: Value Firms by F-Score


Regression Analysis To check the robustness of our statistical results and to formally test the relationship between the

accounting screens and annual returns, we ran regression analysis of market returns on control variables comprised of firm fundamental characteristics, including company size, market value, market risk, as well as the total rank based on MagicP, and the F-score measure. There were a total of 553 distinct companies in our sample between 2007 and 2014, which comprised a panel dataset with 3605 observations. We chose to use fixed effects regressions to examine the relationship between independent control variables on market return. Fixed effects (FE) explore the relationship between predictors and outcome variable within companies. Ordinary least squares (OLS) regression is inappropriate in this case because it assumes homogeneity of firm characteristics. Our sample includes companies from ten S&P sectors and differences between companies’ metrics should certainly impact the overall market performance. We need to control for that and the FE regression allows us to do that by removing the effect of time-invariant characteristics so we can assess the net effect of the predictors on the outcome variable.2. Another important assumption of the FE model is that each entity is different; therefore, the entity’s error term and the constant (which captures individual characteristics) should not be correlated with other entities. The equation for fixed effects model is written as:

𝑌𝑖𝑡 = 𝛼𝑖 + 𝜷𝑿𝑖𝑡 + 𝜀𝑖𝑡 (1) where Yit is the dependent variable (return) for company i in year t; αi is the unknown intercept for each company i; Xit represent independent control variables for each company in time t and β are their coefficients; and εit is the error term (Baltagi, 1985; Greene, 1983, 2003; Wooldridge, 2012). Another way to see the fixed effects model is by using binary variables (dummies) for firm effects, so the expanded equation becomes:

𝑌𝑖𝑡 = 𝛼 + 𝛽1𝑋1,𝑖𝑡 + ⋯+ 𝛽𝑘𝑋𝑘,𝑖𝑡 + 𝛾2𝐶2 + ⋯+ 𝛾𝑛𝐶𝑛 + 𝜀𝑖𝑡 (2) where Yit is the dependent variable for company i in year t; α is the intercept; Xit represent independent control variables and βk are the coefficients for independent variables; Cn are the company dummies and γn are the coefficients for the binary regressors associated with each company (these capture the firm effects and there are n-1 of these observations); and εit is the error term. Control variables include F-score, total rank based on the MagicP ranking, market risk beta, the natural logarithm of total assets (ln_TA), market value (MV), and book-to-market ratio (BM). We ran the model using STATA (Allison, 2009), and the results are presented in Table 3. Column 1 shows the results of regressions with control variable and firm dummies. There is a positive relationship between F-score and total market return, but there is a negative association between MagicP total rank and market return. The coefficients are statistically and economically significant, suggesting that the higher the F-score, the more financially stable the company is, the higher is the market return, while the greater the total rank (the more removed the company is from the top), the lower the return. These findings are consistent with our hypotheses outlined in Section 3.

We also document a negative association between market returns and ln_TA, market beta, and market values indicating that a rapid growth and taking on additional risk did not necessarily translate into better performance for this sample. Finally, BM ratio is positively and significantly related to the market return, confirming that value investing strategy pays off not just for top 50 firms, but also for the entire sample. The observed R2 is 16.71%.

We also ran the FE model that in addition to company dummies included time dummy effects (Tt where t ranges to t-1) to capture variability in performance due to economic and market cycles.3

𝑌𝑖𝑡 = 𝛼 + 𝛽1𝑋1,𝑖𝑡 + ⋯+ 𝛽𝑘𝑋𝑘,𝑖𝑡 + 𝛾2𝐶2 + ⋯+ 𝛾𝑛𝐶𝑛 + 𝜕2𝑇2 + ⋯+ 𝜕𝑡𝑇𝑡 + 𝜀𝑖𝑡 (3)

As explained above, our sample period coincides with one of the worst credit cycle phenomenon of

Great Recession (2008-2010) and we wanted to capture its effects. The results are reported in the column


for Model 2 of Table 3. While the levels of coefficients and their statistical significance do not change considerably compared to the first model specification, R2 rises to 39.28%, thus including time fixed effects improved the model. In sum, the relationship between annual market returns and the accounting screen variables of F-score and MagicP remained robust after controlling for the firm and time effects.

TABLE 3

DETERMINANTS OF MARKET RETURNS BASED ON FIXED EFFECTS REGRESSION

The table reports coefficient estimates of the determinants of market returns for the entire sample of companies listed on S&P 500 between 2007 and 2014. The sample was adjusted for companies that did not include all information necessary to derive the MagicP and Piotroski F-score. The panel included 553 companies over 8 years with a total number of observations of 3605. The standard errors are in parenthesis below each estimate. The dependent variable Market Return. Model 1: 𝑌𝑖𝑡 = 𝛼 + 𝛽1𝑋1,𝑖𝑡 + ⋯+ 𝛽𝑘𝑋𝑘,𝑖𝑡 + 𝛾2𝐶2 + ⋯+ 𝛾𝑛𝐶𝑛 + 𝜀𝑖𝑡 Model 2: 𝑌𝑖𝑡 = 𝛼 + 𝛽1𝑋1,𝑖𝑡 + ⋯+ 𝛽𝑘𝑋𝑘,𝑖𝑡 + 𝛾2𝐶2 + ⋯+ 𝛾𝑛𝐶𝑛 + 𝜕2𝑇2 + ⋯+ 𝜕𝑡𝑇𝑡 + 𝜀𝑖𝑡. ***, **, and * signify 1%, 5%, and 10% significance, correspondingly.

Variable Model 1 Model 2 F-Score 0.008947**

(0.00555) 0.008152**

(0.00493) Total Rank -0.000216***

(0.00005) -0.000204***

(0.00004) Beta -0.08806***

(0.02012) -0.0389** (0.01748)

Log of Total Assets -0.07483*** (0.02285)

-0.19291*** (0.0222)

Market Value -0.000186*** (0.000297)

-0.00012*** (0.00026)

Book-to-Market Ratio 0.35181*** (0.0172)

0.24729*** (0.0155)

Constant 0.85122*** (0.2221)

1.879*** (0.2131)

Company Dummies Yes Yes Year Dummies No Yes Number of obs. 3605

3605

R2 0.1671 0.3928 CONCLUSIONS

Using both the Piotroski score and the derived MagicP formulae proved to be an effective screening strategy that resulted in risk-adjusted outperformance of chosen value stocks over a market weighted benchmark portfolio and its subset of growth stocks. Unlike other studies that utilized similar tests on small firms with high book-to-value (BM) ratios, we examined the performance of large value stocks between 2007 and 2014 and found that: 1) Financially strong firms selected by the means of the Piotroski F-score and the MagicP formulae outperformed the average returns of S&P 500 stocks; 2) The highest F-score and MagicP stocks consistently beat the performance of lower BM (growth) stocks, indicating that investors seeking above average returns should concentrate on investing in value stocks; 3) Using a variety of indicators, including market beta and return volatility, the chosen value stocks were not riskier than growth stocks; 4) Regarding the impacts of economic downturns, we found that value stocks suffered less severely during periods of market corrections. In fact, top 50 value stocks based on the Piotroski score outperformed S&P and growth stocks during the entire sample period, and the outperformance was more pronounced during the worst market conditions.


Since the value stock screening is based on the objective financial statement analysis, we assert that it can help investors reduce the need for complex extensive market and firm research, thus lowering a possibility of suboptimal strategies driven by judgmental biases inherent in investment behavior. This study has another implication. It is typically assumed that individual and institutional investors can choose from a diverse array of stocks; however, this screening strategy may become a valuable tool for investors or agents who are constrained to invest within a universe of large-cap stocks.

Limitations

Some of the limitations of this study include the fact that the original Piotroski screen (2000) was performed on smaller firms in financial distress and reportedly worked best for short investment time horizons in order to capitalize on the improved share price when the first good earnings announcements follow portfolio formation. However, our study was an attempt to check the strategy on S&P 500 firms that are larger and have more transparent information. The results held true for this sample implying that investors can rely on objective financial reports in an effort to differentiate the market value and intrinsic value effects of a high BM firm.

Supporting the EMH, is the observation that once anomalies are published investors tend to exploit these anomalies until they disappear (Green et al., 2011). Thus, it is necessary to test the screen’s effectiveness over a longer history. This study makes a contribution to current knowledge by determining whether the accounting-based filtering process has been consistently successful during the recent past, specifically during the market downturn of 2008-2010. The study concludes that a combination of high F-score and MagicP rankings resulted in outperformance over the market weighted portfolio and growth stocks. ENDNOTES

1. For example, Chrysler Corp. was listed as ticker “C”; however, due to mergers and acquisitions is now listed as FCAU due to the merger with Fiat. Similarly, since Chrysler relinquished ticker “C”, Citicorp abandoned “CITI” in favor of “C.”

2. To confirm our preference of fixed effects versus random effects, we ran a Hausman test that basically tests whether the unique error terms are correlated with regressors, which would suggest that a random effects should be used. The Hausman statistic rejected the random effects model in favor of fixed effects (Green, 2009).

3. We tested the time parameters effects to examine whether the coefficients on time dummies are jointly equal to 0, but this hypothesis was rejected according to the F statistic; therefore, time fixed effects were warranted.

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The Necessity for a New Kind of Accounting: Conscious Accounting

Miriam Gerstein Brooklyn College of the City University of New York

Hershey H. Friedman

Brooklyn College of the City University of New York

Conscious capitalism is about creating businesses that are concerned with all stakeholders and do not simply focus on maximizing profits for shareholders. It is also about compassion and making the United States and the entire world a better place. Conscious capitalists want to provide employees with meaningful work at fair wages. Organization leaders who follow its principles have to be people of integrity and motivated by a higher purpose than greed and have a desire to serve the public. This paper posits that accountants and auditors must become the conscience of the organization and therefore have an obligation to ensure an ethical tone at the top and have to practice conscious accounting. This paper shows how accountants can provide firms with a competitive edge and create sustainable, flourishing businesses with a higher purpose. INTRODUCTION

In order to succeed, the organization of today has to be willing to change the way it conducts business

not only by being innovative, resilient, and quick to adapt to changing conditions, but most importantly, by acting in a socially responsible manner. In the Information Age / Knowledge Economy, a firm needs accountants and auditors that understand the value of the triple bottom line (TBL)—people, profits, planet (Friedman & Lewis, 2015). The accountant of today has to be concerned with ensuring that a firm is sustainable. Short term thinking can destroy a firm. In fact, Wartzman (2013) offers two compelling arguments why the objective of maximizing shareholder value may finally be coming to an end.

First, there are graduate students, many of whom are passionate about changing the world—and not just getting rich. The trouble is that all too many business and law schools undermine this spirit by teaching traditional classes that reinforce a short-term mindset. As Cornell law professor Lynn Stout, one of those at the Claremont gathering, has made abundantly clear, by the time these students hit the job market, they’ve come to falsely believe that the primary purpose of the corporation is to “maximize shareholder value.”… The second group where there’s dissonance can actually be found in the executive suite. Yeah, sure, some people will always be greedy and manipulate short-term financial results because it’s in their narrow self-interest. But to be cynical is to miss a major


opportunity: Most people go into business because they’re eager to offer a product or service that provides customers—and, by extension, society as a whole—something of value. They hate the pressure, from Wall Street and elsewhere, to focus on short-term financial metrics.

It is now quite obvious to almost everyone except some avaricious executives that greed does not pay

and indeed can destroy a healthy company. Greed may work in the short term, but can result in a disaster in the long term. This is what The Economist had to say about greed before the Great Recession of 2008:

Morally, also, there is a world of difference between greed and self-interest. The first, even if it were not self-defeating, would still be a gross perversion of the second. Failing to see this distinction, and thus concluding without further thought that private enterprise is tainted, is a kind of ethical stupidity. Greed is ugly (The Economist, 2005).

Greed caused many CEOs to find creative ways (including the use of irregular accounting and even fraud) to boost short-term earnings at the expense of long-term value creation. The simplest way to accomplish this was to take on huge amounts of risk “in search of easy profits that would lead to a higher stock price” (Nocera, 2012).

The assumption that what worked in the past will continue to work in the future is a good way to destroy a healthy organization (Raphan and Friedman, 2014; Friedman & Lewis , 2014). A number of formerly prominent American companies - AOL, Blockbuster, Blackberry, Dell, Digital Equipment, General Motors, Kodak, MySpace, Radio Shack, Sears, Toys “R” Us, Yahoo, to name just a few -have either lost a great deal of their luster or have disappeared. Competitive conditions are very different today thanks to the Internet and globalization. If one examines Friedman & Friedman’s (2015) technology timeline, it becomes very apparent that the number of innovations today is staggering and more than at “any time in human history.” This exponentially increasing rate of change does not bode well for short-term focused organizations that are slow to respond to changes. Friedman & Friedman (2015) conclude:

It is hoped that by examining the above timeline, it will become obvious that the job of corporate leaders today is to foster creativity. It should not come as a surprise that the average life of a public firm is about 10 years.

Vijay Govindarajan, professor of business and author, believes that successful companies tend to fall into three traps that make them obsolete:

First is the physical trap, in which big investments in old systems or equipment prevent the pursuit of fresher, more relevant investments. There's a psychological trap, in which company leaders fixate on what made them successful and fail to notice when something new is displacing it. Then there's the strategic trap, when a company focuses purely on the marketplace of today and fails to anticipate the future. Some unlucky companies manage a trifecta and fall into all three traps (Newman, 2010).

CONSCIOUS CAPITALISM

A number of CEOs feel the ideal way to grow a company is to understand that business is about considerably more than just maximizing shareholder value and making profits. Executives have to run their companies understanding that business has a higher purpose. The credo of conscious capitalists is at their website (http://www.consciouscapitalism.org/).

We believe that business is good because it creates value, it is ethical because it is based on voluntary exchange, it is noble because it can elevate our existence and it is heroic


because it lifts people out of poverty and creates prosperity. Free enterprise capitalism is the most powerful system for social cooperation and human progress ever conceived. It is one of the most compelling ideas we humans have ever had. But we can aspire to even more. Conscious Capitalism is a way of thinking about capitalism and business that better reflects where we are in the human journey, the state of our world today, and the innate potential of business to make a positive impact on the world. Conscious businesses are galvanized by higher purposes that serve, align, and integrate the interests of all their major stakeholders. Their higher state of consciousness makes visible to them the interdependencies that exist across all stakeholders, allowing them to discover and harvest synergies from situations that otherwise seem replete with trade-offs. They have conscious leaders who are driven by service to the company’s purpose, all the people the business touches and the planet we all share together. Conscious businesses have trusting, authentic, innovative and caring cultures that make working there a source of both personal growth and professional fulfillment. They endeavor to create financial, intellectual, social, cultural, emotional, spiritual, physical and ecological wealth for all their stakeholders. Conscious businesses will help evolve our world so that billions of people can flourish, leading lives infused with passion, purpose, love and creativity; a world of freedom, harmony, prosperity and compassion.

The Great Recession of 2008 has shown us what happens when capitalism becomes predatory. The

CEOs who are joining the Conscious Capitalism organization want to use capitalism to “elevate humanity” by serving all stakeholders, not just shareholders. They want to use capitalism to improve the world. Mackey & Sisodia (2013) note that the term “capitalism” was coined by Karl Marx, an individual who was extremely critical of it. Marx believed that capitalism could only result in a nightmare for most of the world. Greedy capitalists would be using cheap labor working for subsistence wages to enrich themselves. Actually, capitalism has done more to reduce poverty than any economic system the world has known.

After tens of millennia in which 85-90% of human beings lived on less than a dollar a day in today’s terms, worldwide per capita incomes have increased nearly fifteen-fold in constant dollars. Today, about 16% of the world’s population lives on less than a dollar a day. Adjusting for quality and affordability, it is estimated that the average American is 100 times better off today than 200 years ago. Average life expectancy has climbed from about 30 to over 67 years in that time span, and human population has risen from one billion in 1820 to over seven billion today (Mackey & Sisodia, 2013).

Businesses run by conscious capitalists create caring cultures where there is concern for all stakeholders. Employees feel that they are engaged in meaningful work and have a sense of professional fulfillment (Mackey & Sisodia, 2013). There is evidence that the investment returns to brands run on conscious capitalistic principles have been 1025% over the past 10 years vs. 122% for the S&P 500 (King & Fromm, 2013). Lewis (2014) provides examples of firms that are practicing conscious capitalism.

One advantage of being a conscious capitalist is that consumers are willing to spend more for products or services made by socially responsible companies (King & Fromm, 2013). A 2013 Cone Communications study dealing with corporate social responsibility found that more than 90% of consumers in ten countries would boycott companies that behaved in a socially irresponsible manner. More than 50% of consumers claimed to have avoided buying products from companies because of what they felt was “bad corporate behavior” (O’Donnell, 2013). Moreover, organizations that are seen as


socially responsible find it easier to attract and retain engaged employees than those that are not perceived this way. Having engaged employees will ultimately have positive effects on net income and growth (Gross and Holland, 2011).

Alperovitz & Hanna (2015) assert that minorities and young people are very skeptical about capitalism. Pope Francis has also attacked capitalism and referred to unfettered capitalism as the “dung of the devil” (Reuters, 2015). It is time for people to understand that conscious capitalism is the only way for a company or country to prosper. Nothing good can come from a “greed is good” approach to capitalism. CONSCIOUS ACCOUNTING

Tamayo de-Guzman (2012) asserts that:

Today’s professional accountants are less involved in traditional accounting functions and are more concerned with leadership and management. Today’s accountants are leaders in their field providing key support to senior management and are directly involved in many important decisions.

The authors agree and feel that the accounting profession has to be in the vanguard in encouraging firms to be part of the conscious capitalism movement. It is not only the business environment that has changed; the discipline of accounting has transformed itself from its traditional “bean counter” image. In the post Sarbanes-Oxley world, accounting is more than just the “language of business”; it is the “conscience of an organization.” According to The Institute of Internal Auditors (IIA):

Serving as the conscience of an organization is one facet of the internal auditor’s function. A strong sense of ethics is required to fulfill this responsibility. Like any skill or ability, a strong sense of ethics requires training and understanding (Putra, 2010).

Examination of the COSO 2013 Internal Control – Integrated Framework (2013) makes it apparent that the role of the accounting and auditing profession is not what it used to be. Today, accountants and auditors have an obligation to ensure an ethical tone at the top. Moreover, they have a responsibility to all stakeholders including customers, employees, and society, not just management, investors, and creditors. In particular, they have an obligation to ensure that a company does not engage in risky behavior that jeopardizes the long-term health of the organization. In the knowledge economy, accountants have to be able to see the big picture and provide guidance that will enable an organization to thrive. The Institute of Internal Auditors (2012) has the following to say about the crucial need for auditors to create a corporate culture where ethical decisions are made:

What rationalization does a company make to justify a corporate culture where ethics are ignored? In recent years, greed, fraud, and a lack of ethical conduct have led to the collapse of many organizations. A variety of internal and external pressures can lead companies down the wrong path. And once the first misstep is taken, it’s a slippery slope to hurting stakeholders, the community, and your reputation.

Accountants and auditors that provide firms with healthy, ethical recommendations that give them a competitive edge will be of great value.

It is now quite obvious how accounting and auditing irregularities contributed to the last decade’s’ debacles at Enron, Lehman Brothers (the largest bankruptcy in history), Washington Mutual, WorldCom, and others. More recently, accounting scandals have occurred at international institutions such as Britain’s Tesco, the largest supermarket chain in the world; Japan’s Olympus Corporation; and the Vatican’s Institute for Works of Religion, also known as the Vatican Bank (Pullella, 2014; Infinit Accounting, 2014). The SEC filed 99 accounting-fraud investigations (a 46% increase from the previous


year) in the fiscal year that ended September 30, 2014. This year, they started more than 100 investigations (Eaglesham & Rapoport, 2015). Recently, the president of Toshiba as well as several executives resigned over a $1.2 billion accounting scandal involving overstating profits over a 7-year period. A company statement said: "There has been inappropriate accounting going on for a long time, and we deeply apologize for causing this serious trouble for shareholders and other stakeholders.” It seems that "Toshiba had a corporate culture in which management decisions could not be challenged" (Ito, 2015).

Let us examine areas where conscious accounting can make a difference. COMPENSATION OF CEOS AND EMPLOYEES

According to a Watson Wyatt survey, approximately 90% of institutional investors believe that top executives are dramatically overpaid (Watson Wyatt, 2006:3). Warren Buffet asserted that “the ability of corporations to rein in skyrocketing CEO pay is the ‘acid test’ of corporate governance reform” (Heritage Institute, 2007). Buffett stated:

Too often, executive compensation in the U.S. is ridiculously out of line with performance. Getting fired can produce a particularly bountiful payday for a CEO. Indeed, he can “earn” more in that single day, while cleaning out his desk, than an American worker earns in a lifetime of cleaning toilets. Forget the old maxim about nothing succeeding like success: Today, in the executive suite, the all-too-prevalent rule is that nothing succeeds like failure (Heritage Institute, 2007).

That was before the Great Recession of 2008; Excessive compensation of CEOs is a problem that has gotten worse. A study conducted by Alyssa Davis and Lawrence Mishel at the Economic Policy Institute found that the CEO to average worker ratio was 295.9 in 2013 (it was 20 in 1965) (Morgenson, 2015). A different 2013 study showed that CEOs earn approximately 331 times as much as the average worker; and the CEO-to-minimum-wage worker pay ratio was an outrageous 774:1 (Dill, 2014). Accountants and auditors should encourage companies to work to reduce the CEO to average worker salary ratio. This would send a strong message to employees that a company cares about employees. In fact, Mark Bertolini, CEO of Aetna, following a near-death experience, gave thousands of the lowest paid workers at Aetna a 33% raise; and the minimum wage went from $12 to $16 an hour (Gelles, 2015). Often, paying a fair wage to workers does not add to costs since it results in reduced turnover and more productive and engaged employees (Friedman & Lewis, 2015). It is not easy to antagonize millions of people and make it to the list of “America’s Most Hated Companies” (Hess & McIntyre, 2015). Several companies that have made the list, such as McDonald’s and Wal-Mart, are there partly because they pay low wages to employees (McDonald’s and Wal-Mart).

Employee distrust of management is quite prevalent among American workers; this also may have a great deal to do with how poorly workers are being compensated relative to top management. Accountants who are concerned about the long-term health of a company have an obligation to warn management that this kind of negative image can ultimately destroy a company. Organizations that are seen as having no soul will find it difficult to attract the most creative and engaged employees. There is hope that the new rule of the Securities and Exchange Commission requiring public disclosure of the ratio of CEO pay to median pay of employees will finally rein in the runaway, excessive pay of CEOs (Delamaide, 2015). Delamaide (2015) avers:

The noxious practice of "aligning" executive compensation with shareholder interests as measured by the stock price — the standard promoted by former General Electric CEO Jack Welch and his ilk — has subordinated every other interest of the company to juicing the stock price.


Some of the smarter companies in this country are shifting away from a narrow view of shareholder value in favor of a more comprehensive view of sustainable value that embraces other stakeholders in a company besides the investors — employees, customers, suppliers, communities and society as a whole (Delamaide, 2015).

It also suggests that the only person responsible for the success of the organization is the CEO, an assumption that is ridiculous. In any case, CEO pay that has run amok is not sustainable or good for the long-term health or image of a company. ENSURING THAT EMPLOYEES ARE ENGAGED

The second biggest problem facing business is “retention and engagement” (Bersin, 2015). There is a great deal of evidence that “being interested in a task is essential to being good at it” (O’Keefe, 2014). Employees that are engaged see their jobs as more than a way to make a living; they are passionate, enthusiastic, energetic, creative, and care about the organizations for which they work (Gross & Holland, 2011). Because they are emotionally committed to their organizations, they will do everything possible to enhance its reputation. This is why there is a strong relationship between performing meaningful work and employee engagement (Bersin, 2015). One survey found that 80% of respondents aged 13-25 want to work for a socially responsible firm that “cares about how it impacts and contributes to society” (Meister, 2012).

The Gallup organization has been measuring employee engagement since 2000 and its research shows that approximately 70% of American workers are disengaged (Harter & Adkins, 2015). There is quite a bit of evidence that employee engagement can provide a company with a huge competitive advantage. It is therefore important for accountants to be aware of this key metric and encourage CEOs to increase the percentage of employees who are engaged (Christian, Garza & Slaughter, 2011; Crim & Seijts, 2006). Among American workers, “job satisfaction is at its lowest level – 45 percent – since record-keeping began over two decades ago” (Barker, 2014).

It is not that difficult to increase employee engagement. Much of employee disengagement has to do with poor management and leadership. The following is just one example of what managers do wrong:

Gallup researchers have studied human behavior and strengths for decades and discovered that building employees’ strengths is a far more effective approach than a fixation on weaknesses. A strengths-based culture is one in which employees learn their roles more quickly, produce more and significantly better work, stay with their company longer, and are more engaged. In the current study, a vast majority (67%) of employees who strongly agree that their manager focuses on their strengths or positive characteristics are engaged, compared with just 31% of the employees who indicate strongly that their manager focuses on their weaknesses (Harter & Adkins, 2015).

A simple way to improve employee engagement is to communicate in a positive, honest manner with employees. According to the American Psychological Association’s 2014 Work and Well-Being Survey, only 50% of American workers feel that their “employer is open and upfront with them” (APA, 2014). Only 47% of respondents indicated that they were satisfied with employee recognition practices (APA, 2014). It is important for employees to feel valued by employers since those who do feel appreciated are more likely to be in good psychological health, more likely to be engaged in their jobs, and less likely to feel stressed than those who do not feel valued (APA, 2014). There is evidence that the more autonomy workers have, the happier they are with their jobs (Barker, 2014). Old-style autocratic leaders that micromanage employees harm the productivity as well as the happiness of employees.


Thomas (2009) feels that employee engagement is influenced by the following four intrinsic rewards:

Sense of meaningfulness. This reward involves the meaningfulness or importance of the purpose you are trying to fulfill. You feel that you have an opportunity to accomplish something of real value—something that matters in the larger scheme of things. You feel that you are on a path that is worth your time and energy, giving you a strong sense of purpose or direction. Sense of choice. You feel free to choose how to accomplish your work—to use your best judgment to select those work activities that make the most sense to you and to perform them in ways that seem appropriate. You feel ownership of your work, believe in the approach you are taking, and feel responsible for making it work. Sense of competence. You feel that you are handling your work activities well—that your performance of these activities meets or exceeds your personal standards, and that you are doing good, high-quality work. You feel a sense of satisfaction, pride, or even artistry in how well you handle these activities. Sense of progress. You are encouraged that your efforts are really accomplishing something. You feel that your work is on track and moving in the right direction. You see convincing signs that things are working out, giving you confidence in the choices you have made and confidence in the future.

It should be noted that management engagement and employee engagement are related. Management and leadership engagement was at 40.4% in June 2015, which means that the majority, about 60%, were not engaged (Adkins, 2015):

Gallup research shows that a manager's engagement -- or lack thereof -- affects his or her employees' engagement, creating a "cascade effect." Essentially, employees' engagement is directly influenced by their managers' engagement -- whose engagement is directly influenced by their managers' engagement. Although managers represent the most engaged workgroup in the U.S., nearly 60% of this group is not engaged or is actively disengaged. Managers' lack of engagement may be at least somewhat responsible for their employees' same lack of engagement. If the nation's employers can continue to raise the levels of engagement among their leaders and managers, they finally may be able to accelerate their overall levels of employee engagement (Adkins, 2015).

A company that wants managers and employees engaged has to be mission-driven. The mission should indicate that the organization has a powerful sense of purpose and it should be defined in terms of all stakeholders, not just shareholders. Bersin (2015) avers that “’mission-driven companies have 30 percent higher levels of innovation and 40 percent higher levels of retention and tend to be first or second in their market segment. Another way of improving productivity and engagement is by providing workers with profit sharing and/or stock ownership. Southwest Airlines did just that, by giving its workers $355 million of its $1 billion in corporate profits (Blasi, Freeman & Kruse, 2015).

Crim & Seijts (2006) stress that “Leaders should actively try to identify the level of engagement in their organization, find the reasons behind the lack of full engagement, strive to eliminate those reasons, and implement behavioral strategies that will facilitate full engagement.” We feel that accountants and auditors must also be concerned with this key metric since there is a strong correlation between employee engagement and customer satisfaction, productivity, creativity, reduced turnover, and profits (Bersin, 2015, Sorenson, 2013; Gross and Holland, 2011; Thottam, 2005). All these are important for the long-term health of an organization.


CONCERN FOR QUALITY

Executives often make the mistake of believing that higher quality results in higher cost. This comes from short-term thinking and not looking at the big picture. Ross (2008) describes three different opinions held by managers involved in quality.

Higher quality means higher cost: Quality attributes such as performance and features cost more in terms of labor, material, design, and other costly resources. The additional benefits from improved quality do not compensate for the additional expenses. The cost of improving quality is less than the resultant savings: Deming promoted this view, which is still widely accepted in Japan. The savings result from less rework, scrap, and other direct expenses related to defects. This paved the way of continuous process improvement among Japanese firms. Quality costs are those incurred in excess of those that would have been incurred if product were built or service performed exactly right the first time: This view is held by adherents of the TQM philosophy. Costs include not only those that are direct, but also those resulting from lost customers, lost market share, and many hidden costs and foregone opportunities not identified by modern cost accounting systems.

In many cases, skimping on quality results in lower short-term costs but increases costs in the long run. The benefits of spending a little more when a product is manufactured can save a company a great deal of money when it comes to product returns and warranties. Moreover, there is a hidden expense in producing a low-quality product that does not last long. The image of the brand (and related products) may be adversely affected. Several companies on the list of “America’s Most Hated Companies” are there because of shoddy products and/or shoddy service (Spirit Airways, Sprint, Comcast, General Motors) (Hess & McIntyre, 2015). The standard of Six Sigma (3.4 defects per million opportunities, DPMO) is often used to represent high quality. Firms operating at Six Sigma quality levels usually spend less than 5% of revenues on fixing problems; those operating at Three or Four Sigma quality levels -- DPMOs of 66,807 and 6,210 resp.-- spend somewhere between 25% and 40% of revenues repairing problems (Bentley & Davis, 2010:4) CONCERN FOR THE ENVIRONMENT

Firms that are concerned not only about profit but about the environment as well have a greater likelihood of surviving in the long run. The future does not look promising for companies that are indifferent to the planet (Slaper & Hall, 2011). Friedman & Lewis (2015) feel that:

Accountants have an important role to play in measuring the social, environmental, and financial impact of various business decisions. Possessing the tools and the ability to facilitate crucial adjustments in direction, accountants’ decisions will determine whether a corporation is sustainable … By understanding the importance of the triple bottom line, accountants and auditors can increase their value to a firm and have a more important role to play as consultants.

Encouraging a business to engage in sustainable practices such as using solar panels or building factories that have LEED green building certification may seem costly in the short run but can pay for themselves in the long run. Accountants have the tools to make these decisions. As Sneirson (2011) observes: “sustainable business practices tend to pay for themselves and frequently turn a profit.”

A CEO who does not expect to be around for more than a few years may be antagonistic to changes that can hurt short-term profitability. Executives that are overly concerned with meeting short-term profit goals may miss the big picture. Take the problem of water shortages in the west. A fast-food restaurant


chain selling inexpensive hamburgers may not recognize the seriousness of the situation. It takes 1,847 gallons of water to produce one pound of beef [it takes 222 gallons of water to produce a pound of pasta, 46 gallons of water to produce a pound of sweet potatoes, and 518 gallons of water to produce a pound of chicken] (Boehrer, 2014). As long as water is subsidized, the water costs may not be a consideration. A forward-thinking accountant might ask management to consider alternative strategies if the price of water were to, say, quadruple. Another issue that should be addressed before it becomes a huge problem is use of plastic water bottles. Americans use billions of these bottles each year. If companies used recycled materials in making the bottles, it would reduce the amount of plastic pollution and use of fossil fuels.

Accountants are now being used by the Federal Bureau of Reclamation to ensure that cities and farms around the Colorado River are not taking more than their fair share (Lustgarten, 2015). Water has become a huge problem in states such as California and Arizona. Even with respect to the amount of water being tallied there have been problems in getting an accurate count. Double counting results from the fact that surface water and groundwater are often interconnected (Lustgarten, 2015). PROMOTING LEADERSHIP AND ORGANIZATIONAL INTEGRITY

The Committee of Sponsoring Organizations (COSO, 2013) highlighted the importance of leaders setting a “tone at the top” by establishing integrity as the very first principle of internal controls (COSO, 2013). The expression “tone at the top” refers to the fact that the leadership of an organization creates the “tone of an ethical – or unethical – atmosphere in the workplace” (Malley, 2013). If the executives and auditors at the top of the hierarchy are concerned about integrity and ethics, their moral imperative will work its way down to all employees. Organizations that do not have a moral compass are destined to find themselves in serious trouble as we saw during the Great Recession of 2008. Lennick and Kiel (2011) state:

The integrity crises of the first decade of the 21st century have been devastating. But they have not yet convinced enough leaders of the importance of morally intelligent leadership. How many wake-up calls do leaders need to get the message that their ultimate success depends on moral leadership? Will leaders get another chance to do the right thing? Given the precarious nature of today’s global economy, we fear that this wake-up call to choose integrity over greed might very well be our last … how can any leader afford to ignore the call to put moral values at the center of what they do? (Lennick and Kiel, 2011:xxxii)

Gentry (2013) believes that integrity is the most important character strength (closely followed by

bravery) in predicting performance of top-level executives. It certainly seems that many leaders do not feel that integrity is of great importance in becoming an effective leader. This may be the reason that the number of scandals continues to grow. What seems to matter most to most corporate boards is that the price of the corporate stock should rise. But Doty (2015) cites several studies that demonstrate that companies with integrity are significantly more profitable than those that lack it. Doty (2015) observes:

Integrity — or lack thereof — remains a critical challenge for companies today. Whether it involves promising a client that our software will work in their setting, adhering to investment guidelines with people’s retirement savings, or performing a correct medical procedure, we owe it to our customers, employees, shareholders, and the world at large to be responsible about what we commit to and what we deliver. But integrity isn’t easy: It stretches the imagination to envision a world in which businesses deliver on 99.99966 percent of their commitments, as factories do with Six Sigma quality methods. Every day, every leader faces opportunities or even pressure to side step the truth, fudge the numbers, play politics, or pass the buck on hard decisions. In the moment doing the right thing, or doing things right, always seems to cost more (Doty, 2015).


Hess (2013) studied effective CEOs and found that they tended to be servant leaders and cared about all stakeholders.

These leaders were servants in the best sense of the word. They were people-centric, valued service to others and believed they had a duty of stewardship. Nearly all were humble and passionate operators who were deeply involved in the details of the business. Most had long tenures in their organizations. They had not forgotten what it was like to be a line employee. They believed that every employee should be treated with respect and have the opportunity to do meaningful work. They led by example, lived the “Golden Rule,” and understood that good intentions are not enough — behaviors count. These leaders serve the organization and its multiple stakeholders. They are servant leaders (Hess, 2013).

One can learn from Mary Barra, CEO at GM, what needs to be done to change a corporate culture. She started by firing 15 people and then created the “Speaking Up for Safety” program, which has been called “a sort of internal whistleblower protection act” (Geier, 2014). This is a big change from the GM culture described above. Firms that are truly concerned about an ethical tone at the top have to encourage employees to speak up when they observe unethical behavior. These employees have to be assured that no one will retaliate against them for blowing the whistle. Conflicts of interest are responsible for much ethical wrongdoing (Bell, Friedman & Friedman, 2005). Firms that are serious about ethics have to do everything possible to eliminate conflicts of interest whenever possible. Indeed, conflicts of interest played a major role in the Great Recession of 2008 (Biktimirov & Cyr, 2013). New prescription drugs are the number four health risk (along with strokes) and have a 20% chance of harming people. The reason for this has a lot to do with the fact that “commercially funded clinical trials are at least 2.5 times more likely to favor the sponsor’s drug than non-commercially funded trials.” It seems that drug companies, in their quest to make money, design biased trials that “skew the results.” They also “distort the evidence by selective reporting and biased interpretation” (Light, 2014).

Many papers have been written about the problem of auditor independence and conflicts of interest (e.g., Hilton (2010); Moore, Tetlock, Tanlu, & Bazerman, 2006). Rating agencies such as Standard & Poor, which contributed to the Great Recession of 2008, are comparable to auditors insofar as both are required to issue nonbiased reports about companies that pay for their services. The credit-rating agencies were reluctant to provide poor ratings for the sub-prime mortgage-backed securities of clients that were paying for the ratings. Auditors are similarly reluctant to be tough with an important client who can easily switch to another accounting firm.

Ethical organizations should be concerned with stock buybacks. When a company buys their own stock, who benefits? Lazonick (2014) claims that between 2003 and 2012, 449 of the 500 S&P companies spent $2.4 trillion to buy back their stock. The purpose of most buybacks is to enrich executives who hold many shares and stock options. The problem with buybacks is that this money is not being used to enhance the future of the company by making capital investments. It is also a narrow-minded strategy that means fewer jobs for Americans and weakens the entire economy. Capital investment means more employment; the more jobs, the greater the profits for all firms. Everyone gains with a growing and more prosperous middle class. Stock buybacks enrich the few at the top. This is another area where auditors should voice their opinions and give advice. After all, they have to be concerned about what is best for the long-term health of a company. SUPPORTING AMERICA

A CEO of Exxon was asked by an executive from another firm to consider building additional U.S. refinery capacity for security against possible supply disruptions. His response was, “I’m not a U.S. company and I don’t make decisions based on what’s good for the U.S” (Gore, 2013). Corporations are also finding creative ways to avoid paying their fair share of taxes. The corporate tax rate in the United


States is 35% but firms take advantage of various deductions and credits and actually pay only about 12.6% (Gelles, 2014). Some of the methods used by corporations to reduce their taxes include inversions (where an American company acquires an overseas company where corporate taxes are low so that it can reincorporate and lower its tax bill); becoming S-corporations to avoid corporate income taxes; and keeping money abroad to avoid paying the taxes (Apple and General Electric each keep more than $100 billion offshore) (Lobosco, 2014). In fact, as much as $2 trillion in earnings of American companies have not been repatriated to the United States in order to avoid paying the taxes (Nocera, 2014).

Only 6% of corporations today (as opposed to 17% in 1980) are traditional corporations that are required to pay the corporate income tax. The Treasury Department did announce new regulations in September 2014 to make it more difficult to reduce taxes through inversions. What seems to have happened is that companies trying to reduce taxes have reversed the process and large foreign companies are now taking over smaller American companies in order to reduce taxes (Yan, 2015).

One can argue that there is nothing wrong with avoiding taxes. However, the authors feel that conscious accounting includes being fair. Using accounting gimmicks to avoid paying a fair share of taxes – and indirectly weakening the United States – is not the way an ethical firm should behave. Corporations that are based in the United States owe something to this country since they make use of the infrastructure. These companies have a moral obligation to share in the tax burden and show some gratitude for what the country has done for their organizations. Although Walmart is not a company known for treating its employees well, it announced that over the next decade it would purchase an additional $250 billion worth of American-made products to help the economy (Lewis, 2014). This will not only help increase employment in the United States, it will also help increase sales in Walmart stores by giving Americans more spending power. Accountants should encourage firms to make similar commitments on the premise that such commitments will ultimately benefit the company as workers will have more money to spend in the national marketplace. It is hoped that Walmart’s auditors will use the same reasoning to convince the CEO of Walmart to raise the minimum pay of employees to $16 per hour emulating organizations such as Aetna. CONCLUSION

Johan Karlstrom, CEO of Skanska, has his company focus on the “five zeros: zero accidents, zero ethical breaches, zero environmental incidents, zero losing projects, and zero defects.” He also believes that business is not only about shareholder value but also about making the world a little bit better (Brzezinski, 2014). The authors posit that accountants and auditors have an important role to play in making firms behave in an ethical manner that considers all stakeholders. We refer to this as conscious accounting. The accountants and auditors have an important role to play in the “five zeros” since they are the conscience of the organization. In fact, we believe that there should be at least “eleven zeros”: zero accidents, zero ethical breaches, zero conflicts of interest, zero environmental incidents, zero losing projects, zero defects, zero disengaged employees, zero tolerance for excessive compensation for CEOs, zero tolerance for low pay for employees, zero tolerance for workplace bullying and bigotry, and zero tolerance for people living in dire poverty. This paper describes some of the key decisions that they must make in order to ensure the sustainability of their organizations. Sadly, CEOs often have a very short-term perspective and focus more on doing everything possible to maximize their own compensation, even at the expense of the long-term health of their firms. This means that accountants and auditors, the conscience of an organization, have an obligation to make sure that an organization will thrive in the highly competitive knowledge economy by being committed to conscious capitalism and conscious accounting.


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Capital Structure, Firm Performance and the Recent Financial Crisis

Ashrafee Tanvir Hossain Memorial University of Newfoundland

Dao Xuan Nguyen

Memorial University of Newfoundland

We examine the impact of financial leverage on firm performance. By analyzing stock and operating performances of top ten Canadian oil & gas companies for a ten year period (2004-2013), we find that leverage has a strong negative relationship with performance, for all three periods in concern, that is the pre-crisis (2004-2006), crisis (2007-2009), and post-crisis recovery (2010-2013) periods. These results hold both in univariate and cross-sectional set up even after controlling for firm specific variables. INTRODUCTION

One of the most difficult challenges that management faces is the decision on capital structure

(Pouraghajan, et al., 2012). In short, capital structure is a firm’s financing through the combination of equity and debt (Mujahid and Akhtar, 2014). One direct effect of the capital structure decision is that it determines the cost of capital, resulting in changes in a company’s market value. To a larger extent, capital structure also influences other factors, such as company performances and profitability.

In 1958, Modigliani and Miller were one of the first to introduce a general theory of capital structure, known as the M&M propositions. While the original concept offered a fundamental explanation for capital structure, it was also controversial. According to the original Modigliani-Miller theorem, in a perfect market, a firm’ market value is independent of capital structure choices and dividend policies. Subsequent to the original M&M propositions, researchers incorporated market imperfections such as taxes, transaction costs, bankruptcy and agency costs, adverse selection etc., in order to further develop the theory (Mujahid and Sorin, 2009).

In recent years, there has been robust research on the capital structure’s effect on firm financial performance. Research has been made in different disciplines, such as in the financial sector with the banking industry (Skopljak and Luo, 2012), or in the non-financial sector with the iron and steel (Banerjee and De, 2014), the oil and gas (Sabir and Malik, 2012), and the textile industry (Mujahid and Akhtar, 2014). There has also been research that consists of both financial and non-financial firms (Gabrijelcic, et al., 2014). In general, available research evaluates the relationship of capital structure and firm performance by economic crisis periods (Gabrijelcic, et al., 2014), or in general timeframe (Sabir and Malik, 2012).

Based on the agency cost theory, Jiraporn et al. (2011) identifies a negative impact of capital structure on firm performance. Similarly, Sabir and Malik (2012), and Gabrijelcic et al. (2014) also find a negative relationship between capital structure and financial performance. A research by Skopljak and Luo (2012)


shows that different variation in capital structure leads to variation in firm performance. Agnihotri (2014), however, proposes a flexible structure. According to Agnihotri (2014), depending on its competitive strategies and market condition, if a firm can decide an appropriate capital structure, both high and low leverage will result in lower cost of debts, and thus enhancing its performance. Finally, a particular financial ratio can have either the same or different relation with capital structures under different circumstances. For example, in their research, Saeedi and Mahmoodi (2011) discover a negative relation between capital structure and return on assets (ROA), but no significant relationship between capital structure and return on equity (ROE), while Ebrati et al. (2013) find a similar result for ROA but positive relation for ROE.

Canadian economy is increasing its dependence on oil & gas revenue over the past two decades. It is very important to understand the dynamics of oil & gas industry in Canada. As discussed above, we acknowledge the fact that there have been papers that analyze the impact of capital structure on firm performance. However, a specific focused study on oil and gas industry is missing. We believe that it is a very important industry and that this industry acts little differently during the others during crisis period like we encountered in 2007-2009. Therefore, our study will add value by analyzing this specific industry for an oil revenue dependent G7 country like Canada.

In this study, we developed hypotheses to test the effect of the financial leverage on firm performance. We find a strong negative relation between leverage and firm profitability. The testing process, the results, and their implications are discussed in the remaining parts of this paper. Section 2 proposes the development of the hypotheses; sections 3 and 4 describe the data and testing methods, respectively; section 5 discusses the result and their implications; and section 6 concludes the paper.

HYPOTHESIS DEVELOPMENT

The agency cost theory is a basic financial concept elaborated by Jensen amd Meckling (1976), which

is widely used to explain the relation of a firm’s capital structure and performance. They find that a firm’s managers’ and owners’ goals are not aligned if they are separate entities. This conflict arises when managers are key decision-makers who try to maximize their utilities instead of acting in the best interests of shareholders and the firm. In addition to the conflict between managers and owners, there is also the conflict between debtholders and equity investors where there is a risk of default (Jensen and Meckling, 1976). The agency cost theory proposes two different outcomes for leverage’s impact on firm performance. First, as the firm’s leverage is increased, so is the agency cost. In this case, the conflict between debt owners and equity holders increases because shareholders are likely to adopt riskier projects at the expense of debtholders. Thus, there is a negative relationship between higher leverage and firm performance (Soumadi and Hayajneh, 2012). However, from a counter perspective, leverage can positively affect firms’ performance. In this case, as more debts lead to more interest expense, it creates higher risk of bankruptcy; as a result, managers have to perform better to avoid bankruptcy and associated costs, which in turns improves firm performance (Soumadi and Hayajneh, 2012).

Nevertheless, considering the specific nature of the Canadian oil and gas industry, one impact may be greater than the other. First, the oil and gas industry is capital intensive, which requires a significant amount of investments in properties and technology. At the same time, it is highly unpredictable, especially in the stage of exploration. Therefore, the industry is associated with high risks, coming with high return. Also, once natural sources have been found, additional investment is made as long as the oil gained can offset out-of-pocket expenses (Committee on Price Research, 1939). Second, on average, Canadian interest rates have been relatively low in the last ten years, except for 2006, 2007 and 2009 (World Bank, n.d). Thus, default risks have been reduced. Finally, larger oil and gas firms have shown much higher stability and survival rates (Mansell, et al., 2012) over the same period. These conditions would probably allow managers to engage in riskier projects to satisfy the need for large investments at low costs of debts. Hence, the negative effects of the financial leverage would likely outweigh the positive ones.


H1: We will expect that in the pre-crisis period (2004-2006), firms will show regular phenomenon as discussed above, and therefore, firms with above median leverage (higher D/E ratio) will have a negative impact on performance.

H2: However, we expect the scenario to be different during the financial crisis period of the last decade. When the world economies were tumbling one after another, Canada stood tall amongst the G20 countries. However, the growth slowed down and Canada did go into brief period of recession during that period. It is to be noted that the oil price did not take a hit due to the financial crisis; as a matter of fact it peaked during the crisis period. Therefore, during the financial crisis (2007-2009), we expect leverage will continue to have a negative influence on firm performance but the impact will not be as strong as it used to be in the pre-crisis period.

H3: As firms are recovering in the post-crisis (2010-2013) period, performances are getting better as well. However, we have observed an unprecedented dip in oil prices in the recent years. As oil prices are plummeting, it is having an impact on the equity prices of oil companies. We anticipate a lower equity pricing will cause the debt-to-equity ratio to rise in general, and therefore, the impact of leverage on firm performance will show a stronger negative influence in the post-crisis recovery period than that was in the crisis period.

DATA DESCRIPTION

We collect our data for a ten year period (2004-2013) from the financial statements of the ten largest

Canadian oil & gas companies. The detailed list of sample companies is provided in Appendix 1. We divide the sample period into three sub-periods—pre-crisis (2004-2006), crisis (2007-2009), and post-crisis recovery (2010-2013) periods. The definition of the financial ratios used in this study is provided in Appendix 2.

In table 1, we provide some descriptive statistics about our sample firms. We report mean values for cash, fixed assets, current assets, total assets, total debts, current liabilities, and shareholders’ equity. We also provide the ratios, including fixed asset ratio, current ratio, cash ratio, and earnings per share.

In panel A, we split our sample based on the three subperiods mentioned earlier (pre-crisis, crisis, and post-crisis). In panel B we divide the data correspondent to highly leveraged group, low leveraged group, and overall performance. In panel C we break down the data on a year-by-year basis. In absolute terms, there is a steady growth of all accounting parameters (cash, fixed assets, current assets, total debts, current liabilities, and shareholders’ equity) over the ten-year period. This increase is also conforming to the pre-crisis, during crisis and post-crisis interval. With regards to specific ratios, fixed asset ratio is highest before the crisis (0.8), and then steadily declines during and after the crisis (0.78 and 0.72, respectively). Current ratio increases from 0.72 pre-crisis to 0.95 in the crisis and 0.96 post-crisis. Similarly, cash ratio increases from 0.019 pre-crisis to 0.025 during the crisis, and to 0.03 post-crisis. EPS, on the other hand, performs best during the crisis (2.97) and drops post-crisis (1.47) to a level even lower than pre-crisis (2.61). With regards to comparison by high leverage and low leverage, most ratios and indicators are higher for firms having lower leverage, except for absolute amount of fix assets, total assets and debts. For the overall ten-year period, current ratio and cash ratio increase from 0.67 to 0.89 and from 0.02 to 0.04, respectively. Fixed assets ratio and EPS decrease from 0.81 to 0.66 and from 2.22 to 1.33, respectively.

In table 2 we report the level of leverage on a year-by-year basis, and by each subperiod. We report leverage levels for above median (hi D/E), and below median (low D/E) subsamples for each of the two criteria mentioned above.

In general, the level of leverage reduces during the crisis period. The average difference in leverage ratios of hi and low D/E companies are 0.84 before the crisis, 0.63 during the crisis and 0.78 after the crisis. The mean and median for the difference across the ten year period are 0.75 and 0.79 respectively. However, we observe a spike in 2006 (0.98) and 2011 (0.91) and a dip in 2008 (0.39). It is to be noted that 2006 was immediately before the crisis, 2011 was when recovery was gaining pace, and 2008 was the peak of the crisis. The year 2008 was the worst year in of the crisis period marked by the failures of large


TABLE 1 DESCRIPTIVE STATISTICS

PANEL A – BREAKDOWN BY PERIOD

Pre-crisis 2004-06

Crisis 2007-09

Post-crisis 2010-13

Cash (millions of CAD) 338.69 633.05 1,075.75

Fixed assets (millions of CAD) 14,485.96 21,350.86 26,584.15

Current assets (millions of CAD) 2,196.07 3,218.90 4,463.45

Total assets (millions of CAD) 18,151.16 27,381.13 35,752.87

Total debts (millions of CAD) 5,056.00 7,075.03 8,851.98

Current liabilities (millions of CAD) 2,757.42 3,433.79 4,599.70

Shareholders' equity (millions of CAD) 7,087.01 11,829.05 16,167.32

Fixed asset/Total assets 0.80 0.78 0.72

Current assets/current liabilities 0.72 0.95 0.96

Cash/Total assets 0.02 0.02 0.03

EPS 2.61 2.97 1.47 No of Observations 30 30 40

PANEL B – BREAKDOWN BY CAPITAL STRUCTURE

Hi D/E Lo D/E Overall

Cash (millions of CAD) 655.22 817.13 733.67

Fixed assets (millions of CAD) 21,968.55 21,203.94 21,598.07

Current assets (millions of CAD) 3,020.77 3,901.29 3,447.41

Total assets (millions of CAD) 28,857.90 27,632.66 28,264.23

Total debts (millions of CAD) 10,335.35 3,959.03 7,245.79

Current liabilities (millions of CAD) 3,584.02 3,877.68 3,726.31

Shareholders' equity (millions of CAD) 10,718.28 13,978.71 12,298.08

Fixed asset/Total assets 0.75 0.77 0.76

Current assets/current liabilities 0.87 0.91 0.89

Cash/Total assets 0.02 0.03 0.03

EPS 2.07 2.44 2.25 No of Observations 50 50 100


PANEL C – BREAKDOWN BY YEAR 2004 2005 2006 2007 2008

Cash (millions of CAD) 251.73 299.02 465.32 406.77 605.27

Fixed assets (millions of CAD) 12,367.27 14,112.42 16,978.21 18,822.50 21,513.45

Current assets (millions of CAD) 1,673.67 2,357.77 2,556.77 2,822.69 3,283.67

Total assets (millions of CAD) 15,425.05 18,112.69 20,915.75 23,828.68 28,028.77

Total debts (millions of CAD) 4,495.86 4,881.73 5,790.39 6,218.14 7,317.25

Current liabilities (millions of CAD) 2,192.74 3,117.23 2,962.29 3,386.05 3,196.56

Shareholders' equity (millions of CAD)

6,074.19 6,891.40 8,295.44 9,766.02 12,129.88

Fixed asset/Total assets 0.81 0.78 0.81 0.79 0.76

Current assets/current liabilities 0.67 0.70 0.79 0.79 1.08

Cash/Total assets 0.02 0.02 0.03 0.02 0.02

EPS 2.22 2.49 3.13 2.85 4.50 No of Observations 10 10 10 10 10 2009 2010 2011 2012 2013

Cash (millions of CAD) 887.10 529.90 972.30 1,433.99 1,366.79

Fixed assets (millions of CAD) 23,716.63 25,200.17 25,126.45 27,236.29 28,773.69

Current assets (millions of CAD) 3,550.35 3,612.07 4,567.75 4,765.37 4,908.61

Total assets (millions of CAD) 30,285.94 32,066.59 35,943.54 35,958.66 39,042.68

Total debts (millions of CAD) 7,689.71 7,905.45 8,419.40 8,804.56 10,278.51

Current liabilities (millions of CAD) 3,718.75 4,057.19 4,475.53 4,417.97 5,448.10

Shareholders' equity (millions of CAD)

13,591.25 14,733.31 15,829.97 16,471.18 17,634.81

Fixed asset/Total assets 0.79 0.79 0.70 0.74 0.66

Current assets/current liabilities 0.98 0.89 0.97 1.08 0.89

Cash/Total assets 0.03 0.02 0.02 0.04 0.04

EPS 1.55 1.65 1.82 1.08 1.33 No of Observations 10 10 10 10 10


financial institutes and stock market crash in the U.S, which may explain why firms reduced their leverages, resulting in a much smaller gap.

For overall performance, the leverage after the crisis only slightly increased, up by 0.01 from period 2007-2009. Firms seem to be more cautious with regards to finance structure since the financial crisis.

TABLE 2

SUMMARY STATISTICS – ACTUAL D/E

PANEL A – YEAR-BY-YEAR SUMMARY STAT

Year Hi D/E Low D/E Overall Diff (High-Low)

2004 1.10 0.36 0.77 0.73

2005 1.15 0.35 0.79 0.80

2006 1.21 0.22 0.77 0.98

2007 1.09 0.28 0.69 0.82

2008 0.65 0.00 0.00 0.65

2009 0.93 0.24 0.59 0.69

2010 0.88 0.26 0.57 0.62

2011 1.18 0.27 0.73 0.91

2012 1.04 0.22 0.63 0.81

2013 1.05 0.28 0.67 0.78

PANEL B – PERIOD-BY-PERIOD SUMMARY STAT Period Hi D/E Low D/E Overall Diff

Pre-crisis period 2004-2006

1.15 0.31 0.78 0.84

Crisis period 2007-2009

0.89 0.17 0.42 0.72

Post crisis period 2010-2013

1.04 0.26 0.65 0.78

METHODOLOGY

We test the proposed hypotheses by using financial measures collected from our sample companies.

We use Debt-to-equity ratio as our measurement for financial leverage. We implement both operating and stock performance measures. We use the return-on-asset ratio (ROA) and market-adjusted stock return ratio as our primary performance measures. However, we also use other alternate variables (such as ROE) to check robustness of our results.

We assess our samples using univariate analysis and regression model, based on which a conclusion to whether accept or reject the hypothesis is drawn from.

The following model is developed for testing each of the financial measure:


𝑌 = 𝛽0 + 𝛽1𝑋1(𝑡) + 𝛽2𝑋2(𝑡) + 𝛽3𝑋3(𝑡) + 𝛽4𝑋4(𝑡) + 𝛽5𝑋5(𝑡) + 𝛽6𝑋6(𝑡) Where: Y = Dependent variable (ROE, ROE, Market-adjusted stock return)

𝑋1(𝑡)= Leverage ratio at time t 𝑋2(𝑡)= Cash ratio at time t 𝑋3(𝑡)= Current ratio at time t 𝑋4(𝑡)= Fixed asset ratio at time t 𝑋5(𝑡)= EPS at time t 𝑋6(𝑡)= Lagged ROA, lagged ROE or lagged Market-adjusted stock return at time t

DISCUSSION OF RESULTS Univariate Analysis

We use return-on-asset (ROA), return-on-equity (ROE) and market-adjusted stock returns to evaluate firms’ performance. Debt and equity, as main components of capital structure, directly affect the company’s asset values. Thus, it is reasonable to use profitability ratios such as ROA and ROE in order to understand firms’ profitability fluctuation corresponding to capital structure change. We use market adjusted stock returns for robustness.

We report the ROA, ROE and market-adjusted stock returns in tables 3, 4, and 5 respectively. In general, we find a negative relationship between firm performance and its leverage ratio.

TABLE 3

OPERATING PERFORMANCE – RETURN ON ASSETS

PANEL A – YEAR-BY-YEAR BREAKDOWN OF ROA

Year Hi D/E Low D/E Overall Diff (High-Low) t-stat

2004 6.6% 9.8% 8.0% -3.2% -1.53*

2005 5.3% 11.8% 8.2% -6.5% -3.30***

2006 8.1% 13.7% 10.6% -5.6% -1.52*

2007 6.6% 10.7% 8.7% -4.1% -1.12

2008 8.3% 15.0% 11.6% -6.8% -2.40**

2009 4.5% 3.5% 4.0% 0.9% 0.53

2010 4.2% 5.2% 4.7% -1.0% -0.54

2011 2.1% 7.6% 4.9% -5.5% -3.58***

2012 -1.2% 5.5% 2.1% -6.7% -1.70*

2013 0.4% 4.6% 2.5% -4.3% -2.16** This table reports the results for the return on assets. In panel A, we introduce the year-by-year

breakdown of the results, and in panel B, we provide the period by period breakdown of the ROAs. It is to be noted that, hi D/E firms have above median leverage ratio and vice versa for low D/E firms. We find that high leverage firms consistently underperform their low leverage counterparts. The only exception


PANEL B – PERIOD-BY-PERIOD BREAKDOWN OF ROA

Period Hi D/E Low D/E Overall Diff (High-Low) t-stat


6.7% 11.8% 8.9% -5.1% -3.63**


6.4% 9.8% 8.1% -3.3% -0.95


1.4% 5.7% 3.5% -4.4% -3.27***

being year 2009 where high leverage firms outperform low leverage firms by 0.9%. The biggest difference between high and low leverage firms’ performance was found in 2008 (high leverage firms underperformed by 6.8%) which was during the height of the financial crisis. After 2008, we observe a short term gradual decline in performance gap; however, the trend is reversing in the last 3-4 years, and the performance gap is increasing again. We believe it may be due to the falling oil prices worldwide.

In panel B, we report the period by period breakdown. We find that for all three periods (pre-crisis, crisis, and post-crisis) high leveraged firms underperformed their low leveraged counterparts. In the pre-crisis (2004-2006) period hi D/E firms underperformed low D/E firms by 5.1%. However, the performance gap narrowed to just 3.3% during the actual crisis (2007-2009). We believe two factors might have contributed to this—Canada’s strong financial system and stability in crude oil prices during the crisis. Canada’s financial system was rated amongst the best during the crisis, and none of the Canadian big banks face any monetary issues during the crisis. Finally, we observe that the gap is increasing again (high leverage firms are underperforming by 4.4%) in the post-crisis recovery period (2010-2013). The falling oil prices in the past few years could be attributed to this increasing gap.

FIGURE 1

CANADIAN UPSTREAM OIL AND GAS INVESTMENT ANNUAL CAPEX; REAL 2013 DOLLARS

Source: CAPP, Statistics Canada, and ARC Finance Research


As discussed in the summary statistics section, in the pre-crisis period, firms were more risky in their investments, demonstrated by higher D/E ratios. Also, in this period, capital investments in the oil and gas industry drastically increased, then suddenly dropped in 2009 and recovered in 2010. However, the recovery after the crisis was not comparable to that before the crisis.

FIGURE 2

WTI OIL PRICE AND YEAR-OVER-YEAR LOAN GROWTH IN CANADA

Source: Bloomberg, Bank of Canada, BMO CM

TABLE 4 ROBUSTNESS CHECK – RETURN ON EQUITY

PANEL A – YEAR-BY-YEAR BREAKDOWN OF ROE


2004 17.8% 21.2% 19.3% -3.4% -0.80

2005 15.8% 26.6% 20.6% -10.9% -2.06**

2006 22.2% 27.6% 24.6% -5.4% -0.71

2007 18.1% 22.1% 20.1% -4.0% -0.54

2008 19.6% 30.6% 25.1% -11.0% -2.48**

2009 12.0% 6.8% 9.4% 5.2% 1.34

2010 10.2% 10.2% 10.2% 0.0% -0.01

2011 11.6% 15.3% 13.4% -3.7% -0.57

2012 -6.0% 10.1% 2.1% -16.1% -1.31


PANEL B – PERIOD-BY-PERIOD BREAKDOWN OF ROE



18.6% 25.1% 21.5% -6.5% -2.40**


16.6% 19.8% 18.2% -3.3% -0.45


4.4% 11.1% 7.7% -6.7% -1.55*

TABLE 5 STOCK PERFORMANCE - MARKET-ADJUSTED STOCK RETURN

PANEL A – YEAR-BY-YEAR BREAKDOWN OF STOCK RETURNS


2004 16.6% 20.2% 18.2% -3.6% -0.33

2005 32.6% 41.3% 36.4% -8.7% -0.35

2006 -11.4% -3.5% -7.9% -7.9% -0.82

2007 -1.9% 16.2% 6.1% -18.1% -2.07**

2008 10.8% 6.7% 9.0% 4.1% 0.34

2009 -15.9% 7.9% -2.7% -23.8% -0.96

2010 -3.9% -5.2% -4.6% 1.3% 0.15

2011 16.1% 5.2% 10.6% 11.0% 0.77

2012 -10.5% -9.2% -9.9% -1.2% -0.10

2013 -7.9% 4.4% -1.7% -12.3% -2.67**

PANEL B – PERIOD-BY-PERIOD BREAKDOWN OF STOCK RETURNS



12.6% 19.3% 15.6% -6.8% -0.37


-2.4% 10.2% 4.1% -12.6% -1.65*


-1.5% -1.2% -1.4% -0.3% -0.05


Similarly, the oil price grew quickly before the crisis and peaked in early 2008, then plunged in 2009. While the oil price has slightly recovered since 2010, the fluctuation of the oil price has not been as large as before the crisis. As a result, these factors may have magnified the variance in firms’ profitability, leading to a more negative impact of leverage on firms’ performance before the crisis.

We report the return on equity results in table 4. Consistent with our findings in the previous section, we find that high leverage firms are underperforming their low leverage counterparts. The results are qualitatively similar to that of the ROA. The gap decreased during the crisis and it is increasing in the post-crisis period. The performance gap between high and low leverage firms are 6.5% (pre-crisis), 3.3% (crisis), and 6.7% (post-crisis).

For robustness, we analyzed the market adjusted stock returns for the entire sample. We find that high leverage firms still underperform their low leverage counterparts. However, the magnitude of underperformance is greatest during the crisis. This is different from the operating performance results discussed above. As the investor confidence took a big dive and everyone was selling off, we are not surprised to find this phenomenon for the stock returns.

In summary, these results consistently show that high leverage firms underperform their low leverage counterparts. We also observe stronger negative trends in the pre- and post- crisis period compared to that of the crisis-period. These findings support our hypotheses.

Regression Analysis

Table 6 reports the results of ROA regression. The independent variables are leverage ratio, cash ratio, current ratio, fixed asset ratio, EPS and lagged ROA.

TABLE 6

CROSS SECTION ANALYSIS OF ROA

Variables Coefficient t-stat Leverage (D/E) -0.203 -3.18*** Cash ratio 0.183 3.09*** Current ratio -0.98 -1.72* Fixed Asset Ratio -0.7 -1.20 EPS 0.542 7.92*** Lagged ROA 0.289 3.72*** No. of Observation 100 R-square 0.761

TABLE 7

CROSS SECTION ANALYSIS OF ROE

Variables Coefficient t-stat Leverage (D/E) -0.029 -0.41 Cash ratio 0.155 2.09** Current ratio -0.172 -2.40** Fixed Asset Ratio -0.043 -0.59 EPS 0.560 6.62*** Lagged ROE 0.089 3.00*** No. of Observation 100 R-square 0.623


We find that leverage has a strong and significant relationship with return of asset. This finding substantiates our results in the univariate analysis. We also find that firm with high cash reserves, better earning, and better previous performance perform better. For robustness, we performed similar cross-sectional analyses for ROE and stock returns. Those results are reported in tables 7 and 8. The results were qualitatively similar. In summary, the regression analyses reinforce our findings in the univariate analyses.

TABLE 8 CROSS SECTION ANALYSIS OF STOCK RETURNS

Variables Coefficient t-stat Leverage (D/E) -0.038 -0.94 Cash ratio 7.2 -1.83* Current ratio 0.06 1.23 Fixed Asset Ratio 0.194 0.89 EPS 0.016 -0.54 No. of Observation 100 R-square 0.218

CONCLUSION

We conclude that leverage has a negative impact on financial performance. This phenomenon is true

for operating as well as stock performances. We find that the performance gap between highest in the pre-crisis period, and the gap declined during the crisis, and it is increasing again in the post-crisis period. We believe the strong financial system in Canada is responsible for the lower gap during the crisis period; we also believe that the falling oil price is driving the increase in gap in the post-crisis recovery period. This study has certain limitations. As we have used only one industry, the results may not be generalized. Further study may be required to reach a definitive conclusion across all industries. REFERENCE Agnihotri, A. (2014). Impact of Strategy–Capital Structure on Firms’ Overall Financial Performanc. John

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ACKNOWLEDGEMENTS

Hossain thanks Memorial University for providing financial support for this paper. He also thanks Dr.

Alex Faseruk for his comments in earlier version of this paper. All other usual disclosure applies. APPENDIX 1

We selected top ten Canadian oil & gas companies based on market cap from the Globe and Mail,

2013 list:

1. Suncor ($49,819 million) 2. Canadian Natural Resources ($31,277million) 3. Imperial Oil ($36,218 million) 4. Enbridge ($34,631 million) 5. TransCanada Corp ($33,171 million) 6. Husky Energy ($28,878 million) 7. Cenovus Energy ($25,162 million) 8. Encana ($14,476 million) 9. Crescent Point Energy ($14,158 million) 10. Talisman Energy ($11,536 million)


APPENDIX 2 Financial ratios are calculated as follows:

Leverage ratio =Total debt

Total equity

ROA =Net incomeTotal asset

ROE =Net incomeTotal equity

Market − adjusted stock return = Annual stock return − Annual market return (TSX index)

Cash ratio =Total cash and cash equivalent

Total asset

Fixed asset ratio =Tangible �ixed assets

Total asset

Current ratio =Current asset

Current liability

Current ratio =Current asset

Current liability


Behind the Scenes of Mutual Fund Alpha

Qiang Bu Penn State University-Harrisburg

This study examines whether fund alpha exists and whether it comes from manager skill. We found that the probability and the value of fund alpha vary depending on market states and fund styles. Overall, the funds with earned alpha do not exhibit a market-timing ability, though some of them show an ability to select stocks. We also used a sample of bootstrapped funds as the benchmark for funds without skill to explore the topic. Our test results suggest that fund alpha is resulted from pure luck instead of manager skill. INTRODUCTION

Fund alpha and its sources are two important topics in mutual fund research. Research on the former relates to whether fund alpha exists, while that on the latter is about where fund alpha comes from. Answers to these two inquiries not only help investors make wise investment decisions between actively managed funds and low-cost passively managed funds but also provide insightful thoughts on fund management compensation. In this study, we examined three issues in fund performance. First, we examine whether some funds can really earn alpha in real life. Second, we ask, if fund alpha exists, from where does it come? In other words, is fund alpha due to market-timing skill, stock-selection skill, or pure luck? Third, we examine whether fund alpha is a reliable indicator of manager skill, in both the short run and the long run.

Henriksson (1984), Sharpe (1991), Fama and French (1993), Malkiel (1995), and Carhart (1997) have concluded that trying to beat the market through active investing is futile. French (2008) also found that a typical investor would be better off to switch to a passive market portfolio. On the other hand, Ibbotson and Patel (2002) concluded that superior fund performance does exist and that it repeats, even after adjusting for the investment style. Ding and Wermers (2009) also reported outperformance for both large-fund and small-fund managers.

As to the sources of fund alpha, Chevalier and Ellison (1999) found that fund managers who have attended higher-SAT undergraduate institutions earn higher risk-adjusted returns. Berk and Binsbergen (2014) found that manager skill exists and is persistent. Kosowski, Timmermann, Wermers, and White (2006) found that a sizable minority of managers picks stocks well enough to more than cover their costs, and the superior alphas of these managers persist. Baker, Litov, Wachter, and Wurgler (2010) concluded that mutual fund managers are able to trade profitably in part because they are able to forecast earnings-related fundamentals.

A closely related issue is whether fund managers have the ability to time the market return. Overall, the literature suggests that fund managers have poor timing ability and poor overall performance. For example, Treynor and Mauzy (1966), Henriksson and Merton (1981), Chang and Lewellen (1984),


Henriksson (1984), Chen and Stockum (1986), Chen, Lee, Rahman, and Chan (1992), Elton, Gruber, Das, and Blake (2011), and Becker, Ferson, Myers, and Schill (1999) found that the average market-timing performance of mutual funds is insignificant and sometimes even negative. Friesen and Sapp (2007) and Elton, Gruber, and Blake (2011) found no market-timing ability either. On the other hand, Kon (1979), Lehmann and Modest (1987), Lee and Rahman (1990), Grinblatt and Titman (1994), Daniel, Grinblatt, Titman, and Wermers (1997), Kaplan and Sensov (2005), and Jiang, Yao, and Yu (2007) found that mutual funds exhibit significant timing ability. Kacperczyk, Van Nieuwerburgh, and Veldkamp (2011) concluded that fund managers have stock-selecting skill in booms and market-timing skills in recessions. CONTRIBUTIONS OF THE STUDY

In light of the prior research, our study sheds new light on the topics. First, controlling for market states, we estimate the alpha of each and every fund to see whether some funds can actually earn alpha. We also check the styles of the winner funds and the loser funds to determine if fund style matters in the occurrence of fund alpha.1

Second, we investigate the market-timing ability of the winner funds and the loser funds. If winner funds demonstrate a market-timing ability, there are three possible sources of alpha, including market-timing skill, stock-selection skill, and pure luck. If not, fund alpha would come from either stock-selecting skill or pure luck. This test has not been conducted in prior literature. Kosowski et al. (2006) used independent simulations to form the distribution of alphas, assuming no outperformance. Fama and French (2009) used a similar method to assess manager skill. Since bootstrapped funds are generated out of pure luck, their performance can be used as the benchmark for no-skill performance.

To separate manager skill from pure luck, we also use a group of bootstrapped funds in this study as the benchmark for pure luck. Unlike the aforementioned researchers, we make no assumption regarding the return of the bootstrapped funds. If some bootstrapped funds earn alpha, and the bootstrapped winner funds also exhibit market-timing and stock selectivity, then fund alpha is not considered equivalent to manager skill.

Moreover, we introduce a new model to explore the sources of fund alpha. Since fund alpha results from the market exposures of a fund, we use the loadings on the four market factors in Carhart’s (1997) four-factor model as a proxy for manager skill. In this model, fund alpha is used as the dependent variable, and the loadings on the market excess return, the size factor, the style factor, and the momentum factor are the independent variables. This model enables us to compare the sources of fund alpha between the actual winner/loser funds and the bootstrapped winner/loser funds. If the two fund groups exhibit similar loadings on the four independent variables with similar statistical significance, we can say that fund alpha comes from pure luck rather than manager skill, and vice versa.

Finally, since our sample period includes several up and down markets, we can compare the actual winner/loser funds with the bootstrapped winner/loser funds while controlling for market states. Specifically, we use a fixed-effects model to test whether fund alpha is dependent on market states and whether it is transient or persistent. DATA AND METHODOLOGY

Our sample period spans from January 1998 to December 2012. To capture the dynamics of the market, the sample period is divided into five 3-year windows, including an up market from 1998 to 2000, a down market from 2001 to 2003, an up market from 2004 to 2006, a down market from 2007 to 2009, and a down market from 2010 to 2012. We use 15 annual Morningstar Principia discs to cover the entire sample period. 2 Our fund sample is based on domestic equity funds, and fund-level data are used in the study. The index funds, the specialty funds, and the hybrid funds are excluded from the fund sample.

Our methodology consists of three pillars. To test whether some funds have the ability to earn alpha, we use Carhart’s (1997) four-factor model to estimate the alpha of every domestic equity fund in each of the five windows, then we divide funds with alpha statistically significant at 5% into two groups in each


window. Funds with positive alpha are grouped as the winner funds while those with negative alpha are the loser funds.

To test the return gap between the winner funds and the loser funds, we use Henriksson and Merton’s (1981) return-timing model to examine whether the difference is due to an ability to time the market and/or to select stocks. If the winner funds demonstrate any market-timing and/or stock-selecting ability not possessed by the loser funds, then at least part of the fund alpha can be attributed to manager skill. However, if none of the funds exhibit such ability, pure luck cannot be excluded as one of the possible sources for fund alpha.

To separate manager skill from pure luck, we use a bootstrapping technique to randomly select a fund in each month from the actual fund sample while keeping the chronological order to get a bootstrapped fund; we repeat this procedure many times to get a large enough bootstrapped fund sample. Any return generated by the bootstrapped funds would have to come from pure luck. Based on this benchmark for pure luck, we conduct the same tests as those done for the actual funds. That is, we estimate the alpha of each bootstrapped fund in the five windows in the sample period, divide funds with statistically significant alpha into bootstrapped winner/loser funds, and test whether these funds have an ability to time the market and/or to select stocks. A comparison between the actual winner/loser funds and the bootstrapped winner/loser funds provides important information on the relation among fund alpha, manager skill, and pure luck. EMPIRICAL FINDINGS Fund Summary Statistics

Table 1 presents the descriptive statistics of the winner funds and the loser funds at the end of each three-year window. The statistics include the number of funds, the number of the winner/loser funds, the percentage of the winner/loser funds out of the fund sample, the average values of the fund size, the expense ratio, the turnover ratio, the cash ratio, the price to earnings ratio (PE), and the manager tenure.3 We use the Carhart (1997) four-factor model to estimate a fund’s alpha. The basic structure of the model is as follows:

(1) Where αp is the intercept of the model and βi is the loading on the monthly market excess return, the size factor, the style factor, and the momentum factor.

Panel A of Table 1 shows that the outperforming probability ranges from 1.2% in the 2001–2003 down market to 11.5% in the 1998–2000 up market.4 Comparing the outperforming probability across the five windows, we see that funds have a better chance of beating the market in an up market. On the other hand, there is no pattern in the underperforming probability across market states. According to Panel B of Table 1, the underperforming probability ranges from 3.63% in the 1998–2000 up market to 21.50% in the 2001–2003 down market.

In terms of fund size, the loser funds are overall smaller than the winner funds; the only exception occurs in the 2004–2006 up market. Moreover, the winner funds on average have a lower expense ratio and a lower turnover ratio, indicating that higher fund expense does not translate into a higher probability of earning alpha. As for turnover, we can see that the winner funds have much lower turnover than the loser funds in the 1998–2000 up market, the 2001–2003 down market, and the 2004–2006 up market. There is no difference in turnover between winner/loser funds in the other two windows. As to the liquidity position, we do not see any evident difference in liquidity position between the two fund groups, and there is no detectable difference in the price to earnings (PE) ratio between them either. In regard to manager tenure, Table 1 shows that the winner funds on average have longer tenure than the loser funds in all of the five windows.


T

AB

LE

1

SUM

MA

RY

ST

AT

IST

ICS

OF

TH

E W

INN

ER

AN

D T

HE

LO

SER

FU

ND

S

Peri

od

M

arke

t St

ate

Tot

al

Obs

. W

inne

rs

/Los

ers

Pct

Size

($

mill

ion)

E

xpen

se

(%)

Tur

nove

r (%

) C

ash

(%)

PE

Man

ager

T

enur

e

Pane

l A: W

inne

r Fu

nds

1998

-200

0 U

p

1,68

0 19

3 11

.50%

26

50

0.94

77

.69

5.31

28

.05

6.60

20

01-2

003

Dow

n 2,

214

26

1.20

%

1,34

8.85

1.

23

78.0

4 10

.70

16.5

0 5.

66

2004

-200

6 U

p 2,

329

78

3.35

%

901.

38

1.29

76

.63

3.18

19

.47

4.53

20

07-2

009

Dow

n 2,

448

34

1.39

%

1754

.59

1.07

12

9.21

3.

68

18.9

0 6.

99

2010

-201

2 U

p 2,

229

52

2.33

%

1650

.58

0.97

92

.69

7.70

15

.81

8.88

Pa

nel A

: Los

er F

unds

19

98-2

000

Up

1,

680

61

3.63

%

575.

82

1.45

16

7.77

6.

95

32.3

8 3.

97

2001

-200

3 D

own

2,21

4 47

6 21

.50%

43

4.66

1.

33

137.

54

3.72

21

.07

3.68

20

04-2

006

Up

2,32

9 16

1 6.

91%

14

46.2

5 1.

06

79.0

7 3.

62

18.6

8 4.

47

2007

-200

9 D

own

2,44

8 17

9 7.

31%

40

3.32

1.

13

117.

91

5.01

15

.26

4.12

20

10-2

012

Up

2,22

9 19

7 8.

84%

61

8.82

1.

22

94.1

8 4.

37

15.5

1 6.

57


Figure 1 shows the number of the winner/loser funds by style. We see that the large-blend funds, the large-value funds, and the large-growth funds are the top three winner funds, while the small-value funds have the lowest presence in the winner group. On the other hand, the large-blend funds, the large-growth funds, and the large-value funds also represent the top three loser fund groups, and the small-blend funds also have the lowest number of loser funds.

FIGURE 1 THE WINNER FUNDS AND THE LOSER FUNDS BY STYLE

Figure 2 exhibits the percentages of the outperforming/underperforming probabilities across fund

styles. We see that the mid-value funds have a 4.7% chance of outperformance, followed by the large-value funds and the large-blend funds, with outperforming probabilities of 4% and 3.4%, respectively. On the other hand, the average underperforming percentage of the loser funds is much higher. For example, the mid-growth funds have the highest underperforming percentage of around 26%, and even the lowest underperforming probability of the mid-growth funds is as high as 6%.

0

20

40

60

80

100

120

Large Blend

Large Value

Large Growth

Mid-Cap Value

Mid-Cap Growth

Small Growth

Mid-Cap Blend

Small Blend

Small Value

Winner Funds

0

100

200

300

400

500

600

Large Blend

Large Growth

Large Value

Mid-Cap Growth

Small Growth

Small Blend

Mid-Cap Value

Mid-Cap Blend

Small Value

Loser Funds


FIGURE 2 THE WINNER FUND AND THE LOSER FUND PERCENTAGES BY STYLE

Table 2 compares the mean and the median alpha values of the winner funds and the loser funds. We

see that the winner funds have the highest mean and median fund alphas in the 2001–2003 down market, while the lowest mean and median of alpha occur in the 2010–2012 up market. As for the loser funds, the lowest fund alpha is recorded in the period of 1998–2000 and the highest in the 2010–2012 up market. We see that the mean and the median alphas of the winner funds are close to each other, while the distribution of the alphas of the loser funds is skewed positively.

00.5

11.5

22.5

33.5

44.5

5

Mid-Cap Value

Large Value

Large Blend

Mid-Cap Blend

Small Growth

Large Growth

Mid-Cap Growth

Small Blend

Small Value

Winner Funds

0

5

10

15

20

25

30

Mid-Cap Growth

Large Blend

Large Growth

Small Growth

Small Blend

Large Value

Small Value

Mid-Cap Value

Mid-Cap Blend

Loser Funds


TABLE 2 SUMMARY STATISTICS OF THE ACTUAL FUND ALPHAS

Period

Market State

Observations The Winner Funds The Loser Funds Winner Loser Mean Median Mean Median

1998-2000 Up 193 61 0.884 0.804 -1.375 -1.218 2001-2003 Down 26 476 1.082 0.902 -0.809 -0.757 2004-2006 Up 78 161 0.594 0.548 -0.512 -0.364 2007-2009 Down 34 231 0.587 0.604 -0.589 -0.544 2010-2012 Up 52 197 0.460 0.425 -0.406 -0.350

Market Risk Exposure

In light of the tradeoff between risk and return, we compare the market factor exposure between the winner funds and the loser funds to see if it plays a role in fund alpha. Table 3 summarizes the loadings on the four market factors in Carhart’s (1997) four-factor model for both fund groups.

TABLE 3 MARKET RISK EXPOSURES OF THE WINNER FUNDS AND THE LOSER FUNDS

Period Market

State Obs

RMRF SMB HML Momentum +

(avg.) -

(avg.) +

(avg.) - (avg.)

+ (avg.)

- (avg.) +

(avg.) - (avg.)

Panel A: The Winner Funds 1998-2000

Up 195 100% (0.89)

0% 7.18% (0.45)

1.54% (-0.31)

33.85% (0.37)

22.56% (-0.19)

7.21% (0.29)

84.07% (-0.30)

2001-2003

Down 26 100% (0.80)

0% 50%

(0.51) 0% (0)

50% (0.51)

0% (0)

7.69% (0.18)

23.08% (-0.34)

2004-2006

Up 78 100% (0.95)

0% 35.90% (0.53)

15.39% (-0.20)

35.90% (0.29)

8.97% (-0.42)

26.92% (0.25)

28.23% (-0.18)

2007-2009

Down 33 100% (0.97)

0% 33.33% (0.43)

18.18% (-0.25)

9.09% (0.33)

48.50% (-0.34)

33.36% (0.10)

15.13% (-0.23)

2010-2012

Up 52 100% (0.85)

0% 67.31% (0.53)

23.08% (-0.22)

19.28% (0.26)

9.62% (-0.24)

38.46% (0.10)

19.23% (-0.09)

Panel B: The Loser Funds 1998-2000

Up 63 100% (1.21)

0% 52.36% (0.42)

7.96% (-0.20)

60.26% (0.59)

4.75% (-0.34)

63.51% (0.47)

9.49% (-0.28)

2001-2003

Down 483 100% (1.15)

0% 59.25% (0.60)

7.62% (-0.14)

24.85% (0.30)

29.65% (-0.37)

55.68% (0.26)

2.32% (-0.14)

2004-2006

Up 163 100% (0.98)

0% 47.26% (0.57)

12.87% (-0.24)

20.88% (0.29)

31.32% (-0.48)

21.32% (0.29)

19.68% (-0.18)

2007-2009

Down 231 100% (1.03)

0% 60.15% (0.82)

12.58% (-0.21)

41.98% (0.27)

12.12% (-0.26)

18.36% (0.09)

17.64% (-0.15)

2010-2012

Up 198 100% (1.08)

0% 21.71% (0.59)

13.16% (-0.18)

8.07% (0.23)

24.73% (-0.27)

4.60% (0.18)

18.40% (-0.16)


Panel A of Table 3 shows that the average RMRF (market excess return factor) loading of the winner funds is lower than that of the market portfolio, ranging from 0.80 in the 2001–2003 down market to 0.97 in the 2007–2009 down market. All of these loadings are positive, and there seems to be no relation between the winner funds’ market factor exposure and the market state. That is, the winner funds seem not to increase market risk exposure in an up market or reduce this exposure in a down market. Panel B of Table 3 shows that the average RMRF loading of the loser funds is evidently higher than that of the winner funds, ranging from 0.98 in the 2004–2006 up market to 1.21 in the 1998–2000 up market. There seems to be no relation between market state and the market risk exposure of the loser funds.

Based on the size factor SMB, the winner funds tend to increase their holdings in small stocks over time during the sample period. For example, in the 1998–2000 up market, out of the 195 winner funds, only 7.18% exhibit a positive loading statistically significant at 5%, with an average of 0.45, and only 1.54% have a negative statistically significant loading, with an average of -0.31. In the 2010–2012 up market, 67.31% of the winner funds exhibit a statistically significant loading on SMB, with an average of 0.53, while 23.08% have a statistically significant negative loading, with an average value of -0.22. The loading on SMB of the winner funds does not change with market state. On the other hand, the loser funds tend to reduce their small stock holdings toward the end of the sample period. For instance, in the 1998–2000 up market, 52.36% of the winner funds have a positive SMB loading statistically significant at 5%, with an average value of 0.42, and this drops to 21.71%, with an average of 0.59, in the 2010–2012 up market. Meanwhile, the percentage of the loser funds with a negative SMB loading increased from 7.96% in the 1998–2000 up market to 13.16% in the 2010–2012 up market. Similar to the winner funds, the loser funds overall have a higher percentage of positive SMB loadings than negative ones.

As for the style factor HML, the winner funds have more positive loadings on HML in four of the five windows, which means that the winner funds invest more in value stocks. The only exception is in the 2007–2009 up market, where only 9.09% of the winner funds have a positive statistically significant loading, with an average of 0.33, and 48.50% of them have a negative statistically significant loading, with an average of -0.34. In contrast, the loser funds exhibit a higher percentage of negative statistically significant loadings on HML in three windows, including the 2001–2003 down market, the 2004–2006 up market, and the 2010–2012 up market. This indicates that the loser funds tend to hold more growth stocks in these periods. We also notice that in the most recent two windows the average values between the positive loading and the negative loading exhibit a symmetric pattern with opposite signs, and this holds for both fund groups.

The last factor is the momentum factor Momentum. As Panel A of Table 3 indicates, in the 1998–2000 up market, 7.21% of the winner funds exhibit a positive statistically significant momentum factor with an average of 0.29, and 84.07% of them have a negative momentum factor with an average of -0.30. Panel B shows that, in the same period, 63.51% of the loser funds have a positive statistically significant momentum factor while only 9.49% of them are negative. This suggests that most of the winner funds follow a contrarian strategy, and more than half of the loser funds adopt a momentum strategy during this period. A similar pattern is exhibited in the 2001–2003 down market. There is no major difference between the winner funds and the loser funds in the momentum factor loadings in both the 2004–2006 up market and in the 2007–2009 down market. In the 2010–2012 up market, 38.46% of the winner funds have a positive statistically significant momentum factor, and this percentage is 19.23% for funds with negative loading on Momentum. For the loser funds, these percentages are 4.60% and 18.40%, respectively. Moreover, the average values of the positive and the negative loadings on Momentum are almost symmetric to each other.

In summary, the winner funds tend to have lower market risk exposure and hold less small stocks and more value stocks than the loser funds. Based on the characteristics of the loadings on momentum factor, there seems to be no difference between the winner funds and the loser funds. The results indicate that, compared with the loser funds, the winner funds seem to have a more conservative strategy indicated by lower market risk exposure and more holdings in large stocks and value stocks.


Market-Timing and Stock-Selecting Skills Fund alpha is often used to measure manager skill. As previously mentioned, there are several

possible sources for fund alpha: market-timing ability, stock-selecting ability, and pure luck. In this section, we attempt to figure out whether fund alpha owes to the ability to time market changes and/or select stocks; to examine this issue, we add a market-timing factor to Carhart’s four-factor model.5

(2) Where ,p tr is the monthly excess return on a fund; βi is the loading on factor i, representing the monthly market excess return, the size factor, the style factor, and the momentum factor; ω measures the market-timing ability; ,m tr stands for market excess return; and D is a dummy variable with a value of 1 if ,m tr

> 0 and a value of 0 if ,m tr < 0. Table 4 reports the summary statistics of the market-timing ability (ω) and the stock selectivity (α) of

the winner funds and the loser funds in Panel A and Panel B, including the number of observations, the sign, and the statistical significance at the 5% level.

TABLE 4 MARKET TIMING ABILITY AND STOCK SELECTING ABILITY OF THE ACTUAL

WINNER/LOSER FUNDS

Panel A: The Actual Winner Funds Period Market Total Positive

ω Sig. @5%

Neg. ω

Sig. @5%

Positive 𝛼

Sig. @5%

Neg. 𝛼

Sig. @5%

1998-2000 Up 195 122 1 73 0 192 13 3 0 2001-2003 Down 26 15 1 11 0 19 4 7 0 2004-2006 Up 78 62 3 16 0 71 2 7 0 2007-2009 Down 34 13 3 21 2 30 5 4 0 2010-2012 Up 52 22 0 30 3 52 20 0 0 Panel B: The Actual Loser Funds Period Market Total Positive

ω Sig. @5%

Neg. ω

Sig. @5%

Positive 𝛼

Sig. @5%

Neg. 𝛼

Sig. @5%

1998-2000 Up 63 22 0 41 2 4 0 59 3 2001-2003 Down 483 321 11 162 2 10 0 473 174 2004-2006 Up 162 56 1 106 3 19 0 143 23 2007-2009 Down 231 136 1 95 3 9 0 222 49 2010-2012 Up 198 89 4 109 7 20 0 178 34

As Panel A of Table 4 indicates, in the 1998–2000 up market, out of the 195 winner funds, 122 funds have a positive ω, though only one out of the 122 funds exhibits a ω statistically significant at 5%, which is equivalent to a 0.51% probability. Seventy-three funds have a negative ω, but none of them is statistically significant. In terms of stock selectivity, 192 funds have a positive α, and 13 of these are statistically significant. The other three funds have negative α, but none of them exhibits statistical significance. In the same period, as Panel B of Table 4 indicates, out of the 63 loser funds, 22 funds have a positive ω, but none of them is statistically significant. The other 41 funds have negative ω, two of


which are statistically significant. Based on stock selectivity, four of the 63 loser funds have a positive α, but none of them exhibits a statistical significance at the 5% level. On the other hand, 59 of the 63 funds have a negative α, three of which are statistically significant. Test results of this period indicate that the winner funds overall do not possess market-timing ability, though 13 of them (or 6.67%) exhibit stock-selecting skill. The loser funds seem not to have any market-timing ability, while 4.76% of them do exhibit a negative stock-selecting skill.

In the 2001–2003 down market, Panel A of Table 4 shows that 15 out of the 26 winner funds have a positive ω, one of which is statistically significant, while 11 of them have a negative ω, though none of them exhibits any statistical significance. On the side of stock selectivity, 19 of the 26 funds have a positive α, and four of these are statistically significant, while seven of them have a negative α, though none of them is statistically significant. In other words, 15.4% of the winner funds exhibit an ability to select stocks. Panel B of Table 4 shows the results of the loser funds during the same period. We can see that 321 of the 483 loser funds have a positive ω, 11 of which are statistically significant, while 162 loser funds have a negative ω, and two of these are statistically significant. As for stock selectivity, 10 of the loser funds have positive α, but none of them is statistically significant. In contrast, 473 of the 483 loser funds have negative α, and 174 of these are also statistically significant. Put another way, 36% of the loser funds exhibit a negative stock-selecting skill.

In the 2004–2006 up market, as Panel A shows, 62 of the 78 winner funds have positive ω, and three of these are statistically significant; 16 of the 78 funds have a negative ω, though none of them is statistically significant. In regard to stock selectivity, 71 of the winner funds have a positive α, and two of these are statistically significant; on the other hand, seven of them have a negative α, though none of them is statistically significant. Panel B of Table 4 reports the test results of the loser funds. We can see that 56 of the 162 loser funds have a positive ω, and one of them is statistically significant, while 106 of them have negative ω, and three of these are statistically significant. Nineteen of the loser funds have positive α, though none is statistically significant. One hundred and forty-three of the 162 loser funds have negative α, and 23 (or 14.20%) of these are statistically significant.

Table 4 also shows that during the 2007–2009 down market, three of the 34 winners have a positive market-timing skill statistically significant at 5%, and two of the funds have statistically significant negative ability to time the market. In addition, five of the winner funds have a statistically significant positive α. Out of the 231 loser funds, one fund exhibits a statistically significant positive ω, and three funds have negative market-timing ability. None of the loser funds has a statistically significant positive stock selectivity, as indicated by α, while 49 of the loser funds (or 21.21%) have a negative α statistically significant at 5%. In the 2010–2012 up market, none of the 52 winner funds exhibit any market-timing skill, and three of them even have a statistically significant negative ω. In stock selectivity, 20 of the 52 winner funds (or 38.46%) exhibit a statistically significant stock-selecting skill, and none of them has a negative α. As for the 198 loser funds, four of them have a positive ω statistically significant at 5%, and the number is seven for negative ω. Once again none of the loser funds has a statistically significant positive α, and 34 of the loser funds (or 17.17%) have a statistically significant negative stock-selecting skill.

From Table 4 we can see that the winner funds with a statistically significant market-timing factor (ω) range from 0% in the 2010–2012 up market to 8.82% in the 2007–2009 down market;6 overall the winner funds do not possess an ability to time the market changes. In addition, there is no evident difference between the winner funds and the loser funds in terms of the number and probability of funds with positive market-timing factor and their statistical significance.

In contrast, the winner funds and the loser funds demonstrate a clearly different pattern in stock selectivity measured by α. Panel A of Table 4 indicates that very few winner funds have a negative α, and none of them is statistically significant, whereas Panel B shows that very few of the loser funds have a positive α, and none of them is statistically significant. Furthermore, in the 2010–2012 up market, 38.46% of the winner funds demonstrate a positive statistically significant stock selectivity followed by 15.39% in the 2001–2003 down market. In the 2001–2003 down market, 36.02% of the loser funds have a statistically significant negative α, followed by 21.21% in the 2007–2009 down market. Overall the test


results indicate that stock selectivity seems to play a role in differentiating between the winner funds and the loser funds. Bootstrapped Funds

To determine whether fund alpha results from manager skill or pure luck, we need a proxy of pure luck. In this section, we use a bootstrapped fund sample as the benchmark for pure luck (or “no skill”). A bootstrapped fund is constructed by randomly selecting a fund from each month in the sample period while maintaining the chronological order; thus, the performance of a bootstrapped fund is solely driven by luck. Repeating the same procedure 2,500 times, we get a bootstrapped fund sample of 2,500 funds.

Table 5 reports the number and the percentage of the bootstrapped funds with alpha statistically significant at the 5% level. We find that a small group of bootstrapped funds can earn alpha by sheer luck. The percentage of the bootstrapped winner funds ranges from 0.64% in the 2001–2003 down market to 3.12% in the 1998–2000 up market.7 We also see that the percentage of bootstrapped loser funds ranges from 0.92% in the 1998–2000 up market to 9.92% in the 2001–2003 down market. Overall the percentage of bootstrapped loser funds is higher than that of bootstrapped winner funds in four out of the five windows, which is similar to the pattern exhibited by the actual winner and the actual loser funds. In addition, compared with the actual funds, the bootstrapped funds have a lower probability of earning alpha, including both positive alpha and negative alpha.

TABLE 5 SUMMARY STATISTICS OF THE BOOTSTRAPPED WINNER/LOSER FUNDS

Period

Market State Bootstrapped

Funds Observations Percentage Winner Loser Winner Loser

1998-2000 Up 2,500 78 23 3.12% 0.92% 2001-2003 Down 2,500 16 231 0.64% 9.92% 2004-2006 Up 2,500 33 62 1.32% 2.48% 2007-2009 Down 2,500 31 87 1.24% 3.48% 2010-2012 Up 2,500 40 96 1.60% 3.84%

Table 5 shows that some bootstrapped funds can also earn statistically significant positive alpha by luck, and this probability ranges from 0.64% in the 2001–2003 down market to 3.12% in the 1998–2000 up market. Compared with the actual winner funds, the bootstrapped winner funds overall have a lower probability of earning positive alpha. As for the bootstrapped loser funds with a statistically significant negative alpha, the probability ranges from 0.92% in the 1998–2000 down market to 9.92% in the 2001–2003 down market, which is also lower than that of the actual loser funds, as reported in Table 1. In addition, the percentage of the bootstrapped winner funds is higher in four out of the five windows.

In light of this finding, fund alpha can come from pure luck; thus, alpha itself does not warrant a conclusion of manager skill. Next, we use the same model as exhibited in equation (2) to test the market-timing ability and stock selectivity of the bootstrapped winner/loser funds. The results are presented in Table 6.


TABLE 6 MARKET TIMING ABILITY AND STOCK SELECTING ABILITY OF THE BOOTSTRAPPED

WINNER/LOSER FUNDS

Panel A: The Bootstrapped Winner Funds Period Market Total Positive

ω Sig. @5%

Neg. ω

Sig. @5%

Positive 𝛼

Sig. @5%

Neg. 𝛼

Sig. @5%

1998-2000 Up 78 42 3 36 0 66 6 12 0 2001-2003 Down 16 9 1 7 0 14 2 2 0 2004-2006 Up 33 18 3 15 0 29 3 4 0 2007-2009 Down 31 17 1 14 1 29 2 2 0 2010-2012 Up 40 23 0 17 2 35 8 5 0 Panel B: The Bootstrapped Loser Funds Period Market Total Positive

ω Sig. @5%

Neg. ω

Sig. @5%

Positive 𝛼

Sig. @5%

Neg. 𝛼

Sig. @5%

1998-2000 Up 23 8 0 15 0 18 6 5 0 2001-2003 Down 231 108 5 123 2 10 0 221 47 2004-2006 Up 62 35 1 27 1 4 0 58 16 2007-2009 Down 87 39 1 48 2 8 0 79 8 2010-2012 Up 96 48 0 48 2 11 0 85 16

Panel A of Table 6 reports the results of the bootstrapped winner funds. We can see that more winner funds exhibit a positive market-timing ability than the loser funds; however, very few of them are statistically significant. The percentage of bootstrapped winner funds with statistically significant market-timing ability (ω) ranges from 0% in the 2010–2012 down market to 9.09% (or three out of 33) in the 2004–2006 up market. Compared with the actual winner funds, the bootstrapped winner funds overall exhibit a higher chance of market-timing ability. Based on the stock selectivity, most of the bootstrapped winner funds demonstrate positive stock-selecting skill, with a probability ranging from 6.45% (two out of 31) in the 2007–2009 down market to 20% (eight out of 40) in the 2010–2012 up market. Overall the bootstrapped winner funds have a lower probability of exhibiting statistically significant stock selectivity than the actual winner funds. In addition, none of the bootstrapped winner funds shows negative stock selectivity, and this also holds for the actual winner funds.

Panel B of Table 6 reports the market timing and stock selectivity of the bootstrapped loser funds. We can see that there is no pattern in regard to the sign and the statistical significance of the market-timing factor, and the bootstrapped loser funds seem to have a slightly lower chance of exhibiting market-timing ability than the bootstrapped winner funds. As for stock selectivity, the bootstrapped loser funds overall exhibit no positive stock selectivity; this is the same as that of the actual loser funds. Just like the actual loser funds, most of the bootstrapped loser funds exhibit a negative stock selectivity, and some of them are statistically significant.

Since bootstrapped funds can also earn statistically significant alpha, and there is no evident difference in market timing and stock selectivity between the actual winner/loser funds and their bootstrapped counterparts, we cannot equate fund alpha with manager skill. SUMMARY

We found that a small group of funds can earn statistically significant alpha, and funds with positive alphas tend to have lower expense ratio, lower turnover ratio, and lower price to earnings (PE) ratio than


those of their counterparts. In addition, the mid-value funds have the highest chance of earning positive alphas, while the mid-growth funds have the highest chance of earning negative alphas. The probability of fund alpha occurrence changes over time, as does the value of fund alpha.

We examined the market risk exposures of the winner funds and the loser funds and found no evident difference. We also tested market-timing ability and stock selectivity of the winner funds and the loser funds. The test results show that none of the fund groups possesses an ability to time the market, though the winner funds tend to have a better stock selectivity than the loser funds. After examining a sample of bootstrapped funds, which have no skill, we found that fund alpha can also be earned by pure luck, and the bootstrapped winner/loser funds exhibit a similar pattern to that of the actual winner/loser funds in market-timing ability and stock selectivity. This finding suggests that manager skill measured by fund alpha is, at most, a temporary phenomenon; thus, it is not wise for investors to seek persistent fund alpha. ENDNOTES

1. Winner funds refer to the funds with a positive alpha statistically significant at the 5% level, and loser funds refer to the funds with a negative alpha statistically significant at the 5% level.

2. Since each disc covers a one-year period, the survivorship bias and the incubation bias can be minimized. 3. Fund returns are the returns net of expenses. 4. The outperforming/underperforming probability refers to the probability of earning a statistically

significant alpha with a positive/negative value. 5. The market-timing factor is based on Henriksson and Merton’s (1981) return-timing model. 6. Percentages are calculated based on the corresponding observations in Table 4. 7. Bootstrapped winner funds refer to the bootstrapped funds with a positive alpha statistically significant at

the 5% level, and bootstrapped loser funds refer to the bootstrapped funds with a negative alpha statistically at the 5% level.

REFERENCES Baker, M., Litov, L., Wachter, J. A. & Wurgler, J. (2010). Can Mutual Fund Managers Pick Stocks?

Evidence from Their Trades Prior to Earnings Announcements. Journal of Financial and Quantitative Analysis , 45, 1111-1131.

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Elton, E. J., Gruber, M. J. & Blake, C. R. (2011). An Examination of Mutual Fund Timing Using Monthly Holdings Data. Review of Finance, 16, 619-645.


Fama, E. F. & French, K. R. (1993). Common risk factors in the returns on stocks and bonds, Journal of Financial Economics, 33, 3-56.

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techniques. Journal of Financial and Quantitative Analysis, 29, 419-445. Henriksson, R. T. (1984). Market timing and mutual fund performance: An empirical investigation.

Journal of Business, 57, 73-96. Ibbotson, R. G. & Patel, A. K. (2002). Do winners repeat with style? Yale ICF Working Paper, No.00-70. Henriksson, R. D. & Merton, C. (1981). On market timing and investment performance II: Statistical

procedures for evaluating forecasting skills. Journal of Business, 54, 513-534. Jiang, G. J., Yao, T. & Yu, T. (2007). Do Mutual Funds Time the Market? Evidence from Portfolio

Holdings. Journal of Financial Economics, 86, 724-758. Kacperczyk, M. T., Van Nieuwerburgh, S. & Veldkamp, L. (2014). Time-Varying Fund Manager Skill.

Journal of Finance, 69, 1455-1484. Kaplan, S. N. & Berk A. S. (2005). Do Mutual Funds Time their Benchmarks? Working Paper. Kon, S. J. & Jen, F. C. (1979). The Investment Performance of Mutual Funds: An Empirical Investigation

of Timing, Selectivity, and Market Efficiency. The Journal of Business, 52, 263-290. Kosowski, R., Timmermann, A., Wermers, R. & White H. (2006). Can mutual fund “stars” really pick

stocks? New evidence from a bootstrap analysis. Journal of Finance, 61, 2551-2595. Lee, C.F. & Rahman, S. (1990). Market Timing, Selectivity, and Mutual Fund Performance: An empirical

investigation. The Journal of Business, 63, 261-279. Lehmann, B. N. & Modest, D. M. (1987). Mutual fund performance evaluation: A comparison of

benchmark and benchmark comparison. Journal of Finance, 42, 233-265. Malkiel, B. G. (1995). Returns from investing in equity mutual funds 1971 to 1991. Journal of Finance,

50, 549-572. Sharpe, W. F. (1991). The Arithmetic of Active Management, Financial Analyst Journal, 47, (1), 7-9. Treynor, J. L. & Mazuy, K.K. (1966). Can mutual funds outguess the market? Harvard Business Review,

44, 131-136.


Stocks, Bonds, Bills and Long-Run Returns for Retirement Portfolios

Charles Rayhorn Northern Michigan University

A USA Today article, “Investors Look Back on a Decade of Grim Stock Returns,” summed up returns for the first decade of the 21st century—grim. This paper shows the wealth relative, or future value interest factor, for this decade was worse than for the depression years. The other periods of this study are positive. These unpleasant results were nothing that most investors didn’t already ‘feel’. The purpose of this study is to examine various retirement horizons to see how they fared since 1926 with the ups and downs of the stock market. LITERATURE REVIEW, METHODOLOGY AND DATA

Past studies have looked at this very question. Levy (1978), Reichenstein (1986), and Butler (1991) used a single sum, not periodic contributions for various holding periods. They concluded that stocks outperform Treasury bills. Butler & Domian (1992) used Ibbotson’s real returns and sampling with replacement, to form returns for various retirement holding periods from 1926 to 1990. They conclude that the stock market is the better choice for long-term retirement investing. A paper by Hickman, Hunter, Byrd, Beck, & Terpening, (2001) uses a sample with replacement technique to examine the difference in returns between different retirement asset classes for the period. Unlike Butler and Dominan’s work their data isn’t inflation adjusted. They find big penalties for not being in risky assets (common stocks) for long-term investment horizons. They do find marginal support for several switching strategies for investors with shorter investment horizons.

Decade-long wealth relatives (decade-ending price level/ decade-beginning price level) were calculated for all decades, starting in 1930 (1930-1939) through 2010 (2000-2010), and for 1926 to 1929 and 2010-2013. Wealth relatives for the period 1926-1929, 2010-2013, and 1926-2013 are calculated. The purpose of these calculations was to estimate one time, or single sum, investments.

Besides single-sum wealth relatives, we calculated future value interest factors for investors who make payments into a retirement plan yearly. One of the additions that differentiate this paper from Butler & Domian (1992) is adjusting the invested amount by the prior year’s rate of inflation. This adjustment serves as a cost of living adjustment for one’s salary. The investor is assumed to be a wage earner that contributes a fixed proportion of salary, indexed for the prior year’s inflation, each year over a retirement savings period. The Ibbotson Inflation Index serves as the retirement plan contribution inflator. The plan contributions are invested in one of four portfolios. Returns for the four portfolios are based on: the Ibbotson Large Company Total Returns portfolio, the Ibbotson T-bill Total Returns portfolio, the Ibbotson T-bond Total Returns portfolio, and the Ibbotson Large Company Total Returns with a switch to the Ibbotson T-bill Total Returns portfolio at the beginning of the fifth year, five years before retirement.


These data come from the 2014 Ibbotson SBBI Classic Yearbook. The purpose of the switching portfolio is to examine the merit of shifting from risky to safe assets as one approaches retirement.

For the five year savings period the calculation would be: $1.00-5(R-5to-4)(R-4to-3)(R-3to-2)(R-2to-1)(R-1to0) +$1.00-5(I-5to-4)(R-4to-3)(R-3to-2)(R-2to-1)(R-1to0) +$1.00-5(I-5to-4)(I-4to-3)(R-3to-2)(R-2to-1)(R-1to0) +$1.00-5(I-5to-4)(I-4to-3)(I-3to-2)(R-2to-1)(R-1to0) +$1.00-5(I-5to-4)(I-4to-3)(I-3to-2)(I-2to-1)(R-1to0) Where R is 1+r, and I is 1+i. r is the return for the year in question, and i is the inflation rate from the prior year. The subscripts for R and I represent the period relative to the end of the holding period. The future value ‘Due’ situation is assumed—investing starts at the beginning of the period, and no cash-flow at the end of the holding period. Since one dollar is the initial annual contribution, results will be for every dollar invested. This same logic is applied to every time horizon in this study.

Savings and investment periods of 5, 10, 15, 20, 25, 30, 35 and 40 years were examined starting in 1926 and all subsequent years through the beginning of 2009. The year 2009 is the last year one could start a five holding period, given these data. These results are realized, not simulated, returns. Furthermore, since every holding period overlaps, the summary statistics will be biased. This bias isn’t considered a problem. The purpose of this study is to determine how a typical pensioner would have fared investing for retirement starting every year, from 1926 to 2009, assuming various holding periods, with a salary adjustment based on inflation.

One last examination of the data is a simple test of Stochastic (state-by-state) dominance for each investment strategy in every holding period. RESULTS

Table 1 reports annual returns for Large-Company Stocks Total Returns by decade for the study period 1926 through 2013. The first row of data is the wealth relatives for the period in question. These statistics show that the first decade of the 21st century was the worst decade for investing, even surpassing the decade of the great worldwide depression. This result may surprise some. Many articles, using the Dow Jones Industrial average, have appeared stating that it took decades to recover from Black Tuesday in 1929. This study uses total returns, which include reinvested dividends. In any given year, dividends can account for 30% of the total return. This fact affects the holding period returns for the various retirement-saving horizons starting in the 1960s, a fact that has implications for many who read this.

TABLE 1 SUMMARY STATISTICS FOR YEARLY RETURNS BY DECADE

WEALTH RELATIVES ARE THE PRODUCT OF YEARLY 1+r 2009-

2000 1999-1990

1989-1980

1979-1970

1969-1960

1959-1950

1949-1940

1939-1930

1929-1926

2013-1926

2013-2010

WR 0.909 5.328 5.039 1.768 2.121 5.866 2.405 0.995 2.018 4676.4 1.804 Mean 0.012 0.190 0.182 0.075 0.087 0.208 0.103 0.053 0.211 0.118 0.086 Stdev 0.211 0.142 0.127 0.192 0.144 0.198 0.165 0.347 0.241 0.202 0.092 Range 0.657 0.407 0.374 0.637 0.370 0.634 0.480 0.973 0.520 0.973 0.129 Min -0.370 -0.031 -0.049 -0.265 -0.101 -0.108 -0.116 -0.433 -0.084 -0.433 0.021 Max 0.287 0.376 0.325 0.372 0.269 0.526 0.364 0.540 0.436 0.540 0.151 % + ret 60% 90% 90% 70% 70% 80% 70% 40% 75% 73% 100%


Tables 2A-2D report summary statistics for retirement period wealth-relatives for the four portfolios and various retirement saving periods. Table 2A reports the results for the Ibbotson Large Company portfolios. For these returns; there are no holding period horizons where you ‘lose it all.’ However for the 10-year horizon holding periods, for the years starting in 1999, one would have been better off not investing the inflation-adjusted contributions. For the five-year horizons, one would have been fared better being out of the market in the years 1927-1930, 1936, 1937, 1970, 1998, and 2004. The poor performance in the 1920s through the 1930s was due to deflation and negative stock returns. Deflation occurred in 1926-1928, 1930-1932, and in 1938-1939. The poor performance in the decade from 2000-2009 is from returns that were worse than exhibited in the late 1920s and throughout the 1930s. Inflation (CPI-U from Ibbotson) for the first decade of the second millennium was below the long-term average. Of course, the pay reduction was much worse than inflation would indicate during the depression, as well as the period after 2008, for those who became unemployed.

TABLE 2A SUMMARY STATISTICS FOR SAVINGS PERIODS’ WEALTH RELATIVES FROM 1923-2013

Retirement Saving Periods 40 35 30 25 20 15 10 5 Mean 2681.43 1345.45 677.28 327.46 147.63 62.50 24.60 7.77 Stdev 916.11 520.89 316.02 170.73 73.02 28.79 9.00 2.13 Range 4282.64 2400.78 1475.88 783.17 275.35 105.65 31.47 9.60 Min 1265.51 731.32 295.00 118.93 49.01 15.68 9.67 2.33 Max 5548.15 3132.11 1770.88 902.10 324.36 121.33 41.13 11.93 Count 49 54 59 64 69 74 79 84

These relatives are for lagged inflation and Ibbotson large company total returns.

Table 2B reports results for the Ibbotson Treasury Bill Total Returns portfolios. At the 15-year horizon, you do have years where you would have ended with less than if you had taken your contributions and put them into a safety deposit box. This happened in the holding periods that started in 1926 and 1927. For ten-year horizons, this occurs in the years from 1926 through 1931. For the five-year horizon, for the years 1928 through 1932. It is clear that you sacrifice the potential for much larger gains in your retirement account, and you don’t remove the downside risk. In fact, the number of times you wind up with less than if you had done nothing is greater, thirteen versus ten times for Large-Company portfolios.

TABLE 2B SUMMARY STATISTICS FOR SAVINGS PERIODS’ WEALTH RELATIVES FROM 1926-2013

Retirement Saving Periods

40 35 30 25 20 15 10 5 Mean 465.49 286.89 171.99 100.84 57.50 31.35 15.74 6.21 Stdev 234.83 162.54 103.72 59.92 30.91 13.78 4.89 1.07 Range 667.07 458.18 301.93 173.96 99.02 50.34 20.88 5.63 Min 95.64 72.59 53.66 39.61 24.41 14.38 8.96 4.13 Max 762.70 530.77 355.59 213.57 123.43 64.72 29.84 9.76 Count 49 54 59 64 69 74 79 84

These relatives are for lagged inflation and Ibbotson treasury bill total returns.


Table 2C reports results for the Ibbotson Treasury Bond Total Returns portfolios. There are no holding period horizons where you ‘lose it all. Furthermore, there are no holding periods where it would have been better to put your contributions into a safety deposit box.

TABLE 2C SUMMARY STATISTICS FOR SAVINGS’ PERIOD WEALTH RELATIVES FROM 1926-2013

Retirement Saving Periods

40 35 30 25 20 15 10 5 Mean 192.26 133.36 90.63 60.36 39.15 24.25 13.63 5.86 Stdev 69.48 49.78 33.33 20.62 11.51 5.57 2.12 0.48 Range 194.01 137.88 93.68 58.72 33.73 17.12 7.20 1.75 Min 79.69 60.25 45.44 34.85 25.75 18.03 11.32 5.34 Max 273.70 198.13 139.12 93.56 59.48 35.15 18.52 7.09 Count 49 54 59 64 69 74 79 84

These relatives are for lagged inflation and Ibbotson T-bond total returns.

Table 2D reports results for the Ibbotson Large Stock Total Returns series with a switch to Ibbotson Treasury Bill Total Returns returns in the last five years. There are no holding period horizons where you ‘lose it all.’ However, when you get to the ten-year horizons you do wind up with less than if you had taken your contributions and put them into a safety deposit box; this occurred in the holding periods starting in 1928 and 1929. The five-year horizon is the same for Table 2B since you are in T-bills. You do sacrifice the potential for much larger gains in your retirement account, and you don’t remove the downside risk. The number of times that you would have been better off doing nothing is nine, ten and thirteen for the Switch portfolio, Large Stock Total Returns, and T-bills respectively.

TABLE 2D SUMMARY STATISTICS FOR SAVING PERIODS’ WEALTH RELATIVES FROM 1926-2013

Retirement Savings Periods

40 35 30 25 20 15 10 5* Mean 2155.24 1071.52 512.01 232.67 102.41 43.84 17.90 6.21 Stdev 598.76 364.27 207.58 91.46 38.76 14.80 5.18 1.07 Range 3026.10 1861.76 1011.69 370.44 161.36 59.22 24.53 5.63 Min 1003.56 419.06 152.95 65.29 26.25 15.93 7.16 4.13 Max 4029.66 2280.82 1164.64 435.73 187.61 75.15 31.68 9.76 Count 49 54 59 64 69 74 79 84 * Note that the last column is the same as for Table 2b

These relatives are for lagged inflation and Ibbotson large stock total returns with a switch to Ibbotson T-bill total returns for the last five years.

Table 3 lists the Coefficients of Variation (CV) for the three portfolio types in this study. Some interesting results are present. This measure of risk indicates the Inflation/Large Stock Returns portfolio is safer than being in Inflation/T-bills portfolio until fifteen or fewer years remain until retirement. The


Inflation/T-Bond portfolio is safer than the Inflation/T-Bill portfolio for all periods. The Switch portfolios have a lower CV than all the other combinations, except for the Inflation/T-Bond portfolio.

TABLE 3 CV T-BILLS IS FOR INFLATION & T-BILL PORTFOLIOS, CV LG STK IS FOR INFLATION

& LARGE STOCK SERIES, CV T-BILL IS FOR THE INFLATION AND T-BOND PORTFOLIOS AND CV SWITCH IS FOR THE INFLATION & LARGE STOCK

SERIES WITH A SWITCH TO T-BILLS FOR THE LAST FIVE YEARS

Retirement Savings Periods

40 35 30 25 20 15 10 5 CV T-bills 0.504 0.567 0.603 0.594 0.538 0.439 0.311 0.172 CV T-bonds 0.361 0.373 0.368 0.342 0.294 0.230 0.156 0.081 CV Lg stk 0.342 0.387 0.467 0.551 0.495 0.461 0.366 0.274 CV Switch 0.278 0.340 0.405 0.393 0.378 0.338 0.289 0.172

The following figures have two panels. The left side is plots of the Wealth Relatives (FVIF). Each point represents the ending WR for the holding period starting in that year. This illustrates the combined impact of disciplined, systematic retirement savings with raises (and givebacks) based on the Ibbotson Inflation series and the market performance of the Ibbotson Large Stock Total Returns series, the Ibbotson T-Bill Total Returns series, and the Large Stock Ibbotson Total Returns series with a switch to T-Bills in the remaining five years of the holding period respectively, starting in 1926. Unfortunately for most who are reading this paper we didn’t do nearly as well as those who started their careers earlier. You can see that the best time to retire (for all holding periods) would have been about the year 2000. The 1950’s and the years during the Reagan/Clinton bull market were truly phenomenal. The right panel for all the figures are plots of the stochastic (state-by-state) dominance tests of the four portfolios, for all holding periods, are as follows:

40, 35, 30, 25, and 20 Year Holding Periods: the Large-Stock with inflation portfolio is dominant. The switch portfolio dominates the T-Bill and T-Bond (with inflation) portfolios.

15, 10, and 5 Year Holding Periods: None of the portfolios are dominant. However, The Large-Stock with inflation portfolio performs better in most instances.

5 Year Holding Period: No portfolio dominates. Having said that, the Large-Stock with inflation.


FIGURE 1A ENDING WEALTH RELATIVE AND STOCHASTIC (STATE-BY-STATE) DOMINANCE TEST

PLOTS 40YR HORIZONS

FIGURE 1B ENDING WEALTH RELATIVE AND STOCHASTIC (STATE-BY-STATE) DOMINANCE TEST

PLOTS 35YR HORIZONS

FIGURE 1C

ENDING WEALTH RELATIVE AND STOCHASTIC (STATE-BY-STATE) DOMINANCE TEST PLOTS 30YR HORIZONS


FIGURE 1D ENDING WEALTH RELATIVE AND STOCHASTIC (STATE-BY-STATE) DOMINANCE TEST

PLOTS 25 YR HORIZONS

FIGURE 1E

ENDING WEALTH RELATIVE AND STOCHASTIC (STATE-BY-STATE) DOMINANCE TEST PLOTS 20YR HORIZONS

FIGURE 1F ENDING WEALTH RELATIVE AND STOCHASTIC (STATE-BY-STATE) DOMINANCE TEST

PLOTS 15YR HORIZONS


FIGURE 1G ENDING WEALTH RELATIVE AND STOCHASTIC (STATE-BY-STATE) DOMINANCE TEST

PLOTS 10YR HORIZONS

FIGURE 1H ENDING WEALTH RELATIVE AND STOCHASTIC (STATE-BY-STATE) DOMINANCE TEST

PLOTS 5YR HORIZONS

SUMMARY

Table 1 showed that the decades of 1930 and 2000 were the worst decades for the period of this study. Table 1 also shows the decades starting in 1950, 1980 and 1990 were the best for the period of this study. For many readers of this paper, we have had the two best and worst decades for our retirement accounts. Tables 2A and B, and the figures suggest that for normal retirement saving horizons (15 years or more) one would have done fine. Even with the first decade of the second millennium, nothing suggests that we shouldn’t save for retirement. Tables 2A, 2B, 2C, 2D, and 3 suggest that there is little merit for shifting all or some of your portfolios out of the higher-risk asset into a less risky asset—the Large Stock portfolio is better for most periods since 1926—there is hardly anything to be gained and much to be lost by being invested in something other than the stock market (using the Ibbotson Large Stock Total Returns series). These results are consistent with works cited in this paper. While many financial planners recommend that you should subtract your age from 100 (some recommend an age of


110 or 120) to determine the mix of stocks vs. bonds, some famous economist disagree. Paul Samuelson (69) concluded that switching to income producing portfolios as you get older doesn’t hold. Recently, Eugene Fama stated in an interview with Barron’s (22 September 2014): “I have a capitalization-weighted portfolio of all traded stocks. I don’t fool around with bonds. I’m a tenured professor; the university issued me a bond.” While most investors aren’t tenured university professors, if they have a secure job and a defined-contribution plan, they are in a similar situation.

One surprising fact, shown by Table 3 and the figures (above), is the Inflation/T-Bill portfolio had more return than the Inflation/T-Bond portfolio.

Some have suggested that the period of this study is too long, and data before WWII aren’t representative of today’s world. I disagree with that assessment. The data from 1926-1950 are eerily similar throughout the time series starting 1996. The world’s economy has had two run-ups in the stock market beginning in the mid-1990s, followed by two collapses; several periods of disinflation; and according to Pope Francis a piecemeal WWIII. A quote attributed to Mark Twain is still true today, “History doesn’t repeat itself, but it does rhyme.” REFERENCES Butler, K. C. (1991). Risk, Diversification, and the Investment Horizon. The Journal of Portfolio

Management, 17, (3), 41-47. Butler, K. C., & Domian, D. L. (1992). Long-Run Returns on Stock and Bond Portfolios: Implications for

Retirement Planning. Financial Services Review, 2, (1), 41-49. Goodman, B. (2014, September 22). Fama Weighs In on ETFs. Retrieved from

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Nonprofessional Investors’ Framework for Understanding Earnings Quality

Reginald Wilson

The University of Southern Mississippi

The central goal of this article is to provide nonprofessional investors with an understanding of the impact of accrual accounting on earnings quality. Improving nonprofessional investors’ understanding of earnings quality is essential to the capital market, as they have been found to own nearly one-third of all outstanding stock (Bogle, 2005), a number which anecdotally has increased with the advent of employer-directed compensation plans and online stock trading platforms. Following a presentation of the importance of accruals in the earnings process, the framework discusses various earnings management practices that nonprofessional investors should consider when evaluating earnings quality. INTRODUCTION

Investors are routinely faced with complex decisions concerning the quality of a company’s reported

earnings. While the concept of earnings quality is elusive (Givoly et al., 2010, p. 201), earnings quality has been defined as the ability of reported earnings (e.g. income) to predict a company’s future earnings (Spiceland et al. 2015). It is assumed that “high” earnings quality exists when there is a close correspondence between net income and cash flows from operations, especially when this relationship persists over several years (Spiceland et al., 2015). One threat to high earnings quality which nonprofessional investors may be unaware of is earnings management. Earnings management is a practice by which managers attempt to time the recognition of revenues and expenses in the financial statements (Kimmel et al., 2013, p. 185). This term has also been associated with the use of judgment in financial reporting with the intent of misleading investors about the underlying economic performance of a company (Healy et al., 1999, p. 368). Frank and Rego (2006) note that companies are motivated to manage their earnings for several reasons, including the need to meet earnings targets, to smooth earnings, and to demonstrate that the company has positive profits.

A company’s earnings consists of two components: cash flows and accruals (Dechow and Dichev 2002, p. 37). Among several attributes associated with earnings quality, accruals quality has been researched extensively.1 The purpose of accrual accounting is to provide investors with a more comprehensive understanding of a company’s financial health by recording all cash and credit transactions prior to the actual receipt or disbursement of cash. Accrual basis accounting is an improvement over cash basis accounting, due to the fact that accrual basis accounting matches a firm’s revenues and expenses in the same period in which they occur. Accounting standard-setters advocate the superiority of accrual basis accounting over cash basis accounting for the purpose of informing investors of a company’s “present and continuing ability to generate favorable cash flows” (FASB, 1978, para. 44). Academic research also supports these assertions (Subramanyam and Venkatachalam, 2007; Barth et al., 2001; Dechow, 1994).


When analyzing companies’ financial statements, nonprofessional investors should account for the presence of subjectivity in management’s selection of reporting estimates (Healy et al., 1999, p. 366). On one hand, subjectivity in reporting estimates is necessary to improve the value of the accounting information communicated to investors (Healy et al., 1999, p. 366). On the other hand, managerial discretion in the selection of accounting estimates provides the opportunity for earnings management. Managers have been found to manage earnings for a number of reasons, including the need to meet market expectations, the desire to achieve incentives associated with their compensation plans, contractual motives, and regulatory motives (Clikeman, 2003). The goal of earnings management is to misrepresent an organization’s financial performance, which is the same goal that managers seek to accomplish when committing fraud (Clikeman, 2003, p. 76). However, the detection of earnings management is complex, due to the subtlety with which it presents itself. While fraud is an outright lie that involves obvious violations of accounting principles, earnings management is considered a “shading of the truth”, and is discreetly accomplished within the flexibility of a company’s generally accepted accounting principles (Clikeman, 2003, p. 76). This makes earnings management more difficult to detect.

Prior research has generally identified three categories of earnings management: accrual management, manipulation of real economic activities, and earnings management through classification shifting of expenses. The overarching objective of this paper is to develop a framework whereby nonprofessional investors may understand how management employs these earnings management tools to deliberately manipulate a company’s earnings.

The remainder of the paper is organized as follows. The next section provides an overview of the accounting cycle and the components of earnings in order to establish the opportunity for management to manage earnings. This section is followed by a discussion of the costs of earnings management, which discusses five specific forms of earnings management: accruals management, revenue management, classification shifting, management of specific accruals, and the rounding phenomenon. The paper concludes with a discussion and avenues for future research.

THE ACCOUNTING CYCLE AND THE COMPONENTS OF EARNINGS

In order to appreciate accrual accounting, nonprofessional investors must understand the composition of earnings, the impact of earnings on the balance sheet, and the accounting cycle. The accounting cycle is presented in Figure 1. The balance sheet provides a summary measure of companies’ assets, liabilities, and equity. The equity section of the balance sheet consists of two categories: contributed capital and retained earnings. At the end of the accounting reporting period, companies’ current period earnings are transferred to retained earnings, which consist of the sum of companies’ accrual basis income (i.e. earnings) since the company’s inception, net of dividends paid to the investors. Finally, retained earnings is closed to the balance sheet.

A company’s current period earnings are recorded in the income statement. Earnings consists of two components: cash flow and accruals. The cash flow component represents cash which has been collected from customers during the accounting period (e.g. cash flow), whereas, the accrual component of earnings represents cash that is expected to be collected in the future. This relationship between the cash flow component of income and the accruals component of income is presented in Equation 1:

Earnings = Cash Flow (CFp, CFc, CFf) + Accruals (Opening, Closing) (1)

Dechow and Dichev (2002, p. 38) state that the cash flow component of earnings consists of three elements: cash collected from prior period transactions (CFp), cash collected from transactions in the current period (CFc), and cash collected in advance for transactions in the future (CFf). Accruals are created as a result of temporary adjustments to earnings that shift the recognition of cash flows in the income statement over time. Since accruals match companies’ revenues and expenses to the period in which companies actually perform the services, investors are provided with a better understanding of the flow of business activities, not a summary of when cash exchanges hands.


The accrual component of earnings is comprised of opening accruals and closing accruals (DeChow and Dichev, 2002). The origin of opening and closing accruals is as follows. Prior to an accrual adjustment, companies’ revenue and expense accounts, along with their related balance sheet accounts, are understated (Kimmel et al., 2013, p. 175). This understatement is due to the fact that the “cash flow” component of earnings does not capture the full complement of business activities which may occur separately from cash receipts and disbursements. For example, an apartment manager may collect cash for one year’s worth of rent from a tenant at the beginning of the lease, which creates a liability for the apartment manager. As a result, the manager is not allowed to recognize revenue in the financial statements until the business activity has taken place (e.g. the business activity would be the tenant actually living in the apartment each month). As a result of these type of transactions, Dechow and Dichev (2002, p. 38) indicate that two types of accruals are necessary to completely capture business flow and cash flow. They classify these accruals as “opening accruals” which are generated when cash is received or paid before it is recognized in earnings (or vice versa), and “closing accruals”, which occur when a portion (or all) of the original accrual is reversed.

FIGURE 1 ACCOUNTING CYCLE OVERVIEW

Income Statement

For the Period Ending

12/31/XX Statement of

Retained Earnings Balance Sheet As of 12/31/XX

Revenues Retained Earnings (1/1) Assets = Liability + Equity - Cost of Goods Sold + Net Income/Loss Debit (-) Credit (+) Gross Profit - Dividends - Core Expenses Retained Earnings (12/31) Retained Earnings - Special Items Increase Net Income/Loss Revenues *Income and Increase Expenses close to Income Summary Expenses Then to Retained Decrease Earnings Comprehensive

Income Increase

Characteristics of Firms with Low Accrual Quality

A logical first step for investors to begin understanding earnings quality and accrual quality is to understand the impact of firms’ internal controls on accrual quality. Ashbaugh-Skaife et al. (2008) indicate that firms reporting internal control deficiencies tend to have lower quality accruals. Their research also finds that the following firm characteristics are associated with poor accrual quality: high levels of inventory, volatile cash flow operations, volatile sales reports, firms that report losses, firms with merger and acquisition activity, and firms with relatively smaller book-to-market ratios. In addition, the degree of conservatism exerted in the presentation of financial information is associated with an increase in the volatility of a firm’s accruals (Ball and Shivakumar, 2006), and has been debated as to whether it actually improves financial reporting quality (Givoly et al., 2010, p. 205). Other research indicates that distressed firms are likely to have lower accrual quality (Kothari et al., 2005; McNichols, 2000).


Regarding the life cycle of firms, high growth firms have been found to exhibit low accrual quality (Chichernea et al., 2012).

The literature also provides investors with firm characteristics associated with high accrual quality. Ashbaugh-Skaife et al. (2008) indicate that higher accrual quality has been observed in firms with relatively higher investment in property, plant and equipment, firms with substantial off-balance sheet intangible assets, and firms with restructuring activities. Hutchinson et al.’s (2008) findings indicate that the independence of the board of directors and the audit committee are associated with accrual quality.

THE COSTS OF EARNINGS MANAGEMENT

The costs of earnings management differ for varying financial statement items (Marquardt and Wiedman, 2004). Frank and Rego (2006) indicate that earnings management continues to exist within generally accepted accounting principles, even in the post-Sarbanes-Oxley era. Their research notes that even within the scope of Statement of Financial Accounting Standard No. 109 (FASB, 1992), valuation asset accounts are lightning rods for earnings management. Investors should be aware that valuation asset accounts are used to manage earnings upward to meet earnings management forecasts. They also find that firms use valuation allowance accounts to beat analysts’ forecasts when firms’ previously premanaged earnings are below an earnings target (“premanaged earnings” are a company’s reported earnings plus the company’s discretionary changes in valuation allowance accounts).

Marquardt and Wiedman (2014) categorize the costs of earnings management as those that are detected and those that are undetected, with higher costs being associated with detected earnings management than nondetected earnings management. The Securities and Exchange Commission’s detection of earnings management has reportedly led to average stock price declines in the neighborhood of 7.5 percent (Feroz et al., 1991) and nine percent (Dechow et al., 1996). Palmrose and Scholz (2004) discover that firms that restate their earnings are more likely to result in litigation, especially when revenue is restated. Finally, firms whose accounting policies are criticized in the press are also associated with earnings management.

Undetected earnings management in the current period impacts future period earnings when detected (Marquardt and Wiedman, 2014), and are associated with negative stock prices (Teoh et al., 1998). The future reversal of undetected managed accruals in the current period decreases companies’ potential to manage earnings in the future (Barton and Simko, 2002). The audit costs of firms that manage earnings have been found to be directly associated with firms’ level of accruals (Antle et al., 2006), and indirectly associated with the threat of litigation resulting from an audit failure (Lys and Watts, 1994).

Consequences that investors experience resulting from managers’ manipulation of firms’ earnings may vary based on the earnings management strategy employed by a company. The remainder of this section discusses various earnings management approaches and specific accounts used by management to manage earnings.

Earnings Management and Accrual Quality

Prior research asserts that accruals which measure economic performance inherently include both intentional, management-directed errors and unintentional errors (Richardson et al., 2005, p. 442; Dechow and Dichev, 2002, p. 53). Equation 2 extends Equation 1 to capture this error.

Earnings = Cash Flow (CFp, CFc, CFf) + Accruals + Accrual Error (intentional, nonintentional) (2)

In the absence of accrual accounting, “cash” would be the only asset or liability account on the balance sheet (Richardson et al., 2005, p. 445). Without the use of accruals, the true economic performance of companies’ financial statements will be understated. Managers exercise subjectivity regarding the quantification of future economic events, the selection of acceptable accounting methods for reporting (e.g. depreciation; inventory methods), working capital management (e.g. inventory decisions), and the use of discretionary expenses (Healy et al., 1999, p. 369). This subjectivity threatens both the verifiability


and the reliability of accounting estimates (Richardson et al., 2005, p. 440), and may be associated with error in the estimation of accruals.

Poor accrual quality is associated with a number of negative consequences. Dey and Lim (2015) find that lower reliable accrual components lead to lower earnings persistence. Wasan et al. (2013) suggest that poor accrual quality influences the costs of borrowing capital in the syndicated loan market, which may be of interest to institutional investors, as well as those who invest in large banks that heavily participate in the syndicated loan market. The use of discretionary accruals is also associated with the long-run underperformance of convertible bond issuers (Chou et al., 2009).

Dechow and Dichev (2002, p. 47) provide several factors which investors should consider as being highly associated with poor accrual quality, including the amount of unpredictability in estimating accruals, the amount of unpredictability in a company’s earnings, and the frequency which a company reports negative earnings. Richardson et al. (2005) indicate that in addition to managerial subjectivity, noncurrent operating asset accruals and noncurrent liability accruals may be associated with poor accrual quality. Prior research has also found that specific accounts are more susceptible to earnings management (i.e. poor accrual quality) resulting from managerial subjectivity, including bad debt provision (McNichols and Wilson, 1988), depreciation (Teoh et al., 1998), loan loss reserves (Healy et al., 1999), loan loss provisions for banks (Beatty et al., 1995), deferred tax valuation allowances (Visvanathan 1998), and timing the realization of investment gains and losses (Collins et al., 1995). It is incumbent upon investors to petition accounting regulators for more transparency in the disclosures regarding the assumptions used to develop firms’ accruals.

Earnings Management via Revenues Management

Revenue is the largest earnings component of most companies. It is highly subject to discretion (Stuben, 2010, p. 696) and is the most common type of misstatement (Turner et al., 2001). Approximately seventy-percent of the Securities and Exchange Commission’s enforcement actions in the mid-1980s resulted from overstatements of accounts receivables resulting from premature revenue recognition (Feroz et al., 1991). Marquardt and Wiedman (2004, p. 467) indicate that the Securities and Exchange Commission is most likely to detect earnings management when revenues are overstated. Plummer and Mest (2001) find that one primary reason which managers overstate their revenues (and understate operating expenses) is to meet their earnings forecasts. However, their research does not consider the use of discretionary revenues as an earnings management tool.

Discretionary revenue, which is defined as the difference between the change in a company’s actual accounts receivables and the amount of receivables, has been operationalized to detect earnings management, especially for growth firms (Stuben, 2010). Discretionary revenues are identified by analyzing a company’s accounts receivables. If the accounts receivables balance reported in the balance sheet differs from the forecasted accounts receivables, then it is possible that the company’s revenue has not been properly accrued.

Limited research suggests that discretionary revenues are used to manage revenue. Marquardt and Wiedman (2004) find that managers use discretionary revenues to increase a company’s earnings prior to the firm issuing equity. Caylor (2009) observes managers’ discretionary use of accounts receivables in reporting earnings surprises (accounts receivable is the balance sheet-related revenue account). His model considers the use of deferred revenues in revenue management. Finally, Stuben (2010) indicates that managers utilize a number of tactics when engaging in discretionary revenue management, including the manipulation of business activities that impact sales revenues (e.g. sales discounts, bill and hold sales, subjective customer credit policies), the misapplication of accounting principles, the reporting of non-factual revenues, or by the deferral of revenue that should recognized in the current period.

Investors should account for a variety of circumstances which may influence managers’ discretion in the presentation of revenues when interpreting the financial statements for investment purposes. Of interest to investors is that managers tend to use the sales revenue account to improve earnings by “managing” their sales upward (Plummer and Mest, 2001, p. 302). Their research also finds that firms with high operating margin percentages and firms whose current assets are high at the beginning of the


year are likely to engage in this type of behavior. Investors should also be aware that managers are likely to manage their earnings to meet analysts’ forecasts when the firm itself has strong investment potential (Plummer and Mest, 2001; Abarbanell and Lehavy, 2003). In addition, Plummer and Mest (2001) find that firms are likely to manage their earnings upward when analysts assign a “buy” rating to the firm. Thus, investors may consider that the “buy” rating may motivate managers to manage their sales upward to solidify their firms’ strong investment potential. Finally, investors should understand the implications of sales and accounts receivables on financial ratios that include sales in the calculations.

Earnings Management via Classification Shifting

Classification shifting is a form of earnings management that distorts a company’s economic performance by shifting “favorable” line items closer to the ‘Revenue’ accounts, which is indicative of their permanence in nature (e.g. they are more likely to recur). Classification shifting may also occur by shifting negative expenses further away from sales to indicate that they may not occur frequently (McVay, 2006). Research suggests that managers who classify core items as special items will use positive special items (such as gains on the sale of assets) to smooth earnings (Bartov, 1993) and to avoid earnings declines (Marquardt and Wiedman, 2004). The use of classification shifting as an earnings management tool allows managers to inflate the core profitability of a company while not changing GAAP earnings (Lail et al., 2014). Bradshaw and Sloan (2002) indicate that investors and analysts rely on core performance, which may motivate managers to manipulate core profitability (Lail et al., 2014, p. 458).

McVay (2006, p. 506) classifies “core expenses” as being more permanent in nature. These expenses include cost of goods sold and selling, general, and administrative expenses. His research finds that managerial discretion exists in the assignment of income statement accounts to core expenses (relatively stable) versus special item expenses (unusual and/or infrequent in nature). Examples of these transitory expenses include expenses related to restructurings and mergers. Marquardt and Wiedman (2004, p. 465) suggest that earnings management is relatively easier to accomplish and less costly using special items compared to managing earnings using recurring items. McVay’s (2006) core earnings model was adjusted by Causholli et al. (2014, p. 689) to define core earnings in Equation 3:

Core earnings = Core Earnings (Sales – COG – SG&A) / Sales

+ Asset Turnover + Working Capital Accruals Change in total current assets (net of change

in cash) minus the change in current liabilities (net of change in current portion of long-term debt)

+ Percent Change in Sales + Percent Change in Negative Sales + The company’s current and prior

year returns To measure performance (3)

Classification shifting occurs under a number of different conditions. Lail et al. (2014, p. 458) suggest that an ideal environment for discreetly reclassifying expenses is one in which (1) the information is important to investors, (2) disclosure requirements are vague, and (3) auditors’ materiality constraints do not warrant an extensive verification of the disclosure requirements. They find that managers shift expenses from their core operations to a “corporate/other” segment when agency problems exist in the organization, especially since segment reporting rules are not highly monitored, which is consistent with Fan et al. (2010). Abernathy et al. (2014) observe classification shifting when (1) when real earnings management is constrained by poor financial conditions (2) when high levels of institutional ownership and low industry market share is prevalent, and (3) when accruals earnings management is constrained by the use of an accounting system with low flexibility and a cash flow forecast. Classification shifting is


also likely to exist when a firm is pressured to meet or beat analysts’ earnings forecasts, but this likelihood may be reduced when a quality auditor is appointed or when the firm has a well-functioning legal structure (Haw et al., 2011, p. 550). However, classification shifting may also be reduced when a high level of financial analyst following exists (Behn et al., 2013), even in an environment with relatively weak investor protection. Finally, investors should be aware that managers may shift core expenses into special items that are valued differently than other core items (e.g. research and development; Aboody and Lev, 2000) or non-operating items that are not included in analysts’ forecasts (Abarbanell and Lehavy, 2002).

The most sinister aspect of classification shifting is that neither the earnings number nor the balance sheet changes as a result of classification shifting. Although the “bottom line” of these financial statements does not change, investors should be aware that their financial statement ratio analyses will be imparted as the composition of core earnings and special item earnings changes. One method which investors may use to detect this form of earnings management is the use of analytical analysis. Any significant findings that are not explained in management’s discussion and analysis may raise suspicions that classification shifting is present. Investors may also lobby for more rigorous accounting disclosures in order to ensure transparency in this area. Finally, investors may wish to gain a general understanding of the compensation structure of key executives who manage the firms in which they invest, to draw conclusions as the pressures which may lead the executives to engage in classification shifting. Earnings Management via Pension Expense

Managers have been found to manage pension expense. Reasons for earnings management via pension expense mirror those for other accounts, including the necessity to meet analysts’ forecasts, to demonstrate earnings persistence, and to window dress earnings (Perols and Lougee, 2011; Bartov and Cohen, 2009; Moehrle, 2002; Burgstahler and Dichev, 1997). Parker et al. (2013, p. 24) find that managers continue to manage pension expense even after the passage of Sarbanes-Oxley Act. The Rounding Phenomenon

When evaluating financial statements, investors must consider “the rounding phenomenon”, a method used by managers to enhance the presentation of income in order to achieve key metrics during the reporting period (Thomas, 1989; Carslaw, 1988). Their research suggests that investors should consider whether the numbers presented in the financial statements have been rounded to meet earnings expectations when more “zeroes” and fewer “nines” exist in the second digit of the reported earnings. Investors should also be aware that firms improve their reported earnings per share numbers in order to meet analysts’ forecasts, report positive results and sustain recent performance by managing their working capital accruals (Das et al., 2003, p. 32). Investors must also consider the rounding phenomenon when examining financial statements prepared internationally (Skousen et al., 2004; Kinnunen and Koskela, 2003). In addition, investors should be aware of the prevalence of rounding in high-tech firms’ reported earnings (Guan et al., 2008).

The literature is scarce regarding specific accounts and accruals that are rounded to meet expectations. He et al. (2012) find that revenues are more likely to be rounded when firms experience a loss, whereas earnings are revenues are not rounded as severely when the firm reports a profit. Regarding the rounding of expenses, He and Tian (2014) indicate that both profit and loss firms are likely to round up research and development expenses in order to positively impact investors’ perceptions of the firm’s future profitability, albeit this behavior is more prevalent for profit firms than for loss firms.

DISCUSSION AND AVENUES FOR FUTURE RESEARCH

Thoughtful investors who assume responsibility for ensuring that their capital and retirement

investments earn a desired return should understand the impact of earnings management on the financial statements. Although the statement of cash flows provides both investors with a reconciliation of the firm’s beginning and ending balances, it does not inform investors of the potential drawbacks associated


with manager’s subjectivity in the selection of accrual estimates, which may impact a firm’s current and future cash flows. The purpose of this paper is to provide nonprofessional investors with a framework to identify the potential for earnings management when analyzing financial statements prepared on an accrual-basis. This paper may also be valuable to accounting professors who seek to expand accounting students’ understanding of the impact of manager’s subjectivity on earnings quality.

It is nearly improbable to eradicate earnings management. Marquardt and Wiedman (2004) indicate that the release of Securities and Exchange enforcement actions, earnings restatements, shareholder litigations, qualified audit opinions, and negative press coverage about a firm may provide investors with sources to investigate whether their investees have engaged in earnings management. However, they note that undetected earnings management is usually not obvious to investors.

Clikeman (2003) offers several tools that may be used to detect earnings management. Monthly profit margin reviews, trend analysis and ratio analysis may identify unexpected revenue and profit margins that point to accelerated revenue recognition. Investors should also exercise a healthy level of skepticism when reviewing a company’s significant financial statement estimates and accounting assumptions to ensure that they reflect the organization’s financial performance in a manner consistent with the current macroeconomic conditions. Finally, Beattrice and Dacian (2011) suggest that in order to detect earnings management, one must first identify a company’s most managed accruals, then identify the incentives for managing the accruals, and finally recognize the context in which accruals are managed in order to understand the impact of these management criteria on the firm’s resource allocations. Future research may apply this framework to the each of the three major earnings management opportunities discussed in this paper to develop a comprehensive framework to assist investors in their understanding of earnings quality. Future research may also examine the impact that compensation clawback provisions may have on the reduction of different earnings management schemes as well as the reduction of earnings management for specific accruals. ENDNOTE

1. Francis et al. (2004) observes seven attributes of earnings: accruals quality, persistence, predictability, smoothness, value relevance, timeliness, and conservatism.

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The Effects of Capital Infusions after IPO on Diversification and Cash Holdings

Soohyung Kim

University of Wisconsin–La Crosse

Hoontaek Seo Niagara University

Daniel L. Tompkins Niagara University

This paper examines how the number and the timing of capital infusions after the IPO affect the firm’s diversification decision and the firm’s level of cash-holdings. We find that the frequent capital infusions ultimately affect the firm’s liquidity management policy, resulting in holding less cash consistent with the behavior life-cycle hypothesis. At the same time, the hurried external financings after the IPO influence managers to be more conservative in management, resulting in a high propensity for the firm’s diversification and a relatively high level of firm’s cash holdings. INTRODUCTION

In this study, we endeavor to determine the fundamental factors that affect the firm’s diversification decision and their relative level of cash-holdings as influenced by their managers’ behavioral perspective. Many studies in finance have focused on cash-holdings in companies that recorded a high aggregated level over time. For example, Bates, Kahle and Stulz (2009) found that the average cash-holdings in U.S. firms increased from 10.5% in 1980 to 23.2% in 2006. They also explained that IPO firms with high cash-holdings mostly drive the increased cash-holdings. Bouwman and Lowry (2012) suggested that the IPO firms’ high level of cash-holdings are consistently maintained up to five years after the IPO and these IPO firms did not have pre-IPO syndicated loans. Duchin (2010) found that diversified firms hold relatively less cash-holdings than single-segment firms. This is mainly because diversification allows firms to possess a low cross-divisional correlation in investment opportunities, the benefit of coinsurance.

Typically previous studies separately focused on the firm’s diversification decision and level of cash-holdings. For the firm diversification, Hyland and Diltz (2002) found that the agency problem is the major factor that affects diversification such as low R&D (research and development) expenses, large executive salaries, and relatively low Tobin’s q. In addition, Aggarwal and Samwick (2003) suggested that managers decide to diversify their firms based on their private benefits related to the agency problem. As one of the early studies in firm’s liquidity management, Opler, Pinkowitz, Stulz, and Williamson (1999) found that the level of cash-holdings is strongly affected by the manager’s precautionary motive


which is related to the firm’s growth opportunities, or the firm’s risky activities, etc. Almeida et al. (2004) presented that financially constrained firms are more likely to maintain a high level of cash-holdings.

In this study, we first hypothesize that the number and the timing of capital infusions in the subsequent five years after the IPO determine the firm’s level of cash-holdings. Second, the hurried capital infusions after the IPO increase the propensity for the manager to favor the diversification decision. We found that frequent external financings after the IPO are negatively related to the firm’s cash level. We also found that early capital infusions after the IPO induce managers’ conservative management style, resulting in a high propensity for a firm’s diversification and a relatively high level of a firm’s cash holdings.

This collection of results implies that the number and timing of capital infusions after the IPO alters a manager’s management style, and is an important factor which influences a firm’s liquidity management policy. The remainder of the paper is organized as follows. Section 2 presents the study’s theory and empirical hypotheses. Section 3 describes the data and summary statistics involved in this study. Section 4 tests how the timing and number of capital infusions affect the managers’ diversification decision. Section 5 discusses how the timing and the number of capital infusions after the IPO affect the level of firm cash-holdings. Section 6 concludes the study. THEORY AND EMPIRICAL HYPOTHESES

Shefrin and Thaler (1988) suggest that, based on the behavioral life-cycle hypothesis (BLC), the marginal propensity to consume lump-sum capital resources is lower than the marginal propensity to consume regular income. Since a person’s mental accounting considers lump-sum capital resources as a current asset rather than current income, one is more likely to increase saving. If we apply the behavior life-cycle model to corporate finance, since most of the sequential financing happens in the subsequent five years (Herzel et al. 2012), we would expect that managers who experienced sequential financing (i.e. short/long-term borrowings, SEO, etc.) after the IPO are more likely to hold a relatively low level of cash-holdings. On the other hand, managers with sufficient capital resources at the beginning of the IPO might maintain the relatively high level of cash-holdings similar to the individual with lump-sum money based on the behavioral life-cycle hypothesis. Thus, we hypothesize that if a firm has sufficient capital resources at the beginning of the IPO, an indicator that the firm does not need external capital financing after the IPO, then the firm’s level of cash-holding is relatively higher than other firms.

In this study, we use two proxies to measure whether or not a firm has sufficient capital resources at the IPO. The first is the Number of capital infusions measured by counting the number of long-term debt borrowings in the five years after the IPO. The second is Timing of capital infusions measured by counting the length of time from a firms’ IPO to its first long-term debt financing in the subsequent five years.

For the number of capital infusions, similar to the behavioral life-cycle hypothesis, managers that experienced frequent external financings are more likely to hold less cash. Since the number of external financings after the IPO is related to the firm’s characteristics such as growth opportunities, if a firm displays strong growth opportunities then the firm will have multiple capital infusions (Hertzel et al. 2012). In addition, the frequent capital infusions ultimately affect the firm’s liquidity management policy, resulting in holding less cash based on the behavior life-cycle hypothesis.

Contrarily, for the timing of capital infusions, if managers have to spend a certain time to set up external financings shortly after the IPO rather than fully exercising their management skills at the beginning of IPO, then those managers are more likely to have conservative managerial behaviors. Studies show that managers who went through hard times to manage their firms (i.e. economic recessions, financial constraints, etc.) tend to have a conservative management style which induces the diversification decision and the strong motive for precautionary cash-holdings (Schoar & Zuo, 2012; Dittmer and Duchin, 2013). Thus, we expect that managers who went through the hurried external financings at the IPO are more like to diversify their firms and prefer to hold a relatively high level of cash-holdings.


DATA AND SUMMARY STATISTICS

We use Compustat Industry Segment database in order to obtain the sample of diversifying firms. The sample is from 1980 to 2012 and the year of firms’ diversification is defined by the year when a firm has more than two segments. In addition, each segment in a firm has to exhibit a different two-digit SIC code to be considered multi-segments. We exclude financial firms (SIC codes 6000 – 6999) and utilities (SIC code 4900 – 4999). This method of defining diversified firms is the same as used by Lang and Stulz (1994), Berger and Ofek (1995,1999), and Hyland and Diltz (2002). Lastly, we require firms in segment data to have at least five consecutive years of data history.

Two major variables in this study are the number and the timing of capital infusion in the first five years after the IPO. We use the issuance of long-term debt (DLTIS) in Compustat and require the amount of long-term debt to be over 1% on total assets (AT) to measure capital infusions. The Number of capital infusions is measured by counting the number of long-term debt borrowings in the five years after the IPO. The Timing of capital infusions is measured by counting the first year of long-term debt financing in the immediate five years following the IPO. The IPO date is the date when a firm listed in the Center for Research in Security Price (CRSP) file. We also require firms in Compustat to have at least consecutive five years of data history after the IPO.

We define firms that diversified as a treatment group and firms that remained single-segment as a control group during the sample period. To define the control group, we used the performance-matched method by Barber & Lyon (1996). This method requires the control firms to share similar performance to the treatment firms in year t-1. Firm performance is measured by return-on-assets (ROA). In total there are 833 firms that decided to transition to multi-segment firms and 1,737 single segment firms.

In Table 1, we compare mean and median values for key firm characteristics of diversified and single-segment firms from 1980 to 2012. In general, there are no significant dissimilar firm characteristics between the treatment and control group except for firm size, R&D intensity and two major variables in this study - the timing and the number of capital infusions. Since we used the performance- matched method to define the control group, the firm size appears to make a significant difference. However, Barber & Lyon (1996) suggested that the performance-matched method is less biased than the size-matched method. For the R&D intensity, the diversified firms display lower R&D expenses than single segment firms.

The mean (median) timing of capital infusions is 1.71 year (1 year) and 1.79 (1 year) for diversified firms and single-segment firms, respectively, and the difference is statistically significant. Regarding the number of capital infusions, the diversified firm (mean: 3.01; median: 2) is higher than the single-segment firms (mean: 2.89; median: 2). PROBIT ANALYSIS OF DIVERSIFICATION

Next we examine diversification decisions based on the results of probit analysis in Table 2. Probit specifications are based upon the model by Hyland and Diltz (2002) in the models (1) and (2) of Table 2. Consistent with their results, firms with low cash flow and low R&D intensity are more likely to diversify. Managers with poor performance (i.e. low cash flow) or diminished growth opportunities (i.e. low R&D expense) are more likely to diversify their firms (Smith and Watt, 1992; Morck et al., 1990) as this strategy could serve to increase their compensation regardless of their performance. On the other hand, if a firm with high growth opportunities has limited access to external capital markets, the firm typically exhibits low R&D expenses. Thus managers in the firm are more likely to diversify their firm in order to enhance the firm’s internal capital market (Hyland and Diltz, 2002).

There is an insignificant coefficient for the number of capital infusions in models (3) and (4). At the same time, the cash flow and R&D intensity are consistently negative and significantly similar to results of the basic models (1) and (2). This finding suggests that since the number of capital infusions might be driven by a firm’s characteristics (i.e. growth opportunities), the necessity to diversify in order to enhance the internal capital market is captured by a firm’s cash flow and R&D intensity in the probit analysis.


The negative relationship between the timing of capital infusions and the manager’s diversification decision in models (5) and (6) suggests that managers who experienced relatively quick capital infusions after their IPO are more likely to diversify their firms. Consistent with our hypothesis, the findings suggest that the timing of capital infusions is related to IPO managers’ success in handling the IPO and eventually fosters a more conservative management style that ultimately triggers the manager’s diversification decision. REGRESSION ANALYSIS OF CORPORATE CASH-HOLDINGS

The previous probit analysis shows that the timing of capital infusion has a direct effect on a firm’s diversification decision. As managers need to allocate more time to secure external capital toward the beginning of the IPO, their management styles are more likely to be conservative and thus eventually increase the probability of diversification. At the same time, we hypothesize that the conservative management style places additional emphasis on the precautionary motive for holding cash and thereby inducing relatively high cash holdings. For the number of capital infusions after the IPO, we hypothesize that as the number of capital infusions are increased, managers are less likely to hold cash. In this section, we conduct multivariate regression on how the number and the timing of capital infusion after the IPO affect the level of firm cash-holdings based on Opler et al. (1999).

We perform multivariate regression analysis of the firm's cash-holdings based on the number and the timing of capital infusion in the subsequent five years after the IPO. The regression specifications are based on the model by Opler et al. (1999) in the model (1) of Table 3.

In the second column of Table 3, we add the diversification dummy variable in the original model in the first column in order to control cash expenses through diversification (i.e. mergers and acquisitions). The negative coefficient on the diversification dummy variable indicates that there might be significant cash expense as a result of the diversification decision.

The results in the model (4) suggest that, consistent with our hypothesis, the early capital infusion after the IPO (i.e. hurried capital infusion after the IPO) induces managers to increase the level of firm cash-holdings. In the model (3), the negative coefficient on the number of capital infusions is also consistent with our hypothesis that as the number of capital infusions is increased, managers increasingly decide to hold relatively less cash.

Almeida et al. (2004) suggest that financial constraints are an important determinant of the level of firm cash-holdings. Specifically, a firm that faces financial constraints is more likely to accumulate cash-holdings from its ongoing cash flow. By running the Opler et al. model in two subsamples based on whether or not a firm faces financial constraints, this study tests whether the number and timing of capital infusions after the IPO are still important factors that affect the firm’s liquidity management. Table 4 presents the result of the regression model on the two subsamples. Across different measures for a firm’s financial constraints, the timing and the number of capital infusions have a significant effect on the level of firm cash-holdings regardless of financial constraints that a firm faces. CONCLUSIONS

We find that the number and the timing of capital infusions after the IPO have significant effects on the firm’s cash-holdings besides the factors that have been previously studied. Based on the behavioral life-cycle hypothesis (Shefrin and Thaler, 1988), frequent external financings after the IPO has the effect of managers retaining a relatively the low level of cash-holdings. Also, the impact of early capital infusions after the IPO seems to have the effect of making the managers’ personality more conservative. Conservative managers are more likely to decide to diversify and ultimately maintain a relatively low level of cash-holdings.


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TABLE 1 SUMMARY STATISTICS

The table presents means and median of key variables for a sample of 833 of firms that decide to diversify and 1,737 of firms that remain single segment in firms from 1980 to 2012. Dollar values are expressed in 2007 dollars. Accounting and segment data comes from Compustat and all variables are measured in year t-1, the year before the firm’s decision to diversify. Two-sample t-tests (Wilcoxon-Mann-Whitney tests) are conducted to compare the difference of means (medians) of diversified firms and single-segment firms. I define firms that diversified as a treatment group and firms that remained single-segment as a control group during the sample period from 1980 to 2012. For the matching samples of single-segment firms, I applied the performance-matched method by Barber & Lyon (1996). This method requires the control firms to share similar performance to the treatment firms in year t-1. Firm performance is measured by return-on-assets (ROA). Number of capital infusions is measured by counting the number of long-term debt borrowings in the subsequent five years after the IPO. Timing of capital infusions is measured by counting the first year of long-term debt financing in the subsequent five years after the IPO. Cash Flow is the firm’s operating income before depreciation over total assets. Cash and Investments is the sum of cash and short-term investments and capital expenditures over total assets. R&D (research and development) intensity is R&D expense divided by total assets. Industry sigma and Industry Q is the mean of standard deviation of cash flow over assets and Tobin’s Q over ten years in the firm's representative industry, respectively, and the firm’s industry is defined by the two digit-SIC code. Firm age is the number of years that the firm has been listed in the Center for Research in Security Price (CRSP) file. Statistical significance at the 1%, 5%, and 10% level is indicated by ***, **, and *, respectively.

Firms with diversification

Single-segment firms Variable Mean

Median

Mean Median

Cash/assets 0.17

0.10

0.18 0.10 Size 5.70 ** 5.67 ** 5.28 5.24 Dividend payout ratio 4.46

1.41

1.81 1.62

Tobin’s q 1.72

1.46

1.75 1.46 Leverage 0.22

0.18 * 0.2 0.14

Cash flow/assets 0.15

0.14

0.16 0.14 Cash & investment/assets 0.24

0.19

0.25 0.19

R&D intensity 0.04 ** 0 ** 0.05 0 Capital expenditures/assets 0.10

0.04

0.09 0.04

Return on asset 0.18

0.16

0.18 0.16 Net working capital/assets 0.09

0.08

0.11 0.1

Acquisitions/assets 0.03

0

0.02 0 Market-to-book ratio 2.08

1.54

2.12 1.57

Industry sigma 0.26

0.15

0.18 0.15 Industry Q 0.84

0.81

0.84 0.81

Timing of capital infusion 1.71 ** 1 * 1.79 1 Number of capital infusion 3.01 ** 2 ** 2.89 2 Firm age 7.21 *** 6 *** 11.09 11 Number of observations 833 1,737


TABLE 2 PROBIT ANALYSIS OF DIVERSIFICATION

This table presents the results of probit analysis with a firm’s decision to diversify as the dependent variable. The dependent variable equals one if the firm diversifies at year t and zero otherwise. All control variables are measured in year t-1, the year before the firm’s decision to diversify. I define firms that are diversified as a treatment group and firms that remained single-segment as a control group during the sample period from 1980 to 2012. For the matching samples of single-segment firms, I applied the performance-matched method by Barber & Lyon (1996). This method requires the control firms to share similar performance to the treatment firms in year t-1. Firm performance is measured by return-on-assets (ROA). Number of capital infusions is measured by counting the number of long-term debt borrowings in the subsequent five years after the IPO. Timing of capital infusions is measured by counting the first year of long-term debt financing in the subsequent five years after the IPO. Dividend payout ratio is a three-year cumulation of cash dividends and repurchases over income before extraordinary items. R&D intensity (prior 3 years) in model (2), (4), and (6) equals the average of R&D intensity for the prior three years before the diversification. Firm age is the number of years that the firm has been listed in the Center for Research in Security Price (CRSP) file. Statistical significance at the 1%, 5%, and 10% level is indicated by ***, **, and *, respectively.

(1) (2) (3) (4) (5) (6) (7) Timing of capital infusion

-0.046** -0.045** -0.045**

(-2.20) (-2.16) (-2.18)

Number of capital infusion

0.020 0.029

0.002

(1.36) (1.55)

(0.14)

Size 0.092*** 0.090*** 0.089*** 0.087*** 0.093*** 0.092*** 0.094***

(6.86) (6.66) (6.57) (6.41) (5.95) (5.83) (5.92)

Tobin’s q 0.013 -0.003 0.016 -0.010 0.011 -0.012 0.005

(0.48) (-0.13) (0.61) (-0.38) (0.36) (-0.39) (0.16)

Leverage -0.076 -0.037 -0.189 -0.131 -0.186 -0.123 -0.143

(-0.69) (-0.34) (-1.59) (-1.11) (-1.35) (-0.90) (-1.03)

Cash flow/assets -0.649* -0.589* -0.923*** -0.636** -0.711* -0.550 -0.358

(-2.29) (-2.08) (-3.86) (-2.88) (-2.38) (-1.86) (-1.07)

Cash & investment/assets -0.048 -0.107 0.026 -0.039 -0.106 -0.165 -0.130

(-0.35) (-0.79) (0.19) (-0.28) (-0.63) (-0.97) (-0.76)

Dividend payout ratio 0.424 0.430 0.436 0.446 -0.136 -0.128 -0.138

(0.45) (0.46) (0.47) (0.48) (-0.84) (-0.79) (-0.85)

R&D intensity -0.971***

-0.023***

-0.325***

-0.236***

(-6.05)

(-6.19)

(-5.51)

(-5.25)

R&D intensity (prior 3 yrs)

-0.760***

-0.729***

-0.736***

(-4.98)

(-4.79)

(-4.11)

Ln(firm age) 0.029 0.052* 0.0312 0.051* 0.025 0.043 0.025

(1.19) (2.07) (1.26) (2.05) (0.77) (1.28) (0.74)

Capital expenditures/assets -0.050 -0.032 -0.052 -0.046 0.095 0.106 0.073

(-0.49) (-0.32) (-0.50) (-0.44) (0.71) (0.79) (0.54)

Number of observations 5,645 5,645 5,645 5,645 5,645 5,645 5,645


TABLE 3 REGRESSION PREDICTING CORPORATE CASH-HOLDINGS

This table reports regression estimates of the impact of the number of and the timing of capital infusion on firm-level cash-holdings from 1980 to 2012. Full sample consists of 2,570 firms (26,082 firm year observations) including 833 firms that diversified (9,983 firm-year observations) and 1,737 firms that remained single-segment (16,099 firm-year observation) over the sample period. As a control group, the single-segment firm is chosen by comparing its firm performance (ROA) to the performance of multi-segment firms in year t-1, the year before the diversification decision. The dependent variable is cash and short-term investment over total assets. Diversification as an indicator variable equals one if the firm decides to become a multi-segment firm in year t and zero otherwise. Number of capital infusions is measured by counting the number of long-term debt borrowings in the five years after IPO. Timing of capital infusions is measured by counting the first year of long-term debt financing in the five years after IPO. Industry sigma is the mean of standard deviation of cash flow over assets over ten years in the firm's representative industry and the firm’s industry is defined by the two digit-SIC code. Number of segments is the natural log of the number of segments in year t. Standard errors are heteroskedasticity consistent and clustered at the firm level and t-statistics are reported in parentheses. Statistical significance at the 1%, 5%, and 10% level is indicated by ***, **, and *, respectively.

(1) (2) (3) (4) (5)

Dependent Variable Cash/assets Cash/assets Cash/assets Cash/assets Cash/assets Diversification -0.094** -0.050 -0.074 -0.018

(-2.08) (-0.94) (-1.52) (-0.33)


-0.727***

-0.755***

(-16.67)

(-17.29)


-0.0547

-0.066 * Diversification

(-0.97)

(-1.19)

Timing of capital infusion

-0.069** -0.219***

(-2.54) (-4.99)


-0.068 -0.083 * Diversification

(-0.91) (-1.13)

Market-to-book ratio 0.021* 0.022* 0.022* 0.022* 0.022*

(2.19) (2.21) (2.31) (2.20) (2.29)

Size -0.075*** -0.076*** -0.053*** -0.076*** -0.053***

(-4.66) (-4.72) (-3.34) (-4.74) (-3.37)

Cash flow/assets 0.634*** 0.637*** 0.631*** 0.637*** 0.627***

(4.39) (4.41) (4.51) (4.40) (4.48)

Net working capital/assets -1.918*** -1.917*** -1.864*** -1.915*** -1.857***

(-20.67) (-20.66) (-20.20) (-20.64) (-20.13)

Capital expenditures/assets 0.028*** 0.028*** 0.029*** 0.028*** 0.031***

(8.89) (8.93) (9.73) (9.07) (10.31)

Leverage -2.262*** -2.260*** -2.065*** -2.260*** -2.059***

(-17.83) (-17.82) (-16.32) (-17.82) (-16.31)

Industry sigma 0.050 0.051 0.015 0.054 0.022

(1.26) (1.29) (0.32) (1.38) (0.46)

R&D intensity 1.445*** 1.446*** 1.149*** 1.444*** 1.131***

(6.14) (6.14) (5.25) (6.13) (5.18)

Ln(number of segments) 0.068 0.084* 0.075 0.0847* 0.076

(1.76) (1.97) (1.77) (1.98) (1.78)

Number of observations 26,082 26,082 26,082 26,082 26,082


TABLE 4 REGRESSION PREDICTING CORPORATE CASH-HOLDINGS – FINANCIAL

CONSTRAINTS CRITERIA

This table display results for multivariate regression estimates of the Opler et al. model in two subsamples. Full samples are separated into two subsamples based on each of four different measures of the financing frictions: Payout ratio scheme, firm size scheme, bond rating scheme, and Kaplan-Zingales index. Each of the measures of the financing frictions is calculated based on Almeida et al. (2004). This table only presents the coefficient on diversification, the number of capital infusions, the timing of capital infusion and the interaction variable between the number and the timing of capital infusion and diversification. Standard errors are heteroskedasticity consistent and clustered at the firm level and t-statistics are reported in parentheses. Statistical significance at the 1%, 5%, and 10% level is indicated by ***, **, and *, respectively.

Financial Constraints Criteria Constrained Firms

Unconstrained Firms

1. Payout ratio (1) (2) (3) (4) (1) (2) (3) (4)

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Diversification 0.048 -0.047 0.118 0.029 -0.086 -0.042 -0.108 -0.064

(0.60) (-0.46) (1.33) (0.28) (-1.71) (-0.89) (-1.78) (-1.28)


-0.811***

-0.851***

-0.466***

-0.480***

(-12.69)

(-13.12)

(-11.54)

(-11.87)


0.114

0.0914

-0.0733

-0.0712

* Diversification

(1.11)

(0.92)

(-0.80)

(-0.78)


-0.284* -0.238***

-0.0683* -0.136***

(-1.96) (-3.67)

(-1.78) (-3.61)


-0.039 -0.248

0.0736 0.072

* Diversification

(-0.60) (-1.74)

(1.01) (1.04) 2. Firm size Constrained Firms Unconstrained Firms

(1) (2) (3) (4) (1) (2) (3) (4)

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Diversification 0.063 0.028 0.069 0.032 -0.069 0.023 -0.056 0.073

(0.64) (0.28) (0.68) (0.35) (-0.90) (0.21) (-0.64) (0.55)


-0.701***

-0.722***

-0.567***

-0.568***

(-10.61)

(-10.95)

(-6.86)

(-6.55)

inter_D5_2

0.045

0.044

-0.088

-0.113

(0.35)

(0.34)

(-0.87)

(-1.05)


-0.225*** -0.293***

0.184** -0.003**

(-3.60) (-4.80)

(2.06) (-2.04)


-0.023 -0.013

-0.044 -0.090

* Diversification

(-0.16) (-0.10)

(-0.38) (-0.75)


Financial Constraints Criteria

Constrained Firms

Unconstrained Firms

3. Bond ratings (1) (2) (3) (4) (1) (2) (3) (4)

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Diversification -0.038 -0.006 -0.053 -0.023 -0.290** -0.205 -0.165 0.123

(-0.82) (-0.12) (-1.05) (-0.43) (-2.58) (-1.26) (-1.24) (0.60)


-0.734***

-0.763***

-0.586***

-0.536***

(-14.18)

(-14.73)

(-5.39)

(-4.31)


-0.046

-0.041

-0.078

-0.246

* Diversification

(-0.79)

(-0.71)

(-0.49)

(-1.45)


-0.128* -0.254***

0.389*** -0.516**

(-2.52) (-5.20)

(3.83) (-2.72)


0.050 0.044

-0.405* 0.140

* Diversification

(0.58) (0.53)

(-2.34) (1.19)

Constrained Firms Unconstrained Firms

4. Kaplan-Zingales index (1) (2) (3) (4) (1) (2) (3) (4)

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Cash/ assets

Diversification -0.136* -0.118 -0.137* -0.116 -0.107 -0.021 -0.056 0.084

(-2.28) (-1.69) (-2.21) (-1.47) (-1.10) (-0.19) (-0.54) (0.79)


-0.564***

-0.570***

-0.383***

-0.426***

(-9.79)

(-9.74)

(-7.10)

(-7.58)


-0.016

-0.017

-0.098

-0.147

* Diversification

(-0.25)

(-0.25)

(-0.89)

(-1.39)


0.050 -0.048

-0.089** -0.207***

(0.90) (-0.88)

(-2.61) (-3.59)


0.001 -0.004

-0.184 -0.238

(0.02) (-0.06)

(-1.33) (-1.76)


Low Volume and Future Changes in the Stock Market

Paul Bursik St. Norbert College

This paper provides an empirical investigation of whether low volume days produce different subsequent results than other days. Daily S&P 500 returns are groups classified by volume as well as market direction. Subsequent returns are tied both to previous market direction and volume. High volume is associated with higher subsequent daily return variability, and low volume is associated with lower subsequent daily return variability. Of particular note, down market days with low volume have the highest next-day average returns and the lowest next-day return standard deviation. INTRODUCTION

Stock market trading volume has been widely studied for a long time. The largest segment of the “volume literature” explores the relationship between trading volume and stock market volatility. For the most part, the idea is that volume is a reaction to information in the market. In two of the most cited papers in this area, (Schwert, 1989) and (Karpoff, 1987) reach a standard conclusion that volume and volatility tend to rise and fall together. Yet the relationship is difficult to tease out as to which may tend to cause the other. (Campbell, et.al., 1991) focus on different types of investors whose trading could lead to subsequent patterns in returns. In particular, they argue that high volume could be caused by less informed investors’ changes in risk aversion. If, for instance, they become more risk averse, we should see high volume (with a falling market) followed by higher average returns in the market due to the greater returns for risk bearing. This would lead to negative serial correlation in returns. Indeed, they reach what they find as a “striking fact,” that daily autocorrelation is lower on high volume days than on low volume days.

Other, more recent work, focuses on the composition of volume and its impact on return predictability. For instance, (Tetlock, 2007) shows an association between excessive media optimism and excessive media pessimism on volume that temporarily pushes stock prices away from their fundamental values. Other papers, such as (Bordino, 2012) focus on predicting volume from web searches. For the most part, these papers point out that the nature of the work involving stock market volume is concerned with high volume rather than low volume. The reason for this is usually related to some sort of potentially irrational trading by uninformed investors giving rise to future predictability in returns.

DATA, METHODOLOGY, AND RESULTS

The idea of this paper follows from the original motivation for exploring the issue – the claim that volume, in particular low volume, is a market “event.” That is, if low volume is noteworthy, then those days that have low volume should produce subsequent returns that are different in some way. In


particular, the commentary implies that an investor should be wary of low volume. To investigate the impact of volume on subsequent returns, the default approach would be to simply use volume as an explanatory variable in a predictive model of stock returns. This is well-ploughed ground, and mostly relies on small changes in volume producing marginal effects in returns (or vice versa) over all ranges of volume and returns. Instead, this paper separates S&P 500 Index return days (all days from January 2005 to December 2014) into three main groups – High Volume, Normal Volume, and Low Volume. While it seems at first to be a bit odd to take a quantitative, nearly continuous variable like volume and transform it into a qualitative one, there are some advantages. First, this investigation is most concerned with values in the tails of the volume distribution. Often it is what happens in the more extreme cases that is of interest. For instance, consider possible behavioral factors. Perhaps low volume days are associated with market under-reaction to the news of the day leading to positively correlated returns between the low volume day and the subsequent day or days; perhaps high volume days are associated with market over-reaction to the news of the day leading to negatively correlated returns between the high volume day and the subsequent day or days; perhaps “normal volume days,” within a large range of values, are not associated with either under-reaction or over-reaction. To be clear, this paper is not making this behavioral claim. Instead, the example is used to highlight why it might be advantageous to transform volume into a qualitative variable in “event space.” If this were an event study of earnings announcements, this would be similar to classifying some earnings as “large negative surprises” and “large positive surprises.”

Of course, one problem is to determine what constitutes “high volume” and “low volume.” Two factors dominated the approach taken here to define the dividing line. First, high and low volume thresholds need to be adjusted over time, since average volume changes over time. Second, high or low volume should not be exceedingly rare. In a calendar year, there are about 250 trading days. Thinking in terms of deciles, the 25 highest trading volume days during the year were put into the High group, while the lowest 25 trading volume days during the year were put into the Low group. Of course, this leaves about 200 days per year in the Normal group.

Another factor that also needs to be taken into account is the tendency for returns to reverse from one day to the next, which was particularly pronounced during this time horizon. So all of the days were also classified as to whether the index closed up or down (from the previous day’s close). For the overall data and each of the groups and subgroups, subsequent returns were examined over the following 10 days. Average returns along with standard deviations were calculated for each day and for cumulative returns. Selected descriptive statistics for the daily returns for each class of observations are reported in Table 1. Probably the most noteworthy numbers on the table are related to the “All Up” and “All Down” groupings. We see there the strong tendency of return reversals from the previous day. The other result of note (in bold Table 1) is that the “Low Down” group has the highest one-day returns and lowest one-day standard deviation of returns.


TABLE 1 DAILY RETURNS BY GROUP (DAY 0 THROUGH DAY 5)

GROUP VALUE DAY 0 DAY 1 DAY 2 DAY 3 DAY 4 DAY 5 All Days Mean 0.0293% 0.0297% 0.0294% 0.0292% 0.0295% 0.0303% N = 2517 St. Dev. 1.2870% 1.2868% 1.2872% 1.2873% 1.2874% 1.2879% All Up Mean 0.7677% -0.0437% 0.0123% 0.0265% 0.0209% 0.0156% N = 1390 St. Dev. 0.9362% 1.1443% 1.1831% 1.1871% 1.1462% 1.1652% All Down Mean -0.8813% 0.1201% 0.0505% 0.0325% 0.0401% 0.0484% N = 1127 St. Dev. 1.0567% 1.4388% 1.4052% 1.4015% 1.4432% 1.4252% All High Mean -0.0720% -0.0180% 0.1342% -0.0099% 0.0480% 0.2003% N = 250 St. Dev. 2.2749% 2.0655% 1.8800% 2.1139% 1.9202% 2.0020% All Low Mean 0.0080% 0.0158% 0.0416% 0.1463% 0.0519% 0.1018% N = 250 St. Dev. 0.8971% 1.1143% 1.0028% 1.0848% 0.8639% 0.8929% All Normal Mean 0.0446% 0.0373% 0.0149% 0.0195% 0.0244% 0.0003% N = 2017 St. Dev. 1.1519% 1.1773% 1.2269% 1.1699% 1.2333% 1.2131% Low Down Mean -0.6231% 0.2021% 0.0194% 0.1840% 0.0340% 0.1368% N = 106 St. Dev. 0.8737% 1.0217% 1.0428% 1.2012% 0.8719% 0.8422% Normal Down Mean -0.8108% 0.1128% 0.0289% 0.0250% 0.0219% 0.0056% N = 896 St. Dev. 0.9834% 1.2906% 1.3359% 1.2907% 1.3914% 1.3521% High Down Mean -1.6187% 0.1237% 0.2312% -0.0279% 0.1657% 0.2763% N = 124 St. Dev. 1.3730% 2.4248% 2.0307% 2.1390% 2.0732% 2.1465% Low Up Mean 0.4726% -0.1213% 0.0580% 0.1185% 0.0650% 0.0761% N = 144 St. Dev. 0.5729% 1.1624% 0.9758% 0.9940% 0.8607% 0.9305% Normal Up Mean 0.7282% -0.0231% 0.0037% 0.0152% 0.0264% -0.0039% N = 1121 St. Dev. 0.7499% 1.0749% 1.1328% 1.0642% 1.0912% 1.0898% High Up Mean 1.4501% -0.1575% 0.0387% 0.0078% -0.0678% 0.1255% N = 126 St. Dev. 1.9354% 1.6351% 1.7217% 2.0973% 1.7573% 1.8543%

In Table 2 and Table 3 we see the tests for significant differences for daily returns and cumulative returns for selected groups. For each group, an F-test of variances is conducted to check for significance in terms of the difference from the “All Days” distribution. Two overarching themes emerge when considering the F-test results. First, after “Up” days, there is less variability in returns than after “Down” days; and this persists throughout the 10-day return horizon examined here. Second, high volume is associated with greater than average subsequent variability in returns; and low volume is associated with less than average subsequent variability in returns. Again, the association persists throughout the 10-day return horizon. The finding that volume has a positive association with volatility is consistent with the previous literature, but the persistence may be stronger than expected.

The tests for differences in returns from the “All Days” mean yields two significant results. The first is that we observe one-day return reversals, and the strength of the day one return is enough to significantly impact cumulative returns over a number of days. Second, low volume on down days is associated with above-average returns over a number of days. The significance of this lies in the fact that this group is also associated with below average volatility.


TABLE 2 TESTS FOR DAILY RETURN DIFFERENCES FROM THE “ALL DAYS” DISTRIBUTION

Group Value Day1 Day2 Day3 Day4 Day5 Day6 Day7 Day8 Day9 Day10 All Up Mean <** >* Variance <** <** <** <** <** <** <** <** <** <** All Down Mean >** Variance >** >** >** >** >** >** >** >** >** >** All High Mean >* Variance >** >** >** >** >** >** >** >** >** >** All Low Mean Variance <** <** <** <** <** <** <** <** <** <** Low Down Mean >* <** Variance <** <** <** <** <** <** <** <** <** High Down Mean Variance >** >** >** >** >** >** >** >** >** >** Low Up Mean Variance <* <** <** <** <** <** <** <** <** <** High Up Mean Variance >** >** >** >** >** >** >** >** >** >**

* Denotes a 10% Significance Level ** Denotes a 5% Significance Level

TABLE 3 TESTS FOR CUMULATIVE RETURN DIFFERENCES FROM THE “ALL DAYS”

DISTRIBUTION

Group Statistic 1Day 2Day 3Day 4Day 5Day 6Day 7Day 8Day 9Day 10Day All Up Mean <** <** <* <* <* <* <* <* <* Variance <** <** <** <** <** <** <** <** <** <** All Down Mean >** >* >* >* >* >* >* Variance >** >** >** >** >** >** >* >** >* >** All High Mean Variance >** >** >** >** >** >** >** >** >** >** All Low Mean >* Variance <** <** <** <** <** <** <** <** <** <** Low Down Mean >* >* >* >* Variance <** <** <* <** <** <** <** <** High Down Mean Variance >** >** >** >** >** >** >** >** >** >** Low Up Mean Variance <* <** <** <** <** <** <** <** <** <** High Up Mean Variance >** >** >** >** >** >** >** >** >**

* Denotes a 10% Significance Level ** Denotes a 5% Significance Level


In Tables 4-6, this mean-variance relationship is further explored. Table 4 shows 3-day Returns for each group, ranked by the standard deviation of returns. The low volume groups are in bold. All of the low volume groups have lower-than-average volatility (the All Days group is italicized for comparison purposes). Of particular note is that the “Low Down” group has below average volatility while having the largest 3-day returns.

TABLE 4 THREE-DAY COMPOUND RETURNS

Group Observations Day 0 Returns

3-Day Returns (Low to High)

Mean St. Dev. Mean St. Dev.

Low Up 144 0.4726% 0.5729% 0.0486% 1.4209% All Low 250 0.0080% 0.8971% 0.2006% 1.6804% Normal Up 1121 0.7282% 0.7499% -0.0069% 1.7404% All Up 1390 0.7677% 0.9362% -0.0092% 1.8059% All Normal 2017 0.0446% 1.1519% 0.0684% 1.8936% Low Down 106 -0.6231% 0.8737% 0.4072% 1.9678% All Days 2517 0.0293% 1.2870% 0.0836% 2.0032% Normal Down 896 -0.8108% 0.9834% 0.1627% 2.0665% All Down 1127 -0.8813% 1.0567% 0.1980% 2.2181% High Up 126 1.4501% 1.9354% -0.1265% 2.6255% All High 250 -0.0720% 2.2749% 0.0887% 2.9453% High Down 124 -1.6187% 1.3730% 0.3074% 3.2343%

TABLE 5 FIVE-DAY COMPOUND RETURNS




Low Up 144 0.4726% 0.5729% 0.1883% 1.8309% All Low 250 0.0080% 0.8971% 0.3535% 2.0373% Normal Up 1121 0.7282% 0.7499% 0.0135% 2.2230% Low Down 106 -0.6231% 0.8737% 0.5779% 2.2780% All Up 1390 0.7677% 0.9362% 0.0241% 2.2906% All Normal 2017 0.0446% 1.1519% 0.0903% 2.4348% All Days 2517 0.0293% 1.2870% 0.1387% 2.5180% Normal Down 896 -0.8108% 0.9834% 0.1864% 2.6745% Down Days 1127 -0.8813% 1.0567% 0.2800% 2.7674% High Up 126 1.4501% 1.9354% -0.0840% 3.1896% All High 250 -0.0720% 2.2749% 0.3143% 3.4377% High Down 124 -1.6187% 1.3730% 0.7190% 3.6410%


Tables 5 and 6 tell a similar story – the low volume days are followed by strong returns with low volatility. Table 6, in particular shows how different low volume days translate into future market performance. Consider that without the low volume groups, the mean-variance ranks are very close to what we would predict from an efficient market. The higher volatility groups experience higher returns. However, this is not the case with the low volume groups, where the returns are far higher than the standard deviation ranking would predict.

TABLE 6 TEN-DAY COMPOUND RETURNS




Low Up 144 0.4726% 0.5729% 0.2350% 2.2599% All Low 250 0.0080% 0.8971% 0.3293% 2.5722% Normal Up 1121 0.7282% 0.7499% 0.1460% 3.0919% Low Down 106 -0.6231% 0.8737% 0.4575% 2.9506% All Up 1390 0.7677% 0.9362% 0.1764% 3.0850% All Normal 2017 0.0446% 1.1519% 0.2277% 3.3125% All Days 2517 0.0293% 1.2870% 0.2700% 3.3382% Normal Down 896 -0.8108% 0.9834% 0.3300% 3.5685% Down Days 1127 -0.8813% 1.0567% 0.3855% 3.6244% High Up 126 1.4501% 1.9354% 0.3152% 3.8395% All High 250 -0.0720% 2.2749% 0.5517% 4.1291% High Down 124 -1.6187% 1.3730% 0.7921% 4.4066%

DISCUSSION AND CONCLUSIONS

It would appear that any adage warning about investing on low volume days is probably not warranted. Low volume days are associated with below-average volatility on subsequent days, but with no sacrifice in returns. Indeed, it would seem that low volume days constitute an anomaly such that investors tend to earn high returns while bearing low risk. However, a caveat is in order. To call the previously mentioned result a tradable anomaly would be premature. First, no attempt was made here to create a trading rule that could have been implemented in real time. For instance, the high and low volume days were determined after the fact. Still, it appears that investors should not be shying away from trading when volume is low, especially when the market closes down. REFERENCES Bordino, I. & Battiston, S. & Caldarelli, G. & Cristelli, M. & Ukkonen, A. & Weber, I. (2012). Web

search queries can predict stock market volumes. Plos One, 7(7), e40014. Campbell, J.Y. & Grossman, S.J. & Wang, J. (1992) Trading volume and serial correlation in stock

returns. No. w4193. National Bureau of Economic Research. Karpoff, J.M. (1987). The relation between price changes and trading volume: A survey. Journal of

Financial and Quantitative Analysis 22.01 109-126. Schwert, G. W. (1989). Why Does Stock Market Volatility Change Over Time?. The Journal of Finance,

44: 1115–1153. Tetlock, P. C. (2007). Giving Content to Investor Sentiment: The Role of Media in the Stock Market. The

Journal of Finance, 62: 1139–1168.


Integrated Reporting & the Future of Auditing

Sean Stein Smith Fairleigh Dickinson University

Disruptive technologies and innovations have caused dramatic changes throughout numerous industries, most notably in the expectations and requirements of businesses operating on a global scale. Additionally, requirements from both financial and non-financial stakeholders continue to increase in both complexity and time sensitivity; users of organizational data require that it be produced and distributed in a timely and cost-efficient manner. In order to produce and report the information required by increasingly important non-traditional stakeholders, accounting and financial professionals must evolve and adapt to a rapidly changing and evolving marketplace. Systems and processes associated with auditing and forensics must be improved upon and integrated into the real time requirements of the market. In essence, accounting and finance must evolve into a more strategic function, and embrace a role as a strategic business partner. INTRODUCTION

The global business landscape has been, some would argue, irreversibly disrupted and continues to be affected by several mega trends that do not appear to be diminishing. Entire industries and organizations have emerged to fill and address the voids in current market offerings. Examples routinely cited include Tesla, Facebook, Twitter, and the creation of touch-screen phone and computer industry that was spearheaded by Apple. In addition to competitive pressures, it is important to also remember that the global regulatory environment has also become more complex and multi-faceted following the financial crisis of 2007-2008. Regulators, particularly of firms operating in finance and consumer facing organizations, have become increasingly interested and opinionated concerning ongoing operations, future performance, and financial results. Organizations and individuals alike, have been tasked with reporting on, and managing, the expectations of a wider range of stakeholders. These parties include shareholders, environmental groups, regulators, consumers, and even partner corporations that are often utilized as partners. That said, it is interesting to note that, despite that rapidly evolving nature of the business marketplace, and the effect that such changes have had on business at large, the internal audit function has remained relatively unchanged. This places organizations and accounting professionals at a disadvantage in the marketplace. This research piece reviews the current academic literature focusing on the evolving and changing nature of the accounting function and proposes a framework for developing a more strategic and engaged accounting function. Accounting and financial professionals must become more involved in the managerial decision making process, and a broader audit function is central to this strategic and engaged accounting function.


A NEW MODEL OF BUSINESS

Prior to drilling deep into the specific actions and steps required by organizations to adapt to a new business environment, it is important to understand and analyze what changes are driving this change. Put simply, doing business as usual, in a traditional manner, is not a methodology and managerial practice that will generate continuing success. As both shareholder and non-financial stakeholders become increasingly interested, not only in current performance, but in future operations and directions of the organization, it is critical that business respond. Several competing methodologies and types of business organizations have arisen as possible solutions to incorporate the needs of shareholder groups into business management and reporting, and a specific reporting framework appears to represent an effort among existing corporations to address these needs. Benefit and Flexible Purpose Corporations

Benefit corporations, as well as flexible purpose corporations, offer a way in which business leaders can meld together traditional financial metrics and goals with longer-term, sustainability-oriented, and strategic objectives of the organization. One high-profile example of organizations that have embraced the benefit corporation model include Ben & Jerry's, as well a multitude of other organizations, and a full list of organizations embracing this business model can be found in the B Corp Best for the World List www.bcorporation.net. Additionally, and arguably more importantly, the b-corporation model is becoming increasingly embedded within the legislative and regulatory framework. At the time of this writing, over 35 states have legislated the concept of a b-corporation into law. The key essence, and distinguishing feature of a b-corporation revolves around how specifically goals and objectives are linked to sustainability and built into operations and evaluation.

One key issue with sustainability and other non-traditional goals associated with management and performance, involves the way in which such goals are pursued. Often, such goals are added-on, inconsistently enforced, and generally not regarded as core to organizational goals. B-corporations, in contrast to this approach, embed and align such longer-term goals directly into the corporate charter and evaluative tools used to engage and rank organizational performance. Specifically, the business question is whether or not the emergence of benefit corporations and other such alternative business models will influence business decision making in a substantive manner (Andre, 2012). Generally, these specific goals and objectives are also embedded within internal and external reporting, i.e., the reporting and monitoring of such metrics and objectives becomes equally as important as financial results. Such integration represents a convergence of sustainability, corporate governance, and accounting professionals. Interestingly enough, it appears that organizations that have embraced a more comprehensive or holistic model of business performance and evaluation achieve superior financial results.

These benefits are not exclusively accrued to organizations that embrace the b-corporation model, or any of the other flexible purpose, or "grey," corporate models that have debuted in the marketplace since the financial crisis. Overall, it is clear that organizations that have embraced the so-called conscious capitalism model have achieved superior financial results versus both the marketplace at large as well as competitors (Simpson, Fischer & Rhode, 2013). This linkage between academic theory and marketplace performance demonstrates that not only is the emergence of stakeholder theory prominent in academic studies, but it also appears to be demonstrating that it is at home in the market. That being said, is it important to understand that a critical step in the development and proliferation of an more comprehensive financial reporting process is the development of a standardized template. That template appears to be integrated financial reporting. Integrated Financial Reporting

Integrated reporting (IR) has been a development several years in the making, and this framework has been embraced by organizations, institutions, and shareholders in a wide variety of firms and industries. The broad-based implementation appears to represent the favorable nature in which the market views the development and spread of IR. Such a methodology, that is different and inherently more complex than


current financial reporting, requires that organizations embrace changes and developments both from an internal point of view and as an external construct. It links together such disparate trends as corporate governance, sustainability, strategic thinking and planning, and a more proactive and engaged finance and accounting function.

Specifically, the rise of integrated financial reporting represents an opportunity for accounting and finance professionals to leverage existing skill sets and competencies to play a more strategic role in organizations. Drilling down to the specific concepts that are most applicable to accountants and other financial professionals, it appears that information technology, quantifying sustainability, ranking governance, and creating a system of reporting that can be applied to a broad range of activities reside within the areas of strength for accounting professionals. Integrated financial reporting, as defined by Bendt, Bilolo, and Muller (2014), represents a new frontier in how organizations interact with both internal and external shareholders, and accounting and finance professionals must seize the emerging opportunities in the marketplace. Governance, represented by Governance Metrics International, is clearly becoming an increasingly important aspect of how organizations are judged and evaluated in the marketplace, and it is imperative that financial reporting and accounting professionals integrate such shifts into work products and services. Put simply, stakeholders and users of financial information expect organizations to report both on current operations as well as on future prospects in a real-time manner. Additionally, it is critical that, while organizations are reporting on a more real-time basis, quality does not suffer in the process, and such a conundrum presents an opportunity for accountants and financial professionals. TRENDS INFLUENCING THE FUTURE OF AUDIT

Trends and paradigms related to business models will, undoubtedly, have an effect on financial reporting and operations, and there are two trends in particular that appear to be especially important as they relate to auditing an forensics. Sustainability, which until relatively recently was considered a rather amorphous concept related to regulatory concerns, is increasingly becoming a more important aspect of business and managerial decision making. Simultaneously, with the increasing usage of technology in every aspect of business operations, it appears that analytics, especially analytics that enable managers to look forward, will play an ever-increasing role in the strategic planning of an organization. Both trends will shape, not only business strategy at large, but also the role of the accounting function within organizations. Sustainability

Lastly, and perhaps most important of all, is the dual rise of sustainability and analytics as core competencies for both organizations as well as for financial professionals. Sustainability, moving from somewhat of an add-on measure, or something that is merely to comply with regulatory requirements, to a full-fledged business theme, represents both opportunities and challenges. Obviously, organizations that operate and employ sustainable measures have reduced carbon footprints and lower energy costs, both of which help reduce the likelihood of fines and other penalties from regulators. A less apparent benefit, however, is the competitive advantage that arises from embracing a more ecologically oriented and sustainable business model. Akin to organizations such as Wal-Mart and SouthWest that have achieved positions of market dominance due to a low cost model of operations, it is reasonable to foresee that organizations with lower costs, over time, might very well be able to translate sustainability into a competitive advantage. That said, there are significant challenges that lay ahead of such initiatives the least of which relates to, specifically, to track and evaluate such measures. Building and reporting on sustainability standards is a unique opportunity for organizations to both report on innovative information as well as more specific ways in which accountants can integrate such measures into financial reporting (D'Aquila, 2012). Since every organization is unique, and every industry has its own set of competitive pressures and forces, it is critical that financial professionals and business decision makers be equipped


with the correct information. This, among other trends, appears to have directly led to the increase in important of analytics as a business tool. Analytics

Analytics have been long recognized as powerful tools that enable management to analyze past performance, benchmark against the competition, and assist in planning for the future. Until fairly recently, however, the utility of analytics and other statistical analyses was limited to breaking down historical information. Financial statements, the core of the accounting and finance professions, are backward looking and can be 6-12 months out of date by the time they are published. Linking together internally generated information, related to both operations and external financial reporting, is a clearly defined market need, as well as an area in which accounting professionals can leverage existing skills (Cokins, 2014). Specifically, there is a gap between information that is currently generated and analyzed by organizations, and what types of data are published to external stakeholders. This gap, between the information actually occurring and being analyzed, and when the data was reported upon and made available to end-users, presents an opportunity for both organizations and accounting professionals. Harnessing the advances in technology, it is simpler and faster to analyze information in real time, construct and present summarizations of the raw data flowing into and out of the organization, and use such information to make timelier and better informed business decisions. THE FUTURE OF AUDITING AND FORENSICS

Auditing and forensic accounting have long played an important role in both the accounting field and in the area of business management generally. Auditors provide reasonable assurance that the financial statements issued by organizations are prepared and presented in adherence with generally accepted accounting principles (GAAP). Forensic accountants, on the other hand, play a critical role in both due diligence as well as in divorce, bankruptcy, and other civil matters. In light of the changes outlined above, as well as market forces linked to the need for real-time data and information, it is clear that both subsets of the accounting profession need to evolve. A More Proactive Audit Function

Sample sizes, stratifications, and random selections have long been the hallmark of audits, both internal and external. Based on these quantitative methodologies, coupled with testing of internal controls, tracing of internal documents, and staff interviews, auditors have formed an opinion on both the effectiveness of internal controls as well as the likelihood of material misstatements. In light of the two trends mentioned previously, sustainability and analytics, it is clear that such an approach cannot be brought forward into the future. With an amorphous concept, such as sustainability initiatives, with a wide variety of metrics and guidelines, it is critical to obtain as much quantitative and verifiable information as possible. Linked to this is the increased usage of analytics software throughout organizations – why should auditors be restricted to a sample selection when, via learning and leveraging the appropriate software packages, it may be possible to test virtually every transaction? As both business decision makers and external stakeholders demand and expect information to be available in a real-time manner, it is imperative that the audit profession keep pace with the changing marketplace.

Building on this point it is important to recognize that the internal audit function is an integral part of the internal management accounting function, and that in order to produce the timely and quantitative information demanded by stakeholders, the entire accounting team must be engaged. Internal audit requires the creation and tracking of quantitative metrics, and it is logical to conclude that internal audit and forensics should play a more active role in managerial decision making. Tracking, developing, and updating metrics on a real-time basis is a goal that involves the entire internal finance and accounting function, and internal audit, in addition to performing periodic tests to detect fraud and misstatements, should also play a role in developing metrics to make such testing simpler and more reliable. Regardless of the specific steps of metrics developed by individual organizations, it is clear that organizations must


produce, and verify, larger amounts of quantitative data and deliver it to stakeholders. Involving internal audit in this process will ensure a more consistent work product, simpler and less contentious external audits, and a more dynamic accounting function overall. Continuous Fraud and Forensics Testing

Leveraging, once again, the growing importance of analytics in business decision making, it is apparent that forensic examination and fraud investigation need not be limited to periodic, and limited, time frames, and scopes. If technology enables management to monitor internal operations on a continuous and on-going basis, it is also possible, and reasonable, for forensic accountants and investigators to have access to information on a real-time basis. Whether this is enabled using an existing feed from existing ERP and risk management systems, the installation of new systems, or the purchase of specific data tools is less important than the end result of the initiative. While cost, and cost benefit analyses are always a factor concerning new projects or ideas, it is important to realize both the benefits of doing so, as well as the potential costs of not providing accounting professionals with the tools they need to perform duties and analyses in real time. Forensic analysis and fraud examination, in addition to being of assistance in resolving matters related to taxes, business bankruptcy, and asset valuation, can also be of assistance in evaluating internal operations as well as potential business partnerships or mergers. This approach, although somewhat innovative and outside of the traditional scope of forensics and fraud investigators, provides an opportunity for growth for professionals as well as an opportunity for organizations to more effectively leverage existing talents within the organization. THE PATH FORWARD

The idea of a more vigorous and engaged forensic division of the accounting function, or the creation and development of entire organizations centered on a more engaged subset of the professions is a clear reflection of market forces. In addition, this train of thought is reinforced by existing research and academic literature. Specifically, as it pertains to the value that internal audit systems and forensic professionals can bring to the table, it is important to acknowledge the unavoidable fact that financial statement misstatement and fraud are increasingly acknowledged as risks to both operations and reputation. Interestingly enough, and perhaps most nascent at this time in the marketplace as well as in the existing research, is the role that accounting systems can have in managing the risk of shareholder activism. A more strategic accounting function must, by default, integrate the needs of both internal and external stakeholders in both periodic reporting as well as continuous operations (Nixon & Burns, 2012). In spite of this inherent limitations, it is clear that such a dynamic accounting function is necessary in the rapidly evolving business marketplace. Although shareholders large and small are increasingly supportive and interested in the financial returns generated by activist investors, and many have become well-known market commentators, it is important to understand what exactly activism does to operations. An activist campaign usually beings with an accusation that current management and directors are not allocating capital and personnel to the most lucrative internal projects. At the core of this criticism is, by default, the accounting, finance, and financial information systems.

Activist campaigns of note, during the post financial crisis era, have generated market- moving headlines in organizations such as eBay, Apple, Pepsi, Coca-Cola, and HP. While it is noteworthy that the activist campaigns have appeared to have generated a positive financial benefit, it is also as important to acknowledge that the opportunity cost of engaging with activist investors on a continuing basis is high. Business decision makers who are not engaged in making business decisions, but rather preparing for and meeting with activist investor groups, simply cannot be expected to generate high quality decisions. In that light, it becomes clear that the future for fraud investigation, forensic accounting, and auditing personnel in general need not be limited to merely examining financial statements after the fact. Possibilities for future growth include the need for standardization and metrics within the growing field of sustainability accounting, utilizing the growth in analytics to create and track such metrics, and giving management to information it needs to most effectively manage the business. This convergence provide a


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