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CFA Certification Program and Sell-Side Analysts
Qiang Kang College of Business Administration
Florida International University Phone: 305-348-4379 Fax: 305-348-4245
Xi Li School of Business
Hong Kong University of Science and Technology Phone: 852-2358-7560
E-mail: [email protected]
Tie Su School of Business
University of Miami Phone: 305-284-1885 Fax: 305-284-4800
JEL codes: J24, G24, G28 Keywords: Capital Markets, CFA, Analyst
We are thankful for suggestions from Wayne Ferson, John Stowe, and seminar participants at the China International Conference in Finance. We gratefully acknowledge the data provided by I/B/E/S. Any errors are our own responsibility. Please address correspondence to Tie Su, Department of Finance, 514 Jenkins, University of Miami, Coral Gables, FL 33124-6552. Phone: 305-284-1885. Fax: 305-284-4800. Email: [email protected].
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CFA Certification Program and Sell-Side Analysts
The CFA program substantially improves the recommendation performance of analysts
that participate in the program. The positive program impact is much stronger for analysts who
cover smaller companies or companies with thinner overall analyst coverage. Analysts improve
their performance when going through the CFA program as candidates but stop such
improvements after completing the program. The learning from the CFA program also
significantly reduces risk-taking and bias behavior in analyst recommendations. Moreover, CFA
designations increase analysts’ mobility to larger brokerage firms. The results survive various
robustness checks, including using different methods to deal with the selection bias.
JEL codes: J24, G24, G28 Keywords: Capital Markets, CFA, Analyst
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“Lawyers have to pass the bar, doctors have medical school and even stockbrokers need a license before practicing their crafts. But stock analysts, who can make or break a company’s stock with their research, don’t need any credentials to hang their shingles on Wall Street.”
-- Kelleher (2001)
1. Introduction
Sell-side analysts are prominent in the investment process.1 In spite of their influence on
the market, however, neither brokerage firms nor the government have certification requirements
for analysts. This is surprising given such requirements exist in many other professions.
Following the sharp market correction since 2000 and the $1.4 billion Global Research Analyst
Settlement (Global Settlement thereafter) in 2003 between the largest ten brokerage firms and
regulators over the practice of exchanging biased research for investment banking business
[Smith, Craig, and Solomon (2003)], many people have argued for the certification of analysts
on investment knowledge and on ethics training. They argue that the lack of certification
requirements may result in inferior performance and excessively biased research.
The Chartered Financial Analyst (CFA) program is an existing voluntary certification
program offered by the CFA Institute, the trade association for buy-side and sell-side financial
analysts.2 Financial analysts receive the CFA designation after successfully completing the
program. By 2012, there are more than 99,000 CFA charterholders. In 2011, more than 200,000
candidates from over 150 countries registered to take the CFA examinations, with more than
60% of candidates from outside of North America. The CFA program and CFA charterholders
exert substantial influence on global financial markets, corporate decision-making, and the
1 Researchers make enormous efforts to identify analysts with more informative earnings forecasts and investment recommendations [e.g., Kothari (2001) and Lee (2001)], and investors pay millions of dollars to access analyst research. 2 Buy-side analysts working for money managers provide research for in-house use by money managers, and sell-side analysts working for brokerage firms provide research for the firms’ clients. Unless otherwise stated, we use the term “analysts” to refer to sell-side analysts in this paper. Also, we use “brokerage firm” or “firm” to refer to an analyst’s employer and we use “company” to refer to the entity that an analyst covers. Finally, we use “recommendation” and “forecasts” to refer to investment recommendations and earnings forecasts, respectively.
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specific training received by industry practitioners. Our paper investigates the degree to which
the CFA program affects the performance, behavior, and career outcomes of sell-side analysts
who participate in the CFA program and complete the required curriculums to receive
designations.
Using a comprehensive sample of investment recommendations and earnings forecasts
over the 1994 to 2000 period,3 we find robust evidence that the CFA program has a significantly
positive impact on program participants’ recommendation performance. The positive program
impact is economically meaningful: for program participants, their recommendation portfolios’
performance increases by about seven percentage points in annualized excess returns (relative to
their pre-program performance). The positive impact is particularly significant for analysts
covering smaller companies or companies with thinner analyst coverage, consistent with the idea
that the impact is greater for analysts that face more opaque information environment. A caution
is in order, though. Because it takes years for an analyst to complete the program curriculum and
obtain the CFA designation, the above documented economic significance of the CFA program
is not attainable in a trading strategy that simultaneously goes short in stocks covered by CFA
analysts before they become CFA charterholders and long in stocks covered by CFA analysts
after they become CFA charterholders. Nor is it our objective to promote a new trading strategy
because we focus on examining the program effect of the CFA designation process per se.
Moreover, when we examine the subset of analysts who complete the CFA program
during our sample period, we find that they improve performance significantly when going
through the program, but they do not continue to improve performance after finishing the
3 Given regulatory reforms on analyst research such as Regulation FD and Global Settlement in the early 2000s, we focus on the 1994-2000 sample period because it provides us a clean environment to identify the impact of the CFA program without the confounding influence of regulatory reforms. This is important for us to understand whether the CFA program can complement the regulatory reforms to improve analyst performance and behavior.
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program. Although on-the-job experience, as required by the CFA program, and the learning
through the CFA program curriculum could both improve analyst performance, the positive
impact that we observe is likely attributable to the latter because we control for experience
throughout our analysis. While the pass rate of the CFA exams has been declining, we show that
the effect of the CFA program has been consistent over time, which suggests that the declining
pass rate is not due to incumbent CFA charterholders trying to reduce the supply of CFA
charterholders in order to extract higher compensation. Taken together, the results suggest that
the improvement when going through the CFA program is likely due to the learning from the
preparation for the exams.
We also examine the impact of CFA program on analyst behavior and career outcomes.
We find that the CFA program reduces risk-taking and bias behavior in recommendations. For
example, the CFA program increases the proportion of negative recommendations that analysts
make by about 15%. The CFA designation also significantly improves the probability that an
analyst will move up to a larger brokerage firm. Because larger firms are likely to offer higher
pay [Hong and Kubik (2003)], this evidence suggests potential benefits of the designation on
analyst compensation.
Our paper contributes to prior research on occupational regulation in two respects. First,
Kleiner (2000) notes the extant evidence on occupational certification and regulation is limited,
largely due to lack of data. Kleiner also writes, “Typically, direct estimates of the quality of a
service…are not available,” and further, sometimes, “it is not even altogether clear how one
would measure quality.” Our paper provides evidence on the securities industry. In comparison
to the difficulty of quality measurement in prior research, it is easy to produce objective
measures of performance and behavior for analysts. Second, the CFA program does not involve
5
formal specialty education as required by many certification programs widely examined so far,
such as those for lawyers, physicians, and public school teachers. Because formal education is
known to increase human capital, it is easier to separately examine the effects of certification
programs using the CFA program.
Our paper is also related to prior research on the determinants of analyst performance,
behavior, and career outcomes [e.g., Lys and Sohn (1990), Stickel (1995), Clement (1999),
Jacob, Lys, and Neale (1999), Hong and Kubik (2003), and De Franco and Zhou (2009)].
Compared to prior research, we examine the impact of the CFA program on analysts’ human
capital and career outcomes, measured respectively by the abnormal returns from investment
recommendations and mobility among brokerage firms of different sizes. In particular, we focus
on investment recommendations instead of earnings forecasts because investment
recommendations provide an unequivocal assessment of companies by sell-side analysts and are
arguably more important to investors than earnings forecasts.
There is also significant practical importance to determining the effects of the CFA
program on analyst performance and behavior. First, investors have suggested that the CFA
program can be a mechanism to improve analyst performance and behavior. Some even argue for
the program to become a licensing requirement.4 The first step of any policy making is to
determine whether in fact the program benefits analyst performance and behavior. If it does, the
benefits could be substantial given that investors use analyst research extensively. Second,
completing the CFA program usually requires lengthy preparation and costs thousands of dollars.
4 Many analysts became celebrities in the late 1990s for continuously recommending risky stocks that later reached the analysts’ hefty price targets before collapsing. The most well known among these analysts, Henry Blodget and Mary Meeker, were dubbed “King Henry” and “Queen of the Net,” respectively, for this reason [DeBaise and Wingfield (2001)]. Jack Grubman and Henry Blodget, star analysts in the nineties, were also later fined and barred from the securities industry for life for touting stocks with which their firms maintain investment banking relationships. Supporters of the CFA program point out that none of the three analysts have the CFA designation.
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Because in recent years more than 200,000 candidates from over 150 countries enroll for the
exams each year, it is important to know whether the substantial resources spent on preparing
and administrating the exams are justified. Third, as a result of the growing investors’ call for
ethics training following recent corporate scandals, business schools and certification programs
around the world have recently started to incorporate ethics training into their programs. Given
the short history of these training programs, it has been difficult to evaluate their effects on
behavior bias so far. In contrast, the CFA program has long included ethics training in its
curriculum, so this paper could shed light on the effectiveness of such training. Above all, our
study not only helps rationalize spending considerable resources on preparing and administrating
the CFA exams, but also lends support to the increased practice of including ethics training in the
curriculum of education and certification programs.
The rest of the article is organized as follows. Section 2 provides a summary of the CFA
program and the related literature. Section 3 describes the data and the sample construction.
Section 4 discusses in detail the empirical strategy and presents the empirical results. Section 5
concludes.
2. The CFA Program and Related Literature
The CFA program has received heightened attention subsequent to a series of corporate
scandals around the turn of the century. Both the number of CFA charterholders and the number
of CFA candidates have grown substantially in recent years, regulators in countries such as the
United Kingdom, Singapore, Canada, and the U.S. have adopted the CFA designation as a
competency requirement, and the curriculum of the CFA program is being integrated into many
business school curricula. The CFA Institute advocates that all analysts be required to complete
7
its program both to improve research skills and to secure a commitment to abide by its ethics
code [Kelleher (2001)].
The CFA program seems to fit well with certification demand. Indeed, the CFA Institute
strives to set “a globally recognized standard for measuring the competence and integrity of
financial analysts.” According to the CFA Institute, “The CFA Program is comprised of three
levels, each culminating in an examination. You must pass each level sequentially, and fulfill
other requirements of the program…In general, each level of the program requires 250 hours of
preparation” through a “self-study curriculum.” The exams cover a substantial amount and
diversity of material.5 The combined pass rate of the three levels of exams has been below 50%
in recent years. Other key requirements of the program include a bachelor degree and at least
three years of acceptable professional experience in the investment decision-making process
(four years for candidates that register for the 2005 program for the first time and all candidates
that remain in the program after 2007). CFA charterholders also need to pledge to the Code of
Ethics and are disciplined for violations with various measures, including loss of the designation.
While each of these requirements appears to be constructive, it is still unclear whether the
CFA program, or certification programs in general, can improve the performance and behavior
of those that are certified. On the one hand, the 750 hours of preparation that is generally
required to study the CFA program curriculum in order to pass the CFA exams may improve
analyst performance and behavior, given that formal schooling generally increases human capital
[see Card (1999) for a review]. Certification requirements may also establish a minimum
5 The four parts of its Candidate Body of Knowledge include ethical and professional standards, tools, asset valuation, and portfolio management. Each exam level covers all four parts, but with a different focus. Level I exams focus on tools, which include quantitative methods, economics, financial statement analysis, and corporate finance. Level II exams focus on asset valuation, which includes analyses of equity and debt investments, derivatives, and alternative investments. Level III exams focus on portfolio management. Each exam level also gives 10% to 15% weight to ethical and professional standards.
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achievement standard, minimizing poor quality service and the associated risks of disruptions to
the financial markets. On the other hand, the self-studying from the CFA program curriculum
may have no effect on performance and behavior because self-studying is different from formal
schooling and because the curriculum may not be transformable to improved performance. A
code of ethics may not moderate analyst bias [Dobson (2003)]. Even worse, certification
requirements could create costly barriers to entry, which may deter qualified applicants from
entering, and help incumbents extract rents [e.g., Goldhaber and Brewer (2000) and Friedman
and Kuznets (1945)].
Prior research on the impact of occupational regulation generally shows that while
licensing and certification requirements generate increased earnings in the affected industry, they
produce no improvements in the quality of the service provided or the quality of applicants [see
Rottenberg (1980) for a review of earlier literature and Angrist and Guryan (2004), Goldhaber
and Brewer (2000), Kleiner and Kudrle (2000), Kugler and Sauer (2005), Wilensky and Rossiter
(1983), and Wolverton and Epley (1999) for more recent examples].6 In particular, Young (1988)
shows that licensing rules are administered to advance the interest of licensed practitioners.
Similarly, Kandel and Lazear (1992) find that individuals react to the way that the rules are
administrated when choosing licensing locations.
The importance of the CFA program calls for a careful analysis of the program’s impact on
the human capital of (sell-side) analysts; it also stipulates distinguishing between alternative
explanations. The paper closest in spirit to ours is De Franco and Zhou (2009) 7. They report
6 This literature differentiates between mandatory licensing and certification, such as mandatory licensing for lawyers and dentists, and voluntary certification for auto mechanics, physicians, and teachers. 7 Other prior studies usually use small samples and do not examine sell-side analysts. For 223 equity mutual funds in the 1988-1992 period, Shukla and Singh (1994) find that although funds managed by at least one CFA manager do not outperform the S&P 500 index, they are riskier yet better diversified and have higher risk-adjusted returns than funds without CFA managers. Brockman and Brooks (1998) find a positive correlation between the growth in the number of CFA charterholders and the growth in S&P 500 index in the 1963-1995 period. From a survey of 41 U.S.
9
somewhat mixed evidence when comparing the earnings forecasts of CFA charterholders and
non-charterholders. Specifically, the forecasts of CFA charterholders are 3-4% timelier and
bolder and generate greater short-term market reactions (about 0.66% more positive for positive
revisions and 3.30% more negative for the negative revisions) but are less accurate and similarly
optimistic. They also try to differentiate the signaling vs. human capital theories as the
explanation for the weakly better performance of CFA charterholders. They find that the
forecasts of CFA charterholders are timelier and bolder when compared to non-charterholders in
both the periods before and after the CFA designation. They conclude that the CFA
charterholders perform better in the pre-designation period and that the difference indicates
better innate ability of CFA candidates, providing supports for the signaling theory. They also
conclude that CFA charterholders perform better in the post-designation period and that the
difference is a reflection of the skills acquired through the CFA program, lending support to the
human capital theory.
Nevertheless, there could be other explanations for De Franco and Zhou (2009)’s evidence.
The performance difference before and after the CFA designation might be solely due to the
difference in innate ability, and the CFA analysts use the designation solely for the purpose of
signaling, instead of acquiring human capital. Alternatively, because the CFA program usually
takes years to complete, CFA analysts are likely to participate in the program years before the
actual designations. Thus, the performance difference before the CFA designation might be due
to the human capital acquired in the process of completing the CFA program during the pre-
designation period and the acquired human capital enables the CFA analysts to continue to
outperform after the designations, without any signaling effect.
public pensions, Miller and Tobe (1999) find that pensions employing at least one CFA charterholder in investment teams have lower investment management costs than those without CFA charterholders.
10
Different from De Franco and Zhou (2009), we examine the question whether the CFA
program increases the human capital of sell-side analysts, and we focus on investment
recommendations instead of earnings forecasts. To carefully differentiate the signaling vs.
human capital theories, we draw from prior research and explicitly control for innate ability with
analyst fixed effects, in addition to some common control variables, in cross-sectional
regressions [e.g., Hausman and Taylor (1981), and Jacob, Lys, and Neale (1999)]. Analyst fixed
effects are likely to adjust for the analyst-specific characteristics, particularly those related to
innate ability, that we are unable to include in regressions due to data constraints. Our results
thus control for analyst fixed effects and suggest that the CFA program significantly improves
analyst human capital in terms of their making more profitable recommendations.
We also examine the changes in performance within both the pre- and post-designation
periods for the subset of analysts who complete the CFA program during our sample period.
These time series results are unaffected by innate ability because we examine the same analysts
in both periods. We find that the subset of CFA analysts improve performance significantly
when going through the program but do not continue to improve performance after finishing the
program, which provides further support for the positive effect of the CFA program on the
human capital of sell-side analysts.
Complementing De Franco and Zhou (2009), we focus on examining investment
recommendations instead of earnings forecasts. Investment recommendations and earnings
forecasts contain independent information, and many people consider recommendations to be
more important to investors than forecasts [e.g., Francis and Soffer (1997), and Womack (1996)].
Also, investment recommendations provide an unequivocal assessment of companies by sell-side
analysts. In comparison, because there are several dimension of earnings forecast performance
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such as accuracy, timeliness, and boldness, it is difficult to draw an unambiguous conclusion in
case of divergent results among the various dimensions of performance. Moreover, our approach
tracks the performance of investment recommendations from the recommendation dates to
revision dates and thus simulates an investor’s hypothetical investment experience of following
analysts’ investment recommendations. In contrast, it is difficult to assess the impact of greater
accuracy by a few cents and of greater timeliness by a fraction of days. Further, bias is more
severe in recommendations than in forecasts [e.g., Lin and McNichols (1998)] and is the focus of
the $1.4 billion regulatory settlement noted earlier. In untabulated results, we find evidence that
CFA charterholders outperform non-charterholders in terms of forecast accuracy, but this result
disappears after controlling for analyst fixed effects, indicating that this difference is likely due
to other differences between CFA charterholders and non-charterholders. Finally, we examine
the impact of the CFA designation on career outcomes.
3. Data and Analyst Characteristics
3.1. Data
We obtain our primary data from the Institutional Brokers Estimate System (I/B/E/S).
The I/B/E/S database provides individual name, brokerage affiliation, earnings forecasts, and
stock recommendations of each analyst, as well as a unique code for each analyst that allows us
to track analysts should they change affiliations. Its earnings forecast database starts in 1983, and
its stock recommendation database starts in October 1993. I/B/E/S provides standardized
recommendations, with integer ratings from 1 through 5 corresponding to “strong buy,” “buy,”
“hold,” “underperform,” and “sell,” respectively.
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Following Clement (1999) and Jacob, Lys, and Neale (1999), we exclude analysts with
forecasts in the I/B/E/S database prior to 1984 to avoid a left-censored bias in the experience
measure. We also exclude analysts with only “hold” recommendations. We restrict our sample to
the period from 1994 through 2000 for two reasons. First, the recommendations data start in late
1993. Second, given regulatory reforms on analyst research such as Regulation FD and Global
Settlement in the early 2000s, our sample can provide the evidence on the impact of the CFA
program without the confounding influence of regulatory reforms. This is important, for
example from the viewpoint of regulatory reforms, because we want to understand whether it is
possible for the CFA program to complement the above regulatory reforms to improve analyst
performance and behavior.
Our sample selection procedure yields a sample of 4,051 analysts and 15,178 analyst-year
observations. Because the I/B/E/S database sometimes assigns multiple codes to a single analyst,
merging the data for these analysts reduces the sample to 4,019 analysts. For estimation purposes
we require CRSP stock returns and create a three-month recommendation portfolio within each
year (details to be discussed in Section 3.2), which further reduces the sample to 3,510 analysts
and 12,398 analyst-year observations.
The I/B/E/S database only provides the last name and first initial of analysts. We search
news articles in databases such as Lexis-Nexis and ProQuest to find the first name of each
analyst. If our search results in multiple analysts with the same first and last names, we match
information on brokerage firm affiliation with the I/B/E/S database to identify the analyst. We
then hand-collect information about whether and when analysts receive the CFA designation
from the annual Membership Directory of Association for Investment Management and
Research.
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Panel A of Table 2 reports the proportion of analysts by CFA designation. Of the 3,474
sample analysts, 1,259 analysts, or about 36%, are CFA charterholders. Given there are presently
no certification requirements for analysts, the high proportion of analysts having completed the
CFA program is an anecdotal evidence that CFA designation is beneficial to analysts.
3.2. Measures of Performance, Risk-Taking, and Bias
We first create an analyst’s recommendation portfolio following prior research [e.g., Li
(2005), Emery and Li (2009)]. An analyst’s recommendation portfolio is made up of long (short)
positions in stocks the analyst rated 1 or 2 (4 or 5). Stocks are added to the portfolio on the
recommendation date, and removed from the portfolio on the date of any revision to the rating of
3. A stock’s classification changes when a superseding recommendation alters the stock’s
classification. For example, a revision from 1 or 2 to 4 or 5 is a revision, whereas an upgrade
from 2 to 1 is not a revision because the stock would already be classified as a long position.
Reiteration of a previous recommendation does not change a stock’s classification. Returns
within each year accumulate from the recommendation date until either (1) the date of revision,
or (2) the end of the year, if there is no revision during the remainder of the year. CRSP daily
returns for each recommendation are equally weighted to calculate the portfolio’s return. We
require a minimum time period of three months for the overall recommendation portfolio within
each year for estimation purposes.8 We estimate the Carhart (1997) model
Rit = αi + ∑4j=1 jRjt + εit , (1)
where Rit is the return on the recommendation portfolio of analyst i in excess of the three-month
T-bill return on day t, αi is the multifactor model Jensen’s alpha which measures the average
8 Our approach is similar to that of WSJ rankings which gives a weight of 2, 1, 0, -1, and -2 to stocks that the analysts rated 1 through 5. Alternative weighting schemes of recommended stocks do not affect our results.
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daily abnormal return on the portfolio of analyst i given the daily frequency of our data, j is the
regression coefficient for factor j, Rjt is the return of factor j on day t, and εit is an error term for
the portfolio of analyst i on day t. The factors are the return on the CRSP value-weighted
NYSE/AMEX/Nasdaq market index in excess of the three-month T-bill return, the size and
book-to-market factors of Fama and French (1993), and the return momentum factor of Carhart
(1997). Prior research identifies these factors as related to systematic risk or investment styles
that have nothing to do with the contribution of skill. We include them in our analysis to avoid
rewarding analysts for simply exploiting these factors.
We use ALPHA, the intercept of the Carhart model regression, and INFORATIO, the t-
statistic of the intercept, to measure analyst recommendation performance. INFORATIO, which
stands for “information ratio,” is essentially the Sharpe ratio in a multifactor model setting. This
metric is used extensively as a performance measure, because it controls for both systematic and
idiosyncratic risks of an investment. Following Chevalier and Ellison’s (1999) examination of the
risk-taking behavior of fund managers, we measure analyst risk-taking in recommendations with
RESIRISK, the residual return standard deviation in the Carhart model regression.
Because one objective of the recent efforts to reduce analyst bias following the $1.4
billion settlement mentioned above is to increase the proportion of negative recommendations
[Opdyke (2002)], we use the percentage of negative recommendations among an analyst’s
recommendations (PCTSELL), including both underperforms and sells, to measure the bias in
investment recommendations.
For part of our analysis, we also control for analyst performance and behavior reflected in
earnings forecasts. We use Hong, Kubik, and Solomon’s (2000) relative forecast accuracy
(ACCURACY) and relative forecast boldness (BOLDNESS) to measure performance and risk-
15
taking behavior in earnings forecasts. We use Hong and Kubik’s (2003) relative forecast
optimism (OPTIMISM) on a one-year basis to measure analyst bias. These relative measures
account for both the forecasts of the other analysts covering the same stock and the number of
other analysts covering a particular stock. See the Appendix for details about the construction of
these variables.
3.3. Control Variables
We use several control variables. Following Stickel (1992), among others, we measure
analyst reputation using IISTAR and WSJSTAR, dummy variables that equal one if the analyst is
an Institutional Investor (I.I.) All-American and Wall Street Journal (WSJ) All-Star analyst,
respectively, and zero otherwise.9 Jacob, Lys, and Neale (1999) use the number of research
reports issued by an analyst (NREPORT) to measure the timeliness of reports, which should
proxy for the willingness of analysts to exert effort. Clement (1999) and Jacob, Lys, and Neale
(1999) argue that an increase in the number of companies covered by one analyst
(NCOMPANY), that is, broader coverage, increases task complexity. Jacob, Lys, and Neale
(1999) also argue that broader coverage broadens industry knowledge. Stickel (1995) and Hong
and Kubik (2003) use brokerage firm size (BROKERSIZE) as a proxy for marketing ability and
the reputation of analysts’ firms, respectively. We use COMPANYSIZE as a proxy for the
information environment of the companies under coverage, because prior research argues that
smaller companies have a more opaque information environment due to less information
disclosure, and less news and research coverage [Stickel (1995)]. We also use COVERAGE, the
9 Formerly, WSJ published two sets of rankings. One was based on investment recommendations and the other was based on earnings forecasts. WSJ stopped providing the ranking based on earnings forecasts in 2002. For brevity, we only present the results based on the WSJ’s investment recommendation-based rankings. The results for the WSJ’s rankings based on earning forecasts are similar and are available upon request.
16
average number of analysts that cover the same firm as a particular analyst does, as another
measure of the information environment [e.g., Piotroski and Roulstone (2004)]. In addition, we
include EXPERIENCE, the number of years that an analyst has been submitting reports to
I/B/E/S, to measure the impact of learning-by-doing [e.g., Clement (1999) and Jacob et al.
(1999)]. Following the prior literature, we use logarithm values of all the above control variables but
the two dummy variables, IISTAR and WSJSTAR, in our empirical analysis.
3.4. Summary Statistics
Panels B to D of Table 2 report summary statistics on the measures of analyst
performance, behavior, and control variables for the full sample, the CFA subsample, and the
non-CFA subsample, respectively. CFA charterholders are different from non-charterholders in
various aspects. CFA charterholders have smaller RESIRISK and issue a greater proportion of
negative recommendations than non-charterholders. Interestingly, CFA charterholders have
slightly lower ALPHA and ACCURACY, but the differences are statistically insignificant. The
proportion of I.I. stars is lower among CFA charterholders, whereas that of WSJ stars is higher
among CFA charterholders. On average, CFA charterholders have about 1.8 years more
experience, issue more research reports, and cover more companies.
4. Testing Methodologies and Empirical Results
4.1. Empirical Strategy
To assess the impact of the CFA program on the performance and behavior of analysts,
we focus mainly on estimating the following model:
17
0 1 2 3 4
5 6 7 8
9 .
t t t t t
t t t t
t t
Dependent Variable a a CFA a IISTAR a WSJSTAR a EXPERIENCE
a COVERAGE a NREPORT a NCOMPANY a BROKERSIZE
a COMPANYSIZE Year Effects Analyst Effects Brokerage Firm Effects
(2)
Here, the dependent variables include the measures of analyst performance, ALPHA and
INFORATIO, and the measures of analyst risk-taking and bias behavior, RESIRISK and
PCTSELL. As Table 2 shows that CFA charterholders and non-charterholders are different
across several characteristics and to the extent that these characteristics affect analyst
performance and behavior, we include these characteristics to control for performance/behavior
differences that are unrelated to the CFA program. We also include year dummies to control for
time variations in performance/behavior that are related to changes in macroeconomic
conditions.
The specification of Equation (2) deserves a further discussion. First, the key parameter
of interest in this model is the coefficient on CFA, a binary variable that equals one for the years
that an analyst is a CFA charterholder and zero otherwise. Clearly, the choice of becoming a
CFA charterholder is not random. Heckman (1979) explains that using non-randomly selected
samples when estimating behavioral relations results in a unique type of “omitted variables” bias,
namely, selection bias. For example, CFA analysts may have different innate abilities and
motivations from non-charterholders. These differences may affect their initial decisions of
whether to go through the CFA program, which therefore makes it difficult to know whether the
potential performance differences between CFA and non-CFA analysts would be due to the CFA
program. Prior research uses econometric methods such as instrumental variables, the fixed
effects model, and Heckman’s (1979) two-step procedure to control for selection bias [Heckman,
Ichimura, Smith, and Todd (1998)]. Here, appropriate instrumental variables such as measures of
18
innate ability are not available.10 In addition, unlike events such as mergers and acquisitions, the
choice to finish the CFA program again is not available to CFA analysts who have already
finished the program, and this non-reversibility is likely to render Heckman’s (1979) procedure
inaccurate. Moreover, the timing of an analyst’s decision to go through the CFA program is
unknown, further weakening the efficacy of Heckman’s procedure.
Given the limitations of the alternative methods to deal with the selection bias in this
particular scope of study, we therefore adopt the fixed effects model to control for the potential
selection bias. Hausman and Taylor (1981) argue that the fixed effects model offers a common,
unbiased technique for controlling for omitted variables in panel data sets. Jacob, Lys, and Neale
(1999) use analyst fixed effects to adjust analyst aptitude and innate ability. Accordingly, we
include analyst fixed effects in Equation (2). We also add brokerage firm fixed effects to address
omitted brokerage characteristics that may affect analyst performance and behavior. In Section
4.6, we also conduct several tests à la Heckman’s (1979) two-step procedure, combined with the
fixed-effects model; the results are qualitatively similar.
It is worth discussing the interpretation of the coefficient estimate on the dummy
variable, CFA, in a model including analyst-fixed-effects. It is well known in econometrics that
the fixed-effect model essentially captures the within-group variation but not the between-group
variation. Note that the dummy variable always equals one after an analyst obtains the CFA
designation and zero otherwise. Therefore, after controlling for the usual suspects and the time-
invariant unobserved characteristics that affect the analyst performance, the parameter estimate
on the dummy in an analyst-fixed-effect model mainly characterizes the impact of obtaining the
10 Matching methodology would face essentially the same issue as the instrumental variable approach. Matching based on aspects other than proxies for innate ability is unlikely to be useful here. Further, because CFA charterholders are about 36% of our sample analysts, the choices in the non-charterholder sample are too limited to provide decent matches.
19
CFA designation on the analyst’s performance (relative to his pre-designation performance); the
particular parameter estimate generally does not capture the overall difference in performance
between CFA analysts and non-charterholders.
Second, the inclusion of analyst experience is particularly interesting. As we discuss
above, CFA candidates may improve performance due to the learning from the CFA program
curriculum, or due to the buildup of work experience as required by the CFA program. Prior
literature finds mixed evidence with respect to the extent of learning-by-doing as measured by
experience. For example, Clement (1999) find that forecast accuracy increases with experience,
whereas Jacob et al. (1999) find that the experience effect disappears after adjusting for analyst
aptitude. By controlling for analyst experience, we effectively focus our study on the impact that
corresponds to the learning from the CFA program curriculum.
Third, because each level of the CFA exams is offered once a year and analysts need to
have at least three years of relevant work experience,11 it takes at least three years to finish the
CFA program. Because there is no time limit within which a candidate has to complete the
program, and thus analysts may take a few years off between the preparations for the exams, the
actual time for an analyst to finish the program may be well more than three years. Further, some
analysts may finish the exams before gaining the required experience, while other analysts may
meet the minimum experience requirement before finishing all three exams. Likely due to these
reasons, according to the CFA Institute, 30% of the analysts who entered the CFA program in
1990 obtained their designation within five years, whereas only 19% of the analysts who entered
the CFA program in 1996 obtained their designation within five years. The lengthy period of
time to complete the program creates a non-charterholder sample that is tainted with CFA
candidates. For example, if a particular analyst receives the CFA designation in year t, this 11 Since 2003, Level I exams have been offered twice a year in some sites.
20
analyst may be a CFA charterholder, a CFA candidate, or a non-candidate, during the few years
before the designation. While we cannot clearly measure the impact of this bias on our estimates,
this bias will only hinder our ability to detect the performance difference between CFA
charterholders and non-charterholders in our paper. Thus, any beneficial effects that we may find
for the CFA program are likely to provide only an estimate of the lower bound of these
benefits.12
Someone may concern that our model specification still suffers from the omitted-variable
bias. Ideally, Equation (2) should include analyst characteristics such as measures of
education/innate ability (e.g., MBA degree and SAT score), other types of experience, or
indicators for other important certifications (e.g., Certified Public Accountant designation).
However, these characteristics, interesting by themselves, are extremely difficult, if not
impossible, to obtain for the universe of analysts. To the extent that the analyst status does not
change on these aspects (e.g., an analyst has an MBA degree or a CPA designation when
entering our sample, or the analyst never obtains those characteristics), our use of fixed-effects
models helps lessen and address such concern.
4.2. Does the CFA Program Improve Analyst Performance?
We first look at the effects of the CFA program on analyst performance measured by
ALPHA and INFORATIO, respectively. We report the results of estimating the fixed effects
model in the first two columns of Table 3. The coefficient estimate of CFA is positive and
12 When we exclude from the non-charterholder sample all the analyst-year observations for which CFA analysts are likely to be still CFA candidates (e.g., four years before they obtain their designations), we find slightly stronger benefits of the CFA program. We also find similar results when we combine the CFA candidate sample with the CFA sample.
21
significant at the 1% level for ALPHA and INFORATIO. Thus, the CFA program significantly
improves analyst recommendation performance.
The impact of the CFA program is also economically significant. In Column 1 of Table 3,
the magnitude of the coefficient estimate of CFA is 2.86. Using untabulated information on the
detailed percentiles of ALPHA for CFA charterholders and non-charterholders, we infer that
obtaining the CFA designation helps move up the analyst’s position, if starting from the median
level of the distribution, to approximately 60-65 percentile of the pack (see also Panels B to D of
Table 2 for a general assessment of the improvement). Also note that the coefficient estimate of
ALPHA is essentially a measure of the daily return performance in basis points. Thus, this point
estimate implies an equivalent increase in the annualized excess return by 7.47 percentage points
(= (1 + 0.0286%)252 – 1) as a result of completing the CFA program. Further note that, as
discussed above, the coefficient estimate on the dummy variable in the analyst-fixed-effect
model mainly captures the CFA program effect, i.e., the average impact of completing the CFA
program on the analysts’ performance (relative to their pre-program performance). This effect
does not imply that a CFA analyst outperforms a non-charterholder by 2.86 basis points in daily
return or 7.47 percentage points in annualized return.13
If the CFA program helps analyst performance through improved analytic skills, the
benefits should be greater for CFA charterholders who face a more opaque information
environment. Prior research argues that analysts that cover smaller companies and companies
with less coverage from other analysts should face a more opaque information environment. We
therefore expect these analysts to benefit more from the CFA program. We test this conjecture by
adding the interaction of CFA with the two information environment measures, COMPANYSIZE
13 If we estimate equation (2) over the subsample of analysts who obtain the CFA designations during the 1984-2000 period, the point estimate on CFA equals 2.95 with a t-value of 4.09, equivalent to an increase of 7.72 percentage points in annualized return.
22
and COVERAGE, in Equation (2). In columns (3)-(4) of Table 3, we report the results of
estimating Equation (2) when we add the interaction of CFA and COMPANYSIZE. In columns
(5)-(6) of Table 3, we report the results when we add the interaction of CFA and COVERAGE.
The coefficient estimates of both types of interaction terms are negative for both performance
measures. The coefficient estimate of the interaction between CFA and COMPANYSIZE is
significant at the 5% level for ALPHA, whereas that of the interaction between CFA and
COVERAGE is significant at the 1% level for ALPHA and at the 5% level for INFORATIO.
These results are consistent with the hypothesis that analysts that face a more opaque information
environment benefit more from the CFA program.
In summary, the CFA program significantly improves recommendation performance, and
its economic significance is substantial. The average impact of the CFA program on performance
is equivalent to an increase of 7.47 percentage points in the abnormal returns generated by
analyst recommendations. The positive impact is particularly strong for analysts that face an
opaque information environment. Among the variables that we examine, the magnitude of the
coefficient estimates of CFA suggests that the CFA program has the largest positive effect on
analysts’ recommendation performance.
4.3. The Improvement Rate in Performance before and after Finishing the CFA Program
We further examine the changes in performance of analysts before and after their
finishing the CFA program and receiving the designation. For this purpose, we focus on the
analysts who earned the CFA designation during the 1995 to 2000 period. Specifically, we define
a new variable YTOCFA, the number of years away from the completion of the CFA program.
For example, YTOCFA is equal to -3 for the third year before the designation and to +3 for the
23
third year after the designation. We then use the following model to examine the relation
between annual changes in the three performance measures and YTOCFA either for the three
years before analysts complete the CFA program (i.e., the pre-designation period) or for the three
years after the completion of the CFA program (i.e., the post-designation period):
0 1 2 3
4 5 6 .t t t t
t t t t
DependentVariable a a YTOCFA a EXPERIENCE a NREPORT
a NCOMPANY a BROKERSIZE a COMPANYSIZE
(3)
In this analysis, we include analysts who are in our sample for at least two consecutive years.
This requirement allows us to calculate the change in performance measures.
Note in Equation (3), we do not include change in experience because it is always equal
to one year. Thus, our approach here naturally eliminates any effects on analyst performance
changes that are due to the accumulation of experience. We do include EXPERIENCE to control
for an analyst’s experience. One concern is that if learning-by-doing effects do exist, they may
weaken as analysts gain more experience. Thus, because analysts have less experience before
they complete the CFA program, we need to ensure that our results do not reflect a natural
plateau in the learning curve of analysts. By including EXPERIENCE, any effects of the CFA
program that we observe should be attributable to the learning from the CFA program
curriculum, as opposed to experience.
The above approach has several advantages. First, it eliminates concerns about selection
bias by focusing on those analysts who earned CFA designations. Second, a gradual
improvement in analyst performance in the years leading up to the designation would lend
additional support to the positive effect of the CFA program on analyst performance. Further, if
CFA charterholders do not continue to improve performance in the post-designation period, i.e.,
after they finish the CFA program, the performance improvement in the pre-designation period is
likely to be due to the learning from the CFA program curriculum while one is going through the
24
CFA program. Finally, the coefficient estimates of YTOCFA provide estimates for the
performance improvement rate of CFA candidates. The disadvantage of this approach is that the
resulting sample size is small.
Over the 1995 to 2000 period, 563 analysts received the CFA designations. Of these
analysts, we obtain a sample of 232 analysts (415 analyst-year observations) in the pre-
designation period and a sample of 474 analysts (878 analyst-year observations) in the post-
designation period, respectively.
We report the results in Table 4. The coefficient estimates of YTOCFA are positive and
significant at the 5% level for both ALPHA and INFORATIO for the pre-designation period.
Further, the estimates of YTOCFA are insignificant for both performance measures in the post-
designation period. Thus, CFA candidates tend to improve their recommendation performance
significantly while going through the CFA program but they do not continue to improve after
finishing the CFA program. Taken together, the performance improvement in the pre-designation
period is likely to be attributable to the CFA program. These results are consistent with our
findings in Section 4.2.
To understand the economic significance of these results, the increase in the annualized
excess return of going through the CFA program is 5.62 percentage points (= (1 + 0.0217%) 252 -
1) per year. Thus, the total improvement over the three-year period would be a substantial 16.86
percentage points. Given the small sample in this analysis, we have to be cautious when
interpreting coefficient estimates. Nonetheless, the results provide strong evidence that the CFA
program significantly improves analyst recommendation performance.
4.4. Risk-Taking and Bias
25
We then examine separately the effects of the CFA program on risk-taking and bias
behavior. Column (1) of Table 5 reports the results of estimating Equation (2) with RESIRISK as
the dependent variable. The coefficient estimate of CFA for RESIRISK is -0.49 basis points and
is significant at the 1% level. In comparison, RESIRISK averages at 10.70 basis points for CFA
charterholders in Panel C of Table 2. The point estimate suggests that the CFA program on
average reduces RESIRISK of CFA charterholders by 4.38% (= 0.49 / (10.70 + 0.49)), and the
economic significance of the CFA program on analyst risk-taking behavior seems to be modest.
Column (2) of Table 5 presents the results of estimating Equation (2) with PCTSELL as
the dependent variable. The coefficient estimate of CFA is positive and significant at the 10%
level for PCTSELL. Again, for comparison, Panel C of Table 2 reports that the average
proportion of negative recommendations is 4.75% for CFA charterholders. Thus, completing the
CFA program significantly increases the proportion of negative recommendations made by CFA
analysts by about 15% (= 0.63 / (4.75 – 0.63)).
To summarize, the CFA program significantly reduces analyst risk-taking and bias in
recommendations, suggesting additional benefits of completing the CFA program. The
program’s effect on analyst bias is particularly interesting. Research demonstrates strong analyst
bias in the presence of investment banking relationships [e.g. Lin and McNichols (1998) and
Michaely and Womack (1999)]. As a consequence, regulators and investors have exerted
considerable effort to reduce analyst bias, particularly after the $1.4 billion settlement we discuss
above. The CFA program can therefore be a valuable tool to reduce analyst bias. The negative
effect of the CFA program on bias behavior also provides support for the increased interest in
incorporating ethics training in the curriculum of education and certification programs.
26
4.5. Career Outcomes
The popularity of the CFA program indicates its potential benefit to enhancing analysts’
career outcomes. According to the annual surveys of the CFA Institute, CFA charterholders with
10 or more years of experience earn about 21% more than their non-CFA contemporaries. About
90% of CFA charterholders say the designation broadens their career opportunities or chances
for promotion. In this section, we examine the impact of completing the CFA program on analyst
career outcomes. Prior research examines job separations such as termination from the analyst
profession [Hong et al. (2000)] and job mobility among brokerage firms [Hong and Kubik
(2003)]. We examine the determinants of job mobility using the following ordered probit model:
0 1 1 2 1 3 1
4 1 5 1 6 1
7 1 8 1 9 1
Pr
t t t
t t tt
t t t
a a CFA a IISTAR a WSJSTAR
a ACCURACY a INFORATIO a EXPERIENCEJob Mobility
a NREPORT a NCOMPANY a COMPANYSIZE
Year Effects
(4)
where Job Mobility is equal to 0 if an analyst was working for a brokerage firm that was above
the 95th percentile in terms of the number of analysts employed in year t-1 and moves in year t to
a firm below the 95th percentile; 1 if an analyst switches between firms below the 95th percentile
or switches between firms above the 95th percentile from year t-1 to year t; 2 if an analyst does
not change brokerage firms; and 3 if an analyst was working for a firm below the 95th percentile
in year t-1 and moves to a firm above the 95th percentile in year t. We define job mobility in this
way because larger brokerage firms are likely to offer higher pay [Hong and Kubik (2003)]. We
include other analyst characteristics to isolate the effects of the CFA designation.
Table 6 reports the results of estimating Equation (4). The coefficient estimate of CFA is
positive and significant at the 1% level, which suggests that the CFA designation enhances
analyst job mobility. Because the coefficient estimates of the ordered probit model only provide
27
limited information about the marginal effects of particular variables and are therefore hard to
interpret, we follow Greene (1997) and calculate the marginal effects to understand the economic
significance of the CFA designation. The marginal effect of the CFA designation is -0.38%,
-1.18%, 1.07%, and 0.49%, when Job Mobility is equal to 0 through 3, respectively. In
comparison, the corresponding expected probability for the four outcomes of Job Mobility is
2.20%, 11.09%, 83.77%, and 2.94%, respectively.
Turning to the performance measures, ACCURACY and INFORATIO both increase the
chance of favorable career outcomes when switching among brokerage firms, with significance
levels of 1% and 10%, respectively. Our result for ACCURACY is consistent with Hong and
Kubik (2003). The marginal effect of forecast accuracy is -0.01%, -0.04%, 0.04%, and 0.02%
when Job Mobility is equal to 0 through 3, respectively, whereas the same marginal effect of
recommendation performance is -0.01%, -0.03%, 0.29%, and 0.13%, respectively. Thus, the
economic significance of recommendation performance is as important as that of forecast
accuracy.
With respect to other significant independent variables, the chances of a favorable career
outcome declines with experience and the number of research reports, and increases with the
number of companies covered. Although we do not tabulate here the marginal effects of these
variables for brevity, CFA has the largest marginal effect among all the variables. In column (2)
of Table 6, we present the results when we include the measures of analyst behavior in Equation
(4). None of the four measures of analyst behavior is significant at conventional levels.
To summarize, the CFA designation significantly increases the probability of favorable
career outcomes as measured by mobility to larger brokerage firms. Because larger firms are
28
likely to offer higher pay [Hong and Kubik (2003)], our results suggest that completing the CFA
program benefits analyst compensation.
4.6. Robustness Analysis
To determine whether our results are robust, we perform a battery of sensitivity tests.
We use different methods to address the selection bias embedded in obtaining the CFA
designation. As someone may concern that our above approach of using fixed effects is too
conservative to deal with the selection bias, we combine Heckman’s (1979) two-step procedure
with the fixed-effects model as in Fich and Shivdasani (2005). Specifically, in the first step, we
estimate the following probit model, where Selection equals one for all the sample years during
which an analyst is a CFA charterholder by the end of our sample period, and zero otherwise:
0 1 2 1 3 1 4 1
5 6 1 7 1 8 1
9 1 10 1 11 1 .
t t t t
t t t
t t t t
Selection a a FEMALE a IISTAR a WSJSTAR a EXPERIENCE
a COVERAGE a NREPORT a NCOMPANY a BROKERSIZE
a COMPANYSIZE a INFORATIO a ACCURACY
(5)
We use Selection instead of CFA as the dependent variable because becoming a CFA
charterholder is a non-reversible event. If we were to use CFA as the dependent variable in
Equation (5), we would have to assume that an analyst could make the decision to become a
CFA charterholder even after they have obtained the designation, which is impossible. In the
second step, per Maddala (1983) we construct a hazard ratio based on the inverse Mill’s ratio
resulting from Equation (5), and we include the hazard ratio in Equation (2) with analyst-fixed
effects.
Table 7 reports the results of the second-step estimation. Interestingly, the estimated
coefficients on the hazard ratio are not significantly different zero in each of the specifications.
In unreported results, without controlling for analyst-fixed effects in the second-step estimation,
29
the coefficient estimates on the hazard ratios are all significant at the 1% level. This evidence
suggests that, after controlling for time-invariant characteristics with an analyst-fixed effect, the
selection bias due to time-varying characteristics does not pose a severe concern here.
Consequently, the estimation results from the two-step fixed-effect approach do not differ much
from the estimation results from the one-step fixed-effect model. In particular, the CFA program
effect on analyst performance is estimated to be significantly positive with a point estimate
around 2.60 to 2.70.
In addition to the above Heckman’s two-step approach, we conduct several other
robustness checks. For brevity we do not report the results in the text and they are available
upon request. First, we use two alternate tests to control for the potential selection bias. We use
CFA as the dependent variable in the first-step probit model, i.e., Equation (5), and repeat the
second step. Also, instead of using Heckman’s (1979) two-step procedure, we directly include
Selection as one additional explanatory variable in Equation (2). If CFA analysts perform better
only because they are different from non-charterholders, the significance of CFA should
disappear with the inclusion of Selection. The results from both the two alternate tests are similar
to the results reported above for the fixed-effects model.
Second, we conduct tests with a variety of other performance measures. We use
alternative methodologies to measure recommendation performance and risk-taking behavior,
such as the value-weighted analyst portfolio, the market model, and the Fama-French (1993)
three-factor model. Because analysts cover related industries, we create an analyst-specific index
by matching the stocks in individual analyst portfolios with industry indexes based on two-digit
SIC codes. We replace the market index with the analyst-specific industry indexes in the factor
models to control for industry momentum. To address the potential effect of bias on analyst
30
performance, we exclude IPO research coverage in cases in which the brokerage house of an
analyst is the lead underwriter. None of the performance measurements qualitatively affect our
results.
Third, we test different model specifications. Because some of the dependent variables
are fractions, e.g., PCTSELL, we also perform logistic regressions for these variables. Moreover,
in addition to panel regressions, we also use Fama-MacBeth (1973) regressions to examine
performance and behavior. These different specifications do not modify our conclusions.
Chevalier and Ellison (1999) use several measures to examine the risk-taking behavior of
mutual fund managers, namely, residual return standard deviations from the market model
regression in which the mutual fund returns are the dependent variables, and absolute deviations
of market betas and residual return standard deviations of individual managers from the means of
these two variables across all managers within a year. Similarly, we examine the same measures
for analysts. The untabulated results are similar to those for the residual return standard
deviation.
Fourth, we apply different methodologies to investigate the improvement rate in
performance. For each analyst who obtains the CFA designation during our sample period, we
find a matched analyst based on performance in the third year before the CFA analysts obtain
their designation. We include only the matched analysts who do not become CFA charterholders
by the end of 2000. We then compare the performance improvement rate of the matched analysts
in the three years around the year that the corresponding CFA analysts finish the CFA program.
This method is conservative because the matched sample may include analysts who are CFA
candidates and who attain their designation after 2000. In contrast to our findings for CFA
31
analysts in Section 4.3, the improvement rate of matched analysts is not significantly different
between the first and last three years of the performance measurement period.
Instead of using regressions, another way to examine the performance improvement rate
around the CFA designation is to compare the performance of the same group of analysts before
and after the designation. Because this approach requires that analysts survive a minimum
number of years, it results in a very small sample. For example, the sample for the three years
around the designation would require inclusion in our sample for seven consecutive years and
would leave us with only seven analysts. Further, this approach cannot address the potential
influence of other factors such as experience. Nonetheless, we use this approach to examine
whether there is any consistent performance improvement in the years around the designation.
We find performance improvement in the pre-designation period but little performance
improvement in the post-designation period. The results are statistically insignificant, which is
not surprising given the small sample.
We examine the change in performance for the period from three years before to three
years after the designation. The choice of three-year lag can be arbitrary. Alternative choices of
lags yield similar results.
It is possible that individual forecasts and recommendations do not occur randomly
across firms or over time. For example, Stickel (1990) and Welch (2000) find evidence of
herding in forecasts and recommendations, respectively. Interdependence across forecasts or
recommendations is likely to introduce cross-sectional correlation in performance measures and,
in turn, inflate test statistics. To address this potential problem, we exclude the forecasts within
three days of any prior forecast issued for the same stock. We also exclude recommendations
with overlapping time spans between the recommendation and revision dates with any prior
32
recommendation for the same stock. While the resulting sample is a much smaller subset of the
original sample, we obtain similar results from this smaller sample.
For job mobility, we define top brokerage firms differently using other percentiles or the
annual IPO deal volume. We find similar results. The I.I. magazine argues that “because of
ratings by this magazine and others, research is the most closely monitored Wall Street field of
all, making it eminently clear who the outstanding analysts are, male or female” [Galant (1996)].
We thus modify Equation (3) by replacing the dependent variable with an indicator variable that
equals one if analysts are I.I. stars in the subsequent year and zero otherwise. We find that the
CFA designation has no impact on increasing the probability of becoming an I.I. star.
We also estimate Equation (4) by adding analyst and brokerage firm fixed effects and
find similar results. Because the coefficient estimates of fixed effect probit models are
inconsistent [e.g., Greene (1997) and Greene (2004)], the results are not presented in the paper
but are available upon request.
Given the significant benefit of the CFA designation on investment recommendations, a
natural question is whether the designation also has similar effects on earnings forecasts. In
untabulated results, we do not find any effect of the CFA program on analyst performance and
behavior as reflected in earnings forecasts: ACCURACY, BOLDNESS, and OPTIMISM. Our
results are somewhat different from De Franco and Zhou (2009), probably due to sampling
differences.
5. Conclusion
Sell-side analysts are prominent in the investment process. With the onset of market
malaise in 2000 and following the $1.4 billion settlement between regulators and the largest ten
33
investment banks over the exchange of biased analyst research for investment banking business,
investors have called for certification of analysts on investment knowledge and ethics training.
Using a comprehensive sample of investment recommendations and earnings forecasts over the
period from 1994 to 2000, we investigate whether the Chartered Financial Analyst (CFA)
program affects the performance, behavior, and career outcomes of analysts.
We find that the CFA program significantly benefits analyst recommendation
performance. Specifically, by completing the CFA program analysts can boost the performance
of their recommendation portfolios by about seven percentage points in annualized risk-adjusted
return. CFA charterholders who cover smaller companies and companies with less overall
analyst coverage experience even greater performance improvements, consistent with the idea
that the benefits of the CFA program are larger for analysts that face a more opaque information
environment. Focusing on those analysts that complete the CFA program during our sample
period, we find that their performances substantially improve while they are going through the
program as candidates, but such improvements appear to stop upon their completing the
program. Because we control for experience throughout our paper, these effects are likely
attributable to the learning from the CFA program curriculum rather than to the accumulated
experience as required by the CFA program.
Further, the CFA program significantly improves analyst behavior by reducing risk-
taking and bias in recommendations. For example, going through the CFA program increases the
proportion of negative recommendations made by analysts by about 15%. The CFA designation
also significantly increases the probability of favorable career outcomes as measured by mobility
to larger brokerage firms. Because larger firms are likely to offer higher compensation, mobility
34
to larger firms should increase analyst compensation. Thus, the designation is likely to benefit
analyst compensation.
In conclusion, the CFA program substantially benefits the performance, behavior, and
compensation of a large population of analysts. Given the importance of analyst research, the
documented impact of the CFA program cannot be underestimated and can rationalize spending
considerable resources in preparing and administrating the CFA exams. The fact that the CFA
program significantly reduces analyst bias also lends support to the increased practice of
including ethics training in the curriculum of education and certification programs, and suggests
that the CFA program can be a valuable tool to reduce analyst bias.
35
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39
Appendix. The Construction of ACCURACY, BOLDNESS, and OPTIMISM,
Let Fi,j,t be the last earnings per share (EPS) forecast of year-end earnings issued by
analyst i on company j between January 1st and July 1st of year t. Let the actual year-t EPS of
company j be Aj,t, let the company’s stock price be Pj, and let F-i,j,t be the average Fi,j,t made by all
other analysts except analyst i. Thus, F-i,,j,t is a measure of the consensus forecast.
We calculate analyst forecast error as | Fi,j,t - Aj,t | / Pj and an analyst’s deviation from
consensus as | Fi,j,t - F-i,j,t |. We can then construct ACCURACY and BOLDNESS according to the
following procedure, which we illustrate using the case of ACCURACY. First, all analysts that
cover company j in year t are assigned a ranking based on their forecast errors for that company.
For example, the best analyst (the one with the smallest forecast error) receives a rank of 1, the
second-best receives a rank of 2, and so on. If more than one analyst has the same forecast error,
each analyst in the tie receives the midpoint value of the ranks they take up. Second, we calculate
the score measure
, ,, ,
,
1100 [ ] 100
1i j t
i j tj t
RankScore
N
,
where Nj,t is the number of analysts who cover company j in year t. The relative accuracy
measure, ACCURACY, is the average accuracy scores over all the companies covered by analyst i
in year t.
We also create an indicator variable Ii,j,t that equals one if Fi,j,t - F-i,j,t > 0. The relative
optimism measure, OPTIMISM, is the average of these indicator variables over all the companies
covered by analyst i.
40
Table 1. Variable Definitions This table defines the variables in our paper. All variables are calculated within a calendar year. See the Appendix for details on the construction of ACCURACY, BOLDNESS and OPTIMISM. We measure performance and risk-taking behavior of individual analysts using the Carhart (1997) four-factor model regression in which the daily returns on recommendation portfolios of individual analysts are regressed on the CRSP value-weighted NYSE/AMEX/NASDAQ market index returns in excess of the three-month T-bill returns, size, book-to-market, and momentum factors. We follow Li (2005) and Emery and Li (2009) to create an analyst’s recommendation portfolio which is made up of long (and short) positions in stocks rated 1 or 2 (and 4 or 5) by the analyst. Stocks are added to the portfolio on the recommendation date and removed from the portfolio on the date of any revision to the rating of 3.
CFA Dummy variable that equals one for the years that an analyst is a CFA charterholder, and zero otherwise.
ALPHA The intercept of the Carhart (1997) model regression in basis points.
INFORATIO t-statistic for the intercept of the Carhart (1997) model regression.
RESIRISK Residual return standard deviation of the Carhart (1997) model regression in basis points.
PCTSELL Percentage of “sells” and “underperforms” among the analyst’s recommendations.
ACCURACY Hong et al.’s (2000) measure of relative earnings forecast accuracy.
BOLDNESS Hong et al.’s (2000) measure of boldness in earnings forecasts.
OPTIMISM Hong and Kubik’s (2003) measure of relative earnings forecast optimism on a one-year basis.
IISTAR and WSJSTAR
Dummy variable that equals 1 if the analyst is an Institutional Investor All-American or Wall Street Journal All-Star analyst, respectively, and 0 otherwise.
NREPORT Logarithm of the number of research reports that an analyst issues.
NCOMPANY Logarithm of the number of companies that an analyst covers.
BROKERSIZE Logarithm of the number of analysts employed by the analyst’s house. For analysts who switch houses within a given year, we use the time-weighted average of the two houses.
COMPANYSIZE Logarithm of the mean market capitalization of the companies that an analyst covers at the end of the prior calendar year.
COVERAGE Logarithm of the average number of analysts that cover the same companies that an analyst covers at the end of the prior calendar year.
EXPERIENCE Logarithm of the number of years that an analyst has been submitting reports to I/B/E/S.
41
Table 2: Summary Statistics of the Variables
This table reports the characteristics of sample analysts. See Table 1 for variable definitions. The sample consists of 12,270 analyst-year observations for 3,474 unique analysts. Panel A shows the number of analysts in each category and the number as a percentage of the overall sample of 3,474 analysts. Panels B to D report the summary statistics including the mean, standard deviation, and selected key percentiles (1% to 99%) of the variables for the full sample, the CFA subsample, and the non-CFA subsample, respectively. We conduct a t-test for the difference in the means between CFA charterholders and non-charterholders and mark in Panels C and D those variables that have significant t-test results. ***, **, and * indicate that the t-statistics are significant at the 1%, 5%, and 10% levels, respectively. The sample period is from January 1994 through December 2000. Panel A. CFA Composition
CFA Non-CFA Sum
Number of analysts 1,259 (36.24%) 2,215 (63.76%) 3,474 (100.00%) Panel B. Analyst Characteristics: Full Sample (12,270 observations)
Variable Mean Stdev 1% 10% 25% Median 75% 90% 99% ALPHA (basis points) 2.20 15.84 -40.38 -13.15 -5.24 1.39 8.67 19.34 51.05INFORATIO 0.17 1.06 -2.26 -1.19 -0.54 0.16 0.88 1.52 2.65 ACCURACY 50.13 15.46 0 31.76 41.68 50.70 59.43 67.5 90.91RESIRISK (basis points) 12.26 8.59 2.88 4.90 6.79 10.04 14.95 21.72 45.38BOLDNESS 50.19 15.35 0 32.22 41.73 50.76 59.3 67.31 93.20PCTSELL (%) 4.18 11.39 0 0 0 0 0 12.86 55.56OPTIMISM 48.01 21.25 0 23.08 33.33 50 60 75 100 IISTAR (%) 12.77 33.38 0 0 0 0 0 100 100 WSJSTAR (%) 8.28 27.56 0 0 0 0 0 0 100 EXPERIENCE 1.62 0.69 0 0.69 1.10 1.61 2.20 2.48 2.83 COVERAGE 2.05 0.50 0.69 1.39 1.73 2.09 2.41 2.67 3.06 NREPORT 2.31 0.71 0.69 1.39 1.79 2.3 2.77 3.18 3.95 NCOMPANY 2.46 0.70 0.69 1.61 2.10 2.56 2.94 3.22 3.89 BROKERSIZE 3.30 1.02 0 1.95 2.56 3.50 4.11 4.49 4.88 COMPANYSIZE ($Billion) 0.74 1.50 -2.60 -1.16 -0.34 0.73 1.73 2.73 4.23
42
Panel C. Analyst Characteristics: CFA Subsample (3,955 observations)
Variable Mean Stdev 1% 10% 25% Median 75% 90% 99% ALPHA (basis points) 1.93 14.57 -36.32 -11.18 -4.41 1.23 7.40 16.08 49.07INFORATIO 0.16 1.05 -2.23 -1.20 -0.53 0.16 0.87 1.48 2.65 ACCURACY 50.01 15.33 0 31.53 41.67 50.61 59.36 67.29 89.64RESIRISK (basis points)*** 10.70 8.04 2.62 4.37 5.94 8.61 12.82 18.85 42.65BOLDNESS 50.19 15.15 0 32.14 42.09 50.96 59.28 66.67 95.45PCTSELL (%)*** 4.75 11.54 0 0 0 0 4.76 14.29 53.85OPTIMISM 48.16 20.68 0 25 33.33 50 60 73.33 100 IISTAR (%) 12.64 33.24 0 0 0 0 0 100 100 WSJSTAR (%)*** 10.27 30.35 0 0 0 0 0 100 100 EXPERIENCE*** 1.83 0.66 0 0.69 1.39 1.95 2.40 2.56 2.83 COVERAGE*** 2.03 0.50 0.69 1.37 1.70 2.06 2.38 2.65 3.06 NREPORT*** 2.38 0.71 0.69 1.39 1.95 2.40 2.83 3.26 3.97 NCOMPANY*** 2.58 0.68 0.69 1.61 2.20 2.71 3.00 3.33 3.90 BROKERSIZE*** 3.19 1.04 0 1.79 2.48 3.26 4.04 4.47 4.88 COMPANYSIZE ($Billion) 0.76 1.49 -2.60 -1.11 -0.33 0.78 1.75 2.72 4.22 Panel D. Analyst Characteristics: Non-CFA Subsample (8,315 observations)
Variable Mean Stdev 1% 10% 25% Median 75% 90% 99% ALPHA (basis points) 2.33 16.41 -43.34 -14.19 -5.27 1.49 9.27 20.82 51.91INFORATIO 0.17 1.06 -2.26 -1.18 -0.55 0.15 0.89 1.54 2.65 ACCURACY 50.18 15.52 0 31.90 41.69 50.74 59.46 67.58 92 RESIRISK (basis points)*** 13.00 8.75 3.00 5.25 7.35 10.85 15.81 23.08 46.96BOLDNESS 50.20 15.44 0 32.27 41.65 50.65 59.33 67.82 92.86PCTSELL (%)*** 3.91 11.30 0 0 0 0 0 12.5 55.56OPTIMISM 47.95 21.51 0 22.22 33.33 50 60 75 100 IISTAR (%) 12.83 33.45 0 0 0 0 0 100 100 WSJSTAR (%)*** 7.34 26.07 0 0 0 0 0 0 100 EXPERIENCE*** 1.52 0.68 0 0.69 1.10 1.61 2.08 2.48 2.77 COVERAGE*** 2.06 0.50 0.69 1.39 1.75 2.11 2.43 2.67 3.06 NREPORT*** 2.28 0.71 0.69 1.39 1.79 2.30 2.77 3.18 3.93 NCOMPANY*** 2.40 0.70 0.69 1.39 1.95 2.48 2.89 3.18 3.89 BROKERSIZE*** 3.36 1.00 0.69 1.95 2.64 3.60 4.14 4.50 4.88 COMPANYSIZE ($Billion) 0.73 1.50 -2.59 -1.18 -0.34 0.72 1.72 2.74 4.23
43
Table 3. Analyst Performance and the CFA Program This table reports the results of estimating the following fixed effects model in which the dependent variables are measures of analyst performance: ALPHA and INFORATIO.
0 1 2 3 4
5 6 7 8 9
10
*t t t t t
t t t t t
t
Dependent Variable a a CFA a CFA InformationUncertainty a IISTAR a WSJSTAR
a EXPERIENCE a COVERAGE a NREPORT a NCOMPANY a BROKERSIZE
a COMPANYSIZE Year Effects Analyst Effects Brokerage Firm E
.tffects
The measure of information uncertainty is the average size of companies that an analyst covers and the average number of analysts covering the companies that an analyst covers. The coefficient estimates are in basis points when ALPHA is the dependent variable. See Table 1 for variable definitions. Heteroscedasticity-consistent t-statistics are reported in parentheses. ***, **, and * indicate that t-statistics are significant at the 1%, 5%, and 10% levels, respectively. The data are from January 1994 through December 2000.
ALPHA INFORATIO ALPHA INFORATIO ALPHA INFORATIO (1) (2) (3) (4) (5) (6)
CFA t 2.86 *** 0.15 *** 3.55 *** 0.17 *** 8.50 *** 0.41 *** (3.70 ) (3.23 ) (3.96 ) (3.45 ) (3.15 ) (3.09 ) CFA t*COMPANYSIZE t -0.95 *** -0.02 (-2.68 ) (-1.20 ) CFA t* COVERAGE t -2.75 *** -0.12 ** (-2.40 ) (-2.04 ) IISTAR t 0.13 -0.01 0.10 -0.01 0.10 -0.01 (0.23 ) (-0.08 ) (0.17 ) (-0.10 ) (0.17 ) (-0.11 ) WSJSTAR t -1.10 *** -0.16 *** -1.10 *** -0.16 *** -1.09 *** -0.16 *** (-2.48 ) (-3.84 ) (-2.49 ) (-3.84 ) (-2.47 ) (-3.83 ) EXPERIENCE t -0.37 0.02 -0.45 0.02 -0.44 0.02 (-0.38 ) (0.28 ) (-0.47 ) (0.25 ) (-0.45 ) (0.23 ) COVERAGE t -0.85 0.03 -0.80 0.03 0.05 0.07 (-1.22 ) (0.66 ) (-1.16 ) (0.69 ) (0.06 ) (1.46 ) NREPORT t -0.12 0.01 -0.11 0.01 -0.13 0.01 (-0.46 ) (0.48 ) (-0.41 ) (0.50 ) (-0.48 ) (0.47 ) NCOMPANY t -0.90 0.01 -0.97 0.01 -0.91 0.01 (-1.32 ) (0.13 ) (-1.42 ) (0.08 ) (-1.33 ) (0.12 ) BROKERSIZE t -0.95 -0.11 ** -0.92 -0.11 ** -0.92 -0.11 ** (-1.41 ) (-2.35 ) (-1.37 ) (-2.34 ) (-1.36 ) (-2.32 ) COMPANYSIZE t 0.17 -0.02 0.46 * -0.01 0.16 -0.02 (0.75 ) (-1.06 ) (1.72 ) (-0.52 ) (0.72 ) (-1.08 ) Intercept 3.83 0.04 5.54 0.09 2.73 -0.01 (0.52 ) (0.09 ) (0.76 ) (0.19 ) (0.37 ) (-0.02 ) R-square 0.40 0.32 0.40 0.32 0.40 0.32 N 12,270 12,270 12,270 12,270 12,270 12,270
44
Table 4. The CFA Program and Improvement Rate in Analyst Performance
This table reports on the relation between the changes in analyst performance and the changes in analyst characteristics for the three years before and after they obtain the CFA designation, respectively, for CFA analysts who obtain their designation over the 1995 to 2000 period. We report the results of estimating the following model
0 1 2 3
4 5 6 .t t t t
t t t t
Dependent Variable a a YTOCFA a EXPERIENCE a NREPORT
a NCOMPANY a BROKERSIZE a COMPANYSIZE
See Table 1 for variable definitions. YTOCFA measures the number of years from the designation. For example, for the third year before (after) the designation, it equals -3 (+3), respectively. Columns (1)-(2) report the results for the three years before the designation. Columns (3)-(4) report the results for the three years after the designation. The coefficient estimates are in basis points when ALPHA is the dependent variable. The models are estimated with ordinary least squares. Heteroscedasticity-consistent t-statistics are reported in parentheses. ***, **, and * indicate that t-statistics are significant at the 1%, 5%, and 10% levels, respectively. The data are from January 1994 through December 2000.
Before the Designation After the Designation ΔALPHA t ΔINFORATIO t ΔALPHA t ΔINFORATIO (1) (2) (3) (4)
YTOCFA t 2.17 ** 0.23 ** 1.11 0.07 (2.03 ) (2.12 ) (1.06 ) (0.88 ) Experience t 1.27 0.04 0.40 0.06 (0.69 ) (0.25 ) (0.28 ) (0.50 ) ΔCOVERAGE t 2.15 0.11 4.52 * 0.09 (0.62 ) (0.32 ) (1.85 ) (0.56 ) ΔNREPORT t 1.71 0.16 0.15 0.03 (1.23 ) (1.19 ) (0.13 ) (0.30 ) ΔNCOMPANY t -2.62 -0.31 0.39 0.16 (-0.68 ) (-0.81 ) (0.14 ) (0.98 ) ΔBROKERSIZE t -2.36 -0.20 -1.92 -0.13 (-0.57 ) (-0.49 ) (-0.91 ) (-1.00 ) ΔCOMPANYSIZE t 1.99 0.22 -1.19 -0.05 (1.35 ) (1.51 ) (-1.43 ) (-0.86 ) Intercept 0.58 0.23 -0.79 -0.04 (0.15 ) (0.59 ) (-0.25 ) (-0.19 ) R-square 0.01 0.01 0.01 0.01 N 415 415 878 878
45
Table 5. Analyst Behavior and the CFA Program This table reports the results of estimating the following fixed effects model in which the dependent variables are measures of analyst risk-taking and bias behavior.
0 1 2 3 4
5 6 7 8
9 .
t t t t t
t t t t
t t
Dependent Variable a a CFA a IISTAR a WSJSTAR a EXPERIENCE
a COVERAGE a NREPORT a NCOMPANY a BROKERSIZE
a COMPANYSIZE Year Effects Analyst Effects Brokerage Firm Effects
See Table 1 for variable definitions. The coefficient estimates are in basis points when RESIRISK is the dependent variable. The coefficient estimates are in percentages when PCTSELL is the dependent variable. Heteroscedasticity-consistent t-statistics are reported in parentheses. ***, **, and * indicate that t-statistics are significant at the 1%, 5%, and 10% levels, respectively. The data are from January 1994 through December 2000.
Risk-Taking Behavior Bias Behavior RESIRISKt PCTSELLt (1) (2)
CFA t -0.49 *** 0.64 * (-2.38 ) (1.93 ) IISTAR t 0.01 0.55 ** (-0.02 ) (2.15 ) WSJSTAR t 0.23 ** -0.16 (2.17 ) (-0.97 ) EXPERIENCE t -2.73 *** 0.60 (-8.19 ) (1.31 ) COVERAGE t 0.12 0.89 *** (0.54 ) (3.08 ) NREPORT t 0.33 *** 1.47 *** (3.62 ) (9.31 ) NCOMPANY t -5.96 *** -0.68 ** (-24.21 ) (-2.21 ) BROKERSIZE t 0.11 -0.26 (0.55 ) (-0.66 ) COMPANYSIZE t -0.35 *** -0.14 (-4.77 ) (-1.41 ) Intercept 27.87 *** -0.64 (11.07 ) (-0.17 ) R-square 0.76 0.72 N 12,270 12,270
46
Table 6. Mobility among Brokerage Firms This table reports the results of estimating the following ordered probit model about the mobility of analysts among brokerage firms:
0 1 1 2 1 3 1
4 1 5 1 6 1
7 1 8 1
9 1
Pr
t t t
t t tt
t t
t
a a CFA a IISTAR a WSJSTAR
a ACCURACY a INFORATIO a EXPERIENCEJob Mobility
a NREPORT a NCOMPANY
a COMPANYSIZE Year Effects
.
The dependent variable, Job Mobility, is 0 if an analyst was working for a brokerage firm that was above the 95th percentile in terms of the number of analysts employed in year t-1 and moves in year t to a firm below the 95th percentile; 1 if an analyst switches between firms below the 95th percentile or switches between firms above the 95th percentile from year t-1 to year t; 2 if an analyst does not change brokerage firms; and 3 if an analyst was working for a firm below the 95th percentile in year t-1 and moves to a firm above the 95th percentile in year t. See Table 1 for variable definitions. ***, **, and * indicate that t-statistics are significant at the 1%, 5%, and 10% levels, respectively. The data are from January 1994 through December 2000.
Ordered Probit Model
Coefficientt-statistic Coefficientt-statistic
CFA t-1 0.07*** (2.43) 0.07*** (2.43)
IISTAR t-1 -0.06 (-1.38) -0.06 (-1.38)
WSJSTAR t-1 -0.01 (-0.13) -0.01 (-0.12)
ALPHA t-1 0.02* (1.69) 0.02* (1.70)
ACCURACY t-1 0.01*** (2.62) 0.01*** (2.43)
EXPERIENCE t-1 -0.07*** (-2.81) -0.07*** (-2.84)
COVERAGE t -0.01 (-0.11) -0.01 (-0.13)
NREPORT t-1 -0.15*** (-5.77) -0.15*** (-5.79)
NCOMPANY t-1 0.12*** (4.11) 0.12*** (4.13)
COMPANYSIZE t-1 0.01 (0.49) 0.01 (0.55)
RESIRISK -0.01 (-1.09)
BOLDNESS -0.01 (-0.17)
PCTSELL 0.02 (0.42)
OPTIMISM -0.04 (-0.60)
INTERCEPT 1.89*** (15.31) 1.90*** (14.32)
Pseudo-RSQ 0.13 0.15
N 10,129 10,129
47
Table 7. Analyst Performance and the CFA Program: Controlling for Selection Bias Using Heckman’s (1979) Two-Step Approach
This table reports the results of estimating the following fixed effects model in which the dependent variables are measures of analyst performance: ALPHA and INFORATIO.
0 1 2 3 4
5 6 7 8 9
10
*t t t t t
t t t t t
t
Dependent Variable a a CFA a CFA InformationUncertainty a IISTAR a WSJSTAR
a EXPERIENCE a COVERAGE a NREPORT a NCOMPANY a BROKERSIZE
a COMPANYSIZE Year Effects Analyst Effects Brokerage Firm E
.tffects
The hazard ratio LAMBDA is estimated from the following first-step regression in the Heckman’s (1979) two-step approach to deal with the endogenous selection bias:
0 1 2 1 3 1 4 1
5 6 1 7 1 8 1
9 1 10 1 11 1 .
t t t t
t t t
t t t t
Selection a a FEMALE a IISTAR a WSJSTAR a EXPERIENCE
a COVERAGE a NREPORT a NCOMPANY a BROKERSIZE
a COMPANYSIZE a INFORATIO a ACCURACY
Here, Selection equals one for all the sample years during which an analyst is a CFA charterholder by the end of our sample period, and zero otherwise, FEMALE equals one for female analysts and zero for male, and all the other variables are defined in Table 1. The coefficient estimates are in basis points when ALPHA is the dependent variable. Heteroscedasticity-consistent t-statistics are reported in parentheses. ***, **, and * indicate that t-statistics are significant at the 1%, 5%, and 10% levels, respectively. The data are from January 1994 through December 2000.
ALPHA INFORATIO ALPHA INFORATIO ALPHA INFORATIO (1) (2) (3) (4) (5) (6)
CFA t 2.74 *** 0.13 ** 2.62 *** 0.13 ** 2.60 *** 0.13 ** (3.59 ) (2.41 ) (3.47 ) (2.47 ) (3.45 ) (2.45 ) IISTAR t -0.30 0.02 0.43 0.02 (-0.29 ) (0.31 ) (0.53 ) (0.31 ) WSJSTAR t -0.34 -0.17 ** -1.32 ** -0.17 *** -1.36 ** -0.17 *** (-0.31 ) (-2.26 ) (-2.00 ) (-3.73 ) (-2.12 ) (-3.82 ) EXPERIENCE t 2.00 -0.95 -1.15 -0.05 -1.26 -0.05 (0.64 ) (-0.24 ) (-0.85 ) (-0.53 ) (-1.03 ) (-0.56 ) COVERAGE t -2.02 0.05 -0.36 2.95e-3 (-1.24 ) (0.44 ) (-0.58 ) (0.07 ) NREPORT t 0.14 0.02 0.10 0.02 (0.40 ) (0.95 ) (0.31 ) (0.73 ) NCOMPANY t -1.08 -0.03 -1.41 ** -0.03 -1.33 ** -0.01 (-1.58 ) (-0.50 ) (-2.29 ) (-0.66 ) (-2.49 ) (-0.31 ) BROKERSIZE t -1.26 -0.03 (-1.10 ) (-0.40 ) COMPANYSIZE t 0.44 -0.03 (1.01 ) (-0.89 ) LAMBDA 12.41 -0.19 -2.22 -0.18 -2.77 -0.15 (0.90 ) (-0.20 ) (-0.50 ) (-0.56 ) (-0.74 ) (-0.56 ) R-square 0.37 0.30 0.37 0.30 0.37 0.30 N 12,270 12,270 12,270 12,270 12,270 12,270
