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THE IMPACT OF BEHAVIORAL FINANCE ON INVESTMENT
DECISIONS BY INVESTMENT BANKS IN KENYA
BY
ANNETTE NAKAMYA
UNITED STATES INTERNATIONAL UNIVERSITY-AFRICA
SUMMER 2020
THE IMPACT OF BEHAVIORAL FINANCE ON INVESTMENT
DECISIONS BY INVESTMENT BANKS IN KENYA
BY
ANNETTE NAKAMYA
A Research Project Report Submitted to the Chandaria School of
Business in Partial Fulfillment of the Requirements for the Degree of
Master’s in Business Administration (MBA)
UNITED STATES INTERNATIONAL UNIVERSITY-
AFRICA
SUMMER 2020
i
STUDENT DECLARATION
I the undersigned, declare that this is my original work and has not been submitted to any
other college, institution or university other than United States International University-
Africa for academic credit
Signed: Date:______________________
Annette Nakamya (ID 653919)
This project has been presented for examination with my approval as the appointed
supervisor.
Signed: Date:_________________________
Dr. Francis Mambo Gatumo,
Signed: Date:__________________________
Dean, Chandaria School of Business
ii
COPYRIGHT
All rights reserved. No part of this dissertation report may be photocopied, recorded or
otherwise reproduced, stored in retrieval system or transmitted in any electronic or
mechanical means without prior permission of USIU-Africa or the author.
Annette Nakamya © 2020.
iii
ABSTRACT
The purpose of this study was to establish the impact of behavioral finance on investment
decisions by investment banks in Kenya. This research was guided by the following
research questions. What are the effects of mental accounting on the investment decisions
by investment banks in Kenya?, What is the impact of framing on investment decisions by
Kenyan Investment Banks? And What are the effects of heuristics on investment decisions
by Investment Banks in Kenya?
The study adopted a causal research design. The respondents for the data collection process
consisted of investment analysts, investment brokers, investment underwriters, the sales
team and tellers working in investment banks in Kenya. The expected sample size was 76
respondents but only 64 filled the questionnaires and a response rate of 84.2% was
achieved. Primary data was collected through semi structured and Likert scale
questionnaires for the purpose of this study. Prior to the data collection exercise, a pilot test
of the tool was administered to 10 respondents from the sample frame and the tool was
confirmed in accordance with the feedback received. The collected data was cleaned, coded
and analyzed using the Statistical Package for Statistical Sciences software (SPSS). The
data analysis techniques used for this research included descriptive and inferential statistics.
The results were presented using tables and figures.
The first research question was to assess the effects of mental accounting on investment
decision making, the Pearson correlation results showed a positive correlation which
implied that mental accounting factors and investment decision making have a linear
relationship but not enough statistical significance showed since the R value was less than
0.5. This presented that mental accounting and investment decision making have a direct
relationship and but with moderate significance and vice versa.
The second research question aimed at establishing the impact of framing on investment
decision making. The results indicated a positive relationship which implied that framing
and investment decision making were positively correlated with moderate statistical
significance as the R value was below 0.5.
iv
To determine the effects of heuristics on investment decision making, the Pearson
correlation results showed a positive relationship which implied that heuristics biases and
investment decision making were positively correlated with a moderate statistical
significance. This meant that the effects of heuristics biases had a direct relationship with
the investment decision making.
The study concluded a higher presence behavioral finance biases which include mental
accounting, framing and heuristics lead to an increase in investment decision making.
Recommendations for further research suggested use of other research design models such
as action research and semi-mixed method design.
The study also recommended that further researchers should address how behavioral
finance and investment decisions affect investors by linking investment banks and either
stock brokers, non-dealing online foreign exchange brokers, authorized dealers, fund
managers and REITS managers.
v
ACKNOWLEDGEMENT
First and foremost, I would like to thank God for blessing me with good health and giving
me this opportunity to pursue my Master of Business Administration degree at United
States International University-Africa.
I thank my husband, Michael Mburia Gatari, daughter Janelle Elizabeth Wanjiku Mburia,
son Jeremy Gatari Mburia, mother Betty Kwelyowa Kamya and my parents in law Sarah
and Johnson Gatari for being there for me through this journey. They have been my pillars
of strength and encouragement even through the difficult times.
I am also taking this opportunity to show my appreciation towards Dr. Francis Mambo
Gatumo for guiding me through a journey that has been difficult but filled with excitement
and immense knowledge.
I would also like to thank my family, friends and everyone else who participated and
encouraged me in one way or another through the course of this journey. Getting to this
stage would never have been possible without them. May God bless you all.
vi
DEDICATION
I dedicate this work to my husband Michael Mburia Gatari, my daughter Janelle Elizabeth
Wanjiku Mburia, my son Jeremy Gatari Mburia and the rest of my family members and
friends for their continuous support throughout my Masters studies.
vii
TABLE OF CONTENTS
STUDENT DECLARATIO...………………………………………………………….. i
COPYRIGHT…………………………………………………………………………...ii
ABSTRACT…………………………………………………………………………….iii
ACKNOWLEDGEMENT……………………………………………………………...v
DEDICATION………………………………………………………………………….vi
TABLE OF CONTENTS………………………………………………………….......vii
LIST OF TABLES……………………………………………………………………...ix
LIST OF FUGURES……………………………………………………………………x
CHAPTER ONE………………………………………………………………………...1
1.0 INTRODUCTION…………………………………………………………………1
1.1 Background of the Study.....…………………………………………..…………….1
1.2 Problem Statement..……..……………………………………………..……………7
1.3 Purpose of Study…………….………....……………………………..…..…………8
1.4 Research Questions……….…………....……………………………..…..…………8
1.5 Justification of the Study.……………………………………………..….…………9
1.6 Scope of the Study…....………………………………………….……..….……….11
1.7 Definition of Terms…………………………………………….……….….………11
1.8 Chapter Summary…………………………………………….………….….……...13
CHAPTER TWO…………..…………………………………….………….….………14
2.0 LITERATURE REVIEW………………………………….………….….………14
2.1 Introduction………………………………………………….………….….………14
2.2 Mental Accounting and Investment Decisions……………….………….….……...14
2.3 Framing and Investment Decisions………………………………………….…….19
2.4 Heuristics and Investment Decisions………………………………………….…...23
2.5 Chapter Summary………………………………………………………………….32
CHAPTER THREE……………………………………………………………………33
3.0 RESEARCH METHODOLOGY………………………………………………..33
3.1 Introduction………………………………………………………………………...33
3.2 Research Design…………………………………………………………………...33
3.3 Population and Sampling Design………………………………………………….34
viii
3.4 Data Collection Methods……………………………………………………..…....37
3.5 Research Procedures…………………………………………………………….....38
3.6 Data Analysis Methods…………………………………………………………….39
3.7 Chapter Summary………………………………………………………………….40
CH APTER FOUR……………………………………………………………….…....41
4.0 RESULTS AND FINDINGS……………………………………………………..41
4.1 Introduction………………………………………………………………………...41
4.2 Demographics……………………………………………………………………...41
4.3 Effects of Mental accounting on Investment decisions…………………………....46
4.4 Impact of Framing on Investment decisions..…………………………………...…51
4.5 Effects of Heuristics and Investment decisions…………………………………....56
4.6 Chapter Summary…………………………………………………………...……..70
CHAPTER FIVE………………………………………………………………………71
5.0 DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS………….....71
5.1 Introduction………………………………………………………………………...71
5.1 Summary…………………………………………………………………………...71
5.3 Discussion……………………………………………………………………….....73
5.4 Conclusions……………………………………………………………………...…78
5.5 Recommendations…………………………………………………………….……80
REFERENCES…………………………………………………….…………………...83
APPENDICES……………………………………………………..………....……..….98
Appendix I: Research Letter……………………………………...………………….….98
Appendix II: Nacosti Letter…………………………………….….………………..…..99
Appendix III: Cover Letter…………………………………………………………….101
Apendix IV: Questionnair……………………………………………………………...102
ix
LIST OF TABLES
Table 3.1 Population Distribution…….…………………………………………………35
Table 3.2 Sample Size Distribution …………………………………………………….37
Table 3.3 Results of the Reliability Test on the Study Variables…..…………………. .38
Table 4.1 Age Group……………………………………………………………....…....43
Table 4.2 Level of Education……………. ……………………………………………..44
Table 4.3 Current Job ……………………...………...…………………………………44
Table 4.4 Duration in Investment Banking Practice…….………………………………45
Table 4.5 Mental Accounting and Investment Decisions……………………………….46
Table 4.6 Framing and Investment Decisions……………………….………………….52
Table 4.7 Heuristics and Investment Decisions ………………..................…………….57
Table 4.8 Correlation of Mental Accounting and Investment Decision Making….……62
Table 4.9 Coefficients on Mental Accounting and Investment Decisions……………...63
Table 4.10 Model Summary on Mental Accounting and Investment Decisions………..64
Table 4.11 Correlation of Framing and Investment Decision Making……………….....64
Table 4.12 Coefficients on Framing and Investment Decisions……..……….………....65
Table 4.13 Model Summary of Framing and Investment Decisions………….………...66
Table 4.14 Correlation of Heuristics and Investment Decision Making……...…….......67
Table 4.15 Coefficients on Heuristics and Investment Decisions……..………..……....68
Table 4.16 Model Summary of Heuristics and Investment Decisions……..…………...68
Table 4.17 ANOVA……………………………………………………………………..69
Table 4.18 Model Coefficients………..………………………………………………...70
x
LIST OF FUGURES
Figure 4.1 Response Rate……………………………………………………………….42
Figure 4.2 Gender Distribution………………………………………………………….42
Figure 4.3 Marital Status………………………………………………………………..43
Figure 4.4 Risk Orientation……………………………………………………………..45
Figure 4.5 Evaluate Transactions……………………………………………………….47
Figure 4.6 Ask Customer’s Preference………………………………………………….48
Figure 4.7 Categorize and Code economic outcome….………………………………...48
Figure 4.8 Separate money different mental accounts……………………………….….49
Figure 4.9 Ask about source of funds and its intended use………………………….….49
Figure 4.10 Divide investments between safe and speculative portfolios…..……….….50
Figure 4.11 Thinking about risk through mental accounts .…………………………….51
Figure 4.12 Investment decisions and the way information is being presented..…….…53
Figure 4.13 Reacting to particular opportunities differently..………………………..…54
Figure 4.14 Focus on individual gains and losses…………………………………...….54
Figure 4.15 Inconsistent choices of investment decisions………………………...…….55
Figure 4.16 Feel the pain of loss much more than the pleasure of gains……….……….55
Figure 4.17 Decoy Effect..…………………………………...………………………….56
Figure 4.18 Anchoring and Investment decision making……………………………….58
Figure 4.19 Overstatement and Investment decision making….……….……………….59
Figure 4.20 Representative heuristic and Investment decision making..……………….59
Figure 4.21 Availability heuristic and investment decision making..……….………….60
Figure 4.22 Overconfidence and investment decision making….…………...………….60
Figure 4.23 Ambiguity aversion and investment decision making……….…………….61
xi
ABBREVIATIONS
CAPM : Capital Assets Pricing Model
CFAI : Chartered Financial Analyst Institute
CFI : Corporate Finance Institute
CMA : Capital Markets Authority
EBEEC : Economies of Balkan and Eastern Europe Countries
EMH : Efficient Markets Hypothesis
IBF : Institute of Behavioral Finance
JSE : Johannesburg Stock Exchange
NSE : Nairobi Securities Exchange
OECD : Organization of Economic Co-operation and Development
RBA : Retirements Benefits Authority
REITs : Real Estate Investment Trusts
SPSS : Statistical Package for the Social Sciences
USIU-A : United States International University-Africa
1
CHAPTER ONE
1.0 INTRODUCTION
1.1 Background of the Study
Behavioral finance is the study of the influence of psychological factors on financial
markets evolution (Bogdan, Mester, & Matica, 2018). In other words, financial markets
inefficiency is analyzed in the light of psychological theories and perspectives (Pompian,
2015). It is a relatively recent and high impact paradigm which provides an interesting
alternative to traditional finance. The traditional finance assumes that capital markets are
efficient, investors are rational and it’s not possible to outperform the market over the long-
term (Fama, 1998). Psychological principles of behavioral finance include among others
heuristics and biases, overconfidence, emotion and social forces (Kahneman & Tversky,
2013). An important step for all investors is to understand their financial personality. In
other words, in the posture of investor it is vitally important to understand why you make
certain financial decisions or how you are likely to react in common conditions of
uncertainty (Bogdan et al, 2018). This form of analysis is useful in an attempt to understand
how you can temper the irrational components of investment decisions while still satisfying
your individual preferences and requirements.
Baker and Nofsinger (2010) found that behavioral finance provides a different perspective
which is very complex and unconventional whose paradigm suggests that investment
decision is influenced in a large proportion by psychological and emotional factors. Human
emotional complexity includes the following primary approaches: fear, panic, anxiety,
envy, euphoria, greed, satisfaction, ambition or vanity. Very likely that all these emotions
interfere in certain proportions in a financial investment decision making (Baker &
Nofsinger, 2010). Beyond the rhetorical nuance of the events, financial market
imperfections make us wonder if the traditional financial theory is not just an unrealistic
and incomplete solution to a complex and constantly changing market. Most of the financial
market anomalies cannot be explained using traditional models (Bogdan et al, 2018).
Behavioral finance easily explains why the individual has taken a specific decision, but did
not easily find an explanation about how future decisions will be.
2
Jiang and Li (2019) found that Traditional finance has as a cornerstone, the Efficient
Markets Hypothesis, according to whom, since everyone has access to the same
information, it is impossible to regularly beat the market, because stock prices are efficient
and reflect everything we know as investors. A market in which prices always fully reflect
available information is called efficient (Yang & Li, 2013). Efficient Market Hypothesis
(EMH) assumes that capital market are informationally efficient (Fama, 1998). According
to this father of EMH, market efficiency survives the challenge from the literature on long-
term return anomalies. This is consistent with the market efficiency hypothesis that the
anomalies are chance results. Titan (2015) found that apparent overreaction to information
is about as common as under-reaction, and post-event continuation of pre-event abnormal
returns is about as frequent as post-event reversal. The consistence with market efficiency
prediction is that apparent anomalies can be caused by the methodology and most long-
term return anomalies, and they tend to disappear with reasonable changes in technique
(Bogdan et al, 2018). In contrast, behavioral finance assumes that in some circumstances
financial markets are informationally inefficient (Kahneman & Tversky, 1979).
The modern portfolio theory by Markowitz states that there are uncertainties in the security
market, the investor preference cannot be quantified in terms of choices but with the help
of mean and variance of the returns (Markowitz, 1952). The tradeoff of modern finance is
shown as expected utility theory and is concerned with the choice among the alternatives
that have uncertain outcomes (Bernoulli, 1973; Sommer, 1954). The aim is to attain a
tradeoff between risk and return. The Markowitz (1952) approach helps an investor to
achieve his optimal portfolio position and explains how the diversification reduces the risk.
Capital asset pricing model helps to ascertain the relationship between the systematic risk
and expected return of an asset (Treynor, 1961; Sharpe, 1964; Lintner, 1965; Mossin,
1966). It can be used either to price a single security or an entire portfolio of securities.
These theories considered the market to be efficient and investors to be rational. The
efficiency of the stock markets is questionable, as the various stock market anomalies
remain unanswered. These anomalies that are to be answered are as follows: Why there are
bubbles in the market? Why does the market get crashed? How to prevent these bubbles
3
and crashes? When do these bubbles and crashes actually arise in the market? And, what
factors can be held responsible for these uncertainties?
Zahera and Bansal (2018) found that the answers to these questions can be found if the
psychology of the participants is studied and understood properly. The perfect market
conditions as those discussed in the economics and finance books do not always prevail in
the real stock markets. It was by the year 1980s that the solution to this problem was
searched. The result was in the form of behavioral finance which is an emerging area in the
field of finance. It has answered and explained some of the reasons for the behavioral
changes in the investors that deviate them from the rational decision making (Zahera &
Bansal, 2018).
The New York Stock Exchange incorporates behavioral finance biases to understand and
explain actual investor and market behaviors versus theories of investor behavior. The idea
differs from traditional finance, which is based on assumptions of how investors and
markets should behave (Pompian, 2015). According to Barberis and Thaler (2003)
behavioral finance has two building blocks, limits to arbitrage, which reasons that it can be
diffcult for rational traders to undo the dislocations caused by less rational traders and
psychology. Thaler (1980) explained the prospect theory based on an alternative descriptive
theory. Instead of considering investors acting in a cold, irrational way, he argues that
investors act under the influence of behavioral biases often leading to less than optimal
decisions. The theory and assumptions of traditional finance and modern finance have been
challenged by several scholars from time to time. But the theories of behavioral finance
have also been subject to various doubts and challenges. Thaler (1999) explained several
instances where the theories of modern finance give no answer and here the assumptions of
behavioral finance start working. He has selected five areas where the behavior of the
investors in the stock market differs from what have been proposed by the finance theories.
These are volume, volatility, dividends, predictability and equity premium puzzle. Shiller
(2003) proposed substantial literature with the aim of clearing doubts about efficient market
hypothesis. The answers to the various irregularities in the investing patterns of the
investors have been found with the help of behavioral finance.
4
Caginalp and DeSantis (2011) extended the theories that further contradict the efficiency
of the stock market. According to him, the nature of the investments and the participants
that trade or invest in the market are the driving factors of the efficiency of the markets. In
his paper, Marchand (2012) identified the irrationality in the human behavior in the form
of biases and compares the traditional and modern finance theories with the behavioral
finance theories. Nair and Antony (2015) view behavioral finance as not a replacement to
traditional finance theories but as means to understand the irrational investor behavior and
reasons for sudden rise and fall in the market.
Zahera and Bansal (2018) found that if the investors have complete information about the
asset pricing, pricing of securities in the market, the prospect of the company in the future,
government guidelines for investment in the securities, then they are prone to make
irrational decisions. This is because while making any investment decision, they are
influenced by both the potential outcomes and emotional out comes. They can get
influenced by the perceptions of their peers, friends, family or even their competitors. Such
a behavior of the investors to act differently in different situations makes it essential to
combine the concepts of psychology with finance. This can explain the reasons for varying
investor behavior under different circumstances that they face in the market. The strategies
of the investment made keeping in view the principles of behavioral finance can increase
the profits of the investors. It can also guide investors to invest in profitable securities and
to withdraw from the loss-making securities. The rational investors are able to attain the
benefits by investing in those profitable securities and beneficial opportunities that are not
recognized by the irrational investors.
While at the European Investment Conference in France a professor narrates what happens
with the irrational mind and savings for the future, and pops the question of how advisors
can help (Hoover, 2015). Behavioral Economics Professor Dan thinks that financial
advisers are asking the wrong questions like, “How much of your current salary will you
need in retirement?” Ariely (2015). The other question according to Professor Arieley is
“What is your risk attitude on a five, seven, or ten-point scale?” are questions that fail to
5
provide meaningful answers to the European investors. Ariely (2015) looks into the
questions of “How do you want to live in retirement?”, “What activities are you interested
in engaging?” These questions can help clients identify and prepare for their future financial
needs (Ariely & Jones, 2015). Ariely further recognized that the challenge that advisers
face is particularly difficult because “saving money is probably one of the toughest things”
that people can do. Encouraging clients to save for the future is a losing battle (Bebchuk &
Kastiel, 2017). According to Ariely (2015), the minds of the advisors and the surrounding
environments of the investors are normally suited to thinking about money in long term.
Egan (2013) found that the financial consulting profession has an opportunity to reinvent
itself in a way that can be beneficial for financial advisers and their clients. The fact is that
money is hard to think about and investors do need help with making financial decisions
(Hensler, 2013). This means that advisers who position themselves to offer that help will
be performing a valuable service (Laudon & Traver, 2016).
In India, a study by Prosad, Kapoor, and Sengupta (2015) found that behavioral biases are
dependent on investors’ demographics and their trading sophistication with highest
influencing factors being age, profession and trading frequency. Prosad et al. (2015) urgue
that each bias corresponds to a specific set of investor characteristics and overconfidence
comes out to be the most important bias in the Indian context. The Asia-Pacific region, a
study by Chen, Cheng and Lee (2011) found out that risk tolerance is the most important
factor for Taiwanese investors when they design their asset portfolios. However, they prefer
stocks to other assets. Chen et al. (2011) affirms that when market environment and risk
tolerance are considered, mutual funds are chosen over and above stocks. Browning and
Finke (2015) is in agreement with the findings and says that whichever criterion is used,
bonds turn out to be the least favored asset.
In Egypt, Metawa, Metawa, Hassan and Safa (2019) observed that iinvestor sentiments,
overreaction and under reaction, overconfidence and herd behavior significantly affect
investment decisions. Metawa et al. (2019) also, observed that age, gender and the level of
education have significant positive effects on investment decisions by investors. They have
also noted in their journal titled Impact of behavioral factors on investor’s financial
6
decisions, a case of the Egyptian stock market that Experience does not play a significant
role in investment decisions, but as investors gain experience, they tend to overlook the
emotional factors (Metawa et al., 2019).
In Nigeria, Alalade, Okonkwo and Folarin (2014) found that behavioral biases existed but
not very dominant in the Nigeria stock market because a weak negative relationship existed
between behavioral biases and stock market returns in Nigeria. Being aware of behavioral
biases in the Nigerian stock market was a crucial first step in ensuring that investment
decisions were properly controlled to avoid any negative impacts on the individual
investors and on the stock market (Ogunlusi & Obademi, 2019). Again, behavioral biases
might be of relevant consideration in portfolio construction in order to moderate these
biases (Alalade et al., 2014).
South Africa on the other hand through the Institute of Behavioral Finance [IBF] (2018) an
independent research and training institution is promoting the behavioral finance discipline
by offering the practice based tuition. Their mission is to add value and enhance the
professional status of the investment sector in South Africa and in so doing serving the
interests of financial planners and clients (Dickason & Ferreira, 2018). By incorporating
the work of knowledgeable researchers and academics, the Institute tries to find better
explanations for investor decision-making and market anomalies that have been noted but
not really explained within Southern Africa over the past couple of years (IBF, 2018;
Shiller, 2003).
Njuguna, Namusonge and Kanali (2016) in Kenya revealed that existing studies carried out
seem to have a mixed view with regards to market rationality and use of market
fundamentals to make investment decisions. Wera (2006) suggested that the behavior of
investors at the Nairobi Securities Exchange (NSE) is to some extent irrational in regard to
fundamental estimations as a result of anomalies such as herd behavior, regret aversion,
overconfidence and anchoring. He further opined irrationality at NSE while Aduda and
Muimi (2011) found overreaction as an anomaly in the Kenyan stock market.
7
Financial investors as indicated by Fama (1998) are people with a varied number of
deviations from rational behavior, which is the reason why there is a variety of effects,
which explain market anomalies. Phung and Reiff (2015) found that traditional finance
assumes that investors are rational and they are focused to select an efficient portfolio,
which means including a combination of asset classes chosen in such a manner as to achieve
the greatest possible returns over the long term, under the terms of a tolerable level of risk
(Phung & Reiff, 2015). Behavioral finance paradigm suggests that investment decision is
influenced in a large proportion by psychological and emotional factors (Aduda & Muimi,
2011).
1.2 Problem Statement
For ages, standard ordinary finance has constantly presumed that investors are typical and
sensitive in their investment decision making in the stock market and therefore they are
impassive about risk return tradeoffs and exploiting value (Baker & Yi, 2016). Traditional
Finance and the Modern Portfolio Theory (MPT) assume symmetry and therefore
rationality in the investment environment (Markowitz, 1952). Investors must have to
incorporate all the necessary information available according to the efficient market
hypothesis (EMH) and are impartial in analyzing securities and choosing winning stocks.
However, psychologists have found that human beings do not behave as rationally as
economists suppose (Ogunlusi & Obademi, 2019). The occurring of stock market
anomalies according to empirical researches conducted by Babajide and Adetiloye (2012)
revealed that investors are not always as rational as they are portrayed to be. These
anomalies can be explained by a new emerging area of finance called behavioral finance
(Gandhi & Lustig, 2015). Behavioral finance considers how various psychological traits
affect how individuals or groups act as investors, analysts and portfolio managers
(Placeholder7). It tries to understand how emotions and cognitive errors influence
behaviors of individual investors (Kengatharan & Kengatharan, 2014). It also seeks to
explain why and how investors can act beyond the boundary of rationality in ways that
oppose to what they are supposed to (Mumtaz, Saeed, & Ramzan, 2018). Advocates of
8
behavioral finance have been able to explain a number of psychological factors that affect
the decision making of investors in the stock market (Baker & Yi, 2016).
However, much is unknown even till today about the human psychology and investor
irrational behavioral factors that influence the investment bankers’ decision-making
process (Nofsinger, 2017). Research has been conducted about behavioral finance factors
affecting investment performance by retail investors in the NSE by Chami (2017), effect of
behavioral biases on ranking of financing decisions by financial managers of firms listed
in the NSE by Nyakundi (2017), and the effects of behavioral factors in investment decision
making on institutional investors by Waweru, Munyoki, and Uliana (2008) and individual
investors at the NSE by Kimeu, Anyango, and Rotich (2016). Research similar to this study
has been conducted in Nigeria by Ogunlusi and Obademi (2019), Canada by Keswani,
Dhingra, and Wadhwa (2019) and Bombay by Bharath (2019) but none has been conducted
in a Kenyan context, which prompted this research to investigate the impact of behaviorial
finance on investment decisions by investment banks in Kenya.
1.3 Purpose of Study
The purpose of this study was to investigate the impact of behavioral finance on investment
decisions by investment banks in Kenya.
1.4 Research Questions;
To help achieve the purpose of this study, this research answered the following questions
related to the research topic.
1.4.1 What are the effects of mental accounting on the investment decisions by investment
banks in Kenya?
1.4.2 What is the impact of framing on investment decisions by Kenyan Investment Banks?
1.4.3 What are the effects of heuristics on investment decisions by Investment Banks in
Kenya?
9
1.5 Justification of the Study
This study will generate insights that are beneficial to different groups of interest in several
ways.
1.5.1 Policy Formulators.
This study will be beneficial to the policy formulators in the country with regards to the
investment regulations. Policy formulators for example the Capital Markets Authority
(CMA) will find this study helpful in a way that they will involve the individual investors’
psychology among the investment checklist for the investment banks. For instance,
conversations about creating a wealth creation profile for a fresh investor ought to include
a part where the investor’s psychology is considered before their investment portfolio is
created. They may need to include a clause in the application forms that deals with
involving psychological intervention of individuals before they create an investment
portfolio.
1.5.2 The Management of the NSE.
The Nairobi Securities Exchange being another one of the regulatory body for the financial
assets dealt with in the investment banks in Kenya, this study will be helpful to the
management team of in a way that they will now think of involving the investment traders
and dealers of the investment banks in considering the individual’s psychology and
emotions before advising them on which financial assets to onboard when making an
investment decision. In the concept of the Kenyan investment banks, dealers and
investment advisors/ underwriters tend to look at advising the investor to stack their savings
into assets that will benefit the investment intermediaries, not considering whether it is
actually what the investor would have passionately wanted to hold in their portfolio.
1.5.3 The Academia
The academia will find the content of my study helpful in a way that they will find new
discoveries about how individuals are involved in formulating investment decisions. It will
provide more content for interesting research points that have not been taken seriously in
the past, considering, the third world economies have the tendency of wanting to gain from
10
every activity which in the end turns individual investment into another way of predators
(financial intermediaries) taking advantage of the individual savers’ money. The field of
academia will also find this research helpful as it will lay ground for further research about
how behavioral finance is related to traditional finance phenomenon of investment.
1.5.4 Investment Agents
The stock brokers will find my study helpful, as it will work as an eye opener and provide
guidance on the relationship between behavioral finance and peoples’ investment behavior.
They will understand the market dynamics of passion in investment other than looking at
investment as a one sided way of conducting business. They will in the end come to
appreciate that they actually will have more to gain if they advise individuals and
companies to invest in financial facilities where they have passion and strong psychological
appreciation. They will appreciate the saying of if you love what you do, you will never
have to work a day in your life (Moore, 2014). As stated that those who understand the
human behavior are in the best position to predict future actions. This is also true in markets,
which are, let us not forget, composed of human beings (Hagstrom, 2004)
1.5.5 Fund Managers
Fund managers will find my study helpful in dealing with one trillion Kenyan saving funds
investors that are licensed by the CMA and the Retirements Benefits Authority (RBA,
2019). The list of the registered fund managers as of May 2019 are Alpha Africa Asset
managers, Amana capital limited, Apollo asset management company limited, Abraaj
Kenya advisers limited, Britam asset managers Kenya limited, Cannon asset managers
limited, Nabo capital limited, CIC asset management limited, Co-op trust investment
services limited, FCB capital limited, Fusion investment management limited, Genafrica
asset managers limited, ICEA lion asset management limited, Madison investment
managers limited, Old mutual investment group limited, Old mutual investment services
(K)limited, Sanlam investments limited, Stanlib Kenya limited, Standard chartered
investment services limited, Zimele asset management company limited, Natbank trustee
and investment services limited, Seriani asset managers limited, Allan gray (Kenya)
11
limited, Watu capital limited, Cytonn asset managers limited, Altree capital Kenya limited
and Jubilee financial services limited (CMA, 2019)
1.6 Scope of the Study
This study was to establish the impact of behavioral finance on investment decisions by
investment banks in Kenya. The scope of the study was based on the investment advisors/
underwriters working in the 16 investment banks in Kenya who are tasked with the role of
advising investors on which investment decisions to undertake. This study was carried out
in the months of June and July 2019. This study excluded financial analysts that are not
registered by the CMA of Kenya.
1.7 Definition of Terms
1.7.1 Behavioral Finance
Behavioral finance is defined as the application of psychology to finance (Pompian, 2012).
Taffler (2018) refers to it as emotional finance where investment and the unconscious meet.
He discussed that behavioral finance is informed by the psychoanalytic understanding of
the human mind. Thaler, (2019) mentions that the contraversy surrounding behavioral
finance is fading out as scholars accept it as simply a new way of doing financial economic
research.
1.7.2 Mental Accounting
Mental accounting is the process by which individuals and households keep track of and
evaluate their transactions (Thaler, 1980). It serves very much the same function for
households that financial accounting serves for organizations. A study by Phung and Reiff
(2015) found that people have a tendency to separate their money into different accounts
based on miscellaneous subjective criteria, including the source of the money and the
intended use for each account (Phung & Reiff, 2015). According to Nofsinger (2017), the
theory of mental accounting suggests that individuals are likely to assign different functions
to each asset group in this case, the result of which can be an irrational and detrimental set
of behaviors. In this study, mental accounting is an independent variable.
12
1.7.3 Framing
Framing bias occurs when people make a decision based on the way the information is
presented, as opposed to just on the facts themselves (Placeholder0). The same facts
presented in two different ways can lead to people making different judgments or decisions
according to the Chartered Finance Institute (CFI, 2019). In behavioral finance, investors
may react to a particular opportunity differently, depending on how it is presented to them
(Prince, 2019). Bracker (2013) discussed that the concept of frame dependence is a
significant component of behavioral decision making. In this study, framing is an
independent variable.
1.7.4 Heuristics
Bracker (2013) defined heuristics as shortcuts for decision making that are developed from
learned behavior or past experience. In traditional finance according to Barberis and Thaler
(2003), decision making is handled using probability analysis under uncertainty and utility
theory. However, these processes can sometimes be complex and people often rely on
simple heuristics to make decisions instead (Kahneman & Tversky, 1974). In this study,
heuristics is an independent variable.
1.7.5 Investment Decisions
Wamae (2013) defines investment decision-making as a process of choosing a particular
alternative investment from a number of alternative investments. It is an activity that
follows after proper evaluation of all the available alternatives (Wamae, 2013). Decision
making by individual investors is usually based on their personal factors such as age,
education, income, and investment portfolio (Thaler, 2019). A study by Hiriyappa (2008)
defined investment as the production of capital goods, which are not consumed but instead
used in future production. Examples include buildings, a rail road, a factory clearing land,
and putting oneself through college (Hiriyappa, 2008). The finance definition of investment
is buying of financial assets like stocks and bonds, real estate, and mortgages (Van &
Aalbers, 2017). These investments may then provide a future income and increase in value
(Markowitz, 1952). Investment according to Oxford Dictionary means the investing of
13
money (Brown, McLean, & McMillan, 2018). In this study, investment decisions is the
dependent variable.
1.7.6 Investment Bank
Marshall (2019) defines an investment bank as a financial intermediary that performs a
variety of services. Most investment banks tend to specialize in large and complex financial
transactions, such as underwriting, acting as an intermediary between a securities issuer
and the investing public, facilitating mergers and other corporate reorganizations and acting
as a broker or financial adviser for institutional clients (Stowell, 2017). Major investment
banks in Kenya include Faida Investment bank, ABC Capital, Dyre and Blair Investment
bank, Equity investment bank, Genghis capital, Kingdom securities, among others
(CMA,2019). Some investment banks specialize in particular industry sectors. Many
investment banks also have retail operations that serve small, individual customers (CMA,
2019).
1.8 Chapter Summary
This chapter serves as the introductory part of this study with detailed view of the
background of the study, problem statement, purpose of the study, research questions,
justification of the study, Scope of the study and definition of key terms focused on in the
study. The next chapter focuses on literature review where previous studies and findings
related to the research questions will be discussed and chapter three identifies the research
methodology used to carry out the study. The findings of this study are provided in chapter
four after which chapter five gives a summary of the findings, discussions, conclusions and
recommendations.
14
CHAPTER TWO
2.0 LITERATURE REVIEW
2.1 Introduction
This study focused on establishing the impact of behavioral finance on investment
decisions by investment banks in Kenya. This chapter expounded on the research questions
of the study which include what are the effects of mental accounting on the investment
decisions by investment banks in Kenya, what are the impact of framing on decisions made
by Kenyan investment banks, and what are the effects of heuristics on investment decisions
investment banks in Kenya. The following chapter elaborated the research methodology
the research followed.
2.2 Mental Accounting and Investment Decisions
This concept was first named by Thaler (1980) where he described mental/ psychological
accounting as the process, sometimes implicit, by which individuals and households keep
track of and evaluate their transactions. It serves very much the same function for
households that financial accounting serves for organizations. It was found that mental
accounting describes the procedure by which people code, categorize and evaluate
commercial outcomes (Mahapatra, Raveendran, & De, 2018). Hofstede (2010) in his
cultural dimensions analysis argued that culture contributed to individual’s decision
making. He in 2010 introduced the dimension of indulgence and restraint, where he
analyzed individuals and their degree of freedom that societal norms give to citizens in
fulfilling their human desires (Hofstede, 2010). According to Hofstede therefore,
investment decisions are driven by their cultural norms rather than the individual’s mental
accounting. Bracker (2013) on the other hand narrates that mental accounting deals with
the recollection and perception of peoples’ various expenditures. Its purpose is to keep track
of peoples’ money-related decisions so as to give them a model with which to evaluate
future financial decisions.
An investgation by Phung and Reiff ( 2015) found that people have the tendency to separate
their money into different accounts based on miscellaneous subjective criteria, including
the source of the money and the intended use for each account. Their theoretical view of
15
mental accounting suggests that individuals are likely to assign different functions to each
asset group in this case, the result of which can be an irrational and detrimental set of
behaviors (Jain, Jain, & Jain, 2015).
2.2.1 Safe and Speculative Portfolios
According to the Chartered Financial Analyst institute [CFAI] (2019), investment is when
a security or an asset is purchased with an intention of holding it for a long term period
with a view that it will gradually increase in value over that period and speculation can be
considered a more risk based transaction where the sole purpose is to make profit out of
that transaction which is generally a short term and often a single transaction. In the
behavioral lifecycle hypothesis, Shefrin and Thaler (1988) note that people also tend to
experience the mental accounting bias in investing where many investors divide their
investments between safe portfolios and speculative ones on the premise that they can
prevent the negative returns from speculative investments from impacting the total
portfolio. In this case, the difference in net wealth is zero, regardless of whether the investor
holds multiple portfolios or one larger portfolio (Abreu & Mendes, 2010). The only
discrepancy in these two situations is the amount of time and effort the investor takes to
separate out the portfolios from one another (Abreu, & Mendes, 2010).
Thaler (2008) suggests that the psychological self thinks about money and risk through
‘mental accounts’ separating wealth into various buckets or pools. Bracker (2013) notes
that investors often base these pools on goals or time horizon, such as ‘retirement’ and or
‘school fees’. Fisher and Yao (2017) found that accounts can also vary in risk tolerance,
investing some in risky assets for gain while treating others more conservatively. Thaler
(2019) stated mental accounting as the process of separating money into specific categories
rather than evaluating one’s wealth as a whole. Kahneman and Tversky (2013) discussed
similar sentiments that investors pay less attention to the relationship between the
investments held in the different mental accounts than traditional theory suggests. This
natural tendency to create mental buckets causes investors to focus on the individual
buckets rather than thinking broadly, in terms of their entire wealth position.
16
In some early versions of portfolio theory, economists including Markowitz (1952)
contradict this school of thought with their suggestion that most investors seek to balance
security with the small chance for big winnings. Thus portfolio allocations should be based
on a combination of insurance which is a protection against losses and lotteries which is
the small odds of a large gain (Byrne & Utkus, 2013).
2.2.2 Mental Accounting layer of Pyramids
According to Behavioral economists Shefrin and Statman (2011), behavioral portfolios are
formed of a layered of pyramid, with each layer on a separate mental account (Shefrin &
Statman, 2011). Byrne and Utkus (2013) analyzed the same and indicated that the base
layers represent assets designed to provide protection from poverty, which results in
conservative investments designed to avoid loss. Higher layers represent hopes for riches
and are invested in risky assets in the hope of high returns (Statman, 2017).
According to Das, Markowitz, Scheid and Statman, (2011), this idea explains why an
individual investor can simultaneously display risk averse and risk tolerant behavior,
depending on which mental account they are thinking about. Statman, (2014) found that
this model can help explain why individuals can buy at the same time both insurance such
as gilts and lottery tickets such as a handful of small cap stocks. The theory also suggests
that investors treat each layer in isolation and don’t consider the relationship between the
layers (Byrne & Utkus 2013). Established finance theory holds that the relationship
between the different assets in the overall portfolio is one of the key factors in achieving
diversification (Markowitz, 1952).
According to Sudhir et al. (2017), for years, traditional finance has always presumed that
investors are rational in their decision making process in the stock market about risk return
trade-offs and maximizing utility. However Baker and Yi (2016) found that behavioral
finance studies revealed that human beings do not behave as rationally as economists
suppose as their decisions at times are affected by their psychological feelings. Numerous
studies from Asian coutries, the Middle East and Western countries have in fact established
that psychological factors do have relationships and impacts on the decision making of
17
investors in investment decision making. In light of this study, the researcher investigates
the gap that exists between traditional finance and modern portfolio theory as assumed by
Markowitz (1952), investor rationality as assumed by Kahneman and Tversky (1979) and
the conclusion by Kahneman (1978) that investment behavior is based on psychological
factors. There also exist gaps in investment behavior of different geographical regions of
the world and their corresponding demographic profiles.
2.2.3 Overconfidence and Availability bias in Mental Accounting
Jain, Walia, and Gupta (2019) found that investor behavior usually deviates from logic and
reason, and consequently, investors exhibit various behavioral biases which impact their
investment decisions. In Malaysian stock market, Baker and Yi (2016) intimate that
overconfidence and availability bias have significant impact on the investors’ decision
making. It was also found that the psychological factors are dependent of individual’s
gender (Hoffmann, Post, & Pennings, 2013). This research is mostly consistent with the
evidence found in previous studies carried out by Barberis and Huang (2001) in the United
States market where they found that when doing their mental accounting, people engage in
narrow framing, that is, they often appear to pay attention to narrowly defined gains and
losses which points to the availability bias. In other words, individual stock gains and losses
can be carriers of utility in their own right, and the investor may take this into account when
making decisions. Research by Mascarenas and Yan (2017) in europe found that investors
will carry out investment activities only when their psychological needs are met, hence
practicing the overconfidence bias in their decision making. They further discussed that not
all investors want to take risks to obtain profits, and not all investors will give up their
profits for sake of more profits because they are afraid of risks. Investors are different, as
they make decisions according to their own risk and return profiles. Unlike the traditional
CAPM theory by Treynor (1961), the consideration that risk and return portfolios have
different levels in line with the investors’ mental accounts of risk and profits to meet their
investment expectations is paramount.
The other mental accounting factor to consider is Regret avoidance, which is a theory used
to explain the tendency of investors to refuse to admit that a poor investment decision was
18
made (Brewer, DeFrank, & Gilkey, 2016). According to Seiler et al. (2012), when people
fear that their decision will turn out to be wrong in hindsight, they exhibit regret aversion.
Regret-averse people may fear the consequences of both errors of omission for example
not buying the right investment property, and commission for example buying the wrong
investment property (Brewer et al., 2016). In south Africa, research by Dickason and
Ferreira (2018) found that people will attempt to manage situations to avoid regret and be
regret-averse basing on the numbers shown by the Johannesburg Stock Exchange (JSE,
2019). In so doing, information related to specific events is grouped into specific
compartments, which reflect that investors with a low-risk tolerance level and a
conservative investor personality are subject towards loss aversion and mental accounting
biases (Barberis & Thaler, 2003). These investors will compartmentalize information
before making any hasty investment decisions.
Ratemo (2016) in the research carried out for the Kenyan market found that the individual
investors categorize each class of their shares held distinctly when choosing to buy or to
sell them. This was intended to establish whether while making investment choices,
investors consider each type of shares as a separate basket or whether all shares are taken
as homogenous investments in securities. Dickason and Ferreira (2018) discussed that
mental accounting can be explained with two scenarios, the first is when an investor invests
money and receives excessive returns and the second is when an investor invests money
and receives normal returns. The investor then typically compares the two scenarios and
the reaction will be hesitant to dispose investments with normal returns. As a result,the
excessive returns will motivate investors, due to the comparison between compartment
information, to wait until higher returns are achieved. Kahneman (2011) however urgued
that the experience of a moment or an episode is not easily presented by a single happiness
value.
Kahneman and Terversky (2013) discussed that the prospect theory divided investment
choice process into two phases. The first phase involves framing by which mental accounts
are created and the second phase involves the evaluation of mental accounts and making a
choice. This study, therefore aimed to investigate investment underwritters’s level of
19
awareness of the impact of individual psychological factors on their decision making in the
stock market, thus increasing the rationality of investment decisions for enhanced market
efficiency in both money and capital markets investment by investment banks in Kenya.
2.3 Framing and Investment Decisions.
CFI (2019) investigated the phenomena of framing and found that this bias occurs when
people make decisions based on the way the information is presented, as opposed to just on
the facts themselves. They state found that the same facts presented in two different ways
can lead to people making different judgments or decisions. In behavioral finance, investors
may react to a particular opportunity differently, depending on how it is presented to them.
Bracker (2013) expressed that the concept of frame dependence is a significant component
of behavioral decision making. In traditional finance, people make decisions with frame
independence (Koseoglu, 2019). Kahneman and Tversky (1979) found that in frame
independent decision making, the information presented matters, but the way and order in
which that information is presented does not. Frame dependence suggests that the decision
depends not only on the information, but in how that information is presented (Mishra,
Gregson, & Lalumiere, 2012).
2.3.1 Prospect Theory
Barberis (2013) defines prospect theory as a behavioral model that shows how people
decide between alternatives that involve risk and uncertainty like the percentage likelihood
of gains or losses. This theory demonstrates that people think in terms of
expected utility relative to a reference point like current wealth rather than the absolute
outcomes. Prospect theory was developed by framing risky choices and indicates that
people are loss-averse and since individuals dislike losses more than equivalent gains, they
are more willing to take risks to avoid a loss (Kahneman, 2011).
Kahneman and Tversky (1979) discussed the importance of framing when they introduced
prospect theory as an alternative to expected utility theory. Prospect theory is based on the
ideas that individuals do not view gains and losses independently and tend to overweight
small probabilities. Framing becomes relevant because decision makers do not focus on the
20
net change in value, but on the individual gains and losses along the way. Thaler and
Johnson (1990) mentioned that the order of the potential gains and losses can result in
differing decisions even if the end result is the same.
2.3.2 Hedonic Editing
According to Sul, Kim and Choi (2016), hedonic editing refers to the decision strategy of
arranging multiple events in time to maximize hedonic outcomes. Retrospective hedonic
editing according to Cowley (2008) occurs when people combine events to frame a
previous experience in its most positive light. The issue of framing can be addressed to
hedonic editing and in situations where people may choose not to be risk-averse, the way
in which information is presented can cause one to make inconsistent choices, and that one
is likely to become less risk-averse after a win and more risk averse after a “loss” regardless
of the net result (Cherono, Olweny, & Nasieku, 2019). Tversky and Kahneman (1981)
introduced a scenario related to framing which illustrate that we can get confused by
making value judgements based on how the information is worded, but according to frame
independence, there should be no relationship between the choice made and the wording of
the available alternatives.
In addition to addressing framing, prospect theory also addresses the issue of loss aversion
(Guo & Lin, 2019). Yang (2016) stresses that one of the principle concepts of traditional
finance is that of risk-aversion. Bracker (2013) found that according to risk-aversion,
investors will only take on additional risk if they receive adequate compensation. While the
specific degree of risk-aversion is not specified and typically is assumed to vary depending
on the individual, the basic premise of risk-aversion is assumed to underlie financial
decision making. However, Kahneman (2011) argued that people are loss averse rather than
risk averse. This implies that they feel the pain of losses more than they feel the pleasure
of gains and that they may actually take on additional risk to avoid a loss (Paris, 2012).
2.3.3 The Decoy Effect
The decoy effect is a phenomenon whereby consumers will tend to have a specific change
in preferences between two options when also presented a third option that is
21
asymmetrically dominated (Tomer, 2018). According to Agarwal (2019), the decoy Effect
also known as the asymmetric dominance effect is a cognitive bias in which consumers will
tend to have a specific change in preferences between two options when also presented with
a third option that is asymmetrically dominated. In finance, it is used to influence decision
making by presenting decision makers with a third option that is equally good (Ifcher &
Zarghamee,2019). It is also used in marketing and pricing strategies to improve business
results for instance, when there are only two options, people will tend to make decisions
according to their personal preferences, but when they are offered another strategical decoy
option, they will be more likely to choose the more expensive of the two original options
(Simonson, 2014).
The decoy effect is technically known as an asymmetrically dominated choice and occurs
when people’s preference for one option over another changes as a result of adding a third
similar but less attractive option (Tomer, 2018). Paris (2012) conducted a study on how the
decoy effect can influence investors’ decisions and found that the decoy effect caused
investors to increase the perceived importance of growth over income. In other wards,
Kahmen and Tversky (1981) urgue that the psychological principles that govern the
perception of decision problems and the evaluation of probabilities and outcomes produce
predictable shifts of preference when the same problem is framed in different ways.
In europe and the UK, a study done by Spillace and Grehan (2019) found that things are
framed deeply affects how the information is processed and subsequently acted on so much
so that, despite the availability of convincing evidence, it can be extremely hard to shake
off fabled preconception. It seems that once a label sticks, it can be very hard to dislodge.
Kahneman (2011) urged that your framing determines how you imagine the problem, its
possible solutions and its connection with other situations. Research by Stewart and
Shefrin (2000) found that framing also influences how investors manage making more than
one decision simultaneousely.
Narrow framing according to Copur (2015) is another framing factor to look at which is
described as a myopic approach of investors wherein they make investment decisions
22
without considering the context of their entire portfolio. He argues that people affected with
this bias focus their attention to specific, seemingly attractive investment options while
they tend to overlook the full range of options available to them. Willows (2015) notes that
in South Africa, people who frame decisions make narrowly worse investment decisions
overall. They tend to view each individual purchase or sale in terms of that transaction only.
This is more correctly referred to as ‘narrow framing (Fehrenbacher, Roetzel, & Pedell,
2018). A wider frame would be to view the overall share portfolio or your overall wealth.
While this might not seem like it will make much of difference, research shows that it does.
Popmpian, (2015) argued that generally, the wider the frame, the more diversified the
investor’s portfolio will be, but with greater volatility attached thereto. This is linked to the
disposition effect, discussed by (Barberis & Xiong, 2009) as the tendency to sell shares that
have increased in price and hold on to those that have decreased in price. The narrower an
investor’s frame, the more likely he or she will exhibit these tendencies. This can be
expected if investors are narrowly following their individual investments rather than a
wider frame of overall wealth. Nevertheless, investors with a fixed and tight frame
generally think they made all the correct investment decisions.
In Kenya, a study done by Mbaluka, Muthama and Kalunda (2012) found that framing
modifies the investment decision depending on the perspective given to the problem and
that its effects influenced the decisions made by individual investors. According to
Kahneman (1979), an important implication of prospect theory is that the way economic
agents subjectively frame an outcome or transaction in their mind affects the utility they
expect or receive. Loss aversion is a closely tied behavioral effect to framing effects, and
found that investors made statistically significant inconsistent choices between the
decision problems framed positively in terms of gains and negatively in terms of losses
(Mutuku, 2018). His study concluded that people were found to weight losses more heavily
than gains.
Garman and Forgue (2011) discussed loss aversion as the tendency to loathe realizing a
loss to the extent that it is avoided even when it is the better choice. Loss aversion is also a
form of regret aversion, which is a feeling of responsibility for loss or disappointment. Past
23
decisions and their outcomes inform the investor’s current decisions, but regret can bias
their decision making. Regret can anchor a person too firmly in past experience and hinder
them from seeing new circumstances. Framing can affect a person’s risk tolerance in that,
they may be more willing to take risk to avoid a loss if they are loss averse, or they may
simply become unwilling to assume risk, depending on how they define the context
(Grable, 2016). Framing however may influence an investor into missing to make the best
decision, which may involve comparing or combining choices, lack of diversification or
over diversification in portfolio formation, which poses an investment decision gap
(Nofsinger, 2017).
2.4 Heuristics and Investment Decisions.
Heuristics refer to shortcuts for decision making that are developed from learned behavior
or past experience (Braker, 2013). In traditional finance, decision making is done using
probability analysis under uncertainty and utility theory, however, these processes can
sometimes be complex and people often rely on simple heuristics to make decisions instead
(Hirshleifer, Levi, & Lourie, 2019). Kahneman and Tversky (1974) argue that many
decisions are based on beliefs concerning the likelihood of uncertain events such as the
outcome of an election, the guilt of a defendant, or the future value of the dollar. These
beliefs are usually expressed in statements such as "I think that," "chances are ," and "it is
unlikely that" among others. Otuteye and Siddiquee (2015) found that occasionally, beliefs
concerning uncertain events are expressed in numerical form as odds or subjective
probabilities. Their research found that people base their decisions on several heuristic
principles which reduce the difficult tasks of assessing probabilities and predicting values
to simpler judgmental operations (Peng, Chen, Shyu, & Wei, 2011).
Earlier studies therefore described several heuristics that are employed in making
judgements under uncertainty. Chakrabarti and Kumar (2017) found that
representativeness, which is usually employed when people are asked to judge the
probability that an object or event (A) belongs to class or process. Overconfidence and
availability of occurrences which is often employed when people are asked to assess the
frequency of a class or the plausibility of a particular development, adjustment to additional
24
information, anchoring, which is usually employed in numerical prediction when a relevant
value is available, self-attribution, trend-chasing, overreaction and under-reaction are all
heuristic biases (Linsi & Schaffner, 2019).
2.4.1 Representativeness Heuristic
The representativeness heuristic according to Krawczyk and Rachubik (2019) is described
as assessing similarity of objects and organizing them based around the category prototype
for instance, like goes with like, and causes and effects should resemble each other.
This heuristic is used because it is an easy computation. Representativeness is one of the
major general purpose heuristics, along with availability and affect (Benjamin, 2019). It is
used when investors judge the probability that an investment A belongs to class B by
looking at the degree to which A resembles B (Jain et al, 2015). When investors do this,
they neglect information about the general probability of B occurring at its base rate
(Kahneman & Tversky, 2013). Dickason and Ferreira (2018) discussed that investors
should have an understanding of their own investment behavior in order to enable them to
select the most appropriate investment according to their risk personality (Kumari & Sar,
2017). Investor decisions based on stereotypes refers to the representativeness bias of
behavioral finance, as a result, some investors base investment decisions on inaccurate
market participant perceptions and patterns (Kannadhasan, 2009).
Moreover, investors tend to overreact in the market because of the perception of pattern
repetition (Singh, 2012). Overconfidence is a result of investors who tend to overestimate
their investment capabilities. Singh (2012) found that it is because of overconfidence that
investors attempt to time the market in such a manner in order to outperform the market.
Kannadhasan (2009) discussed that much information is available to use in the market to
base investment decisions on, but anchoring exists in the market when investors only rely
on a single piece of information. As discussed by Dickason and Ferreira (2018), due to the
propensity of investors to rely on a single piece of, mostly historical, information the
adjustment to additional information tends to be relatively slow.
Research by Kahneman and Tversky (2013) found that the representativeness heuristic is a
psychological bias which means that, under uncertainty, investors are prone to believe that
25
a history of a remarkable performance of a given firm is "representative" of a general
performance that the firm will continue to generate good performance into the future.
According to Boussaidi (2013), investors subjects to this heuristic overreact, thus, to salient
and similar information about firms past performance such as similar consecutive earnings
surprises. Kahneman and Tversky (1972) define representativeness as a situation where
"subjective probability of an event, or a sample, is determined by the degree to which it is
similar in essential characteristics to its parent population, and the degree to which it
reflects the salient features of the process by which it is generated. Shefrin and Statman
(2000) discusses an experiment where he asks students in his MBA class to estimate an
expected return for two stocks, Dell and Unisys. The results were very high for Dell and
quite low for Unisys.
Based on the yield on Treasuries and betas at the time, the CAPM predicted different returns
for Dell and Unisys respectively (Shefrin & Statman, 2000). Statman (2014) Attributes the
big difference between traditional finance CAPM estimate to the fact that it was because
Unisys was seen as a "bad" company and Dell a "good" company. Shefrin and Statman
(2011) therefore, suggests that students did not base their analysis on risk and return, but
on their image of the company. On the other hand, Biger (1975) contradicts this view by
his analysis that economic research has been carried out under the assumption that
consumption and investment decisions are made with the absence of money illusion. He
found that classical and modern economic theory assume that consumption and investment
decisions are motivated by real rather than nominal considerations. Conversely, portfolio
theory and its later development to the Capital Asset Pricing Model are formulated in a
nominal context, and that the behavioral assumptions in these models refer to nominal
wealth of consumers (Statman, 2017).
In a research carried out by Bracker (2013), he formed questions that address the risk-
return inversion that get results when investors apply the representativeness heuristic to
their evaluations of common stocks. The people were given a list of a number of companies
and asked to rank them based on how they perceive the potential returns from each
company (Chava, Kim, & Weagley, 2019). While the participants did not need to estimate
26
a specific expected return, they based their rankings off of their expectations. In another
experiment, the participants were given the same set of companies in a different order and
asked to rank them based on perceived risk (Rahmani, M., & Nikoomaram, 2019).
According to traditional finance, the expectation was to see a strong correlation between
the rankings for risk and those for return. Specifically, the stocks with the highest expected
returns should be the riskiest while the stocks with the lowest expected returns should be
the least risky (Palazzo, 2012). Instead a different picture forms where investors tend to
identify good stocks and rank them as less risky with higher expected returns. Alternatively,
bad stocks are ranked as riskier with lower expected returns Rahmani et al, (2019).
Another representativeness heuristic is the conjunction fallacy which was introduced by
(Tversky & Khaneman, 1983). This is based on basic probability analysis which states that
the probability of a conjunction, P(A and B), cannot be greater than the probability of the
individual components, P(A) and P(B). In order for both A and B to be true jointly, they
need to each be true individually. However, individuals may perceive the conjunction to be
more likely if it is more representative of how they characterize an event or individual.
Bracker (2013) defines anchoring as a phenomenon in which people make forecasts based
on some initial value and adjust from there. Interestingly, Burton and Shah (2013) found
that there is evidence that the anchor may not even be related to the issue being forecasted.
Earlier scholars Kahneman and Tversky (1974) ran an experiment asking people to forecast
a percentage for the number of African countries in the United Nations. However, before
they made their forecast, they spun a wheel of fortune to generate a random number, then
they answered a two-part question (Kahneman & Tversky, 1974). Was the correct number
higher or lower than the number on the wheel and what was the number? The answers were
correlated to the number spun on the wheel.
2.4.2 Anchoring and Adjustment
Anchoring and adjustment is a psychological heuristic that influences the way people intuitively
assess probabilities (Furnham & Boo, 2011). According to this heuristic, people start with an
implicitly suggested reference point they call the anchor and make adjustments to it to reach their
estimate (Kahneman & Tversky, 1974). Baker, Filbeck and Nofsinger (2019) argued that
27
anchoring happens when the starting point is given to the subject, as well as when the
subject bases her estimate on the result of some incomplete computation. According to the
anchoring heuristic, information that is visibly irrelevant still anchors judgments and
contaminates guesses. Burton and Shah (2013) argue that when people start from
information known to be irrelevant and adjust until they reach a plausible-sounding answer,
they under-adjust. Epley and Gilovich (2006) found that people under-adjust more severely
in cognitively busy situations and other manipulations that make the problem harder. In
their 2016 research, they found that people deny they are anchored or contaminated, even
when experiment shows that they are (Epley & Gilovich, 2016), and therefore these effects
are not diminished or are only slightly diminished by financial incentives, explicit
instruction to avoid contamination and real world situations (Baker et al., 2019).
A study by Burton and Shah (2013) found that the anchoring bias appears when investors
make estimates starting from an initial value which is the anchor that is adjusted to yield
the final answer. The anchor may be implied by the formulation of the problem or may be
irrelevant. The bias occurs when adjustment is insufficient or is too conservative.
According to Kahneman and Tversky (1974), when forming estimates and predictions,
people usually start with some initial arbitrary value and adjust from it. People also make
estimates by starting from an initial value that is adjusted to yield the final answer. The
initial value may be suggested by the formulation of the problem, or it may be the result of
a partial calculation (Baker et al., 2019). Regardless, Kahneman and Tversky (1974) argue
that “adjustments are typically insufficient,” and “Different starting points yield different
estimates which are biased toward the initial value”. Byrne and Utkus (2013) introduces
the diamond anchor and discusses that the there is a conventional wisdom that a diamond
engagement ring should cost about two months’ worth of salary. This standard is in fact an
example of highly illogical anchoring (Iswadi, Marzuki, Yunina, & Haykal, 2019). It’s true
that spending two months of salary can serve as a benchmark when buying a diamond ring,
it is completely arbitrary and irrelevant as a reference point. In fact, it may have been
created by the jewelry industry to maximize profits. Many individuals’ buying a ring cannot
afford to spend two months of salary on this expense, on top of other necessary expenses.
As a result, many people go into debt to meet the “standard.” In these cases, the diamond
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anchor can take on a new meaning as well, as the prospective ring buyer struggles to stay
afloat in a sea of rising debt. In theory, the amount of money spent on an engagement ring
should be dictated by what a person can afford (Iswadi et al, 2019).
The European financial review as discussed by Baker and Ricciardi (2014) found that
investor behavior often deviates from logic and reason, and investors display many
behavior biases that influence their investment decision-making processes. Baker et al.
(2019) Defined Anchoring as the tendency to hold on to a belief and then apply it as a
subjective reference point for making future judgments. In Europe, Anchoring occurs when
an individual lets a specific piece of information control his cognitive decision-making
process. People often base their decisions on the first source of information to which they
are exposed, like an initial purchase price of a stock, and have difficulty adjusting or
changing their views to new information. Many investors still anchor on the financial crisis
of 2007-2008 as a bad experience, this results in a higher degree of worry, which can cause
them to underweight equities in their portfolios because they are excessively risk- and loss-
averse (Ricciardi, 2012).
In Malaysia Tuyon and Ahmad (2016) found that the behavioral finance paradigm provides
an alternative perspective of human behavior based on the positive theory that is open to
the multi-disciplinary understanding of human behavior. Investor decision and preference
are believed to be boundedly and adaptively rational. According to Aharoni, Tihanyi and
Connelley (2011) bounded rational means investor decision making involves both elements
of rational and irrational. Another scholar Zahera (2018) found that there are situations in
which people assess the frequency of a class or the probability of an event by the ease with
which instances or occurrences can be brought to mind. According to Hertwig, Pachur and
Kurzenhäuser (2005), one may assess the risk of heart attack among middle-aged people
by recalling such occurrences among one's acquaintances. Dhingra and Bhattacharjee
(2019) argue that one may evaluate the probability that a given business venture will fail
by imagining various difficulties it could encounter. This judgmental heuristic is called
availability. A study by Bracker (2013) found that availability is a useful clue for assessing
frequency or probability, because instances of large classes are usually recalled better and
29
faster than instances of less frequent classes, however it is affected by factors other than
frequency and probability.
2.4.3 Availability Heuristic
Cherry (2019) defines the availability heuristic is a mental shortcut that relies on immediate
examples that come to a given person's mind when evaluating a specific topic, concept,
method or investment decision. Frederiks, Stenner and Hobman (2015) found that in the
US, availability bias occurs because investors rely on information to make informed
decisions, but not all information is readily available. As discussed by Zahera (2018),
investors tend to give more weight to more available information and to discount
information that is brought to their attention less often, for example, Garman and Forgue
(2011) found that stocks of corporations that get good press, claim to do better than those
of less publicized companies when in reality these high-profile companies may actually
have worse earnings and return potential.
According to Kahneman and Tversky (2013), psychology has found that humans tend to
have unwarranted confidence in their decision making. This means having an inflated view
of one’s own abilities. Crowd behavior dictates that humans behave like animals, feeling
safe in a crowd. According to Akerlof and Shiller (2009); Cartwright (2016), confidence is
one of the most important aspects of animal spirits. They believe that confidence, signifying
the behavior beyond the rational approach to decision-making plays a major role in the
economy. Odean (1999), Barber and Odean (2001, 2002) and Glaser and Weber (2007)
stated that many empirical studies demonstrate that overconfidence leads to excessive
trading and that the more overconfident the investor, the more likely the investor is to
choose higher-risk Investment. Nosic and Weber (2010) demonstrate that overconfidence
and risk perception have a positive effect on the risk-taking behaviors of individual
investors. Therefore, we can say that overconfidence corresponds to individuals who are
too confident and exaggerate in estimating their own competence and underestimate risk.
Willows (2015) found that in South Africa, overconfidence, over reaction, and
underreaction happen due to various events like attention to winner, and loser stocks,
market roumers, and speculative political issues. In Kenya however, investors tend to buy
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shares when stock price has fallen as they gain overconfidence of increase in of the rate of
buying shares to a great extent (Wamae, 2013). Baker and Ricciardi (2014) argue that some
investors suffer from status quo bias in which they tend to default to the same judgment or
accept the current situation and changing this inertia requires strong motivation or
incentives. Status quo bias according to Fernandez and Rodrik (1991) occur when investors
fail to update their economic conditions despite potential gains from doing so, they instead,
stick to a positions such as holding a stock instead of selling it or otherwise act in a
suboptimal manner.
People also tend to defer savings for retirement or postpone opening a retirement account
which makes overconfidence to be summarized as unwarranted faith in one’s intuitive
reasoning, judgments, and cognitive abilities (Blumenthal, 2016). Pompian (2011)
observed that people are poorly calibrated when estimating probabilities and direct
application of over confidence in investment, which can be complex and involve forecasts
of the future. Psychologists found overconfidence to be an all pervasive human
characteristic and overconfident investors may overestimate their ability to identify
winning investments (Thaler 1999). In Kenyan context, Bellows (2019) highlighted that
traditional finance theory suggests holding diversified portfolios so that risk is not
concentrated in any area. Bellows (2019) found that misguided conviction can weigh
against this advice, with investors or their advisers being sure of the good prospects of a
given investment, causing them to believe that diversification is unnecessary.
Baker and Ricciardi (2014) link overconfidence to self-attribution bias, where investors
tend to attribute successful outcomes to their own actions and bad outcomes to external
factors. According to Thaler (1999), they often exhibit this bias as a means of self-
protection or self-enhancement. This can also be linked to the issue of control, where over
confident investors like believing they exercise more control over their investments than
they do (Forbes, 2005). In a research carried out in Europe, Thaler (2019) found that
affluent investors reported that their own stock-picking skills were critical to portfolio
performance. They were unduly optimistic about the performance of the shares they chose
and underestimated the effect of the overall market on their portfolio’s performance. In this
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simple way, investors overestimate their own abilities and overlook broader factors
influencing their investments. Another heuristic to consider in this study is worry, which is
described as an ordinary and unquestionably widespread human experience and also brings
memories and visions of future episodes that alter an investor’s judgment about personal
finances (Baker & Ricciardi, 2014). Based on survey evidence, Ricciardi (2011) found that
a much larger percentage of investors associate the word “worry” with common stocks as
compared to bonds. Thaler et al. (2015) found that more anxiety about an investment
increases its perceived risk and lowers the level of risk tolerance among investors, in turn
this concern increases the likelihood that investors will not buy the security (Thaler et al.,
2015).
Familiarity bias is another heuristic to consider when looking at the impact of behavioral
finance on investment decisions. According to Hester (2017), in psychology, familiarity
bias or heuristics refers to the phenomenon where people opt for the more familiar options,
even though these often result in less favorable outcomes than available alternatives.
Despite information from a wider variety of sources being more readily available than ever
before, familiarity bias remains an issue considering that investors stick with what they
know (Kumar, 2018). Nofsinger (2017) discussed the familiarity bias that occurs when
investors have a preference for familiar investments despite the seemingly obvious gains
from diversification. Investors display a preference for local assets with which they are
more familiar, they call this the local bias, as well portfolios tilted toward domestic
securities, the home bias. An implication of familiarity bias is that investors hold
suboptimal portfolios, for example in Kenyan context, investors need to cast a wider net
and expand their portfolio allocation decisions to gain wider diversification and risk
reduction Waweru, Mwangi and Parkinson (2014). Investing internationally helps to avoid
familiarity bias. Bailey, Kumar and Ng (2011) talked about the trend-chasing bias where
investors often chase past performance in the mistaken belief that historical returns predict
future investment performance. Mutual funds take advantage of investors by increasing
advertising when past performance is high to attract new investors, this is mostly practiced
in Europe. Research by Baker and Ricciardi (2014) and later Baker, Filbeck and Ricciardi
(2017) demonstrates that investors do not benefit because performance typically fails to
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persist in the future and to avoid this bias, investors should resist following the herd or
jumping on the bandwagon. Mayone (2018) in a study he did in South Africa found that
although investors may feel better when investing with the crowd, such an investment
strategy is unlikely to lead to superior long-term performance. This presents a gap between
rationality and human behavior which is the sole reason this study is important to
investigate how heuristics impact investment decision making in investment banks in
Kenya.
2.5 Chapter Summary.
This chapter covered research that was performed by previous scholars related to
behavioral finance and investment decision making. The literature review has been
organized according to the research questions listed in chapter one. Chapter 3 will provide
detailed information related to the research methodology for the project.
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CHAPTER THREE
3.0 RESEARCH METHODOLOGY
3.1 Introduction
This chapter covered the research methodology that was used in the study. The research
methodology comprises of research design, population and sampling design, data collection
methods, research procedures, and data analysis methods.
3.2 Research Design
Cooper and Schindler (2008) defines research design as the plan and structure of
investigation so conceived as to obtain answers to research questions. Sachdeva (2009)
found that research design provides the glue that holds the research project together.
According to Flannelly and Jankowski (2014), research design is the arrangement of
conditions for collections and analysis of data in a manner that aims to give relevance to
the research purpose with economy in procedure. In fact, research design is the conceptual
structure within which research is conducted and It constitutes the blue print for collection,
measurement and analysis of data (Kumar, 2011). Kothari (2004) informs that the
formidable problem that follows the task of defining the research problem is the preparation
of the design of the research project, popularly known as the research design.
This study employed causal research design, providing the relationship between mental
accounting and investment decision, framing and investment decisions and heuristics and
investment decisions. This type of research design is conducted in order to identify the
extent and nature of cause and effect relationships. Dudovskiy (2018) explains that causal
research designs can be conducted in order to assess impacts of specific changes on existing
norms and various processes. He further explains that causal studies focus on an analysis
of a situation or a specific problem to explain the patterns of relationships between
variables. The presence of cause cause-and-effect relationships can be confirmed only if
specific causal evidence exists. Causal evidence has three important components, temporal
sequence where the cause must occur before the effect. The second one is concomitant
variation where by the variation must be systematic between the two variables, for instance,
if a company doesn’t change its employee training and development practices, then changes
34
in customer satisfaction cannot be caused by employee training and development, and
nonspurious association which explains that any covariation between a cause and an effect
must be true and not simply due to other variable, in other words, there should be no a third
factor that relates to both, cause, as well as effect (Sachdeva, 2009).
According to Flannelly and Jankowski (2014), causal experimentation forms the top of the
hierarchy because it establishes the temporal order needed to infer causation, and controls
for other alternative explanations of what might have caused the observed outcome. In this
study, the researcher looks for the possible relationship between the independent variables
that is mental accounting, framing and heuristics, and the dependent variable, being the
investment decisions and how one causes the other to be impacted. Data on mental
accounting, framing and heuristics was analyzed quantitatively to help investigate the
relationship between investment decisions and the three independent variables. This
research design was chosen because it describes what exists and subsequent discovery to
new facts and hence broadening the pool of knowledge (Creswell, 2014).
3.3 Population and Sampling Design
3.3.1 Population
Population according to Anderson, Sweeney, Williams, Camm and Cochran (2015) is
defined as a set of elements of interest in a particular study. Mackey and Gass (2015) Also
defined population as a well-defined collection of individuals or objects known to have
similar characteristics, or all individuals or objects within a certain population usually have
a common, binding characteristic or trait. According to Ghauri and Gronhaug (2015),
usually, the description of the population and the common binding characteristic of its
members are the same. There are two types of population, namely, the accessible and the
target population. The accessible population is the type to which the researcher can apply
their conclusions from a subset of the target population to form a study population. The
target population on the other hand refers to the entire group of individuals or objects to
which researchers are interested in generalizing the conclusions (Burns, Bush, & Shinha,
2014).
35
The target population of this study was the 16 investment banks in Kenya, however, the
sampled respondents were the dealers, brokers, analysts, tellers and the sales team in each
of the 16 investment banks in Kenya. This is justified by the fact that they are more
appropriate in giving a personal account of how they perceive decisions in investment and
how they go about on analyzing capital and money markets, financial assets assessment
and advising investors on which asset to onboard for their investment portfolios. The target
population should have observable characteristics to which the researcher intends to
generalize the result of the study (Mugenda & Mugenda 2003). Table 3.1 below represents
the population distribution used in this study.
Table 3.1 Population Distribution
Investment Bank Population of Employees Percentage (%)
African Alliance Kenya Investment Bank 45 6
Barclays Financial Services Limited 40 6
CBA Capital Limited 45 6
Dyer & Blair Investment Bank Limited 40 6
Equity Investment Bank Limited 40 6
Faida Investment Bank Limited 50 7
Genghis Capital Limited 45 6
KCB Capital Limited 45 6
NIC Capital Limited 45 6
Renaissance Capital (Kenya) Limited 50 7
SBG Securities Limited 45 6
Standard Investment Bank Limited 45 6
Kestrel Capital (East Africa) Limited 45 6
Sterling Capital Limited 45 6
Dry Associates Investment Group 40 6
Salaam Investment Bank Kenya Limited 35 6
Total 700 100
Source: CMA (2019)
3.3.2 Sampling Design
The sampling design describes the sampling frame, sampling techniques and the sample
size for the study (Cooper & Schindler, 2014)
3.3.2.1 Sampling Frame
According to Cooper and Schindler (2014) sampling frame is a list of elements from which
sample is actually drawn and is closely related to the population. Anderson et al. (2015) on
36
the other hand, defined sampling frame as the source material or device from which a
sample is drawn. It is a list of all those within a population who can be samples. Capital
Markets Authority (CMA), indicate that there are 16 registered investment banks in Kenya
which have about 700 employees (CMA, 2019). Therefore, the sample was drawn from the
700 employees of the Kenyan investment banks.
3.3.2.2 Sampling Technique
The OECD (2004) defines sampling technique as the identification of specific process by
which the entities of the sample have been selected. It is the strategy which the researcher
adopts as the procedure for identifying the most qualified respondents to the study questions
(Sachdeva, 2009). The nature of this study required the use of the purposive sampling
technique for selecting respondents. In this method, the researcher was able to identify the
most active investment underwriters in Kenyan investment banks and convince them to
participate in the study. This strategy was useful as it enabled the selection of the
underwriters who have valuable input on the behavioral factors that impact the choice of
investment decisions.
3.3.2.3 Sample Size
Table 3.2 represents the sample size distribution for this study. A sample size is the actual
number of respondents the researcher aims to interview (Israel, 2017). It is also referred to
as the number of sampling units selected from the study population (Chandan, Singh &
Khanna, 2010). Based on the Yamane (1973) formula n= (N/1+Ne^2) this study employed
a confidence level of 0.05 to account for the 700 employees of the investment banks in
Kenya as of May 2019 (CMA, 2019). The sample size for this study therefore was
(700/1+700*0.05^2) making it 254 respondents. Kombo and Tromp (2009) defined a
sample as a subsection of the population that has been selected and represents
characteristics of a population. Because of the nature and structure of the investment banks,
there is a lean number of stuff that actually do the work of investment advisory, which
informed the research to employ a 30% tact as per Mugenda and Mugenda (2006) in getting
the desired number of respondents and ensure a true picture of the subject being researched
is reached. This then made the researcher’s target group 76 respondents, which is a more
37
precise group that ensured reliability in the collected data. A census of all investment banks
in Kenya from which brokers, analysts, tellers, dealers and the sales team was included in
the sample size.
Table 3.2 Sample Size Distribution
Investment Banks Employees Population Percentage Sample Size
Investment Analysts 86 12 25
Investment Brokers 103 15 33
Investment Underwrites 10 1 2
On the Sales team 38 5 11
Tellers 17 2 5
Others 446 64 0
Total 700 100 76
3.4 Data Collection Methods
Johnson and Turner (2003) observed that a method of data collection is simply a technique
that is used to collect empirical research data. It is how researchers get their information.
The six major methods of data collection are questionnaires, interviews, focus groups, tests,
observation and secondary data. According to Dudovskiy (2018), primary data present the
actual information that was obtained for the purpose of the research study. The data
collection instrument is a device used to collect data in an objective and a systematic
manner for the purpose of the research (Orodho, 2009). This study employed a
questionnaire method of data collection. These questionnaires were subdivided into two
parts, with the first part covering the demographic details of the respondent, while the
second part had three sub-sections answering the research questions of the study.
They included; i) what are the effects of mental accounting on investment decisions by
investment banks in Kenya?, ii) what is the impact of framing on investment decisions by
investment banks in Kenya? and iii) what are the effects of heuristics on investment
decisions by investment banks in Kenya?. A commentary section under each part of the
38
research questions was included to cater for any other additional information that had not
been specified in the specific questions. A 5-point Likert scale rating with both open-ended
and close-ended questions was used for asking respondents' opinions and attitudes that are
utilized to ask the individual underwriters to evaluate the degrees of their agreement with
the impacts of behavioral factors on their investment decision, basing on the research
questions. The 5 points in the scale were respectively from 1 to 5: Strongly disagree,
Disagree, Neutral, Agree, and Strongly Agree.
3.5 Research Procedures
According to Sileyew (2019), research procedure is the sequence of activities that are
followed when carrying out field study. The researcher commenced this process by
conducting a pilot test of the questionnaire. This process involved distributing 10
questionnaires to respondents who were not part of the final study. This pilot test was
conducted to ensure that the questionnaires are complete, precise, accurate and clear. This
assisted in assessing the reliability and validity of the data collection instrument (Price,
Jhangiani, & Chiang, 2015). The researcher looked for consistency of responses in the
questions presented for piloting in the questionnaire. This was done through test-retest-
reliability testing with the pilot respondents. The findings in table 3.3 above showed
consistency in responses represented by an Alpha Cronbach of 0.765, which built
confidence in the data collection instrument the researcher used.
Table 3.3 Results of the Reliability Test on the Study Variables
Variables Cronbach’s Alpha. No. Of Items
Effects of Mental
Accounting
0.758 10
Impact of Framing 0.731 8
Effects of Heuristics 0.806 9
Average 0.765 9
The researcher then sought for an official letter from the institution’s research office that
was used to request for permission to conduct a field survey within the Investment Banks
in Kenya. This letter explained all the details on the intents and purposes regarding the field
survey. The researcher also sought for a research permit from the National Commission for
39
Science, Technology and Innovation (NACOSTI) to allow for conduction of research
within the country. Upon receipt of the permit and acceptance of the request to conduct the
survey, the researcher delivered the questionnaire to the respondents through self and field
officers.
There were 76 questionnaires manually distributed among the 16 investment banks of
Kenya. The researcher selected individuals who were given the questionnaire to fill where
as those with any difficulties were guided by a research assistant who also assisted in
disseminating and collecting the questionnaires. During the procedure, the researcher
requested the respondents who could have the questionnaires filled up immediately to do
so, and agreed to pick up the un answered questionnaires from the respondents who were
not able to in two weeks’ period to allow them enough time to fill the questionnaires at
their own pace.
3.6 Data Analysis Methods
Data is a collection of figures and facts relating to a particular activity under study.
According to Blumberg (2014), for data to be useful, it must provide answers to the research
questions. The data that was obtained from the field was systematically organized to
facilitate for its analysis. The instruments used for data collection were tested for reliability.
The responses collected were collated, coded and analyzed using both quantitative and
qualitative techniques. Responses were then entered in the Statistical Package for the Social
Sciences (SPSS) software, version 24 to retrieve results. The quantitative data was analyzed
and presented in tables and figures. After the data was collected, Person’s correlation was
used to measure the systematic and linear relationship between the dependent and
independent variables of the study. According to Zheng, Shi and Zhang (2012), in almost
all statistical problems, dealing with how random variables depend on each other plays a
fundamental role and pearson’s correlation coefficient has been the most dominant
dependence measure that mainly depicts a symetric and linear relationship between two
variables. Allen and McAleer (2018) commented that generalized measure of correlation
can lead to more meaningful conclusions and improved decision making.
40
3.7 Chapter Summary
Chapter three covered the research methodology needed for the study which comprised of
the research design used for the study, population and sampling design of the study, data
collection methods through questionnaires, research procedures, and data analysis methods.
Chapter four presents the results and findings of the study.
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CHAPTER FOUR
4.0 RESULTS AND FINDINGS
4.1 Introduction
This chapter presents results, analysis and findings in relation to the topic of the study, the
respective research questions and interpretation of the findings. To provide a visual
representation, the study used Pearson’s correlations to present statistical relationship
between the dependent variable which is investment decisions by investment banks of
Kenya and the independent variables which are the mental accounting, framing and
heuristics in tables, graphs and charts.
4.2 Demographics
This section consists of information that describes basic demographic characteristics which
include respondents’ current job, gender, age group, education level, marital status,
duration in the investment banking practice, and their risk orientation to influence
investment decision making.
4.2.1 Response Rate
A total of 76 questionnaires were distributed to the respondents to get their reaction about
their practice in respect with the impact of behavioral finance on investment decisions by
investment banks in Kenya. Figure 4.1 presents the response rate from the field survey.
Out of the distributed 76 questionnaires, 64 questionnaires were answered and returned in
time for data analysis. This represents, 84% response rate. This response rate is in
agreement with (Mugenda & Mugenda, 2003), who explained that a response rate of 70%
and above is sufficient for data analysis. The study thus was satisfied with the response
rate, which signifies a representative sample for the study population. The N value in this
study is 64 (N=64).
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Figure 4.1 Response Rate
4.2.2 Gender
The data in figure 4.2 represents the gender distribution among the respondents of the study.
The results in figure 4.2 indicate that, majority of the participants that is 64.1% were male,
and the rest of the respondents 35.9% were female. The study therefore makes a finding
that majority of the investment practitioners in investment banks in Kenya are male. The
results indicate that there is a good progress towards realization of gender balance within
the practice of investment banking in Kenya.
Figure 4.2 Gender Distribution
4.2.3 Marital Status
The figure 4.3 represents the marital status of the respondents using the participant
distribution (N=64) as the population point basis. The findings in figure 4.3 indicate that
56% of the participants are married, 41% are single and the widowed are 3%. There are no
participants or respondents that indicated divorced or other. The findings indicate that
family values play a significant role among the decisions made by investment banks in
43
Kenya. It also forms a basis for encouragement and motivation to work harder for a better
future.
Figure 4.3 Marital Status
4.2.4 Age Group
The findings in table 4.1 show that majority of the respondents, about 54.7%, are in the age
group 21–30 years. This represents the age group of the practitioners in investment banks
in Kenya. This includes investment Analysts, investment brokers, underwriters, tellers,
sales team and others who are carry out investment decisions in investment banks in Kenya
and have a deep understanding of investment banking. The respondents at the age group of
31-40 years occupied the second rank with 32.8% followed by 41-50 years at 7.8%. The
group of 51 years and above had 1.6% and the rest who are below 21 years showed a 3.1%.
The findings in this study implicate that, either investment banks prefer employing younger
people between the age of 21-30 years, or investment bankers do not prefer to stay in the
practice after the age of 40 most likely, that is why the age group of 50 and above had no
respondents.
Table 4.1 Age Group
Age Group Frequency Percentage
Below 21 years 2 3.1%
21-30 years 35 54.7%
31-40 years 21 32.8%
41-50 years 5 7.8%
51 years and above 1 1.6%
Total 64 100%
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4.2.5 Age Level of Education
Table 4.2 represents the level of education of the respondents. The findings indicate that
46.9% have graduate/masters qualifications, 43.8% have undergraduate degree
qualifications, while the remaining 9.3% respondents were undergraduate interns, post
graduate and none under the category of other. This study therefore implicates that while
the investment decision making practitioners are on the job, they further their education to
get more qualifications to help them in betterment of the investment decisions they make
in the investment banks of Kenya.
Table 4.2 Level of Education.
Education Level Frequency Percent
Graduate/Masters 30 46.9%
Post Graduate 3 4.7%
Undergraduate Degree 28 43.7%
Undergraduate Intern 3 4.7%
Total 64 100%
4.2.6 Current Job
Table 4.3 represents the current jobs done by the respondents in this study. The findings
indicate that there is a probable balance between investment brokers and investment
analysts working in the investment banks in Kenya, quantified as 39.1% and 34.4%
respectively. The sales team respondents were 14.1% while the ones on the others category
were 7.8%, investment underwriters were 3.1% and the tellers were 1.6%. The findings
implicate that investment analysts and brokers/underwriters play a significant role in
investment decisions made by investment banks in Kenya.
Table 4.3 Current Job
Current Job Frequency Percent
Investment Analyst 22 34.4%
Investment Broker 25 39.1%
Investment Underwriter 2 3.1%
On the Sales Team 9 14.1%
Other 5 7.8%
Teller 1 1.5%
Total 64 100%
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4.2.7 Duration in Investment Banking Practice
Table 4.4 represents the duration the respondents have worked with investment banking.
The findings indicate a majority of respondents have worked with investment banking for
0-5 years at 50%, followed by those who had worked for 6-10 years at 26.6% and 11-15
years at 14.1%. Those who had worked with investment banking for 16-20 years were
4.7%, 21-25 years were 1.6% and those above 26 years were 3.1%.This implicates that
most of the investment practitioners in investment banks in Kenya are young and energetic
with fresh minds and they operate the day- to- day tasks of investment decision making
with an open perspective, not biased by experience.
Table 4.4 Duration in Investment Banking Practice
Duration in Investment Banking Frequency Percent
0-5 years 32 50.0%
6-10 years 17 26.6%
11-15 years 9 14.1%
16-20 years 3 4.7%
21-25 years 1 1.5%
26 years and above 2 3.1%
Total 64 100%
4.2.7 Risk Orientation
Figure 4.8 represents the risk orientation of the respondents. The findings indicate that a
majority of respondents are risk takers at 76.6% and a few are risk averse at 23.4%. This
implies that investment decision makers in investment banks in Kenya are risk takers and
that helps them make the most risky investment decisions and rip the respective rewards.
Figure 4.4 Risk orientation
46
4.3 Effects of Mental accounting on Investment Decisions
Table 4.5 presents the findings on the effects of mental accounting on investment decisions
by investment banks in Kenya. A scale of 1-5 was used where: 1=Strongly disagree,
2=Disagree, 3=Neutral, 4=Agree and 5=Strongly Agree. The study makes a finding that
with a mean of 4.41 and standard diviation of 0.87, majority of respondents usually evaluate
their transactions before making an investment decision. This indicates that, mental
accounting affects investment decisions made by investment banks in Kenya.
Similarly, the study makes a finidng that the the least used mental accounting factor is that
investment practitioners as about the source of funds and its intended use before making an
investment decision with a mean of 3.78 and standard deviation of 1.16 as shown in the
table 4.5 above.
This implies that investment underwriters greatly consider mental accounting, which gives
behavioral finance a pivotal role in investment decisions made by investment baks in kenya.
Table 4.5 Mental Accounting and Investment Decisions
Mental Accounting N Mean Std. Deviation Variance Skewness Std. Error
Statistic Statistic Statistic Statistic Statistic Statistic
Iusually evaluate my transactions before aking
an investment decision64 4.41 0.868 0.753 -2.260 0.299
I normally ask customers their investment
preference64 4.00 1.155 1.333 -1.405 0.299
I Normally categorize and code economic
outcome of investment choice before making a
choice
64 4.03 0.925 0.856 -0.808 0.299
I frequently separate money into different
accounts based on criteria before making an
investment decision
64 3.84 1.087 1.182 -0.751 0.299
I ask about the source of funds and its intended
use before making an investment decision
64 3.78 1.161 1.348 -1.001 0.299
I divide investments between safe and
speculative portfolios on the premise that they
will prevent the negative returns from impacting
the total portfolio
64 3.91 1.094 1.197 -1.010 0.299
I think about money and risk through mental
accounts, by separating wealth into various
buckets or pools
64 3.95 0.862 0.744 -0.521 0.299
47
The figures below show a Likert scale response to mental accounting contributors in
investment decision making by investment banks in Kenya.
4.3.1 I Usually Evaluate my Transactions Before Making an Investment Decision
Figure 4.5 indicates that 35 respondents who constitute of 54.7% of the respondents
strongly agree to evaluating transactions before making investment decisions. 39.1% agree,
1.6% are neutral, 1.6% disagree and 3.1% strongly disagree to the practice of evaluating
transactions before making an investment decision. This implies that majority of
investment underwriters in investment banks in Kenya use mental accounting before
making an investment decision.
Figure 4.5. Evaluate transactions
4.3.2 I Normally Ask Customers Their Investment Preference
Figure 4.6 represents the responses on the question of asking customers their investment
preference before making an investment decision. 39.1% of the respondents strongly agree,
40.6% agree, 9.4% are neutral, 3.1% disagree and 7.8% strongly disagree. This implies that
majority of the investment practitioners in investment banks of Kenya ask customers their
preferences before making an investment decision.
48
Figure 4.6 Ask customer’s preference
4.3.3 I Usually Categorize and Code the Economic Outcome of an Investment Choice
Before Making a Decision
Figure 4.7 represents the respective responses to categorizing and coding the economic
outcome of an investment choice before making an investment decision. Majority of
respondents strongly agree to this practice with 35.9%, 37.5% agree to this practice, 21.9%
are neutral, 3.1% disagree and 1.6% strongly disagree to this practice. This implies that
most of the investment underwriters in investment banks in Kenya categorize and code
economic outcome of an investment when considering to make an investment decision.
Figure 4.7 Categorize and Code Economic Outcome.
4.3.4 I Frequently Separate Money into Different Mental Accounts Based on Criteria
Before Making an Investment Decision.
Figure 4.8 presents the survey responses of the question of frequently separating money
into different mental accounts based on criteria before making an investment decision.
49
32.8%, and 34.4% of the respondent are in agreement to this practice, 20.3%, are neutral,
9.4% disagree and 3.1% strongly disagree. With 67.2% of the respondents in agreement
with the practice of separating money into different mental accounts, it implies that majority
of investment brokers, investment analysts and underwriters in investment banks in Kenya
practice mental accounting in their day-to-day operations.
Figure 4.8 Separating money into Different Mental Accounts
4.3.5 I Ask About the Source of Funds and Its Intended Use Before Making an
Investment Decision
Figure 4.9 presents the response to the mental accounting element of asking about source
of funds and its intended use before making an investment decision and the findings are
that 29.7% strongly agree to this practice, 39.1% agree, 18.8% are neutral, 4.7% disagree,
and 7.8% strongly disagree. This implies that investment practitioners in investment banks
in Kenya are cautious about the funds being fronted for investment purposes.
Figure 4.9 Ask About Source of Funds and Intended Use
50
4.3.6 I Divide Investments Between Safe and Speculative Portfolios
Figure 4.10 presents field survey responses to the mental accounting element of dividing
investments between safe and speculative portfolios on the premise that they will prevent
the negative returns from impacting the total portfolio, and the findings are that 46 out of
64 respondents are in agreement that they use mental accounting in their daily operations.
This is broken down in 34.4% strongly agree to doing this practice, 37.5% agree, 17.2%
are neutral to this practice, 6.3% disagree and 4.7% strongly disagree to partaking in the
practice of this mental accounting element. This implies that majority of Kenya investment
banking practitioners practice mental accounting in their daily operations.
Figure 4.10 Divide Investments Between Safe and Speculative Portfolios
4.3.7 I Think About Money and Risk Through Mental Accounts
Figure 4.11 presents field survey responses to the question of thinking about money and
risk through mental accounts, by separating wealth into various buckets or pools. The
findings are that 47 respondents agree to this practice, that is 28.1% strongly agree and
45.3% agree, 20.3% are neutral to this practice, 6.3 disagree and none strongly disagrees to
this practice. This implies that majority of the investment brokers, Analysts and
underwriters in investment banks in Kenya practice mental accounting before making an
investment decision.
51
Figure 4.11 Thinking about Risk Through Mental Accounts
Besides the already set mental accounting probes to investigate whether investment practitioners in
investment banks in Kenya apply mental accounting, respondents responded as follow to the
question of which other ways they apply mental accounting in their investment banking practice.
The study finds that respondents normally ask investors to answer a few mental accounting
questions prior to making an investment decision, the study also indicates that respondents use
mental accounting when determining the level of gains to be made from investment and any
potential threats to be addressed. Respondents in this study indicated that they use mental
accounting when timing when to execute a transaction, some of the respondents indicated that
mental accounting is possibly sentimental, others rarely use it in their daily practice of investment
decision making.
The study indicates that respondents use mental accounting when assessing their viability to make
good returns, determining whether the return on investment will be satisfactory, in monthly personal
budgeting, when analyzing price movements, in including inflation while considering returns, when
making a decision on spending behavior and investing.
4.4 Impact of Framing on Investment Decisions
The data in table 4.6 presents the mean and Standard deviation of framing and investment
decisions made by investment banks in Kenya. A Likert scale of 1-5 was used where:
1=Strongly disagree, 2=Disagree, 3=Neutral, 4=Agree and 5=Strongly Agree. The study
makes a finding that in majority of the respondents’ investment practice, they feel the pain
of losses more than they feel the pleasure of gains and they can take on additional risk to
avoid a loss with a mean of 3.8 and standard deviation of 1.106. This indicates that majority
of respondents agree that they use framing in their investment decision making. The study
52
also makes a finding that the least used framing bias is the fact that investment brokers,
analysts, underwriters and sales men make investment decisions based on the way the
information is presented as opposed to on the prevailing facts. This has a mean of 3.3 and
a standard diviation of 1.25. These findings imply that, Investment Brokers, Analysts and
Underwriters in investment banks in Kenya are actively applying framing in their
investment decision making activities as they go about their day-to-day investment
advisory work.
Table 4.6 Framing and Investment Decisions
The Figuress below present the response rate per the used scales in determining the effects
of framing on investment decision making.
Framing N Mean Std. Variance Skewnes Std.
Statistic Statistic Statistic Statistic Statistic Statistic
I make investment decisions based on the way the
information is presented as opposed to on the prevailing
facts
64 3.30 1.256 1.577 -0.489 0.299
I react to a particular opportunity differently depending
on how it is presented to me64 3.70 1.108 1.228 -0.824 0.299
When making investment decisions, I do not focus on
the net change in value but on the individual gains and
losses along the way
64 3.55 1.068 1.141 -0.610 0.299
The way information has been presented has caused me
to make inconsistent choices. I have been more risk
averse after a “loss” and less risk averse after a “win”
regardless of the net result
64 3.73 1.116 1.246 -0.439 0.299
In my investment practice, I feel the pain of losses more
than I feel the pleasure of gains and I can take on
additional risk to avoid a loss
64 3.83 1.106 1.224 -1.028 0.299
I tend to have a specific change in investment decision
preferences between two investment options when also
presented with a third option
64 3.64 0.932 0.869 -0.547 0.299
53
4.4.1 I Make Investment Decisions Based on the Way The Information is Presented
Figure 4.12 presents the field survey likert scale response of the framing aspect of making
investment decisions based on the way information is presented as opposed to on the
prevailing facts. The findings show that majority of the respondents agree to this practice
that is 14.1 strongly agree and 43% agree, 10.9% are neutral to practicing this in their
investment decision making, 20.3% disagree and 10.9% strongly disagree to making
decisions based on the way information is presented as opposed to prevailing facts. This
implies that 31.2% of the investment brokers, analysts and underwriters in investment
banks in Kenya make investment decisions basing on the prevailing facts, that is the 20.3%
who disagree and the 10.9% who strongly disagree with this practice.
Figure 4.12 Investment Decisions and the Way Information is Presented
4.4.2 I React to a Particular Opportunity Differently Depending on How it is
Presented to me
Figure 4.13 presents the field survey results on the question of reacting to opportunities
differently depending on how it is presented to the respondents. Findings show that a
majority of them agree to doing this when carrying out their day to day activities. 25%
strongly agree, 39.1% agree, 23.4% are neutral and 12.6% both disagree and strongly
disagree to engaging in this practice in their daily activities. This implies that majority of
investment banking practitioners in Kenya use framing in their investment banking decision
making.
54
Figure 4.13 Reacting to Particular Opportunities Differently
4.4.3 When Making Investment Decision, I do Not Focus on the Net Change in Value
This Figure 4.14 presents the response on the question of not focusing on the net change in
value but on the individual gains and losses along the way when making investment
decisions. The study findings show that 59.4% agree to this practice and 23.4% are neutral.
12.5% and 4.7% disagree to using this practice in their investment decision making
practice.
Figure 4.14 Focus on Individual Gains and Losses
4.4.4 The Way Information has been Presented has Caused me to Make Inconsistent
Choices
Figure 4.15 presents the field findings of the framing element of tending to make
inconsistent choices. Findings show that 55.2 % of the respondents are in agreement that
the way information has been presented has caused them to make inconsistent choices. The
survey finds that respondents have been more risk averse after a “loss” and less risk averse
55
after a “win” regardless of the net result. 31.3% are neutral to this practice while 12.4%
disagree to having been influenced by this framing element in their investment decision
making.
Figure 4.15 Inconsistent Choices of Investment Decisions
4.4.5 I Can Take Additional Risk to Avoid a Loss
Figure 4.16 presents the likert scale research results on the question of feeling the pain of
losses much more than the pleasure of gains. Findings show that 70.3% of respondents
agree to the fact that in their investment practice, they feel the pain of losses more than they
feel the pleasure of gains and they can take on additional risks to a void a loss. 18.8% are
neutral and 11% are in disagreement with this element of framing affecting their investment
decision making.
Figure 4.16 Feel the Pain of Loss Much More Than the Pleasure of Gains.
56
4.4.6 The Decoy Effect
Figure 4.17 presents the field survey results on the decoy effect of framing. Findings show
that a majority of respondents agree to being affected by the decoy effect in their investment
decision making. 62.5% of the respondents tend to have a specific change in investment
decision preferences between investment options when also presented with a third option.
25% are neutral to this practice and 12.5% of the respondents are in disagreement with the
decoy effect affecting their investment decision making.
Figure 4.17 Decoy Effect
The field survey of the other ways respondents were using framing in their day-to-day
investment decision making practice, findings show that framing is being used when using
possibilities and assumptions rather than actual figures, in market information and
corporate actions, while other respondents mentioned that they consider every investment
on a case by case basis. The study also finds that respondents use framing when presented
with information that is not in the general market, influencing the size of the company based
on its assets, and that information presentation is good as it causes last minute sway in
decision making, but when the question conflicts with facts, then facts prevail.
4.5 Effects of Heuristics and Investment Decisions
The data in table 4.7 presents the mean and Standard deviation of heuristics and investment
decisions made by investment banks in Kenya. A Likert scale of 1-5 was used where:
1=Strongly disagree, 2=Disagree, 3=Neutral, 4=Agree and 5=Strongly Agree. The study
makes a finding when making investment decisions, majority of respondents usually
recommend investing with companies that get good press and high profile corporations as
57
more than the less publicized companies with a mean of 3.73 and standard diviation of 1.06.
this implies that investment bank practitioners in Kenya take into account company
profiling when makind investment decisions. The study also makes a finding that the least
used heuristic bias is the overconfidence heuristic, where investment practitioners tend to
attribute successful outcomes to their own actions and bad outcomes to external factors
with a mean of 3.3 and standard diviation of 1.18. This means that most investment
analysts, brokers, underwriters and sales men take responsibility for their actions and do
not blame it on the prevailing investment environmental factors
Table 4.7 Heuristics and Investment Decisions
Heuristics N Mean Std. Varianc Skewnes Std.
Statistic Statistic Statistic Statistic Statistic Statistic
I frequently anchor too heavily on initial piece of
information when evaluating choices of an
investment decision to make
64 3.61 1.190 1.416 -0.647 0.299
I usually overestimate the investment’s yield
capabilities when making an investment decision64 3.55 1.154 1.331 -0.630 0.299
When making an investment decision under
uncertainty, I tend to believe that a history of a
remarkable performance of a given firm is
representative of its general performance and it
will continue to perform well in the future.
64 3.67 1.142 1.303 -0.770 0.299
When making investment decisions, I usually
recommend investing with companies that get
good press and high profile corporations as
more than the less publicized companies
64 3.73 1.058 1.119 -0.438 0.299
I tend to attribute successful outcomes to my
own actions and bad outcomes to external
factors
64 3.34 1.185 1.404 -0.589 0.299
I usually have preference for familiar investments
despite the seemingly obvious gains from
diversification when making investment decisions
64 3.70 1.079 1.164 -0.704 0.299
58
The figures below show the Likert scale findings on the effect of heuristics on investment
decision making by investment banks in Kenya.
4.5.1 The Anchoring Heuristic and Investment Decisions
Figure 4.18 presents the likert scale field survey investigation of the element of anchoring
and its effect on investment decision making. The findings show that 25% of the
respondents strongly agree to frequently anchoring too heavily on initial piece of
information when evaluating choices of an investment to make. 37.5% agree, 17.2% are
neutral, 14.1% disagree and 6.3% strongly disagree. These results imply that anchoring is
a factor that is used in the investment decision making by investment banks in Kenya.
Figure 4.18 Anchoring and Investment Decision Making
4.5.2 Overstatement and Investment Decisions
Figure 4.19 shows field responses for the question of I usually overstate the investment’s
yield capabilities when making an investment decision and the findings show that more
than half of the respondents agree to this practice, that is 39 out of 64 respondents. This has
been broken down as 20.3% strongly agree to usually overestimating the investment’s yield
capabilities when making an investment decision. 40.6% agree to engaging in the practice
of overstatement, 18.8% are neutral to heuristics influence, 14.1% disagree and 6.3%
strongly disagree.
59
Figure 4.19 Overstatement and Investment Decision Making
4.5.3 Representativeness Heuristic and Investment Decisions
Figure 4.20 presents field survey findings on the element of anchoring on past performance.
Findings reveal that 25.4% of respondents strongly agree that when making an investment
decision under uncertainty, they tend to believe that a history of a remarkable performance
of a given firm is representative of its general performance and it will continue to perform
well in the future. 38.1% agree to representative heuristic affecting their decision making
practice, 20.6% are neutral, 9.5% disagree and 6.3% strongly disagree.
Figure 4.20 Representativeness Heuristic and Investment Decision Making
4.5.4 Availability Heuristic and Investment Decisions
Figure 4.21 presents field survey results of the availability heuristic and its effects to
investment decision making. 27% of the respondents strongly agree that When making
investment decisions, they usually recommend investing with companies that get good
press and high profile corporations as more than the less publicized companies, 38.1%
60
agree, 20.6% are neutral. This finding implies that investors tend to give more weight to
more available information and to discount information that is brought to their attention
less often. 12.7% disagree and 1.6% strongly disagree to this practice affecting their
investment decision making operations.
Figure 4.21 Availability Heuristic and Investment Decision Making
4.5.5 Overconfidence and Investment Decisions
Figure 4.22 presents likert scale field survey results of the overconfidence heuristic and
response on how it affects investment decision making. 14.3% of respondents strongly
agree that they tend to attribute successful outcome to their own actions and bad outcomes
to external factors. 38.1% agree, and 16.4% are neutral. This implies that investment
practitioners in investment banks in Kenya have too much faith in the precision of their
estimates. 11.1% disagree and 11.1% of respondents strongly disagree to this practice
affecting their investment decision making.
Figure 4.22 Overconfidence and Investment Decision Making.
61
4.5.6 Ambiguity Aversion and Investment Decisions
Figure 4.23 presents the likert scale survey results of the ambiguity aversion connection
with investment decision making. Findings show that 25% and 37.5% of respondents
strongly agree and agree to usually having preference for familiar investments despite the
seemingly obvious gains from diversification when making investment decisions. 25% are
neutral, 7.8% disagree and 4.7% strongly disagree to this. This implies that investment
banking practitioners in investment banks in Kenya tend to stick to what they know when
making investment decisions.
Figure 4.23 Ambiguity Aversion and Investment Decision Making
When respondents were asked which other ways they apply heuristic biases when making
investment decisions, they responded that they apply heuristic biases when involved in
foreign participation in stock, when buying shares as a group and they are being influenced
about an investment choice between shouting a company and a silent one, for example
safaricom and Limuru tea. Findings also revealed that respondents mentioned that they will
involve in poor decision making due to lack of enough data, and also that risk appetite and
investment personality may at times deceive even the best of the pickers. In conclusion, the
study found that behavioral finance has a big impact to investment decisions made by
investment banks in Kenya.
4.5.1 Correlation Tests
Correlation test is used to evaluate the association between two or more variables by
assessing for existence of statistical relationship between the independent variable and the
dependent variable (Li, Tan, & Wang, 2019). The tests also provides the significant factor
62
which helps in evaluating whether the findings of the study can be inferred for the target
population.
4.5.1.1 Correlations on Mental Accounting and Investment Decisions
The data in table 4.8 presents the results of computation on the effect of mental accounting
on investment decision making. The findings there in present the correlation between the
mental accounting and investment decisions made by investment banks in Kenya.
The correlation shows a positive linear relationship between mental accounting and
investment decision making. The significance is reflected at level 0.01, recording a p value
of 0.000 which points to a significant statistical evidence existing in the sample suggesting
that the relationship between mental accounting and investment decision making does exist
as shown with (p < 0.01, Correlation Coefficient = 0.429). The Correlation value of 0.429,
indicates that the relationship is positive but moderate as it is below 0.5. This implies that,
a positive change in mental accounting factors will trigger a change in investment decision
making.
Table 4.8 Correlation on Mental Accounting and Investment Decision Making
Investment
Decision Making
Mental
Accounting
Investment
Decisions
Making
Pearson Correlation 1 .429**
Sig. (2-tailed) .000
N 64 64
Mental
Accounting
Pearson Correlation .429** 1
Sig. (2-tailed) .000
N 64 64
**.Correlation is significant at 0.01 level (2-tailed).
Coefficients on Mental Accounting and Investment Decision
The Table 4.9 presents a coefficients equation analysis done between mental accounting
and investment decision making. The results showed that there is a positive correlation
between the independent variable, that is mental accounting and the dependent variable,
which is investment decisions. The investment decisions are critical at 1.970, and the
relationship is positive depicted by the positive correlation value R = 0.472. This means
that a unit change in the mental accounting factors will positively change the level of
63
investment decisions made and vice versa. With an estimated critical value of 0.05 (α=0.05,
95% confidence level), The range in the confidence level will vary from plus or minus the
standard of error, that is (1.970 + 0.510=2.48) and (1.970 -0.510=1.46) which both falls on
the positive side of the curve. This implies a positive relationship existence between mental
accounting factors and investment decisions making. The t score is simply the B value
divided by the std. Error, hence the figures 3.866 and 3.741. The significance value, p value
confirms that there is enough statistical evidence to show that the relationship between
mental accounting and investment decisions exists since it is less than the level of
significance 0.05 (p = 0.000 < 0.05). This significance is however moderately strong since
the relationship is below the estimated level of significance at 0.5, that is (0.472 < 0.5).
The coefficient equation on mental accounting and Investment decisions is as below;
Y+X. =1.970 + 0.472
Where Y=Dependent Variable(Constant)/Investment Decisions and
X is the Independent Variable.(Mental Accounting)
Table 4.9 Coefficients on Mental Accounting and Investment Decisions
Unstandardized
Coefficients
Standardized
Coefficients
Model B Std. Error Beta T Sig.
1 (Constant) 1.970 .510 3.866 .000
Mental Accounting .472 .126 .429 3.741 .000
a. Dependent Variable: Investment_Decision_Making
Model Summary on Mental Accounting and Investment Decisions.
Table 4.10 presents the model summary on mental accounting. The R value represents the
strength of the relationship between the independent variable which is mental accounting
and the dependent variable which is investment decision making. The Correlation value, of
0.429, indicates that there is a positive relationship between mental accounting and
investment decisions but it is moderately strong since the R value is less than 0.5. This
implies that, a positive change in mental accounting factors will trigger a positive change
in investment decision making and vice versa. The adjusted R2 Value, 0.171, implies that,
64
mental accounting factors account for 17.1% in variability for the performance in
investments, whereas, 82.9% in variability in the investment decision making can be
attributed to other factors external from the mental accounting factors the survey was
testing.
Table 4.10 Model Summary on Mental Accounting and Investment Decisions.
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .429a .184 .171 .63034
a. Predictors: (Constant), Mental Accounting
4.5.1.2 Correlations on Framing and Investment Decisions
The data in table 4.11 presents the results of correlation test between framing and the
investment decision making. The findings show a positive linear relationship between
framing and investment decisions at 0.428. This means that the relationship is moderate as
it is below 0.5. The calculations at a significance level of 0.01 registered a p value of 0.000,
which implies that there exists significant statistical relationship between framing biases
and investment decisions as shown in (p<0.01, p=0.000, R=0.428). The R value generated
0.428, indicates that the relationship is a moderate positive correlation between framing
and investment decisions. These findings imply that, a positive change in framing biases
will cause moderately significant positive change on the investment decision making.
Table 4.11 Correlation on Framing and Investment Decision Making
Investment
Decision Making
Framing
Investment
Decisions
Making
Pearson Correlation 1 .428**
Sig. (2-tailed) .000
N 64 64
Framing Pearson Correlation .428** 1
Sig. (2-tailed) .000
N 64 64
**.Correlation is significant at 0.01 level (2-tailed).
65
Coefficients on Framing and Investment Decisions
The Table 4.12 presents a coefficients equation analysis done between framing and
investment decision making. The results showed that there exists a positive correlation
between the independent variable, that is framing and the dependent variable, which is
investment decisions. The investment decisions are critical at point 2.419, and the
relationship is positive depicted by the positive correlation value R = 0.401. This means
that a unit change in the framing factors will positively change the level of investment
decisions made and vice versa. With an estimated critical value of 0.05 (α=0.05, 95%
confidence level), the range in the confidence level will vary from plus or minus the
standard of error, that is (2.419 + 0.393 = 2.812) and (2.419 - 0.393 = 2.026) which both
falls on the positive side of the curve. This implies a positive relationship existence between
framing factors and investment decisions making. The t score is simply the B value divided
by the std. Error, hence the figures 6.148 and 3.725. The significance value, p value shows
that there is enough statistical evidence to confirm that the relationship between framing
and investment decisions exists since it is less than the level of significance 0.05 (p = 0.000
< 0.05). This significance is however moderately strong since the relationship is below the
estimated level of significance at 0.5, that is (0.401 < 0.5). The coefficient equation on
framing and Investment decisions is as below;
Y+X. =2.419 + 0.401
Where Y=Dependent Variable(Constant)/Investment Decisions and
X is the Independent Variable (Framing)
Table 4.12 Coefficients on Framing and Investment Decisions
Unstandardized
Coefficients
Standardized
Coefficients
Model B Std. Error Beta T Sig.
1 (Constant) 2.419 .393 6.148 .000
Framing .401 .108 .428 3.725 .000
a. Dependent Variable: Investment_Decision_Making
66
Model Summary on Framing and Investment Decisions.
Table 4.13 presents the model summary on framing. The R value represents the strength of
the relationship between the independent variable which is framing and the dependent
variable which is investment decision making. The Correlation value, of 0.428, indicates
that there is a positive relationship between framing and investment decisions but it is
moderately strong since the R value is less than 0.5. This implies that, a positive change in
framing factors will trigger a positive change in investment decision making and vice versa.
The adjusted R2 Value, 0.170, implies that, framing factors account for 17.0% in variability
for the performance in investments, whereas, 83% in variability in the investment decision
making can be attributed to other factors external from what the framing factors the survey
was testing.
Table 4.13 Model Summary on Framing and Investment Decisions.
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .428a .183 .170 .63083
a. Predictors: (Constant), Framing1
4.5.1.3 Correlations on Heuristics and Investment Decisions
The data in table 4.14 presents the results of correlation test between heuristics and the
investment decision making. The findings present that the relationship between heuristics
and investment decisions by investment banks in Kenya of 0.447 is a positive linear
relationship. The computations at a significance level of 0.01 registered a P value of 0.000,
which is interpreted to mean that there exists moderately significant statistical relationship
between heuristics biases and investment decisions as shown in (p<0.01, p=0.000,
R=0.447). The R value generated 0.447, suggests that the relationship is a moderate positive
correlation between heuristics and investment decisions and implies that a change in
heuristics biases results into a positive change in investment decision making and vice
versa. The results showed that the independent variables that is mental accounting, framing
and heuristics have a positive correlation with investment decisions by investment bankers.
The R value represents the strong correlation which is 0.525 and R Square represents the
total variation in the dependent variable (Investment Decisions), as a result of change in the
independent variables. From the analysis, the R Square value was 0.276 which implies 28%
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of the variation in decision making was caused by changes in mental accounting, framing
and heuristics, as shown in table 4.10 above. The significance had a P value of 0.000 <
0.05, implying that there is significant statistical evidence existing to suggest that the
correlation exists.
Table 4.14 Correlation on Heuristics and Investment Decision Making
Investment
Decision Making
Heuristics
Investment
Decisions
Making
Pearson Correlation 1 .447**
Sig. (2-tailed) .000
N 64 64
Heuristics Pearson Correlation .447** 1
Sig. (2-tailed) .000
N 64 64
**.Correlation is significant at 0.01 level (2-tailed).
Coefficients on Framing and Investment Decisions
The Table 4.15 presents a coefficients equation analysis done between heuristics and
investment decision making. The results showed that there is a positive correlation between
the independent variable, that is heuristics and the dependent variable, which is investment
decisions. The investment decisions are critical at point 2.443, and the relationship is
positive depicted by the positive correlation value R = 0.395. This means that a unit change
in the heuristics factors will positively change the level of investment decisions made and
vice versa. With an estimated critical value of 0.05 (α=0.05, 95% confidence level), the
range in the confidence level will vary from plus or minus the standard of error, that is
(2.443 + 0.367 = 2.81) and (2.443 - 0.367 = 2.076) which both falls on the positive side of
the curve. This implies that a positive relationship existence between heuristics biases and
investment decisions making. The t score is simply the B value divided by the std. Error,
hence the figures 6.660 and 3.397. The significance value, p value shows that there is
enough statistical evidence to confirm that the relationship between heuristics biases and
investment decisions exists since it is less than the level of significance 0.05 (p = 0.000 <
0.05). This significance is however moderately strong since the relationship is below the
estimated level of significance at 0.5, that is (0.447 < 0.5). The coefficient equation on
framing and Investment decisions is as below;
68
Y+X. =2.443 + 0.395
Where Y=Dependent Variable (Constant)/Investment Decisions and
X is the Independent Variable (Heuristics)
Table 4.15 Coefficients on Heuristics and Investment Decisions
Unstandardized
Coefficients
Standardized
Coefficients
Model B Std. Error Beta T Sig.
1 (Constant) 2.443 .367 6.660 .000
Framing .395 .100 .447 3.937 .000
b. Dependent Variable: Investment_Decision_Making
Model Summary on Heuristics and Investment Decisions.
Table 4.16 presents a model summary on heuristics and investment decisions. The R value
represents the strength of the relationship between the independent variable which is
heuristics and the dependent variable which is investment decision making. The Correlation
value, of 0.447, indicates that there is a positive relationship between heuristics and
investment decisions but it is moderately strong since the R value is less than 0.5. This
implies that, a positive change in framing factors will trigger a positive change in
investment decision making and vice versa. The adjusted R2 Value, 0.200, implies that,
heuristics biases account for 20.0% in variability for the performance in investments,
whereas, 80% in variability in the investment decision making can be attributed to other
factors external from what the heuristics biases the survey was testing.
Table 4.16 Model Summary on Heuristics and Investment Decisions.
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .447a .200 .187 .62418
b. Predictors: (Constant), Heuristics
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ANOVAa of Behavioral Finance and Investment Decision Making
An ANOVA table reports how well the regression equation fits the data. An analysis done
at 95% of confidence level produced an F critical of 7.619 and the P value of 0.000. This
analysis confirmed that investment decision making had a significant relationship with
behavioral finance, as shown in table 4.17 above.
Table 4.17 ANOVAa of Behavioral Finance and Investment Decision Making
Model Sum of
Squares
df Mean
Square
F Sig.
1 Regression 8.329 3 2.776 7.619 .000b
Residual 21.865 60 .364
Total 30.194 63
a. Dependent Variable: Investment Decision Making
b. Predictors: (Constant), Framing, Mental Accounting, Heuristics
Regression Equation on Mental Accounting, Framing and Heuristics and
Investment Decision Making.
The equation below represents regression between the dependent variable and the three
independent variables.
Y = a+bX1+bX2+bX3
Where, 𝑌 = 1.537 + 0.255𝑋1 + 0.154𝑋2 + 0.210𝑋3
Y = Investment Decision Making (%) - Dependent variable
𝑋1= Mental Accounting biases (%) - Independent variable
𝑋2= Framing Biases (%) - Independent variable
𝑋3= Heuristics Biases (%) - Independent variable
The regression equation above expresses that considering all other factors are held constant
investment decision making increases by 1.537.
Model Coefficients
The findings in Table 4.18 presented that with all other variables held at zero, a unit change
in mental accounting biases would lead to a positive change of 0.255 in investment decision
making (Beta -0.231, p > 0.05), a unit change in framing biases would lead to positive
70
change of 0.154 in investment decision making with (Beta 0.165, p > 0.05), and a unit
change in Heuristics biases would result to a positive change of 0.210 in investment
decision making with (Beta 0.237, p > 0.05). This equation provides significant statistical
evidence of a positive relationship between behavioral finance biases which are mental
accounting, framing and heuristics and investment decision making considering a a
confidence level of 0.05, p=0.004 (p<0.05).
Table 4.18 Model Coefficientsa
Unstandadized
Coefficients
Standadized
Coefficients
Sig 95.0% confidence
interval for B
Model B Std.
Error
Beta T Lower
Bound
Upper
Bound
1 (Constant) 1.537 .513 2.993 .004 .501 2.564
Mental Accounting .255 .145 .231 1.752 .085 -.036 .564
Framing .154 .137 .165 1.127 .264 -.119 .428
Heuristics .210 .126 .237 1.669 .100 -.042 .461
a. Dependent Variable: Investment Decision Making
4.6 Chapter Summary
The chapter has presented data presentation and analysis of the questionnaires. This
included the results and analysis of demographic data, descriptive statistics, likert scale
findings and correlation tests, coefficients analysis, and regression tests to assess the effects
of mental accounting, to establish the impact of framing, and to determine the effects of
heuristics on investment decisions by investment banks in Kenya. The next chapter presents
discussion, conclusions and recommendations.
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CHAPTER FIVE
5.0 DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS
5.1 Introduction
This section of the study covers the summary of the findings which highlights the key main
findings in this study. The section will then cover the study findings discussions, where the
thematic association is made between the main findings of this study and the past literature
on the behavioral finance and investment decisions by investment banks in Kenya. The
chapter will then draw the main conclusions from the field study and offer
recommendations drawn from the findings.
5.2 Summary
The purpose of this study was to investigate the impact of behavioral finance on investment
decisions by investment banks in Kenya. The research questions of the study were: What
are the effects of mental accounting? What is the impact of framing? And what are the
effects of heuristics on investment decisions by investment banks in Kenya?
The study employed a causal research design to investigate the relationship between the
independent variables which included mental accounting, framing and heuristics and the
dependent variable which is investment decision making. Primary data collection was used
with a drop and pick of 76 questionnaires with both open-ended and close-ended questions
where 84% of the responses were recorded. Among the respondents, 64.1% were male and
35.9% were female. Among the male and female respondents, majority were aged between
21-30 years. This indicated that majority of investment bankers were young to middle aged
males. The responses on level of education were 43.3% masters’ degree holders and 46.9%
undergraduate degree holders, which strikes a balance in the level of education and
ultimately meant that the level of education has no significant influence on the quality of
investment decisions made by investment bankers.
The population of the research was 700 employees of all 16 investment banks in Kenya,
with 36% in investment baking practice, which included Investment Analysts, investment
brokers, investment underwriters, tellers and the sales team. The study research used
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Pearson's correlation, regression model summary analysis for the three research regression
and data was analyzed by IBM SPSS. A multiple linear regression model was used to
evaluate the relationship between three independent variables which are mental accounting,
framing and heuristics, and the dependent variable which is Investment decision making.
The results were presented in Tables and figures.
On the effects of mental accounting on investment decision making, the Pearson correlation
results showed a positive where r = 0.429, p = 0.000. This implies that mental accounting
and investment decision making are positively correlated and the relationship is statistically
significant since the P-value is less than the 0.01. This confirms that mental accounting has
a direct relationship with investment decision making. According to regression analysis,
the adjusted R square value for all behavioral biases was 0.240 which implies that 24% of
the variations in investment decision was caused by mental accounting. An ANOVA
analysis and coefficient were computed at 95% confidence level, and it has confirmed a
statistically significant relationship since the F critical is 7.619 and p value is 0.000.
On the impact of framing on investment decisions making, the Pearson correlation results
indicated a positive relationship as r = 0.428, p = 0.000. This implies that investment
decision making and framing are positively correlated, and the relationship is a statistically
significant since the P value is less than the level of significance, p<0.05. The regression
analysis showed that adjusted R square value was 0.240 which implies 24.0% of the
variation in investment decision making was caused by variations in behavioral finance
biases. An ANOVA analysis and coefficient did at 95% confidence level and the F critical
was 7.619 and P value of 0.000, confirming that the relationship between framing and
investment decision making is statistically significant.
On the effects of heuristics and investment decision making, the Pearson correlation results
showed a positive relationship as r = 0.447, p = 0.000. This implies that heuristics and
investment decision making are positively correlated with a moderate statistical
significance because the p value is less than the level of significance at 0.05. This signifies
that heuristics have a direct relationship with investment decision making.
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A multiple linear regression analysis was done between the dependent variable (Investment
decision making) and independent variables (Mental accounting, framing and heuristics).
The results showed that it is positively correlated between independent variables and the
dependent variable (r = 0.525, p = 0.000). This implies that independent variables and
dependent variable are positively correlated and there is statistical significance since the p
value is less than the level of significance value 0.05, (p<0.05). The adjusted R square value
was 240, which implies 24% variation in investment decision making as a result of
behavioral finance biases variations. Holding all other factors constant, mental accounting,
framing and heuristics on investment decision making reduced by 1.537. Multiple linear
regression results showed a positive relationship between Investment decision making and
behavioral finance factors. P value was 0.000 at the 0.05 significant level. Therefore, this
relationship has a moderately significant effect of mental accounting, framing and
heuristics on investment decision making by investment banks in Kenya.
5.3 Discussion
5.3.1 Effects of Mental Accounting on the Investment Decisions
Mental accounting is a concept in the field of behavioral economics. Developed by
economist Richard H. Thaler, it contends that individuals classify funds differently and
therefore are prone to irrational decision-making in their spending and investment behavior
(Konstantinidis & Katarachia, 2015). This research investigated the effects of mental
accounting on investment decision making and found that there was a positive effect. The
(r= 0.429, R2 =0.184 and P-value= 0.000) in the analysis implied that mental accounting
factors had an 18.4% effect 0n investment bankers’ investment decisions which is a positive
correlation with a moderate statistically significant level. This was in agreement with
Dickason and Ferreira (2018), whose findings examined risk level tolerance and mental
accounting biases and concluded that companies can more accurately profile their investors
and also offer more refined investment options using mental accounting. Braga and Favero
(2017) agree with this study with relation to the disposition effect where investors are
swayed towards selling the winning stock earlier than the losing stock in their investment
decision making practice.
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However, Seiler, Seiler and Lane (2012) disagree with the findings of this study and argue
mental accounting and investment decision making is a false reference, as they consider
transaction cost as a major determining factor for investment decision making. Seiler et al.
(2012) seem to agree with Ryan, Harrison and Schkade (2015) arguing that the inclusion
of cost benefit equation and that firm and situational factors that affect consideration of
social subsystems should be put into account when making investment decisions.
There was a presence separating money into different accounts based on criteria before
making an investment decision among investment banks’ investment decisions which
confirms Gupta and Agarwal (2018) findings that investors segregate resources and
consumptions into different categories. Further evidence by Pompian (2011) agreed that
mental accounting biases contribute greatly in influencing investment decision making and
that investors divide investments between safe and speculative portfolios on the premise
that they will prevent the negative returns from impacting the total portfolio. This is in
agreement with Konstantinidis and Katarachia (2015)’s research which stressed that mental
accounting, is a fundamental part of the behavioral finance theory and goes beyond the
constraints of traditional paradigms and contributes to the development of new funding
models. They also found that mental accounting involves people’s tendency to create,
depending on their individual characteristics, different mental accounts to which they
assign experiences.
In this research, mental accounting and investment decisions confirmed a statistically
significant relationship between both variables which is in agreement with Jain, Walia and
Gupta (2019) that investment behaviour usually diviates from logic and reason, and
consequently investors exhibit various behaviourial biases which impact their investment
decisions. According to Becher (2011), generally people employ mental accounts to
manage events differently, and their actions are affected by the method of management,
this is in agreement with the findings of this study that investment bankers practice
investment portfolio management by dividing investments between safe and speculative
portfolios on the premise that they will prevent the negative returns from impacting the
portfolio. Bencher (2011) still agrees with the findings of this study that investment
75
managers practice caution in their actions by thinking about money and risk through mental
accounts, by separating wealth into various buckets or pools. He gives an example
demonstrating that people behave differently when they use credit cards rather than cash,
stressing that they tend to bet more money on a lottery game when they use a credit card
instead of cash. Mental behavior has disproved the utility theory, according to which all
decisions are evaluated on the basis of their expected utility. Psychological attitudes and
cognitive thinking generate irrational actions and behaviors, and rational thinking is
abandoned. It becomes, therefore, evident that rational thinking is highly significant and it
is imperative that it be monitored.
5.3.2 Impact of framing on the investment decisions
In the context of investment decision making, framing is defined as the tendency
of investors, in the process of making investment decisions, to respond differently to
a choice, based on the way it is presented or formulated (Konstantinidis, Spinthiropoulos,
& Kokkonis, 2018). This study sought to establish the impact of framing on investment
decisions by investment banks in Kenya. The (r= 0.428, R2 =0.183, P-value= 0.000) in this
research showed that there was a positive impact of 18.3% of investment decisions made
being influenced by framing factors. The analysis implied that framing factors and
investment bankers’ investment decisions are positively correlated in a statistically
significant way. This conforms with Fehrenbacher et al (2018) findings who specify that
strong support that vietnamese participants have a stronger tendency to invest in additional
resources to avoid a loss and evidence that negatively framed information leads to the
higher escalation of commitment.
The findings on the question of investors having a specific change of mind between two
investment options when also presented with a third option (decoy) had a positive response
from the survey and this agrees with Kida, Moreno and Smith (2010) who found that the
paradox of choice phenomenon is evident for participants who are less experienced with
investing. Note that 50% of the study participants had worked with investment banking for
a period of between 0-5 years. According to Kida et al. (2010), individuals who are more
experienced with investment decisions were actually less likely to invest when faced with
76
a limited choice set, centrally to the paradox of choice or decoy effect phenomenon.
Hafenstein and Bassen (2016) findings in fact sugest that the paradox of choice may not
exist when individuals with investment experience make their decisions. This also
contradicts with Konstantinidis et al. (2018) who found that people generally draw
conclusions based on the framework in which a situation is presented or formed. The impact
of framing according to Shefrin and Statman (2000) has been repeatedly demonstrated as
one of the major biases in the decision making process and depends on one’s age, range of
knowledge and psychological state. Thaler (1999) also agrees with the findings of the study
by stressing that the specific framing effect violates the standard finance theory of rational
choice, which assumes frame independence of the problem, namely, that the framing of a
problem does not affect decision making.
The study showed that 76.7% were risk takers an indication that there is a direct relationship
between risk affiliation and investment decision making. This agrees with Meziani and
Noma (2018) findings that on average, women are more risk averse than men. This study
contradicts with the findings of Lam and Ozorio (2013) who argued that men are more
likely to take greater risk after a win while women are more likely to take greater risk after
a loss.
Emami, Welsh, Ramadani, and Davari (2019) also found that investoors when faced with
decision anomalies in the context of mental accounting and framing had little effect on
investment decision making, contrally to the study findings that 60% of the study
respondents make investment decisions based on the way the information is presented as
opposed to on the prevailing facts.
The research findings of 56% of the respondents agreeing that the way the information has
been presented has caused them to make inconsistent investment choices disagrees with
main-stream finance theory which suggeste that investors make investment choices
depending on the potential profit-making out comes they may have (Kahneman & Tversky,
1979). Extensive research in psychology has demonstrated that investors tend to treat every
decision as unique and isolate each choice from others. This is defined as the effect of
77
narrow framing, wherein the conjunctions of complicated choices are neglected Kahneman
(1993) and is in agreement with this study findings where 58% of the respondents agreed
to making investment decisions based on the way the information is presented to them.
5.3.3 Effects of Heuristics on the Investment Decisions
Heuristics in this study have been referred to as the rules of thumb, which makes decision
making easier, especially in complex and uncertain environments by reducing the
complexity of assessing probabilities and predicting values to simpler judgments as
described by (Kahneman & Tversky, 1974).
This research showed in the investigation of effects on heuristics on investment decisions
showed that there was a positive impact where (r= 0.447, R2 =0.200, P-value= 0.000)
interpreted to mean that 20% of investment decisions by investment banks in Kenya were
influenced by heuristics bases. This study is in agreement with Thaler et al.(2015) who
found that there is evidence of the use of heuristics in decision making process and also
highlighted the critical role played by various learning from the experience of others.
Otuteye and Siddiquee (2015) findings that heuristics are useful practical tools for
simplifying decision making in complex environment due to uncertainty, limited
information and bounded rationality agree with the study findings that 79.7% of the study
respondents anchor too heavily on initial piece of information when evaluating choice of
an investment decision to make.
Research by Peng et al. (2011) contradicts with the study findings by stating that bad market
states evoke negative affective states in investors and that investors may rely less on the
use of heuristics and become more careful and logical in their investment decisions (Peng
et al., 2011). Blumenthal (2016) findings that biases and heuristics comprehensively
impacts decision making agree with the study findings that 82% of the study findings that
they overstate the investment’s yield capabilities and also do not focus on the when making
investment decisions and also tend to believe that a history of a remarkable performance of
a given firm is representative of its general performance and it will continue to perform
well in the future this is supported by Chakrabarti and Kumar (2017) findings that heuristic
78
biases which are representative, effect and extrapolation have a positive impact as they
cause india volatility index to be an efficient hedge for extreme negative market
movements.
Other findings that are in agreement with this research findings include Michailova,
Maciulis and Tvaronaviciene (2017) with individual trading activity and performance were
influenced by overconfidence, also pointed out by Khan, Tan and Chong (2019) that
perception of higher past portfolio returns increases investors’ trading, percentage of risky
share investment and the number of financial asset holding and Daniel and Titman (2019)
that investor overconfidence can generate momentum in stock returns and that this
momentum effect is likely to be strongest in those stocks whose valuations require the
interpretation of ambiguous information. Linsi and Schaffner (2019) are neutral to the study
findings with an argument that the degree to which investment heuristics can bias aggregate
capital flows depends on the levels of uncertainty and self-referentiality that structure the
environments under which investment decisions are being made (Linsi & Schaffner 2019).
Waweru et al. (2014) argue that the heuristics are quite useful particularly when time is
limited, but sometimes lead to biases. The representativeness heuristic in the study recorded
an agreement score of 64% which is in agreement with Boussaidi (2013) that investors tend
to form judgement based on stereotypes or the degree of similarity that an event has with
its parent population. Chandra (2016) however disagrees with this saying that while
representativeness maybe a rule of thumb, it can also lead people astray, for example,
investors may be too quick to detect patterns in data that is random, they may believe that
a healthy growth of earnings in the past may be representative of high growth rate in future
and may not realize that there is lot of randomness in earning growth rates.
5.4 Conclusions
5.4.1 Effects of Mental Accounting on Investment Decisions
The findings from the research showed that mental accounting factors were positively
influencing investment decision making. Precisely, mental accounting and investment
bankers’ investment decisions have a direct relationship as a higher presence of mental
79
accounting factors result to an increase in investment decision making of investment banks.
Though some studies found a negative correlation between mental accounting and
investment decision making, the reasons can be explained by the by different perceptions
and perspectives of considering investment decisions factors. The cause of mental
accounting biases can be linked to the behavioral finance theory, which is the study of the
influence of psychology on the behavior of investors or financial analysts. These behaviors
aid human decisions that originate from both cognitive and affective systems. Therefore, if
there is a higher presence of one of mental accounting factors, categorizing and coding of
economic outcome, dividing investments between safe and speculative portfolios, and
thinking about money and risk through mental accounts, it will affect investor’s decision
making.
5.4.2 Impact of Framing on Investment Decisions
Findings from the research recognized that investment decision making was positively
influenced by framing factors. Precisely, investment decisions and framing factors move in
the same direction, so when framing factors increase, the investment bankers’ investment
decisions increase and vice versa. Although there were studies that proposed otherwise, the
research in agreement with the study that framing factors have high statistical evidence of
influencing investment decisions are numerous. Framing biases like making investment
decisions based on the way the information is presented as opposed to the prevailing facts,
reacting particularly to a particular opportunity differently depending on how it is
presented, and having a specific change in investment decision preference between two
investment options when also presented with a third option (the decoy effect).
5.4.3 Effects of Heuristics on Investment Decisions
The findings from the research recognized that investment decisions were positively
influenced by heuristics biases. Precisely, heuristics biases and investment decision making
have a direct relationship as a higher presence of heuristics biases literally results to
increased investment decisions by investment bankers’ as investors become more confident
with the fact that they have previous information to base their investment decisions upon.
There were very many studies that supported this research with positive agreement with the
80
research findings but a few disagreed and others neutral with the idea of heuristics
influencing investment decision making. Heuristics popularly referred to as the rule of
thumb biases include representative, affect, anchoring, loss aversion and availability.
Investment decision making is very critical in the capital markets authority and heuristics
help in the quality and speed of those decisions to be made.
5.5 Recommendations
5.5.1 Recommendations for Improvement
5.5.1.1 Effects of Mental Accounting on Investment Decision Making
The research recommends that use of other research design models such as action research
design model where the researcher seeks deeper understanding of the investment decision
making process, they conceptualize and particularize the problem and move through
several interventions and evaluations, for instance, the researcher can seek an unpaid
internship opportunity to have a hands on practice on how investment decisions in
investment banks are made.
Future researchers can also try with the case study research design where they make one
leading investment bank in Kenya a case study and investigate their full operations to come
up with conclusive evidence of how investment banking top management support
investment decision making by investment banks in Kenya. The researcher can also
investigate the case by using other statistical tests such as the chi-square, independent
sample testing, discriminate analysis and open analysis to confirm whether they will come
to a conclusion different from the findings of this research as all the tests for a relationship
between mental accounting and investment decisions came out positive with significant
statistical evidence.
5.5.1.2 Impact of Framing on Investment Decision Making
The findings in this study on the question of the impact of framing on investment decisions
showed a positive relationship with significant statistical evidence. This means there was
credible evidence that framing biases will affect investment decision making but there is
no proof that it will not. Recommendations for further studies therefore can use survey data
81
with a meta-analysis design method to systematically evaluate and summarize the results
from a number of individual studies, thereby increasing the overall sample size and the
ability of future researchers to study effects of interest. This will help in developing a new
understanding of a research problem using synoptic reasoning. Recommendations for
further research can also use vignette-based questionnaire randomly assigned to subsets of
respondents and test for the relationship between framing and investment decision making.
5.5.1.3 Effects of Heuristics on Investment Decision Making
The research recommends the use of further research designs like the mixed-method design
with an examination of a real life contextual understanding, multi-level perspectives and
cultural influences. An objective of drawing on the strengths of quantitative and qualitative
data gathering techniques to formulate a holistic interpretive framework for generating
possible solutions or new understandings of the problem should be explored in further
research.
Further researchers can also use the observational research design to collect in-depth
information about the investment decision making behavior and also reveal
interrelationships among multifaceted dimensions of the investment decision makers’
group interactions. This study found a linear correlation between heuristics and investment
decisions with significant statistical evidence, further research should focus on
investigations to null this hypothesis with statistical testing of the Chi-square, ordinary least
square analysis, bivariate data analysis to test further evidence of the relationship strength
between heuristics biases and investment decision making.
5.5.2. Recommendations for Research
This study mainly focused to investigate the impact of behavioral finance on investment
decisions by investment banks in Kenya. The study recommends that further researchers
should address how behavioral finance and investment decisions affect investors by linking
investment banks and either stock brokers, non-dealing online foreign exchange brokers,
authorized security dealers, fund managers, and REITS managers to compare how they
82
come to make their investment decisions. Further researchers should address the topic with
different research methodologies to evaluate their findings.
83
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APPENDICES
Appendix I: Research Letter
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Appendix II: Nacosti Letter
THE SCIENCE, TECHNOLOGY AND INNOVATION ACT, 2013
100
The Grant of Research Licenses is Guided by the Science, Technology and Innovation (Research Licensing) Regulations,
2014
CONDITIONS
1. The License is valid for the proposed research, location and specified period
2. The License any any rights thereunder are non-transferable
3. The Licensee shall inform the relevant County Governor before commencement of the research
4. Excavation, filming and collection of specimens are subject to further necessary clearence from relevant
Government Agencies
5. The License does not give authority to tranfer research materials
6. NACOSTI may monitor and evaluate the licensed research project
7. The Licensee shall submit one hard copy and upload a soft copy of their final report (thesis) within one of
completion of the research
8. NACOSTI reserves the right to modify the conditions of the License including cancellation without prior notice
National Commission for Science, Technology and Innovation off Waiyaki Way, Upper Kabete,
P. O. Box 30623, 00100 Nairobi, KENYA
Land line: 020 4007000, 020 2241349, 020 3310571, 020 8001077
Mobile: 0713 788 787 / 0735 404 245
E-mail: [email protected] /
[email protected] Website:
www.nacosti.go.ke
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Appendix III: Cover Letter
ANNETTE NAKAYA
USIU-Africa
P. O. BOX 14634 - 00800,
Nairobi, Kenya
Dear Respondent,
I am carrying out a research on the impact of behavioral finance on investment decisions
by investment banks in Kenya. This is in partial fulfilment of the requirements for the award
of the degree of Master of Business Administration at United States International
University-Africa. This study intends to use data from investment Analysts, dealers,
brokers, tellers and the sales teams of the investment banks of kenya from which you are
part of the selected sample of respondents whose views we seek on the above mentioned
matter.
Attached is a questionnaire of which you are kindly requested to answer all the questions
accordingly. All information given in the questionnaire will be treated with strict
confidentiality and used for the purpose of this dissertation only.
A copy of the final report will be availed to the respondents/firms upon request.
Thank you for taking your time to fill in the questionnaire.
Thank you in advance,
Yours sincerely,
Annette Nakamya
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Apendix IV: Questionnaire
SECTION A: DEMOGRAPHICS
1. What is your Gender
Male ☐ Female ☐
2. What is your Marital Status?
Married ☐
Single ☐
Divorced ☐
Widowed ☐
Others ☐, Please Specify ____________________________
3. What is your Age Group
Below 21 Years ☐
21-30 Years ☐
31-40 Years ☐
41-50 Years ☐
51 Years and above ☐
4. How long have you worked with investment banking?
0-5 Years ☐
5-10 Years ☐
10-15 Years ☐
15-20 Years ☐
20 Years and Above ☐
5. What is your highest level of education?
Under Graduate Intern ☐
Under Graduate ☐
Graduate ☐
Post Graduate ☐
Other ☐, Please Specify ____________________________
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6. What is your Position?
Investment Analyst ☐
Investment Underwriter ☐
Investment Broker ☐
Teller ☐
On the Sales Team ☐
Other ☐, Please Specify ____________________________
7. What is your investment orientation?
Risk Averse ☐
Risk Taker ☐
SECTION B: BASED ON RESEARCH QUESTIONS
Given the statements on the table below, please rate your level of acceptance to the
statement with regards to behavioral finance and investment decisions by investment banks
in Kenya. Use the following key to rate your response: (1 = Strongly Disagree; 2 =
Disagree; 3 = Neutral; 4 = Agree; 5 = Strongly Agree).
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8. Effects of Mental Accounting on Investment Decision Making
Please tick the box corresponding to your personal opinion for each statement.
Mental Accounting
Strongly
Disagree
Disagree Neutral Agree Strongly
Agree
1 2 3 4 5
8.1 I usually evaluate my transactions
before making an investment
decision
8.2 I normally ask customers their
investment preference
8.3 I usually categorize and code the
economic outcome of an
investment choice before making
a decision
8.4 I frequently separate money into
different accounts based on
criteria before making an
investment decision
8.5 I think about the source of funds
and its intended use before making
an investment decision
8.6 I divide investments between safe
and speculative portfolios on the
premise that they will prevent the
negative returns from impacting
the total portfolio
8.7 I think about money and risk
through mental accounts, by
separating wealth into various
buckets or pools
In what other ways do you view yourself using mental accounting when making investment
decisions? -
___________________________________________________________________________
__________________________________________________________________
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9. Effects of framing on investment decisions
Please tick the box corresponding to your personal opinion for each statement.
Framing and investment decisions
Strongly
Disagree
Disagree Neutral Agree Strongly
Agree
1 2 3 4 5
9.1 I make investment decisions based on
t the way the information is presented
a as opposed to on the prevailing facts
9.2 I react to a particular opportunity
differently depending on how it is
presented to me
9.3 When making investment decisions, I
do not focus on the net change in value but
on the individual gains and losses along
the way
9.4 The way information has been
presented has caused me to make
inconsistent choices. I have been more
risk averse after a “loss” and less risk
averse after a “win” regardless of the net
result
9.5 In my investment practice, I feel the
pain of losses more than I feel the pleasure
of gains and I can take on additional risk
to avoid a loss
9.6 I tend to have a specific change in
investment decision preferences between
two investment options when also
presented with a third option
In what other ways do you view yourself using framing when making investment decisions?
__________________________________________________________________________
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10. Effects of heuristic on investment decisions
Please tick the box corresponding to your personal opinion for each statement.
Heuristics and investment decisions
Strongly
Disagree
Disagree Neutral Agree Strongly
Agree
1 2 3 4 5
10.1 I frequently anchor too heavily on
initial piece of information when
evaluating choices of an investment
decision to make
10.2 I usually overestimate the
investment’s yield capabilities when
making an investment decision
10.3 When making an investment decision
under uncertainty, I tend to believe that a
history of a remarkable performance of a
given firm is representative of its general
performance and it will continue to
perform well in the future.
10.4 When making investment decisions, I
usually recommend investing with
companies that get good press and high
profile corporations as more than the less
publicized companies
10.5 I tend to attribute successful outcomes
to my own actions and bad outcomes to
external factors
10.6 I usually have preference for familiar
investments despite the seemingly obvious
gains from diversification when making
investment decisions
In what other ways do you view yourself using heuristics when making investment decisions?
___________________________________________________________________________
_____________________________________________________________________
THANK YOU
