A SURVEY OF BEHAVIOURAL FACTORS INFLUENCING INDIVIDUAL INVESTORS CHOICES OF SECURITIES AT THE NAIROBI SECURITIES EXCHANGE BY: KIMANI VICTOR WARUINGI A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF DEGREE OF MASTERS OF BUSINESS ADMINISTRATION (MBA), THE SCHOOL OF BUSINESS, UNIVERSITY OF NAIROBI 2011
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A SURVEY OF BEHAVIOURAL FACTORS INFLUENCING INDIVIDUAL
INVESTORS CHOICES OF SECURITIES AT THE NAIROBI SECURITIES
EXCHANGE
BY:
KIMANI VICTOR WARUINGI
A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE AWARD OF DEGREE OF MASTERS OF
BUSINESS ADMINISTRATION (MBA), THE SCHOOL OF BUSINESS,
UNIVERSITY OF NAIROBI
2011
D E C L A R A T I O N
I, undersigned, declare that this project is my original work and has not been submitted
for a degree at any other university.
S i g n a t u r ^ T l X r ? . ^ . Date:
This research project has been submitted for examination with my approval as university
Ms. Winnie Nyamute
Lecturer,
Department of Finance and Accounting,
School of Business,
University of Nairobi.
Victor Waruingi Kimani
D61/70808/2009
A C K N O W L E D G E M E N T
It is because of the almighty God that I have been able to undertake this program
successfully and 1 have seen His providence in all areas, it is because of Him that I am
writing this today.
1 am also thankful to my supervisor Ms. Winnie Nyamute for her invaluable support in
guiding me in this project through her ideas and support that has seen my research idea
developing to an excellent research project.
Much appreciation to the investors at the NSE who took their time to complete my
questionnaire which facilitated my data analysis and findings.
Lastly I wish to thank my friend Celestine for her guidance and support during my
research.
iii
DEDICATION
To my mum who gave me and has continued give me all the support and encouragement
to complete this program despite the many challenges that 1 was facing during this
period.
iv
ABSTRACT
Although finance has been studied for thousands of years, behavioural finance which
considers the human behaviours in finance is quite a new area. Behavioural finance
theories, which are based on the psychology, attempt to understand how emotions and
future cash flows), so it seems reasonable to speculate that the emotions and feelings of
investors influence their pricing of equities.
One of the centre prepositions of finance theory has been that markets are efficient.
Efficiency means that the price of each security coincides with fundamental value even if
some investors commit error due to biases or frame dependence. Although a case may be
made against efficient markets, existing ev idence does not generally support the ability of
investors to consistently produce excess returns. That is, although market inefficiencies
may exist they are generally not easy to exploit. If stock prices are efficient and
transaction costs and taxes are ignored, investors should not do serious harm to their
wealth if they trade frequently or follow specific investing strategies. Traditional finance
has therefore developed in a normative manner, that is, traditional finance concerns the
rational solution to the decision problem by developing ideas and financial tools for how
investors should behave. As a consequence, traditional finance does not focus on actual
investor behaviour and its consequences (Thaler et al, 1992).
Although cognative and emotional weaknesses affect all people, traditional and standard
finance ignores these biases because it assumes that people always behave rationally
(Statman 1995). The traditional perspective of how people make decisions involving
conditions of risk and uncertainty assumes what Loewenstein et al. (2000) describe as a
' consequential perspective*. In this traditional model, the decision-maker is assumed to
quantitatively weigh the costs and benefits of all possible outcomes and choose the
outcome with the best risk-benefit trade-off. This perspective can be seen in the
traditional finance theories of Markowitz portfolio theory (Markowitz, 1952) and the
Capital Asset Pricing Model (Sharpe. 1964).
Behavioural finance contends that people may not always be rational, but they are always
human. Thus behavioural finance exposes the irrationality of investors in general and
shows human fallibility in competitive markets. Fama (1988) and many others claim not
to be persuaded by evidence about behavioural finance and they point out that long term
anomalies, which challenge the efficient market hypothesis, are sensitive to
methodology.
According to Shleifer (2000), behavioural finance relates the usual assumptions of
traditional finance by incorporating observable, systematic and very human departures
from rationality into models of financial markets and behaviour. By combining
psychology and finance, researchers hope to better explain certain features of securities
markets and investor behaviour that appear irrational.
Shefrin (2000) notes that investors are prone to committing specific errors of which some
are minor and others fatal. By allowing psychological bias and emotion to affect their
investment decision, investors can do serious harm to their wealth. Investors who are
prone to these biases will take risks that they do not acknowledge, experience outcomes
that they do not anticipate, will be prone to unjustified trading, and may end up blaming
themselves or others when outcomes are bad (Kahneman and Riepe, 1998).
•4
1.2 Statement of the Problem
Due to the positive correlation between stock market and economy, the rise of stock
market positively affect the development of the economy and vice versa. Consequently,
decisions of investors on the stock market play an important role in defining the market
trend, which in turn has an influence on the economy. Most behavioural finance studies
have been carried out in developed markets of Europe and the USA (Odean. 1999;
Rockenbach, 2004; Caparrelli et al.. 2004; Fogel and Berry. 2006). Only a few studies
have been completed in emerging markets with no known research having been
conducted locally with regard to individual investors.
A previous study done on the NSE by Okoth (2005) tested whether contrarian investment
strategy offer profitability opportunity at the NSE. The findings suggested that the
strategy offers profitability opportunities at the local security market especially in the
short run then. Another study done on the weekend effect at the NSE by Mokua (2003)
showed that stock returns were equal over all the days of the week hence did not appear
to be a good indicator of the stock returns at the NSE. A similar study done by Kamau
(2003) to test the existence of Turn of the month and January effects at the NSE indicated
absence of January effects at the local bourse. Nyambogi (2005) tested the hypothesis
that weather in Nairobi is correlated to stock returns at the Nairobi Securities Exchange.
The data investigated from NSE and meteorological department revealed that the NSE 20
share index was not affected by the prevailing weather conditions in Nairobi at the time.
Rasugu (2005) investigated the existence of holiday effect at the NSE. The results of this
study were not significant and therefore did not support the existence of holiday effect at
5 .
the NSE. It suggested that technical trading rules could not be applied to attain superior
trading results at the NSE at the time. Kimeu (1991) found random walk trend at the NSE
while Kingori (1995) found no stock reversals at the NSE.
All these studies set out to establish the existence of irrationality at the NSE. However,
they were not intended to and did not establish the factors that drive the actions of
investors at the local security market leading to irrationality. The study therefore seeks to
establish the influence of behavioural factors on trading activities at the NSE. Thus, this
study was aimed at addressing the question; do behavioural factors influence individual
investor choice while investing at the Nairobi Securities Exchange?
1.3 Objective of the Study
To determine the impact levels of behavioural influences on the individual investor
choices of securities at NSE in the equity market
1.4 Significance of the Study
The findings of this study will be beneficial to the following;
1.4.1 Investors at NSE
The study will assist existing and potential investors to make investment decisions based
on many variables. These variables will include not only the fundamental and the
technical aspect, but also the psychological biases or factors that are in play within the
investor and also in the market. •6
1.4.2 The Government
The study will assist the government develop programs that will rectify any distortions or
anomalies that are in the bourse and which investors encounter while making investment
decisions. This will be through policy formulation and regulation by the Capital Markets
Authority and the Nairobi Securities Exchange.
1.4.3 Future Scholars
This study will form a basis for further study in this area that has no known research that
has been published in Kenya and that has also not been widely explored in the emerging
markets. The study will provide a useful basis upon which further studies on behavioural
factors and investment choices could be conducted.
/ i
CHAPTER TWO
LITERATURE REVIEW
/
2.1 Introduction
This chapter will review studies done by various scholars and theories that address the
behavioural factors that influence individual investors while investing in the securities
exchange market. Theoretical review on market efficiency and anomalies, behavioural
finance, investment behaviour and the research gap are discussed.
2.2 The Efficient Market Hypothesis (EMH)
The efficient-market hypothesis emerged as a prominent theory in the mid-1960s. Paul
Samuelson had begun to circulate Bachelier's work among economists. In 1965 Eugene
Fama published his dissertation arguing for the random walk hypothesis, and Samuelson
published a proof for a version of the efficient-market hypothesis. In 1970 Fama
published a review of both the theory and the evidence for the hypothesis.
Bodie (2009) argues that because security prices adjust to all new information the
security prices should reflect all information that is publicly available at any point in
time. Therefore, the security prices that prevail at any time should be an unbiased
reflection of all currently available information, including the risk involved in owning the
security. Therefore, in an efficient market, the expected returns implicit in the current
price of the security should reflect its risk, which means that investors who buy at these
•8
informationally efficient prices should receive a rate of return that is consistent with the
perceived risk of the stock.
/ /
The alternative hypothesis is that security market is inefficient and that result of stock
price is not accurately reflecting the new information. This might result from the
following: the investor is unable to interpret the new information correctly; the investors
have no access to the new information; the transaction cost in trading security is an
obstruction for free trading; the restriction on short sale; and finally, the investors might
be misled by the change in accounting principles (Dyckman and Morse, 1986).
There are three classic misconceptions about EMH: Any share portfolio will perform as
well as or better than a special trading rule designed to outperform the market. The EMH
says that after first eliminating unsystematic risk by holding broadly based portfolios and
then adjusting for the residual systematic risk, investors will not achieve abnormal
returns (Bodie, 2009). Secondly, there should be fewer price fluctuations. If shares are
efficiently priced, why is it that they move every day even when there is no
announcement concerning a particular company? This is what we would expect in an
efficient market. Prices move because new information is coming to the market every
hour, which may have some influence on the performance of a specific company (Bodie,
2009). Lastly, only a minority of investors is actively trading, most are passive therefore
efficiency cannot be achieved. This too. is wrong. It only needs a few trades by informed
investors using all the publicly available information to position (through their buying
and selling actions) a share at its semi-strong form efficient price (Bodie, 2009).
Investors and researchers have disputed the efficient-market hypothesis both empirically
and theoretically. Behavioural economists attribute the imperfections in financial markets
to a combination of cognitive biases such as overconfidence, overreaction. representative
bias, information bias, and various other predictable human errors in reasoning and
information processing. These have been researched by psychologists such as Daniel
Kahneman (1979), Amos Tversky (1979), Richard Thaler (1994), and Paul Slovic
(2000).
2.3Anomalies
Anomalies are empirical results that seem to be inconsistent with maintained theories of
asset-pricing behaviour. They indicate either market inefficiency (profit opportunities) or
inadequacies in the underlying asset-pricing model (Fama. 1970).
2.3.1Accounting Anomalies
At its core, the goal of the literature is to understand how accounting numbers relate to
firm value and how quickly and accurately investors assess the information in financial
reports. Indeed almost any role for accounting numbers relies, implicitly or explicitly, on
their association with firm performance and value (Richardson el al, 2010).
One of the literature's key results is that earnings, cash flows, accruals, valuation ratios,
and asset growth predict cross-sectional variation in expected stock returns Fama (1970).
In order to test whether returns are anomalous, we must know what expected returns
•10
should be in the absence of mispricing. This problem is especially acute for many
accounting anomalies because plausible risk and mispricing explanations both exist.
/
Mispricing stories for accounting anomalies are only loosely grounded in theoretical
models of investor behaviour and rely, instead, on the generic idea that investors do not
understand the properties of earnings and accruals. For post-earnings-announcement
drift, the standard argument is that investors underreact to quarterly earnings
announcements and, in particular, do not fully appreciate the persistence of seasonally
differenced earnings (Bernard, 2003). For the accrual anomaly, the simplest story is that
investors fixate on bottom-line earnings and do not fully appreciate the differential
reliability of its cash flow and accrual components. For the value anomaly, the traditional
argument is that investors overreact to firms' past performance, failing to fully appreciate
its transitory nature (Richardson et al, 2005).
Risk-based stories for the anomalies have evolved and the basic argument is simply that
different types of stocks are exposed to different amounts of systematic risk and,
therefore, carry different expected returns. For example, Fama and French (1988) suggest
that 'distressed' value stocks might be risky because they are especially sensitive to
economic conditions. The risk-based models above largely ignore the crucial issue of
how to measure risk; they take the asset pricing factor(s) as given and focus, instead, on
how risk is likely to vary cross-sectionally and through time as a function of firm
characteristics (Sloan. 2008).
1 1
4
2.3.2 Fundamental Stock Market Anomalies
There is a large body of evidence documenting the fact that historically, investors
mistakenly overestimate the prospects of growth companies and underestimate value
companies. Shleifer et al (1992) concluded that "value strategies yield higher returns
because these strategies exploit the mistakes of the typical investor and not because these
strategies are fundamentally riskier." They concluded that common measures of risk do
not support the argument that the return differential is a result of the higher riskiness of
value stocks, but rather, in their opinion, is due to behavioural considerations and the
agency costs of delegated investment management.
There have been anomalies based on fundamentals and value that have been documented
to outperform the market in long-term studies. The effects are related to varying degrees
and investors using the different techniques will commonly select many of the same
stocks (Shleifer et al, 1992).A classic study on the performance of low price to book
value stocks was by Eugene Fama and Kenneth R. French. It covered the period from
1963-1990 and included nearly all the stocks on the NYSE, AMEX and NASDAQ. The
stocks were divided into ten groups by book/market and were re-ranked annually. The / /
lowest book/market stocks outperformed the highest book/market stocks 21.4% to 8%
with each decile performing worse than the previous. Fama and French also ranked the
deciles by beta and found that the value stocks had lower risk and the growth stocks had
the highest risk (Fama, 1965).
•12
High dividend yield has had anomaly effects that have been documented through
numerous studies and have concluded that high yielding stocks tend to outperform. In
High Yield, Low Payout, Patel et al of Credit Suisse (2006) found that while high /
^ r . /
dividend yield stocks did indeed outperform their lower yield counterparts, the 8th decile
stocks produced the best returns.
2.3.3 Neglected Stocks
Neglected stocks commonly are selected by those that follow a contrarian strategy of
buying stocks that are out of favour. Werner F.M. DeBondt and Richard Thaler
conducted a study of the 35 best and worst performing stocks on the New York Stock
Exchange (NYSE) from 1932 through 1977. They studied the best and worst performers
over the preceding five and three year periods. They found that the best performers over
the previous period subsequently underperformed. while the poor performers from the
prior period produced significantly greater returns than the NYSE index (Thaler et al,
1977).
According to T. J. Peters and R.H. Waterman's in their research on "Lessons from
f America's Best-Run Corporations" (1982), the two formed a list of "Excellent"
companies based on a number of factors including asset growth, book value growth, and
return on assets. Following up on their work, Clayman et al (1987) studied the
performance of the "excellent" firms and another group she termed "excellent" (by going
"in search of disaster") and found that the characteristics of the excellent companies
quickly reverted to the mean in the years following their excellent performance. The
•13
"excellent" companies also reverted to the mean and showed substantial improvement.
The stocks of the "excellent" firms significantly outperformed the excellent companies
over the years that followed.
Jeff Anderson and Gary Smith in July/August (2006) studied Fortune's ten "most
admired companies in the US from 1983-2004 and they outperformed. They concluded
that a portfolio consisting of the stocks identified annually by Fortune magazine as
America's most admired companies outperformed the S&P 500 even with various lags
after publication date, representing a clear challenge to the efficient market hypothesis
since Fortune's picks are readily available public information and they found no
compelling explanation for this anomaly.
2.3.4 Financial Market Anomalies
2.3.4.1 Cross-Sectional Return Patterns
Given certain simplifying assumptions, the CAPM states that the return on a security is
linearly related to the security's non-diversifiable risk (or beta) measured relative to the
market portfolio of all marketable securities. If the model is correct and security markets
( are efficient, security returns will on average conform to this linear relation (Markowitz,
1952).
Empirical tests of the CAPM first became possible with the creation of computerized
databases of stock prices in the U.S. in the 1960's. To implement the tests, researchers
often estimate cross-sectional regressions of the form:
Ri = ao + alpi + I ajcij + ei
•14
Where pi is the security's beta which measures its covariance with the return on the
market and cij represents security-specific characteristic j (size, earnings yield, etc.) for
security i. The CAPM predicts that the aj, for j >1, are zero. Early tests supported the
CAPM (e.g.. significant positive values for a l . insignificant values for aj, for j > 1). The
explanatory power of beta came into question in the late 1970s when researchers
identified security characteristics such as the earnings-to-price ratio and market
capitalization of common equity with more explanatory power than beta (Markowitz.
1952).
2.3.4.2 The Value Effect
The value effect refers to the positive relation between security returns and the ratio of
accounting based measures of cash flow or value to the market price of the security.
Examples of the accounting-based measures are earnings per share and book value of
common equity per share. Investment strategies based on the value effect have a long
tradition in finance and can be traced at least to Graham and Dodd (1940). Ball (1978)
argues that variables like the Earnings-to-Price ratio (E/P) are proxies for expected
returns. Thus, if the CAPM is an incomplete specification of priced risk, it is reasonable
to expect that E/P might explain the portion of expected return that is compensation for
risk variables omitted from the tests.
Basu (1977) was the first to test the notion that value-related variables might explain
violations of the CAPM. He found a significant positive relation between E/P ratios and
average returns for U.S. stocks that could not be explained by the CAPM. Reinganum
1 5 .
(1981) confirmed and extended Basu's findings. Rosenberg, Reid and Lanstein (1985),
DeBondt and Thaler (1987) and many others have documented a significant positive
relation between returns and the Book-to-Price ratio.
2.3.4.3 The Size Effect
The size effect refers to the negative relation between security returns and the market
value of the common equity of a firm. Banz (1981) was the first to document this
phenomenon for U.S. stocks. Banz found that the coefficient on size has more
explanatory power than the coefficient on beta in describing the cross section of returns.
Indeed, Banz finds little explanatory power for market betas. Like the value effect, the
size effect has been reproduced for numerous sample periods and for most major
securities markets around the world (Hawawini and Keim, 2000).
2.4 Traditional Finance Theory and Behavioural Finance
Behavioural finance attempts to explain human behaviours in markets, importing theories
of human behaviour from the social sciences (Shiller, 1998). Psychologists and
economists have researched this field since the early 1970s (Thaler, 1994). The
pioneering work is found as early as the 1950s, for example, Allais (1953) and Ellsberg
(1961).
Behavioural economists study how people behave, learn and make economic decisions in
reality, and what happens when we relax the assumption that everyone is rational all of
the time (Thaler, 1994). Behavioural finance emphasises that rationality cannot be
•16
assumed as something that people should feature, whereas the term 'irrationality' in
conventional economics means something that would and should be eliminated in a
competitive market.
/
Researchers have uncovered a surprisingly large amount of evidence that contradicts this
view. Bernstein (1996) states that the evidence reveals repeated patterns of irrationality,
inconsistency, and incompetence in the ways people arrive at decisions and choices when
faced with uncertainty. Against this background, behavioural finance has evoked much
interest in relation to investment decision-making.
2.4.1 Behavioural Factors Influencing Investor Decision Making
Behavioural finance theories are based on cognitive psychology, which suggests that
human decision processes are subject to several cognitive illusions. These cognitive
illusions can be grouped into two classifications: illusions due to heuristic decision
processes and illusions caused by the adoption of mental frames, which are conveniently
grouped in the prospect theory. These two categories form the basis of the behavioural
theories: (Waweru, 2008).
I i
2.4.1.1 Heuristic Driven Biases
Heuristics are rules of thumb, which people use to make decisions in complex, uncertain
environments. Decision-making is not strictly rational where all relevant information is
collected and objectively evaluated: rather the decision-maker takes mental shortcuts
•17
(Kahneman and Tversky, 1979). Examples of illusions resulting from the use of
heuristics include: Representativeness. Anchoring, and Overconfidence.
/
2.4.1.2 Representativeness
Representativeness can manifest itself when investors seek to buy 'hot' stocks and to
avoid stocks, which have performed poorly in the recent past. Investors may form
judgements based of patterns that are simply random in a data and not representative of
the facts. This behaviour could provide an explanation for investor overreaction
(DeBondt and Thaler. 1995).People tend to categorise events as typical or representative
of a well-known class, and to overstress the importance of such a categorisation. For
example, share prices often rise when a company reports increased earnings several
quarters in a row, because investors tend to infer a high long-term earnings growth rate
(Barberis, 2001).
2.4.1.3 Anchoring
Anchoring arises when a value scale is fixed (anchored) by recent observations. Investors
usually use their purchase price as a reference point (Kahneman and Riepe, 1998) and f / react to changes in price relative to the initial purchase price. According to Shiller (1998),
prices of today are often determined merely by those of the past. Anchoring can lead
investors to expect a share to continue to trade in a defined range or to expect a
company's earnings to be in line with historical trends, leading to possible under-reaction
to trend changes. Investors usually form an opinion about an item and they become
unwilling to change their mind-set despite that there is new information that has huge
1 8 -
significance and may be contrary to what they presently believe. Investors also tend to
become more optimistic when the market rises and more pessimistic when the market
falls. Shiller (2000) found that at the peak of the Japanese market, 14% of investors
expected a crash, but after it did crash, 32% expected a crash.
2.4.1.4 Overconfidence
Overconfidence leads investors to overestimate their predictive skills and believe that
they can time the market. Studies have shown that one side effect of investor
overconfidence is excessive trading (Evans. 2006; Allen and Evans, 2005). There is
evidence (Evans, 2006) that financial analysts are slow to revise their previous
assessment of a company's likely future performance, even when there is strong evidence
that their existing assessment is incorrect. People are overconfident in their own abilities,
and investors and analysts are particularly overconfident in areas where they have some
knowledge (Shiller, 1998; Evans, 2006).
Odean (1999) analysed position data of 10,000 discount brokerage accounts maintained
by a national wide brokerage in the U.S. He found that those investors did tend to sell
more past winners than losers, traded excessively, and their returns were reduced through
trading. Statman and Thorely (1999) reported that high stock returns were associated with
high trading volume in the subsequent periods and the crash of 1987 brought low volume
for years afterwards, which was consistent with the overconfidence theory. Bange (2000)
reported evidence in line with overconfident behaviour that individuals sold past losers
and bought past winners as if past market performance could be extrapolated into the
•19
future. The findings of those studies on the U.S. individual investors were consistent with
the behavioural hypotheses, namely, overconfidence and the disposition effect.
2.4.2 Prospect Theory
Prospect theory provides a framework that explains how behavioural aspects influence
risk tolerance in investment decisions. Value is assigned to gains and losses rather than to
the final net assets, while probabilities are replaced by decision weights (Pious, 1993).
Kahneman and Tversky (1979) found that people underweigh outcomes that are probable
in comparison with those that are certain. They also found that people respond differently
to equivalent situations depending on whether they are presented in the context of losses
or gains. Lebaron (1999) suggests that people become considerably more distressed at
the prospect of losses than they are pleased by equivalent gains. In situations where the
probability of loss is quite large, people exhibit risk-seeking rather than risk-averse
behaviour (Tversky, 1990).
Prospect theory describes several states of mind that can be expected to influence an
individual's decision-making processes. The key concepts include: Regret, Loss aversion,
mental accounting, and herd behaviour.
2.4.2 .1 Loss Aversion
Loss aversion recognises that the mental penalty associated with a loss is greater than the
mental reward from a similar size gain (Shiller, 2000). According to Gomes (2005),
Polkovnichenko (2005), and Thaler (2006), if individuals are loss-averse they will either
•20
not participate in equity markets or will allocate considerable less of their wealth to
equities. If individual are loss-averse, the potential pain from stock market declines
outweigh the pleasure from gains even with a high equity premium. As a result loss-/
averse individuals choose to avoid any exposure to equity. Loss aversion implies that
individuals frame events as either gains or losses relative to a reference point, and loss
aversion in instruments, this phenomenon is believed to manifest in what is known as
"disposition effect". People are believed to realize gains too quickly in the fear that they
may make a loss.
2.4.2 .2 Mental Accounting
Mental accounting is the propensity for individuals to organise their world into separate
mental accounts. Investors tend to treat each element of their investment portfolio
separately, which can lead to inefficiency, and inconsistency in making investment
decisions (Shiller, 2000). According to Richard Thaler (2006), every financial decision
should be based on rational calculation of its effects on overall wealth position. He
further states that individuals separate their money into various mental accounts where
they treat money differently depending on its source. Individual were found to be more
spendthrift on money received as bonus or dividends that money meant to cater for tasks
such as health or education. If investors have a tendency of recognizing immediately in
their mental accounts but postponing acknowledging their bad decisions, they may sell
stocks that have performed well and hold on poorly performing stocks, namely, the
"disposition effect" (Odean, 1998).
•21
2.4.2 .3 Regret Aversion
Regret refers to people's emotional reaction on making a mistake (Pious. 1993).
Investors consistently engage in behaviour that they regret later (Evans, 2002). They /
avoid selling shares that have decreased in value, and readily sell shares that have
increased in value (Shiller, 1998; Lebaron. 1999). Fogel and Berry (2006) found that
investors reported regrets about holding a losing stock too long than about selling a
winning stock too soon and this led to the disposition effect.
Odean (1999), while studying the US market, obtained data by a brokerage house for
10,000 accounts and tested the disposition effect. He found that there is an investors'
preference to sell winners and to hold the losers, except in December, but this, he said
could be explained by tax reasons. He showed that this investor behaviour cannot be
motivated by rebalancing portfolio reasons or by reluctance to increase the trades to
minimise transaction costs.
Regret aversion may also result in what is known as herding behaviour. Shiller (2000)
outlines psychological experiment by Deutsh and Gerrard where the human tendency to
concur with the majority view was shown. In the experiment, people questioned their
own opinions if the found everybody disagreed with it. These human tendencies are
individually sensible, but collectively can lead to irrational and herding behaviour. Any
investor may feel more comfortable investing in a popular stock if everybody else
believed that it is a good one. Responsibility of it falling will be shared with the other
investors who originally expected it to do well (Brabazon, 2000).
•22
2.4.3 Herding Behaviour and Social Influences
This describes how individuals in a group can act together without planned direction. The
term pertains to the behaviour of animals in herds, flocks, and schools and to human
conduct during activities such as stock market bubbles and crashes. Large stock market
trends often begin with and end with periods of frenzied buying (bubbles) or selling
(crushes) (Robert Shiller, Ivo Welch, et al. 2004).
In "herding" models, it is assumed that investors are fully rational, but only have partial
information about the economy. In these models, when a few investors buy some type of
asset, this reveals that they have some positive information about that asset, which
increases the rational incentive of others to buy the asset too. Even though this is a fully
rational decision, it may sometimes lead to mistakenly high asset values (implying,
eventually, a crash) since the first investors may, by chance, have been mistaken (Shiller
et al, 2004).
2.4.4 Impact of Behavioural Factors on Investment Decision Making
Shefrin (2000) contends that heuristic-driven bias and framing effects cause market
prices to deviate from fundamental values. Olsen (1998) suggests that behavioural
finance may explain empirical evidence, which casts doubt on existing financial models
based on rationality. DeBondt and Thaler (1995) argued that because investors rely on
the representativeness heuristics, they could become overly optimistic about past winners
and overly pessimistic about past losers and that this bias could cause prices to deviate
from their fundamental level. Anchoring and overconfidence may lead investment
2 ?
analysts not to adjust their earnings" estimates sufficiently to reduce the impact of
unanticipated events on the stock markets (Allen and Evans, 2005).
DeBondt and Thaler (1995) contend that if the tenets of behavioural finance are correct,
several implications may arise regarding possible behavioural patterns in financial
markets. Additionally, DeBondt and Thaler (1995) were convinced that there may be
over- or under-reaction to price changes or news; extrapolation of past trends into the
future; lack of attention to fundamentals underlying a stock; focus on popular stocks and
seasonal price cycles.
Several studies document that investors are systematically reluctant to sell stocks for a
loss (Shefrin and Statman. 1985; Odean, 1998; Grinblatt and Kelohaiju, 2001). Less is
known about how they make purchases. There are three indications of how likely stocks