Modern Finance v Behavioral Finance - Andrikopoulos

8/9/2019 Modern Finance v Behavioral Finance - Andrikopoulos

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MODERN FINANCE vs. BEHAVIOURAL FINANCE:

AN OVERVIEW OF KEY CONCEPTS AND MAJOR ARGUMENTS

PANAGIOTIS ANDRIKOPOULOSaLeicester Business SchoolDe Montfort University

Abstract

Modern Finance has dominated the area of financial economics for at least four decades.Based on a set of strong but highly unrealistic assumptions its advocates have produced arange of very influential theories and models. Nonetheless, in the last two decades a newacademic school of thought has emerged that refutes the key assumption of a homoeconomicus; an assumption that represents the cornerstone for the development of thetheory of efficient markets. The first empirical evidence against efficient markets in themid-eighties signalled the beginning of a fiercedebate between these two schools ofthought. This paper gives an overview of the key arguments of these two distinctiveacademic doctrines.

a

Department of Accounting and Finance, Leicester Business School, De MontfortUniversity, Tel: +44(0) 116 257 7218, Fax: +44(0) 116 251 7548, E-mail:[email protected]


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MODERN FINANCE vs. BEHAVIOURAL FINANCE:AN OVERVIEW OF KEY CONCEPTS AND MAJOR ARGUMENTS

I. Introduction

The decades of the 1950s and 1960s were the most productive periods in finance

thought. This was the period in which finance changed from a descriptive discipline to a

modern science full of new ideas that needed to be refined. The focus of the academic

community on exploiting the full potential of mathematical probabilistic and

optimisation models and techniques led to the construction of theories and models such

as portfolio optimization theory, the capital asset pricing model and the efficient markets

hypothesis. Their principles would constitute a key influence in the years to come.But within two decades of the introduction of these ideas contradictory evidence began

to emerge. The appearance of many anomalies led some academics to reconsider their

initial beliefs about the applicability of the leading theories of modern finance. This was

the spring of a new era, that of behavioural finance. As the new ideas of behavioural

finance were introduced, a rigorous academic debate commenced on the validity of these

new theories.

This paper focuses on this current theoretical debate. Section two describes the principle

basis of the modern finance school of thought and its historical roots in classical

economic theory. In this section, the assumption of homo economicus and the theory of

EMH will be considered. Section three will introduce the reader to the principles of the

Behavioural Finance theory. In this very section, certain ideas of psychology and

decision-making and how they can be linked with the finance discipline are discussed.

Section four discusses modern finances key arguments against this new theoretical

school. Finally, this paper will conclude in section five.

II. Modern Finance: The End-Product of a Long History of Economic Thought

A. The Homo Economicus as a Key Assumption of Positive Economic Theory

Not many scholars would disagree that economic activity is a social activity. Since the

first works of classical economists such as Adam Smith, David Ricardo and J.S. Mill in

the 18th

and 19th

centuries, the role of humans involved in economic activity wasrigorously investigated. It is these early writings in political economy and in positive

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economic thinking that indirectly introduced the assumption of homo economicus into the

social sciences. However, J.S. Mill was the first to explicitly define this assumption

(Pribram, 1983, p.173).

The whole idea of a rationaleconomic man currently represents a stronghold in modern

investment theory but for other reasons than its early form, when it was briefly discussed

back in the 18th century in the Wealth of Nationsby Adam Smith. The roots of this later

version ofhomo economicus lie in the positivistic doctrine of economic methodology that

was introduced by Von Neumann and Morgenstern (1944) and persistently followed by

the vast majority of the financial economists of the second half of the 20th century.

Under this positivistic doctrine of financial economics, homo economicusrefers to a greatly

simplified model of human behaviour where an individual is characterised by perfect self-

interest, perfect rationality and free access to perfect information regarding a specific

condition. The key rationale for the development of this assumption lies in the complex

nature and unpredictability of human behaviour and its inability to be used effectively as

a means for accurately predicting and explaining human behaviour itself. On the grounds

of simplicity, mathematical applicability and empirical reasoning, human behaviour was

oversimplified and quantified following methodologies developed and used in the field of

the hard sciences.

Since its first appearance in financial economics literature, the over-simplification ofhuman behaviour represented only one part of a more generic empirical deductive

procedure aiming to define price behaviour and create a theory for it. This was the

beginning of modern finance.

B. Random Walks, EMH and the Role of Homo Economicus in a Deductive Process of a Positivistic

Theory

At the dawn of the last century a PhD thesis was about to change the course of history

for financial economics. Louis Jean-Baptiste Alphonse Bachelier produced this thesis in

1900 and his work still represents a creation of exceptional merit in the area of financial

mathematics. The introduction of new ideas in the theory of stochastic processes such as

that of brownian motion1 and martingales soon became a starting point for the

amalgamation of all economics, mathematics, accounting and finance disciplines that

1

These advances took place even before Einsteins mathematical work in Brownianmotion for the development of the molecular-kinetic theory of thermodynamics in 1905.

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created the basis of the modern finance doctrine. Eventually, the end product of this

early work was the creation of the Efficient Market Hypothesis (Fama, 1970), a theory

that still even today represents the cornerstone of modern academic finance.

According to this theory, markets are considered to be efficient relative to a given

information set, if there are no abnormal profit opportunities for investors trading on the

basis of this information (Fama, 1970). Hence, it is practically impossible for investors to

consistently earn abnormal returns on the basis of universally available information. This

proposition has dominated investment theory for the last forty years and mathematically

is illustrated using Famas notation as 0)|( 1, =+ ttjx , where represents the

difference between the actual price of securityjat time t+1 and its expected price based

on the given set of information

1, +tjx

t . If the expectation given by the above equation is

equal to zero there are no available opportunities for investors to beat the market, as no

overpriced or underpriced stocks exist at time t. The stochastic process xj is then

considered to be a fair game (Le Roy, 1989).

The actualabnormal profit achieved is given by the difference of the actual price of stock

jat time t+1 to the expected price of that stock given the available set of information or

where is the price of the security j at time t , and

E is the expectation operator.

)|( 1,1,1, ttjtjtj ppx = +++ 1, +tjp 1+

Famas efficiency framework states that current information flows are the sole

determinant of current asset price movements and that market prices are the best

reflectors of the fundamental values of their underlying assets. This theory implies the

existence of a stochastic process with independent, identically distributed binomial

random variables, or what is commonly known as a random walk (Roberts, 1959;

Osborne, 1959; Granger and Morgenstern, 1970). Similar to the Brownian motion, the

origin of a random walk can also be traced at least as far back as the work of Louis

Bachelier (1900). According to the random walk hypothesis there is no dependency

between sequential returns, or as it is mathematically expressed ttt PP += 1 . In this

equation, is the stock price at time t, the stock price at time t-1, and,tP 1tP t is the

residual series or error value, where E(t)=0 and Cov(Pt,Pt-n)=0, n 0. Furthermore, an

even stronger version of the random walk hypothesis states that the probability

distribution function of returns remains constant over time, i.e. that the residual series

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has a constant distribution. Nevertheless, this stronger version of the random walk

hypothesis is not a direct logical consequence of the EMH.

As regards the fundamental values of securities, according to the EMH these are

determined by expected future cash flows, in the case of equities the future stream of expected

dividends, discounted back to the present, or mathematically expressed as

)()1( 111

tttt dPEP ++= ++

. Thus, fundamental prices at time tshould be equal to the

expected future prices and expected dividend payments at time t+1, on the basis of the

current publicly available information set t discounted at the appropriate discount

rate . On the basis of the above model, any changes in share prices will be the outcome

of a speedy value re-adjustment performed by investors in response to changes in the

information set.In line with all positivistic economic theories, EMH itself follows from certain more

basic assumptions, including that ofhomo economicus. Sufficient conditions for the EMH

can be summarized into four categories relating to:

i) The public availability of information,

ii) The speed with which this information can be absorbed and lead to a new price

equilibrium,

iii) Investor self-interest and

iv) Investor rationality and the extent to which investors exhibit effective and efficient

cognitive behaviour.

Of the above four assumptions the last two are considered to be the most important as

they represent the link between EMH and its positivistic philosophical roots where

reality should be oversimplified to facilitate accurate mathematical predictions. In this

specific case, possible investor irrationality could affect both the way that information is

perceived and the process by which stock market prices adjust in order to reflect any new

information sets.

Any claim that, obviously, not all investors are rational was immediately undermined by

the early scholars in this new academic field using two main theoretical arguments.

According to the first response, irrational investors as a group cannot affect security

prices, as their investment strategies are individually uncorrelated. In effect, irrational

investors are trading randomly and their trades cancel each other out, leaving efficiencyto prevail in the end.

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The second response states that the process of arbitrage (Friedman, 1953; Fama, 1965)

and competition amongst arbitrageurs will ensure that irrational traders will tend to

accumulate cumulative losses and eventually their wealth will diminish, leaving the field

open to rational investors. If either response is correct, prices will be set back to

equilibrium and market efficiency will hold.

The above arguments for market rationality and the existence of perfect human beings

have profound importance for modern finance. If correct, they justify working with the

simplifying assumption that all investors are rational. This simple rational expectations

world is used as a basis for the development of more advanced theoretical models, such

as linear asset pricing ones. Under these simplifying assumptions, which aim to deduct

certain empirical complexities, expected returns on securities are purely a function of

their individual risk characteristics. This risk is determined by the non-diversifiable

component of volatility of the future income stream.

Empirical testing of the theory has proved problematic. As EMH provides the

theoretical framework within which linear asset-pricing models are embedded, critical

testing of a model by itself is impossible. This problem is often referred to as the joint-

hypothesis problem (Fama, 1970; 1991). However, empirical evidence against market

efficiency and the theoretical linear risk/expected-return relationship may undermine the

above sufficient conditions and in particular the assumptions and arguments for marketrationality. Recent empirical tests especially and the literature of fundamental anomalies

proved to be the most challenging to the modern finance position as they provided an

opportunity for a new theoretical school to emerge, that of behavioural finance.

III. Behavioural Finance: A New Perspective in Financial Empiricism

A. The Rule of Bayes, Rational Decision Making and Basic Principles of Behavioural Finance

Thought

Behavioural finance offers alternative explanations on the key question of why prices

deviate from their fundamental values. Its key argument is based on the claim that

human behaviour and perceptions represent two crucial elements of financial decision

making (Hirshleifer, 2001). This has led to the search for new models and ideas that may

be able to explain and predict market behaviour from various psychological biases.

Scholars in the area of behavioural finance have focused their attention on importing

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various psychological theories into the study of finance. To fully appreciate the

behaviourists theoretical propositions, we need to first understand the nature of a

decision making process under the homo economicus assumption whose foundations are

based on the principle of conditional probabilities as was mathematically established in

1763 by the mathematician Thomas Bayes.

Bayes rule represents a fundamental principle of rational decision making. Bayesian

theory argues that the probability of an event can be viewed as the degree of belief of an

ideal person. These ideal persons beliefs are considered the most efficient ones even if

they are completely subjective, as long as they are consistent and follow the basic axioms

of probability theory. According to the Bayesians argument, rational decision making

and probabilistic reasoning should be based on the key axiom of indifference, where if

concrete evidence does not exist regarding the relative likelihood of two events, these

events should be considered equiprobable with one another. In addition, Bayesians

consider these conditional probabilities to be more basic than joint probabilities.

Hence, instead of referring to the joint probability of two events as P(A,B), where P is

the probability of occurrence and where A and B the two events, Bayesian probabilities

emphasise the conditional probabilityP(A|B), where P(A|B)*P(B) = P(A,B). By symmetry

we also have P(B|A)*P(A) = P(A,B). A version of Bayes Rule states that the probability

of event A occurring given that the event B occurs is equal to the probability of both

events occurring divided by the probability of B, or mathematically expressed as

)(

)(*)()(

BP

APABPBAP

|= . According to this Bayesian probabilistic reasoning, the update of

beliefs )( BAP concerning an outcomeA in the light of the evidence B, is a function

of the prior beliefA, the likelihood )( ABP | , and the probability ofB. Using the law of

total probabilities for mutually exclusive and exhaustive events nkiAk ,...,, = , we have

=

==n

k

k PABPBP1

*)())( =

n

k

kABP1

,( kA )( . By substituting this equation from the one

above we can finally obtain the generalisation of Bayes theorem, given by

=

=n

k

P

BP

1

(

()

|

kk

iii

APAB

APABAP

)(*)

)(*)( .

Bayes theorem provides the probabilistic framework within which rational investment

decisions should be made on the basis of all relevant, available information. It gives a

highly structured procedure for rational decision making, which was also adopted in the

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case ofhomo economicusand the pursuit of its rational self-interest objectives. And this is

exactly the point from which the behaviourists main arguments are derived. Behavioural

Finance argues that people often fail to respond rationally to new information as they

completely fail to follow the above idealistic mathematical framework. This is caused by

humans inability to differentiate information that requires probabilistic judgement from

that which requires value judgement. As Garnham and Oakhill (1995) argue although

people make informal judgements of likelihood and about what might and might not happen, it is far

from clear that their judgements conform to these principles. For example, they might judge two events to

be independent, yet their estimate of the likelihood of both happening might be quite different from the

result of multiplying their estimates of the individual events happening. If they make inferences based on

such judgements, those inferences cannot be modeled using probability theory.

Despite the fact that the above statement derives from psychology, it can be considered

central to the principles of behavioural finance. People often fail to compute the

probabilities of outcomes in accordance with Bayes theorem, as past experience and

cognitive biases increase the likelihood of decisions being taken on the basis of

value/personal judgement.

Similar conditions are applicable to investment decision making, as this process requires

both intuition and knowledge gained from past experience. Positive evidences of the

existence of biased judgement in investors heuristic framework would imply thatinvestment behaviour suffers from a number of illusions and market signals

pragmatagnosia2. Hence, due to the illusion of control, knowledge, experience etc. and

investors inability to understand and apply successfully their own knowledge to past and

current information signals3, investment behaviour can lack sound judgement leading to

incorrect decision making, so as to give rise to the appearance of the evidenced

mispricing phenomena. According to behaviourists, these biases need to be fully

comprehended and models developed on the basis of them. This is also supported by

Bowman and Buchanan (1995) who argue that, the knowledge on human behaviour should be

used in order to help us understand how investors may misperceive the results of their actions and, by

2 The term pragmatagnosia refers to the incapability of humans to recognise these signals

and act accordingly.

3 This can be due to the inability of the human mind to weight any kind of information

equally resulting in recent information being weighted more heavily than older and bad

decisions more heavily than good ones.

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extension, the functioning of the share markets. Using these existing investment judgement

biases as a basis, hitherto stock market anomalies can now be analysed and evaluated.

B. Behavioural Finance and the Concept of Noise Traders

Noise traders are defined as those whose investment decisions rely more on

psychological factors than on sound investment management principles (Friedman,

1953). According to the arguments for market rationality, irrational trading cannot be

sustained in the long run as efficient rational arbitrageurs effectively eliminate noise

trading. Additionally, due to the random and uncorrelated nature of their trades, noise

traders cancel each other out leaving asset prices unaffected. These arguments on the

relationship between noise traders and asset prices are not unfamiliar, as the first studies

on the matter date back to the late 1950s and 1960s (Friedman, 1953; Fama, 1965). These

arguments support the claim that the market may be rational, even though individual

investorsmay not.

Behavioural finance has responded to these arguments and demonstrated that they are

valid only if certain other assumptions are made, assumptions that appear to be false by

principle for real markets. The evidence of persistent fundamental anomalies dictates that

noise investors can seriously affect equilibrium prices even in the long term. This isbecause rational arbitrage in reality is not only limited but can also create by itself price

inefficiency under certain circumstances (De Long et al., 1990; Jacobsen, 1999). The

rationale behind the ability of noise traders to affect stock prices lies in two limitations

of rational arbitrageurs, namely short investment horizons and risk aversion. Also, the

relative size of the two groups is relevant.

Rational arbitrageurs cannot entirely wipe out the effect of noise traders on the market if

the size and the ability of the former group to trade are very limited (Camerer, 1992).

Additionally, the risk aversion of arbitrageurs by itself limits their ability to cancel noise

trades even if arbitrageurs have infinite buy-and-hold horizons (Shiller, 1984; Campbell

and Kyle, 1987). If noise traders undervalue or overvalue stocks, industries or markets

over a long period of time, the short horizon under which arbitrageurs performance is

evaluated limits their ability to force asset prices back to their fundamental values (Black,

1986). Noise trading can therefore force asset prices to diverge from fundamental values

for extended periods of time and create the basis for a long-term mean reversion effect.

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This mean reversion is based on the claim that even noise traders will eventually

recognise extreme mispricing, causing prices to revert to fair value over the long term.

Rational arbitrageurs must also bear in mind the risk that noise investors beliefs may

become more extreme and unpredictable, a factor referred to as noise investor risk. Noise

investor risk is non-diversifiable, systematic and rational arbitrageurs can price it.

Consequently, unsophisticated trading should be considered as a new source of

systematic risk, creating additional volatility on the stock markets that rational

arbitrageurs would not bear unless compensated with higher expected returns (Figlewski,

1979; De Long et al., 1990). This again limits the ability of arbitrageurs to enforce

rational asset pricing.

Apart from altering the risk structure of the market, according to behaviourists the

presence of noise investors also affects the expected returns structure. The willingness of

noise investors to bear the risk of holding over-priced assets gives them the opportunity

to earn higher returns than their rational counterparts. This is due to the fact that noise

trading itself can drive prices up leading to abnormal profits performance (De Long et

al., 1990). Given that noise traders can change the systematic risk-expected returns

structure of the market, it may become rational for arbitrageurs to trade in the same

direction as noise investors, driving prices even further from equilibrium, creating further

changes in assets systematic risk and expected returns. Hence, noise trading can beconsidered as one of the most important explanations for the existence of regularities

within stock markets.

C. The Overreaction and Under-Reaction Hypotheses of Behavioural Finance

Two of the most important hypotheses that can partially explain the evident price

equilibrium anomalies are those of overreaction and under-reaction. The overreaction

and under-reaction hypotheses are especially important in behavioural explanations of

the value effect.

The tendency of human beings to overreact and under-react in certain circumstances,

deviating from Bayesian optimum rational decision-making, arises from psychological

biases such as conservatismand the representativeness heuristic(Kahneman and Tversky, 1973;

Kahneman, Slovic and Tversky, 1982; Daniel, Hirshleifer and Subrahmanyam, 1998;

Kaestner, 2005). The former psychological bias, the state of conservatism, refers to the

condition where investors subconsciously are reluctant to alter their beliefs in the face of

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new evidence (Edwards, 1968). The main impact of this bias in investment decision

making is that even if investors beliefs are changed in the light of new information, the

magnitude of that change is relatively low in terms of what it should be under strictly

rational conditions. On the other hand, the representativeness heuristicis the illusion of seeing

patterns in a random walk or more generally order among chaos (Barberis, Shleifer and

Vishny, 1998).

The effects of these biases were studied in an experiment some years ago (Bloomfield

and Hales, 2001). Subjects were exposed to a series of outcomes; continuous regimes,

reversal regimes and various shifting regimes (combinations of continuous and reversal

regimes). They evidenced overreacting behaviour when encountering continuous regimes

followed by reversals, and under-reactive behaviour reversal regimes were followed by

further reversals. The importance of these two psychological biases in the under- and

overreaction hypotheses is that investors under conservatism will only partially evaluate

new publicly available information, or even disregard it altogether if it is not in favour of

their beliefs.

Under the representativeness heuristic, investors will consider a series of positive company

performances as representative of a continuous growth potential, and ignore the

possibility that this performance is of a random nature. This leads to excessive optimism

and overvaluation of the companys prospects. In addition, memory constraints such asmemory loss in humans also provide an explanation why investors tend to weight recent

information more favourably than earlier information. Research in this area shows that

memory loss does play a significant role in forming decisions, and that decisions are

highly influenced by the environment within which the decisions are formed. Thus,

stable environments will trigger inertiawhile in highly volatile environments individuals

show signs ofexcessive impulsiveness(Hirshleifer and Welch, 2000).

Apart from the psychological state of conservatism, another cause of investors under-

reaction to new information is the heterogeneity of the investing public. Not everyone

has equal access to sources of information. Rather, information diffuses gradually across

the public domain, while the ability of investors to successfully extract information from

current prices has been questioned (Hong and Stein, 1998). In the stock market

environment, reactions to new information are asymmetric and industry related. There is

a large reaction to bad news about past winning stocks, while the reaction to bad news

from past losing stocks is comparatively small.

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This asymmetry is considered to be important in behavioural explanations of the value

anomaly (De Bondt and Thaler, 1985; Lakonishok, Shleifer and Vishny, 1994; Griffin

and Lemmon, 2001, Antoniou, Galariotis and Spyrou, 2003). As past winning stocks are

subject to a larger response to negative surprises they tend to be morevolatile than past

losing stocks/value stocks, contradicting the rational relationship between risk and

expected return (Jegadeesh and Titman, 1993; Skinner and Sloan, 2000; Andrikopoulos

and Daynes, 2004). Hence, as investors generally fail to judge correctly when dealing with

uncertain outcomes, trading patterns can be identified such as overreaction following bad

earnings announcements of past winners and under-reaction to good earnings

announcements from past losers (Barberis, Shleifer and Vishny, 1998). The failure of

investors to alter their beliefs about certain stocks and to foresee that a good earnings

signal from a past losing firm is a sign of more to come creates opportunities for certain

investment strategies such as the momentum and contrarian to achieve above average

results.

Apart from individual stocks, according to behavioural finance the phenomenon of

judgement bias is also evident when examining industry performance. Under-reaction to

information related to new and changing industries can explain the higher profitability of

momentum strategies when applied to stocks of a single industry compared to individual

stocks across different industries and markets (Moskowitz and Grinblatt, 1999)Behaviourists also argue that different trading attitudes and information flow in the

market can also trigger irrational behaviour. Further evidence on this hypothesis was

given by Hong and Stein (1998). According to their study both overreaction and under-

reaction of stock prices could be explained by the trading attitudes ofnews-watchers and

momentum traders4as well as the different information flow between these two investment

groups. If information spreads across agents gradually, then prices will adjust slowly to

new information, creating under-reaction. This under-reaction will be followed by

overreaction as the spread of information causes late news-watchers and momentum

traders to enter the market (Hvidkjaer, 2004). This gradual spread and speed of

information flow throughout different investor groups is highly important as slow

information flow can lead to short-run return correlation and subsequently to long-term

strong reversals, a condition that obviously violates market efficiency.

4

Newswatchers analyse fundamentals while momentum traders mainly rely oninformation that can be extracted from price movements.

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Another argument of Behavioural Finance with respect to the overreaction phenomenon

is the impact of analysts coverage on certain stocks. Small stocks with no coverage at all

will not exhibit a strong overreaction effect as they are usually excluded from analysts

recommendations. On the other hand, for small size stocks with low analysts coverage,

the very gradual information flow will result in these stocks exhibiting the strongest

reversal effect of all (Hong and Stein, 1998). Thus, although judgement biases and the

flow of information of investors may be key elements in explaining under-reaction and

overreaction effects, the behaviour of financial agents, such as investment analysts and

brokers, is an additional important feature. The ability of brokers to form and structure

investors beliefs and expectations will contribute to inefficient pricing if their

recommendations are biased in the first place. This idea in conjunction with the

inefficient expectation hypothesis is discussed immediately below.

D. The Overconfidence Hypothesis and Bias of Investment Agents

Overreaction and under-reaction to new information may be viewed as a combination of

three distinct inefficiencies; firstly, the inability of investment players to correctly

distinguish between the length of the short-run and the long-run, leading to a persistent

and gross over-estimation of the mean reversion horizon; secondly, the excessive

optimism of all investment agents due to biased self-attribution, and thirdly, the influence

that one investment group has on another. Where structural inefficiencies and biased

decision making is found in the hypothetically strongestgroup, this will be passed to the

weakerparty.

With regards to the first inefficiency, it is arguable that investors in general fail to

correctly define the length of the short and long run (Jegadeesh and Titman, 1993;

Haugen, 1995). Indeed, although the dynamic nature of modern business dictates that

severe competition in the market place restrains successful firms from retaining

abnormal profits for a long period of time, most investors fail to foresee this reality. This

overestimation leads to the overpricing of currently successful firms. This mistake is then

followed by the second inefficiency, biased self-attribution, whose roots are again found in

the area of psychology. Individuals tend strongly to attribute events that confirm the

validity of their actions to high ability, while at the same time attributing events that

disconfirm their actions to external reasons. A combination of these two factors can

result in the creation of excessive optimism about certain stocks, while simultaneouslyreducing the chance/probability of correcting their beliefs. Both elements reinforce

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behavioural finances overconfidence hypothesis, whereby investors engramic

brachychronic5 behaviour indirectly eliminates consideration of correct alternative

outcomes. This leads them to initially under-react to future unconsidered information

flows, while if this information flow persists it eventually leads to the phenomenon of

overreaction. Similar conditions apply to the theoretically well-informed investing agents,

where their excessive optimism about certain companies can significantly influence their

recommendations and increase public overconfidence. Investors biased self-attribution

represents a key explanation for short-term momentum, positive short-term autocorrelation of

stock returns for individual stocks and the market as a whole (Daniel, Hirshleifer and

Subrahmanyam, 1998).

With regards to the behaviourists argument on analysts and brokers overoptimistic

recommendations, empirical research supports the view that analysts do tend to overstate

their forecasts for various political and career reasons. Positive prospects regarding

glamour companies can motivate investors to increase transactions. On the other hand, if

things go wrong, and things could go wrong at any time, their recommendations can be easily

justified. Empirical research is strongly supportive to the case of positive bias in the

forecasts and recommendations of investment analysts. This can lead investors, whose

beliefs are based on those analysts forecasts, to under-value firms that have past low

performance and over-value those firms with past excellent performance (Dechow andSloan, 1997; Beaver and Ryan, 1998; Billings and Morton, 1999; Krische and Lee, 2000).

Analysts overoptimistic forecasts are also evident during common stock offerings and

initial public offerings, with a plausible explanation being that of career incentives.

Analysts employed by the leading offering managers produce the most optimistic

forecasts concerning the potential growth of the offering firms (Dechow, Hutton and

Sloan, 2000; Chahine, 2001). This is consistent with empirical research on the positive

relationship between the level of fees paid to analysts firms and the level of analysts

growth forecasts and with the fact that companies with the most positive forecasts are

found to significantly under-perform the market averages after the public offering.

Additional explanations can follow from the structure of the financial analysis industry.

Analysts remuneration is based on their ability to generate trading volumes for their

employers, rather than on the accuracy of their predictions. Accuracy of their predictions

suffers, as it is a secondary objective (Mikhail, Walther and Willis, 1999). This positive

5

The author refers to the failure of investors in generating alternative short-term coursesof action due to the unconscious existence of erroneous memory.

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bias mentioned above is evidenced to increase investors confidence generally and to

generate larger trading volumes. Apart from the trend towards buying larger and more

glamorous stocks for political or career reasons, overestimation of firms future

prospects can also create a more general overconfidence in investors that results in

increased trading volume, with subsequent financial benefits for their companies and the

investment industry in general (Statman and Thorley, 1999).

Overall, behaviourists argument that investment professionals preference for easy to sell

glamour stocks, and the fact that the time horizon in which their performance is

evaluated is significantly shorter than the time required for certain investment strategies

to pay off, provides further explanation of the inefficient pricing of past losing stocks

(Lakonishok, Shleifer and Vishny, 1994; Brower, Van Der Put and Veld, 1996). Over

fifty percent of the out-performance of value/contrarian investment strategies has been

attributed to investors nave reliance on analysts biased long-term earnings growth

forecasts (Dechow and Sloan, 1997; Easterwood and Nutt, 1998).

Overoptimistic forecasts can also have substantial impacts on the forecasted companies

management. Managers of companies with poor performance forecasts will tend to

follow and meet those forecasts, contributing to medium term inertia. On the other

hand, poor performance forecasts can result in a management response that can drive

their performance measures away from those forecasts, resulting in possible positivesurprises to the market (Abarbanell and Lehavy, 1999). This can contribute to the

reversals observed in companies with past poor performance.

According to behavioural finance, the psychological, institutional and agency factors

discussed so far not only drive prices away from fair value but create excess volatility in

the markets. This excess volatility is systematic and non-diversifiable and thus itself

becomes a factor of concern in the investment decisions of both rational and irrational

investors. In addition, the ex ante nature of measurement and quantification of these

inefficiencies makes it almost impossible for the excess volatility they create to be

quantified and modelled on a prior basis.

IV. Behavioural Inefficiencies under the Modern Finance View

A. Frequency and Nature of the Behavioural Effects

Soon after the first empirical papers on behavioural finance were published, their claimscame in for considerable criticism from supporters of the modern finance paradigm. One

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important counter-argument disputes the existence of certain regularities and argues for

the existence of research biases and other methodological shortcomings in behavioural

finance studies. More commonly, the evidence on the existence of pricing anomalies is

accepted but in that case, the most important response concerns the existence of

additional risk factors, e.g. value premium can be explained as compensation for bearing additional

systematic risk.

Analytically, according to Modern Finance advocates, the observed long-term and short-

term under-reaction and overreaction phenomena should not be considered as

inconsistent with market efficiency. Over the long term both phenomena eventually

appear with the same frequency. The market under-reacts as frequently as it over-reacts.

This is consistent with market efficiency, which only requires that investors expectations

are unbiased, and correct on average, or over the long term, (Fama, 1998). Despite the fact that

behaviourists have identified apparent anomalies and proposed various explanatory

theories and models, according to the former finance doctrine, none of these theories is

sufficiently developed to realistically challenge the existing efficient markets model.

With regard to badmodel errors (Barber and Lyon, 1996; Fama, 1998; Barber, Lyon and

Tsai, 1999), long-term financial anomalies are found to be extremely sensitive to different

methods of calculating returns. Important issues concern new listing bias, rebalancing

bias and cross-sectional dependence. If long-term returns are calculated using the buyand hold abnormal return metric over long intervals, abnormal returns will be

overestimated. As most of the recent behavioural studies have used these return metrics,

their results should be considered as weak. Buy and hold abnormal return calculations

can be error free in cases where the population mean abnormal stock return of the

portfolio is equal to zero. However, this approach is still subject to a large amount of

cross-sectional dependence (Barber, Lyon and Tsai, 1999). If, instead, average or total

abnormal monthly returns are used and shorter intervals are examined, statistical

inferences will be less problematic. However, where the latter metrics are used, the

anomalies become marginal and tend to disappear. Hence, the phenomena observed in

behavioural studies may be attributed to biased models and erroneous methodologies.

The previous two counter-arguments have not been found totally convincing, even by a

majority within the modern finance school. An example is the existence of a non-

equilibrium relationship between risk and return in contrarian strategies, a phenomenon

also supported by many modern finance authors (Fama and French, 1992; Fama, 1998;

Fama and French, 1998; Davis, Fama and French, 2000). The most common response is

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to argue that this violation of the market efficiency rule is due to an additional systematic

distress factor that is priced in the market. In that case, additional factor loadings to

already existing models, or extensions or variations of existing models, such as ICAPM,

international APT, or the Fama and French three-factor model, may produce far more

reliable results and capture this particular risk element (Fama and French, 1998; Metrick,

1999; Liew and Vassalou, 1999; Doukas, Kim and Pantzalis, 2001). According to these

studies, a pure factor-based portfolio is capable of explaining value out-performance,

contradicting proponents of market irrationality explanations (Daniel and Titman, 1997).

The development and testing of such multi-factor risk models continues to be an

extremely active area of research.

B. Behavioural Studies and Research Bias

Equally important counter-arguments against the behavioural position concern the

adequacy of the data used. Data bias has always represented a source of uneasiness to

researchers. Although certain techniques have evolved to deal with these problems, bias

may not be fully eliminated. In addition there are cases where its existence cannot easily

be identified.

Many studies of the US stock market use COMPUSTAT as a primary source of data.However, COMPUSTAT is subject to both past selection/survivorship bias and look-

ahead bias (Banz and Breen, 1986). Past selection/survivorship biasrefers to the exclusion of

dead companies from the database. Exclusion of dead companies occurs in particular

when the database is back-filled, since only viable companies are added at this time,

excluding dead companies. Look-ahead biasrefers to the situation where data is recorded

in the database at a given point in time although the information was not publicly

available until a later date. According to Banz and Breen (1986), an examination of the

P/E and small firm effects indicated a significant degree of both these biases. Earnings in

the standard COMPUSTAT files seemed to be distorted when compared to earnings in

the more reliable research version of the database, while small companies with high E/P

ratios were excluded from the database after they ceased trading. Consequently, any

statistical examination of securities returns using a sample based on size would tend to

be dominated by larger companies.

The authors proposed certain techniques to alleviate these problems, the most important

one being the formation of stock portfolios in March, three months after the December

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balance sheet date. This would minimise look-ahead bias. Restricting the research sample

to large, liquid stocks, or to stocks comprising an index would eliminate the small firm

bias in COMPUSTAT. However, this places a limit on the kinds of research that can be

undertaken. As a number of behavioural finance studies have been carried out using the

COMPUSTAT facility, some of the conclusions may be vulnerable to criticisms of the

quality of the data used.

Further arguments against the existence of anomalies concern data mining and model

mining (Sullivan, Timmermann and White, 1999). Nevertheless, in responding to the data

biases arguments, behaviourists have countered that the ability of past returns to affect

the cross section of future returns has not only been captured through the use of the

biased accounting indicators included in the COMPUSTAT facility, but in a wider range

of indicators and different databases used from researchers to form characteristic-based

portfolios (Grinblatt and Moskowitz, 1999; Wermers, 1997). Regarding the data mining

and model mining claims, the anomalies are pervasive and have been observed in out-of-

sample tests covering a wide range of time periods and markets. Overall, defenders of the

modern finance position tend to accept the existence of stock market anomalies,

responding to behaviourists claims using the risk-based counter-arguments discussed

above.

V. Final Comments

The last five decades have seen the development of the rational expectations revolution

in finance theory. This has lead to the theories of efficient markets and rational asset

pricing that now dominate the subject. Applications of modern finance have been

extensively applied in financial management in practice.

It has been claimed by proponents of the new behavioural finance paradigm that finance

is undergoing a new revolution. According to this view the modern finance research

programme is in decline. It is also claimed that the positive contributions of modern

finance are at an end and that its energies are now devoted to protecting itself in various

ad hoc ways from the threat posed by the vast and growing anomalies literature. The

simplifying models of modern finance, under this view, should be regarded as merely

rough first approximations to how markets really behave, and that they stand in need of

substantial revision and extension. The models of behavioural finance, incorporating

behavioural, psychological, agency and institutional factors as well as risk factors, are now

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beginning to challenge modern finance models in terms of explanatory and predictive

power. As Haugen (1999) points out, those theories that deliver models with the greatest

predictive power are the ones that will remain at the end of the debate. Nevertheless, the

rational expectations model and the efficient markets model can never become obsolete,

since they represent an ideal market. Should the behavioural finance revolution succeed,

its applications in practice will simply move real markets closer to the ideal of semi-

strong market efficiency.

The behavioural finance revolution, if such a movement indeed exists, is yet at an early

stage, and any advances in the discipline will be made one step at a time. Similarly, any

defeat of the claims of behavioural finance can only be made by careful and rigorous

scientific research. As more scientific evidences come to the surface they add more heat

to this ongoing debate till the time where a new theory will come to finally replace the

old doctrine and the science of financial economics will at last successfully resemble real-

life investing.

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