The Psychology of Financial Decision Making:A Case for Theory-Driven Experimental Enquiry

Priya Raghubir*

Assistant Professor, Haas School of Business,University of California at Berkeley.

and

Sanjiv Ranjan DasAssociate Professor, Harvard Business School, and

Visiting Professor, Haas School of Business, University of California at Berkeley

*: Please address correspondence to Priya Raghubir at Haas School of Business, University of Californiaat Berkeley, Berkeley, CA 94720-1900; Phone: (510) 643-1899; Fax: (510) 643-1420; email:raghubir@haas.berkeley.edu. We are especially grateful to an anonymous referee, Donnie Lichtenstein,Richard Lyons and Meir Statman for their comments on an earlier version of this paper. This research waspartially funded by University of California Junior Faculty Grant, and the Centre for Financial ServicesGrant at Columbia University, awarded to the first author. We appreciate the cooperation of the ParentTeacher Association at the Los Perales Elementary School for assistance with data collection.

The Psychology of Financial Decision Making:A Case for Theory-Driven Experimental Enquiry

Abstract

This paper has three main parts. We first present a brief survey of the behavioral anomalies in the

finance literature classified as: price and return effects, volume and volatility effects, time series patterns

and other miscellaneous effects. For each category, we find that the empirical literature offers a multitude

of explanations. We then develop a theoretical information-processing framework to examine the

psychology of financial decision making. Many extant behavioral anomalies noted in the finance literature

can be derived from this framework. The theoretical framework comprises both cognitive and motivational

antecedents of bias in financial decision making. The model posits five stages at which cognitive biases

may arise: perception, memory-retrieval, information integration, making a judgment, and behavior.

Motivational effects are theorized as either directly affecting the manner in which information is processed,

or indirectly moderating the likelihood that cognitive biases will occur. Finally we present the main thesis

of the paper that theory-driven experimental analysis enables clarification amongst competing explanations,

and should complement existing empirical paradigms. We illustratively use the model to examine financial

decision making biases in the choice between value and glamour stocks. Across a systematic series of 5

mini-experiments, we find support that the primary antecedent driving choices between value and glamour

stocks is cognitive. Concluding thoughts reinforcing the need to examine the why of a seeming anomaly are

offered for both finance theorists as well as practitioners.

The Psychology of Financial Decision Making: A Case for Theory-Driven Experimental Enquiry

1. Introduction

There can be little disagreement about the contention that market behavior may, at times, be

extreme and seemingly inexplicable: October 1987 and its decennial, October 1997, the Asian market

crisis, the hedge fund crisis of 1998, are all cases in point. There is, however, surprisingly little agreement

as to why these events occur even though there appears to be growing consensus that human heterogeneity

and idiosyncrasy play a role in the development of major financial events. Words such as "herding,"

"irrational exuberance," "excessive risk-taking," "cascades," and "bubbles" are now commonplace in the

reporting of financial events. Over the last half-century, researchers have been identifying curious

systematic patterns in this seeming unpredictability. An intriguing aspect of the historical development of

such events is that they have become more frequent and, in some sense, pretty routine. For want of a better

name, these patterns have been termed "financial anomalies" and "biases." Semantics aside, the search for

an explanation for why markets behave like this in an age of increasing financial acumen of the super-

informed, has led to the development of a new field at the confluence of Economics, Finance, and

Psychology. This nascent field of behavioral finance is coming of age with an increasing number of

researchers turning to non-paradigmatic psychological variables and models to understand and explain their

own and others' results.

The introduction of psychological antecedents into the analysis of financial anomalies is far from

being a negation of the rational economic paradigm. On the contrary it may suggest the expansion of the

economic paradigm to account for observed reasons for behavior. There will always be a tension between

how the world should be, based on rational theory, and how the world often really is, based on behavioral

tendencies. The two interact often, and unpredictably, and our theories struggle through continual updating

in a vain attempt to keep pace with the infinite variety of the human landscape. Unexpected outcomes may

be the results of rational behavior applied to the wrong economic model, or a failure of the right economic

model to account for the entire range of behavior, with a growing consensus that it may be the latter.

In this paper, we propose that to fulfill its potential, the field of behavioral finance must expand its

focus to complement the inductive explanation of errant data patterns with a deductive prediction of

behavior, and submit that experimental inquiry based on psychological theoretical models, is the route to

follow. This article examines the role of theory-driven experimental enquiry in the field of behavioral

finance. We argue that this method has the potential to provide a unique understanding of how people

make financial decisions. Such an understanding will lead to a prediction of micro-level behaviors and

testable hypotheses, some of which may be aggregated and manifested in macro-level effects. It has the

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potential to unveil hitherto undocumented patterns in financial decision making. A few psychological

constructs may account for what, at first glance, appears to be a bewildering array of anomalies. This is

not a scenario of "anything can happen," rather one of systematic predictions derived from an underlying

theory.1 The field of economics has already recognized the importance of using experiments to supplement

existing normative modeling and descriptive field research approaches (Plott 1991a, 1991b). Early use of

this paradigm exists with the use of laboratory experiments to address the behavior of financial markets in

the fields of economics and psychology (e.g., Andreassen 1988, Myagkov and Plott 1997, Noussair, Plott

and Riezman 1995, 1997), but experimentation has yet to become a mainstream paradigm in Finance.

An understanding of the antecedents or causes of an effect is crucial to manage that effect--an issue

of importance not only to traders and risk managers, but also to consumers, public policy makers, and

regulators. A unique appreciation of the cause of an effect provides a toolbox to reduce the size of the

effect, leverage off it, or rigorously account for it in mathematical modeling to better capture descriptive

reality rather than normative behavior. Such an appreciation is best provided by experimentation working

hand in hand with current econometric modeling (e.g., DeBondt and Thaler 1985, 1987), survey methods

(e.g., DeBondt 1991, Shefrin and Statman 1993, 1995, Shiller 1990), simulations (e.g., Benartzi and

Thaler 1995) or quasi-experimental approaches (e.g., Barber and Odean 1998). Experimental enquiry is

the single most appropriate methodological tool to isolate causes of behaviors and complement extant

empirical paradigmatic approaches.

We start, in Section 2, by categorizing the documented anomalies/ biases into four distinct

categories pertaining to returns and prices, time series patterns, volumes, and others. These categories are

for convenience and exposition. In Section 3, we then propose a general overarching, yet simple,

framework for understanding the process of financial decision making, and illustratively demonstrate how

some of the extant work in behavioral finance fits within this framework. We also present an illustrative

set of propositions that can be derived from the framework. In Section 4, we present an illustration of the

methodology required to test for an effect, demonstrating how one can set up an experiment appropriately

so as to distinguish between alternate hypotheses.

The main thesis of this article is that theory-driven experimental enquiry is an appropriate

methodological tool at this stage of the life-cycle of behavioral finance when many empirical irregularities

1 In a recent article in the Review of Financial Studies, Glosten (1999) states “...the opportunity for a clear testwith an exogenous change in institution is rare if not nonexistent. This is one thing that experimental markets canprovide, which is one reason I find the results of this form of analysis useful.”

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have been found and shown to be robust, and numerous compelling explanations have been proffered for

their existence. While economists have used the experimental paradigm before, they have used it to detect

anomalous behavior, explore learning ability etc., but not necessarily to distinguish between alternate

explanations for the same empirical fact. The state of the behavioral finance literature shows a many-to-

many mapping between effects (anomalies or biases) and underlying causes for these effects. The overall

situation appears to be one of either multiple explanations for the same effect at best, or of alternative

explanations driving the effect at worst. Theory-based empirical enquiry has the inherent advantage of

eliminating alternative explanations and isolating key causes. More importantly, it can highlight potentially

important gaps in the literature and direct future empirical investigation to uncover patterns that diverge

from rational expectation models. Methodologically, experimentation is the only route to identify

causation, and rigorously eliminate alternative explanations. Its methodological weakness, external

generalizability, is less of a concern given the state of the literature at this point. The biases have been

demonstrated, and replicated; what remains for us to isolate is why they occur.

One example of an effect being explainable by many causes, is the finding of excess returns from

value investing (Graham and Dodd, 1934). The jury is yet out on why this effect occurs. DeBondt and

Thaler (1985) find overreaction and favor the contrarian investor hypothesis, which holds that value

strategies work because they are the opposite of what a naïve investor would do. A naïve investor is

believed to extrapolate recent bad information too much into the future, leading to overreaction.

Overreaction causes market prices to significantly diverge from fundamentals and be followed by a

reversal. While their data is consistent with this explanation, others have contested that the seemingly

anomalous higher returns may simply reflect higher risks (Ball and Kothari 1989, Chan 1988, Lakonishok,

Shleifer and Vishny, 1994, but see DeBondt and Thaler 1987 for a rebuttal). Other explanations that have

been suggested for this effect include investors assuming trends where none exist, thereby creating a trend

(Jegadeesh and Titman 1993, DeBondt 1993), justifiability of glamour stocks by money managers (Shefrin

and Statman 1993), and faulty inferencing that a well-run company is a good investment irrespective of

price (Shefrin and Statman 1995). We shall demonstrate by an experimental example (at the end of the

paper), that further light may be shed on the antecedents of the value-investing anomaly through

experimentally systematically exploring each cause. Most proffered behavioral explanations have been

able to explain a few biases, but have fallen short of parsimoniously accounting for a whole class of biases.

To the extent that many market place anomalies are merely symptomatic of an underlying psychological

construct, in an ideal scenario, the construct should have good predictive ability.

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We first present a summary of financial anomalies that have been shown to occur, and follow this

up with a general information processing model that posits the manner in which people process information

to make financial decisions. Potential sources of bias at each of the stages are highlighted, with

implications for macro-level effects discussed. Some predictions for each type of bias are offered, and an

illustrative experiment showing how to test for one such effect presented.

2. Financial Anomalies and Biases: A Bird's Eye View

With increasing frequency, researchers are uncovering new patterns in data that systematically

violate the tenets of extant rational economic theory. At first blush, each appears to be a new bias, and as

the list of biases and anomalies grows, the likelihood of a parsimonious explanation to account for these

recedes. However, to the extent these data patterns reflect an underlying tendency in human behavior, it

may be useful to view the different patterns as mere manifestations of a larger construct. In short, a

simplification of the literature is called for before one can begin the gargantuan task of understanding it.

Below, we propose one such simplification.

We have categorized the deviations of behavior from rational tenets into four main categories:

Returns and Prices, Volume and Volatility, Time Series Patterns, and Others. Thus, all anomalies are

categorizable into "meta" biases, offering a parsimonious classification scheme. Each of these is described

briefly, and annotated with examples of specific biases summarized in Table 1.

-- Insert Table 1 around here. --

2.1 Returns and Prices

There appears to be a persistent deviation of stock prices from fundamentals, a finding that has

been demonstrated to be fairly robust (e.g., Shiller 1981, Shleifer and Vishny 1990). One example of this

is overreaction in some instances, or under-reaction in others (Barberis, Shleifer and Vishny, 1998; Daniel,

Hirshleifer, and Subrahmanyam, 1998), implying a return predictability such that stock prices do not

display a random walk. This has been found to happen more in small stocks and in January, and is evident

in a 3-day window around quarterly earnings announcements (Chopra, Lakonishok, and Ritter, 1992).

Lehmann (1990) finds evidence for overreaction studying short intervals (one week), eliminating changes in

fundamentals as an alternative explanation for the overreaction effect. Stein (1989) demonstrates that long

maturity options "overreact" to changes in implied volatility of short-maturity options and Arrow (1982)

concludes that daily variations in stock prices are excessive relative to the daily variations in information.

(However, Klein, 1990 and Zarowin 1989 find no evidence of overreaction; while DeBondt and Thaler,

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1987 argue that the "overreaction" effect may not reflect a misinterpretation of earnings.)

A good exemplar of a documented anomaly capturing a deviation of returns from a normative

expectation, are the excess returns from value investing. Graham and Dodd (1934) found that there are

excess returns from "value investing," where buying stocks with a low price/earnings ratio leads to "excess"

risk-adjusted returns, with extreme losers outperforming the market. This empirical finding has spawned a

wealth of research looking for robustness of, and explanations for, this effect. The effect is robust: It has

been replicated in the US (Basu 1977, 1983) and Japan (Chan, Hamao, and Lakonishok 1991); shown to

hold for stocks with a high book relative to market value of assets (Fama and French 1992), as well as for

other measures of "value" including size (Banz 1981), and dividend yield (Keim, 1985), and has been

demonstrated to hold in the long term (Chopra, Lakonishok and Ritter 1992, DeBondt and Thaler 1985). A

more general form of this bias is the general effect that "losers" appear to perform better than winners

(Ball and Kothari 1989; Chan 1988; Chopra, Lakonishok and Ritter 1992; Conrad and Kaul 1993;

DeBondt and Thaler 1985, 1987; Zarowin 1990).

The closed-end fund puzzle is another anomaly that fits within this class. The anomaly is that a

closed end fund starts at a premium, trades at a discount within 120 days of issue and has full price at

closing (Peavy 1990; Weiss 1989); an effect replicated for international funds (Bodurtha, Kim, and Lee

1995). The dominant explanation suggested is one of market sentiment, i.e., individual investors'

sentiments (Lee, Shleifer and Thaler 1990, 1991; Zweig 1973; Fisher and Statman, 1999). This has the

potential to also explain the puzzling correlation between prices of US and non-US closed-end country

funds (Bodurtha, Kim and Lee 1995; Lee, Shleifer and Thaler 1990).

Another seemingly anomalous finding is the equity premium puzzle, which documents that risk-

adjusted returns of stocks are "unjustifiably" higher than those of bonds, with stocks having outperformed

bonds by 8% over the last century (Mehra and Prescott 1985, Weil 1989). Yet another example is that of

momentum strategies, where people appear to buy when a stock starts doing well expecting the momentum

to continue. While efficient market theory posits that today cannot be extrapolated to tomorrow, portfolios

based on a 1-year formation period exhibit momentum (Chopra, Lakonishok, and Ritter, 1992).

While many have explored rational economic reasons for these effects, including that higher returns

reflect higher risks (Ball and Kothari 1989, Chan 1988, Lakonishok, Shleifer and Vishny 1994), studies

incorporating risks have replicated the effect (DeBondt and Thaler 1987). Rather than pitting the rational

economist view against the behavioral anomaly view, some authors have married the two in a noise-trader

or contrarian investing model, where market heterogeneity in rational ability allows for the naïve trader to

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be prone to biased behavior, with the expert behaving in an apparently non-normative manner, for very

rational reasons. Thus, Lakonishok, Shleifer and Vishny (1994) argue that value strategies might work

because they are contrarian to naïve strategies followed by other investors. These naïve strategies might

range from extrapolating past earnings growth too far into the future, to assuming a trend in stock prices,

to overreacting to good or bad news, or to simply equating a good investment with a well-run company

irrespective of price. Regardless of the reason, some investors tend to get overly excited about stocks that

have done very well, buy them, leading to their becoming overpriced (See Daniel and Titman 1999 for an

interesting analysis of this literature).

In summary, this class of anomalies suggests that certain types of financial instruments predictably

outperform others, and this is, at least in part, due to some, if not all, traders behaving in a manner that

violates rational economic models necessitating the need for a psychological explanation for the behavior.

2.2 Volumes and Volatility

Loewenstein (1988) documented that there is "too much" volume and volatility to be justified by

regular investment and portfolio requirements. Trading volumes have also been examined at the individual

consumer level by Barber and Odean (1998), who found that men trade 45% more than women, and earn

1.4% less! Overconfidence often leads to excessive trading (Odean, 1997, 1998), and an illusion of control

may be another factor (Heisler, 1994). Andreassen (1988), also found that trading increases on days when

there are large changes in prices, and reduces on days when there are small changes.

With shorter time intervals, another documented effect is excess volatility in intra-day prices

(Arrow 1982), closely related to the finding that there are large stock price changes that appear to be

unrelated to identifiable economic or company related developments (Cutler, Poterba and Summers 1989).

Another specific example falling within this super-category is the presence of cascades and

bubbles, and the presence of hot markets (for IPO issues among other things). Under the general rubric of

market sentiment, Ritter (1991) suggests that excessive trader optimism leads to existence of hot markets.

Of course, the greatest evidence of excessive volume are market crashes and panics (e.g., Stock Market

Bubble of 1929; Crash of October 1987), which could be due to overreaction (Lehmann 1990), hysteria

(Seyhun 1990), herding (Froot, Scharfstein, and Stein 1992), or over-weighting current information

(DeBondt and Thaler 1985). Such strategies could also lead to "excess" returns.

2.3 Time Series Patterns

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The literature also documents anomalous patterns of prices of a financial instrument over a period

of time. An exemplar of a puzzling pattern is the IPO Pricing pattern: Initial Public Offerings (IPOs) are

(i) underpriced in the short term (e.g., Ibbotson, Sindelar, and Ritter, 1988 estimate the average initial

return from offer price to market price at the end of the first day's trading at 16.4%; see also Carter and

Manaster, 1990); (ii) underperform in the long term (e.g., Ritter, 1991 finds that the average holding period

return for a sample of 1526 IPOs of common stock in 1975-84 is 34.47% in the three years after going

public as compared to a matched sample return of 61.86%; see also Agrawal and Rivoli, 1990 for similar

results for one year horizons with the sample period 1985-86); and (iii) are issued in "hot" markets

(Ibbotson and Jaffe, 1975; Ritter, 1984; and Ibbotson, Sindelar, and Ritter, 1988). (See Wang, Chan, and

Gau, 1992 for anomalous behavior in the IPO offerings for real estate investment trusts). Some behavioral

explanations for this pattern include the cascade hypothesis (Welch 1992) whereby people are posited to

follow the leader and ignore their own information while making a decision (see also Bikhchandani,

Hirshleifer, and Welch 1992). Using information about others' expectations has also been suggested under

the impresario hypothesis tested by Shiller (1990). A survey of 237 individuals and institutions showed

that IPO enthusiasm is related to beliefs regarding how other investors feel about the market.

A second example is an apparent under-reaction to earnings announcements. This has been

recorded in post-earnings announcement drift where cumulative average residuals drift upwards after

announcements of an earnings increase (Bernard 1993, Bernard and Thomas, 1989; Bernard and Thomas,

1990; Freeman and Tse, 1989; Mendenhall, 1991; Wiggins, 1991). For example, Michaely, Thaler, and

Womack, 1995 found that dividend initiations and omissions are associated with large share price reactions

with a mean drop of 7% for omissions and an increase of over 3% for initiations. These represent under-

reactions, and trading strategies based on this earned positive returns 22 out of 25 years. Ofer and Siegel,

1987 have recorded evidence of under-reaction by analysts for EPS expectations, and DeBondt and Bange,

1992 find similarly for inflationary expectations.

Unusual price patterns are manifest in mergers and acquisitions prices where the buying firm

shows a drop, whereas, the acquired company shows an increase in share price, a pattern that does not

appear to correct over the long run (Agrawal, Jaffe, and Mandelkar 1992). Reasons ranging from

overconfidence (Baradwaj, Dubofsky, and Fraser 1992), and use of asymmetric information (Hawawini

and Swary 1990), to ego-enhancing motives (Morck, Shleifer, and Vishny 1990) and the hubris hypothesis

(Roll 1986) have been proposed as explanations for this anomaly.

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2.4 Other Anomalies

The mere presence of certain patterns or aspects of financial instruments is puzzling to some. An

examples is the puzzle as to why dividends exist, in a tax regime that would otherwise discourage it (Elton

and Gruber, 1970; Shefrin and Statman, 1984). Other examples include why credit card returns are sticky

(Ausubel 1991), and the transmission of sentiment across markets (see Lee, Shleifer and Thaler, 1990,

1991; Bodurtha, Kim and Lee, 1995).

This brief glimpse of the documented anomalies in the literature shows that different reasons with

some common themes have been frequently, but variously proposed to explain human behavior in financial

markets. We now offer a structural framework for the causes for the anomalies within a psychological

information-processing model.

3. A process model of financial information processing

In this section, we explore how people formulate perceptions of the risk and return of a financial

asset and the factors that might potentially bias these. Given the growing consensus among behavioral

finance researchers that human limitations in processing ability or motivation can affect the functioning of

rational markets, this model makes a contribution by providing a psychological theory predicting the stages

at which biases may occur, along with the reasons they would (also see Slovic 1972). This is a theory of

individual information processing and does not cover interactions between groups of people that could lead

to an exacerbation (or occasionally an amelioration) of the biases at the individual level. Given this caveat,

to the extent individuals behave in a systematic manner, one may expect markets to mirror their behavior.

The term "bias" is used for want of a better nomenclature to capture systematic deviations from

normative models, rather than implying that the decisions are "wrong" or inaccurate. Theoretically

speaking, biased perceptions of financial risk may occur at any stage in the processing of information prior

to making a judgment. A typical model of information processing includes the stages of perception of

existing information, retrieval of information from memory, integration of multiple sources of

information, with judgments based on the above, and resulting in observable and unobservable types of

behavior (e.g., Bettman 1979). (Note that perception of contextual information and memory-based

information are two alternate sources of information that may be used instead of, or in combination with

each other to make a decision.) Using a similar structure, we examine factors that could bias perception

and retrieval of existing information, and lead to non-normative integration rules and judgment heuristics,

leading to biased judgments. (For a similar conceptualization within the consumer mutual fund choice

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domain see Lichtenstein, Kaufmann and Bhagat, in press)

Biases due to the manner in which people perceive an existing set of returns are termed perceptual

biases; biases due to the manner in which they retrieve such information from memory are termed memory

biases; biases due to the way in which different sources of information are integrated prior to making a

judgement are termed information integration biases; biases due to the type of judgement rule used are

termed criterion biases and biases in the manner in which people act are termed behavioral biases. This

last captures hard-wired rules of action, including mere repetition (or lack thereof) of actions due to

habituation. This typology captures the stage at which biased judgments enter the decision process. The

model is depicted in Figure 1.

-- Figure 1 around here. --

Perceptual and retrieval biases have been less examined than judgment or criterion biases in the

financial literature. However, consistent with a perceptual bias explanation, is the myopic, or excessively

short term, horizon that has been discussed in the context of why stock prices deviate from fundamentals

(Shleifer and Vishny 1990). An example of a recorded bias at the criterion stage includes justification as

an explanation for excess returns from value-investing. Specifically, money managers prefer glamour

stocks because they appear prudent and can be more easily justified to plan sponsors, with poor

performance easily attributable to poor market conditions (Shefrin and Statman 1993). On the other hand,

a behavioral bias is typified by the cascade hypothesis whereby people are posited to follow the leader and

ignore their own information while making a decision (Welch, 1992), which has been invoked to explain the

IPO pricing anomaly. Another example is the investor model of habit formation (Constantinides 1990)

which argues that people's decision to buy stocks is based on current standards of living, and is used to

explain the equity premium puzzle (but see Burge 1993 for a rebuttal).

Factors affecting each of these biases are further categorized into cognitive and motivational

antecedents. The distinction between the two is best understood in the manner described by Lewin (1951):

whereas cognition provides a perceiver's interpretation of the world, determining what a person will do;

motivation predicts whether the behavior will occur at all. Motivations are the motor for cognitive

behavior. The distinction between cognition and motivation is important because while a cognitive cause of

an effect would imply that corrective action be via increasing cognitive capacity through ability or

opportunity (e.g., time available), motivational causes require modifying the goals, or beginning or end

feeling states for a decision maker. Cognition is the mental process or faculty of knowing, including

aspects such as awareness, perception, reasoning and judgement. A cognitive factor is one to do with

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perceptions, thoughts, beliefs, inferences, or decision rules. These include limitations in cognitive ability

(e.g., brainpower) to identify, retrieve, or integrate information, apply appropriate decision rules, and act in

accordance with these decision rules. Cognitive biases are biases relating to shortfalls in the perceiving,

thinking and reasoning process given a set of information to process. Such biases have been considered in

the finance literature in the past. For example, Shefrin and Statman (1994) develop a capital asset pricing

theory where noise traders interact with information traders. Noise traders are traders who commit

cognitive errors while informed traders are free of cognitive errors. Similarly, other cognitive biases that

have been discussed in the literature include the use of the representativeness heuristic, whereby naïve

investors extrapolate recent bad information much into the future, which leads to their overreacting

(DeBondt and Thaler 1985); assuming trends where none exist (Jegadeesh and Titman 1993); and inferring

correlations between well-run companies and investment potential (Shefrin and Statman 1993, 1995).

Motivational factors, on the other hand, capture the desire to achieve, maintain, or reverse feeling

states, including the decision goal (e.g., make a correct or justifiable decision). Given that motivation to

make an accurate (versus justifiable, or easy) decision may moderate the cognitive biases, for ease of

exposition, motivational factors are clubbed together and discussed separately rather than with respect to

their effect at each stage of the decision process. Some of the motivational factors discussed in the

financial literature pertain to loss aversion, whereby people are more sensitive to losses than gains.

Benartzi and Thaler (1995) invoke loss aversion with myopic behavior (short term decisions even when

goals are long term) to explain the equity premium puzzle. Another example is the herding instinct that

has been offered as an explanation for overreaction (Shiller 1990). For example, to the extent people wish

to protect their own reputation, they may wish to engage in the most justifiable course of action, which

simply entails following the crowd (Scharfstein and Stein 1990). Another example consistent with a

motivational bias is why there are heavy trading volumes (Loewenstein 1988, Heisler 1994). An

overarching motivation for most people is to be able to understand the future so that they may better

control it. Living in a random world of random events is inherently unsettling and unsatisfying. Thus,

people overestimate the control they have over future events. Such a feeling of control may be a good

predictor of why trading volumes are so high.

Thus, even a casual examination of the literature suggests easily identifiable cognitive and

motivational antecedents for market anomalies. These antecedents are manifest in each stage of the

financial decision making process, resulting in five types of bias. Later in the paper, we shall provide

simple examples showing how experimental work may be used to arrive at a sharper explanation for any

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anomaly. To set the stage, we now discuss each type of bias in some detail.

3.1 Cognitive Biases

A cognitive bias is defined as a systematic deviation from a norm due to inadequate ability or

opportunity to collect or integrate information. Drawing heavily on the model that humans are cognitive

misers aiming to invest only as many cognitive resources as required by their desire for accuracy (Simon

1967), we describe a framework that shows how shortchanging cognitive resources in attention or

integration tasks may lead to systematic patterns (including errors) in behavior.

3.1.1 Perceptual biases

A perceptual bias is defined as a systematic deviation in the manner in which a person perceives a

string of (financial) data as compared to an objective description of that data. Given that there are two

main aspects of a string of financial data: the trend, and the noise around the trend, what factors influence

which aspect is given more attention? An overarching goal of human agents is to be able to predict the

universe so that they may better control it (Heider 1958). Uncertainty is, in and of itself, aversive as it

undercuts one's ability to predict and control one's environment (Fiske and Taylor 1991). Towards this

end, people look for patterns that will help them predict the future and, therefore, work it to their

advantage. This type of an effect (arguably rational rather than biased) has been suggested in the financial

literature by Jegadeesh and Titman (1993), who suggest that such an effect could turn into a self-fulfilling

prophecy, as investors believing that a trend exists, feed the trend itself, causing prices to diverge from

fundamentals. If we accept that the need to see a trend in data is a basic perceptual bias, then we can draw

upon the perception literature to predict other patterns that might emerge.

There is a large literature on biases in spatial judgements based on visual cues that can speak to

these questions (e.g., McNamara 1986, Thorndyke 1981). This literature essentially invokes differential

attention to data as the underlying mechanism by which biases are manifest. For example, research on uni-

dimensional stimuli (line length) has shown that people differentially focus on the beginning and the end of

the string versus the path the string takes (Raghubir and Krishna, 1996). Over a century of research in

visual perception and cognitive psychology has demonstrated that the eye is fallible, with optical illusions

shown to be robust (Bolton 1897-98, Krishna and Raghubir 1997). There is increasing convergence in the

view that the effects are because people have insufficient cognitive capacity (e.g., mental resources) to

process available information, leading to either an incomplete sampling of available information, or

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inadequate integration of presented information to make judgments.

Further, given that data has many aspects, attention may be directed toward one or the other of

those aspects, leading to those aspects of the data being over-weighted in the judgment (Fiske 1980). For

example, trends may receive more attention than noise in some conditions, and contextual cues may

exacerbate the salience of relative performance of a financial instrument over its absolute performance.

Thus, based on the literature on visual information processing with limited cognitive ability, we summarise:

P1: Attention Deficits: Due to limited cognitive capacity, decision makers may be prone to:a. Initial Anchoring: Decision-makers sample from an information distribution,

with points that are most perceptually salient (such as the end-points ofthe distribution) more likely to be selected as initial anchors to be used asinputs in the decision process.

b. Inadequate adjustment: Decision-makers focus more on perceptually salientaspects of stimuli and inadequately adjust for other information, includingbackground information.

c. Differential attention to perceptually salient aspects of the data. Decision-makers emphasize elements in the salient information subset differentially.

For example, a graphical representation of data can present a near infinite quantity of information.

Humans with their limited information processing ability would reach capacity constraints and be unable

to utilize all the information present. Thus, attention may be drawn to perceptually salient points of the

distribution to simplify the information processing task. Based on prior literature in visual information

processing, the salient points are likely to be the start and end points of a graph (Raghubir and Krishna

1996) and the minimum and maximum points of the graphical space (Krider, Raghubir and Krishna 1999).

A good example of perceptual biases in information processing is Kahneman and Tversky's (1979)

assertion, developed as the Prospect Theory model, that numbers are meaningless in and of themselves, but

attain meaning only with respect to a reference point. Thus, a price represents not so much the amount one

has to pay, but instead, the gain over the reference point, or a loss based on the reference point. Given this,

what specific reference points are chosen may be of critical importance to the manner in which risk and

return are perceived.

For example, with sequential data, the starting point may serve as a starting anchor, or reference

point. However, carving the data from a different starting point, should affect perceptions of how the stock

performed. A very important reference point that has been demonstrated to exist is the price at which a

share was bought. Thus, irrespective of future expectations, if the current market price is lower than the

price at which a stock was purchased, people will be less willing to incur a loss and switch out of the stock,

than if the current price was higher than the price they had bought it at, which would give them a net gain

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on the transaction. This might lead to people selling winners and holding losers. This "disposition effect"

is well studied in both Finance and in the behavioral decision theory literature. For example, Shefrin and

Statman (1995) found that long positions are held open longer for losing positions as compared to winning

positions. Ferris, Haugen and Makhija (1988) found the same effect in year-end trading volumes: those of

winning stocks were greater than those of losing stocks; and Heisler (1994) found that in the treasury bonds

futures markets, losing trades were held longer than winning ones. Encouragingly, he also found that this

was more true of unsuccessful traders, implying that ability factors moderate the extent to which decision

makers can correct for the use of reference points in their integration of information, to make normatively

appropriate choices.

However, the price at which a stock has been purchased may not be the only reference point that is

used. Alternative reference prices that may be used could be the indices against which a current stock is

compared. Thus, attention may be drawn to the fact that stock A has performed better than its index, while

stock B has performed worse than its index. If so, even if stock A returned lower than Stock B, it may

perceived to be a better performing stock, due to its reference point. If people use index information as a

reference (as arguably, they should), fund managers can use deceptive practices to improve consumers'

perceptions of funds' past performance. Similarly, the presence of other contextual information may affect

risk perception. For example, exposure to greater risk in one market, may lead traders to underestimate

risk in other markets, merely because the risk is lower than what they otherwise trade. Understanding, and

incorporating reference points into models of decision making appears to be a promising approach.

3.1.2 Retrieval biases

If information is not contextually available, decision makers need to rely on memory-based

information to make their decision. Memory is fallible. Psychologists examining memory processes have

shown that even if information is available in long term memory, it may be inaccessible and, therefore,

difficult to bring to mind (Feldman and Lynch 1988; Menon, Raghubir, and Schwarz 1995, 1997).

Accessibility refers to ease of retrieving an input from memory (Higgins and Bargh 1987). Arguably,

retrieval biases will be less relevant in the context of financial decision making as many decisions are

stimuli-based (i.e., in the presence of information), rather than memory-based. Retrieval biases are based

on the presumption that people have difficulty accessing information in long term memory due to the high

cost (time and effort) of recalling information, and interference from new information,. They, therefore,

make decisions based on a "sample" of information retrieved from memory, with the sample being biased

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due to the differential accessibility of one piece of information versus another. A number of factors,

including the frequency and recency of information, its valence, and value, affect information accessibility.

These are briefly discussed:

FREQUENCY OF INFORMATION. The more frequently an event occurs, the greater the likelihood

that it will be recalled (Higgins 1989). Thus, the more frequently that decision makers access information

about stock prices (e.g., multiple times a day versus daily versus weekly), the more accessible past price

information is likely to be. This implies that markets for which information is available with differential

frequency (e.g., real time, or once every period-daily, weekly etc.) may behave differently.

RECENCY OF INFORMATION. The more recently an event occurs, the greater the likelihood that it

will be recalled (Higgins 1989). Clarke and Statman (1998) showed that newsletter forecasts varied with

recent market results. Thus, traders or financial investors may weight recent information much more than

past information. Note that this may be rational behavior if prices are auto-correlated or if for other reasons

recent information is more informative, but not otherwise. This cause has been documented in the context

of overreaction by DeBondt and Thaler 1985, who argued that individuals attach disproportionate

importance to short-run economic events, under-weighting random elements and priors. When exaggerated

fears that losers may become bankrupt are not borne out, price reversals occur. While Chan (1988) and

Ball and Kothari (1989) explain the same effect in terms of higher risk of losers over time, DeBondt and

Thaler (1987) correct for this higher risk, and show the anomaly persists.

NEGATIVE INFORMATION. Negative information is more accessible than positive information,

primarily because peoples' base level expectations are that positive events are more likely to occur than

negative ones, making negative occurrences "stick out" (Fiske 1980). The greater accessibility of

"negative" information, compounded by the fact that it is weighted more (Fiske 1980), implies that it may

have a larger than appropriate effect on risk.

Dreman (1993), in offering an information explanation for why value stocks are more profitable,

uses the principles of asymmetric effects of negative and positive information. He hypothesizes that the

value investing anomaly occurs because investors react positively to new information which is favorable

because the prior perception was negative. He tests a value based contrarian strategy where the best stocks

are high P/E and worst stocks are low P/E for the 1973-1990 period. When information is released that

changes the market's view of what is a good or a bad stock, a correction occurs, making a contrarian

strategy profitable. Large surprises are those where announced earnings are 10% greater than expected.

EXTREME INFORMATION. Extreme events are also highly salient and more likely to come to mind

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(Fiske 1980). Thus, for example, the greater the extremity of a movement of a stock across time (i.e., the

larger the jump) the greater the likelihood that the movement will be recalled and, therefore, "sampled."

This leads to a natural hypothesis that perceived risk is greater when there are a few big jumps versus many

small jumps, even though the variance may be higher for the latter. The implication here is that kurtosis,

the fourth moment, has an important impact on risk perception, and is operationalized via the accessibility

heuristic.

Thus, we summarize:

P2: Cognitive Antecedents of Information Retrieval: Past information that is not contextuallyavailable, but needs to be recalled from memory, is more likely to enter a decision if it is more (vs. less):

a. frequentb. recentc. negative, ord. extreme.

3.1.3 Information Integration biases

Information integration pertains to how information that is either contextually available or retrieved

from memory, is combined to make an overall decision. Feldman and Lynch (1988) developed an

accessibility-diagnosticity framework for decision making. While accessibility refers to ease of retrieving

an input from memory, diagnosticity refers to the sufficiency of the retrieved input to arrive at a solution

for the judgment task at hand. The likelihood that a source of information will be used to make a judgment

is: (a) a positive function of the accessibility of the input; (b) a positive function of its diagnosticity for the

judgment; (c) a negative function of the accessibility of alternate inputs; and (d) a negative function of the

diagnosticity of these accessible alternate inputs (Simmons, Bickart and Lynch 1993). This framework

suggests that the types of information that are selected and the manner in which they are integrated, all may

lead to departures from a normative model of decision making.

Inappropriate selection of information: Owing to cognitive limitations, decision makers are more

likely to sample a part of the information available to them, rather than use all the information at their

disposal. This information sample is unlikely to be a random one, and is more likely to be driven by factors

such as the salience of information, its accessibility, its perceived diagnosticity, and its ease of use. A good

example of this is the asymmetric use of negative and positive information.

Negative-Positive Asymmetry: An entire literature on prospect theory addresses this bias.

Prospect theory accounts for the fact that people become risk seeking when making losses and risk averse

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when making gains (DeBondt and Thaler 1994). People overweigh losses as compared to gains. Social

psychological research has also shown that negative information works differently from positive

information (Taylor 1991). An example of such an explanation is proposed by Dreman (1993) who

attempted to explain why there are excess returns from value investing.

Use of Judgmental shortcuts versus systematic processing. Attitude theory (e.g., Chaiken 1980,

Petty and Cacioppo 1986) has proposed that there are two main ways in which an attitude may be formed:

through a systematic integration of all information, weighted by its diagnosticity (or perceived relevance for

a judgment task), or through a heuristic route (where simple rules of thumb are applied to make a

judgment). The higher the motivation to make an accurate judgment and the more the cognitive resources

available to make such a judgment, the greater the likelihood that overall judgments will be based on

systematic integration of information. However, to the extent that people are cognitive misers and unable

to expend the resources required to normatively combine different sources of information (some of which

may contradict each other, and many of which differ in terms of how relevant they are for the judgment

task), heuristic processing may dominate and lead to judgment biases. Heuristic processing is also an

appropriate tool for decision makers to reduce decision complexity in a world where there may be

information overload. Recent work on these theories argues that if the decision makers goals include

criterion other than accuracy: e.g., justifiability of the decision, need to eliminate post-decision regret, or

use of "how-do-I-feel-about it?" heuristic, then heuristic processing may be used to simplify the judgment

task (e.g, Pham 1998, Simonson 1989).

Inappropriate weighting of information. In a normative information integration universe,

information should be weighted by its diagnosticity for a judgment task. However, there exists a wealth of

literature in decision making that has shown that people overweight certain types of information--e.g.,

information that is more vivid, attention getting, or distinct (Nisbett and Ross 1980); or inappropriately use

information that is non-diagnostic (Barberis, Shleifer, and Vishny 1998). The literature on pseudo-

diagnosticity (e.g., Muthukrishnan 1995), shows that some sources of information have the appearance of

being informative, but are merely so in a placebic sense. Other literature in social cognition has shown a

strong effect of source cues -- i.e., the source transmitting the information affects the perceived reliability

of the information, and may do so in non-normative ways (e.g., attractive spokespeople may be more

persuasive than unattractive spokespeople; Chaiken 1980). The attribution of source motives also affects

the perception of their reliability.

Attributional Biases. Social psychologists have for long studied the manner in which people

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attribute causes to events (Heider 1958, Jones and Davis 1965, Kelly 1967). This need to assign a cause to

past outcomes stems from people's innate need to predict the universe, so that they may better control

future outcomes. The attribution literature has documented a number of self-serving biases that allow

people to maintain or improve their self-esteem: for example, attributing failure externally and success

internally (Miller and Ross 1975). Other attributional biases are more cognitive in nature: e.g., attributing

behavior to the entity performing the behavior, rather than external causes, or random events (also known

as the "fundamental attribution error," Heider 1958, Ross 1977).

In the context of financial decision making, this may imply that decision makers may attempt to

over-explain the random nature of events, a suggestion put forward by Jegadeesh and Titman (1993),

where they suggest that people assume trends where none may exist; or hold a particular company more

responsible than it may be for a change in its stock price, even if this change were attributable to industry

or economic events.

Inferencing: For most decisions, people do not have all the information that they may require to

make a decision, or the information may not be available in the form that they need. In such cases, most

decision makers resort to making inferences based on available information to fill in information gaps.

These inferences represent correlational beliefs and schemas that people have (Abelson 1981, Fiske and

Neuberg 1990).

An example from the financial literature exemplifying this type of an effect was proposed by

Shefrin and Statman (1993, 1995) to explain the value-investing anomaly. They argued that naïve

investors equate a well-run company with a "good" investment, irrespective of price. This is but one

example of inferencing and a systematic examination of people's belief structures would uncover other

potentially erroneous inferences they make while deciding on a financial investment.

Thus, we propose that:

P3: Information integration biases are due to: a. Improper sampling of information available to make a decision, b. Inadequate combinatorial methods (e.g., using heuristics). c. Incorrect weighting of information. d. Attributional biases in assigning cause to events. e. Drawing inappropriate inferences to fill in missing data

3.1.4 Biases in the Use of Judgment Criteria

The incentive to use the least-taxing cognitive strategy which yields a reasonable response may be

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fairly high in high pressure settings such as a typical dealing room. This prompts the use of judgment

heuristics to reduce task complexity, and to make the judgment process easier (March and Simon 1958;

Tversky and Kahneman 1974).

These kinds of biases arise when only a few selective criteria are used to arrive at an evaluation of

risk. These criteria may be chosen for "non-rational" reasons, or may simply differ from the actual factors

affecting risk and return. The literature on choice by justification is relevant here. People will often choose

the alternative which is easier to justify (e.g., glamour stocks, Shefrin and Statman 1993), when they need

to justify their decision, rather than the one which may be normatively better on a stated criterion of return/

risk. This effect may play a major role in the investment management industry. Another example of a

judgement criterion bias is when agents suspend their judgement in favor of the judgement of others. An

instance of such a bias is found in informational cascades (Bikhchandani, Hirshleifer, and Welch, 1992).

An informational cascade occurs when it is optimal for an individual, having observed the actions of those

ahead of him/ her, to disregard own information and follow the behavior of the preceding individuals. The

authors argue that localized conformity of behavior and the fragility of mass behaviors can be explained by

informational cascades.

Common heuristic rules of thumb that have been examined in the context of attitude formation,

include the use of consensus information ("consensus implies correctness"), communicator characteristics

(e.g., attractive spokespeople are more persuasive), source likability ("people generally agree with people

they like"), use of experts' opinions ("experts statements can be trusted"), message length or using quantity

of cues rather than quality of cues to make a decision ("length implies strength": for a review see Chaiken,

Liberman and Eagly 1989).

Coming from a slightly different perspective, Tversky and Kahneman (1974), list a number of

heuristics that bias the manner in which people make decisions. Thus, the representativeness heuristic

argues that people base probabilistic judgments on the extent to which a particular stimulus is

representative of a larger domain, leading to an insensitivity to prior probability of outcomes, insensitivity

to sample sizes, misconceptions of chance. insensitivity to predictability, illusions of validity, and

misconceptions of regression. On the other hand, the availability heuristic proposes that the ease with

which information comes to mind (defined by us as "accessibility" earlier in this article) leads to inferences

of the population from which the information has been drawn. This could lead to biases of imaginability,

illusory correlations, as well as biases due to retrievability of instances. Finally, they propose the anchor

and adjust heuristic which proposes that people simplify a decision making task by starting off with an

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initial anchor and then (inadequately) adjusting this anchor in the light of other evidence. This leads to

judgments biased in line with the starting anchor.

Thus, we summarize:

P4: Criterion biases are due to using a heuristic rule of thumb to make decisions.

3.1.5 Behavioral Biases

Judgments may also be biased because of habit persistence (Constantinides, 1990). This occurs

when habit replaces objective judgement criteria. The obverse of this would be the need to seek variety, for

the sake of variety, resulting in distortions in portfolio make-up. Ferson and Constantinides (1991) find

evidence for habit persistence in monthly, quarterly, and annual data. In the social psychology literature

this bias is also called the status quo bias (Kahneman, Knetsch and Thaler 1991, Samuelson and

Zeckhauser 1988).

P5: Behavioral biases are due to people using past behavior as a guide to direct future behavior,resulting in either variety-seeking or habit persistence.

The above was a sketch of a cognitive process theory by which people make financial decisions.

We showed that biases can creep in at any of the stages of processing of information prior to making a

decision, and carry through to the final decision to buy or sell. An understanding of the causes of behavior

is necessary as the ways to correct perceptual biases differs from the manner in which information

integration biases can be corrected.

Similarly, motivational biases require a different prescription compared to cognitive biases. We

now present some motivational factors, followed by ability factors, that directly affect decision making or

moderate the manner in which cognitive factors work.

3.2 Motivational Biases

Motivational biases arise from self-created incentives, which interfere with an individual’s

objective evaluation of a financial opportunity. There are some basic motivational states that drive human

behaviour. Thus, the motivation to maintain a positive self-image, manage an impression, the motivation to

achieve goals, predict the future, or the motivation to feel good are all basic human needs (e.g., Greenwald,

Bellezza, and Banaji 1988). In fact, the need to maintain a positive self-image has been associated with a

number of self-serving biases and shown to be a key factor in maintaining psychological health through an

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illusion of well-being: depressives are one of the few categories that seem immune from self-serving

motivational biases (Taylor and Brown 1988). To the extent these motivational needs may interfere with

(or assist) in making normatively correct decisions, they deserve examination. Below, we categorize

motivational biases into those that exert a direct influence on behaviour, and those that moderate the effect

of cognitive biases, and thus, operate indirectly.

3.2.1 Direct Effects

These are further categorized into motivational biases stemming from the need to improve or

maintain self-image, versus those stemming from the need to improve or maintain positive affective

states. Affect is defined as the set of moods, emotions, and feelings associated with a particular encounter.

The former are discussed below:

Ownership effects. These occur from self-positivity biases: the belief that good things will happen

to you and bad things will happen to others. This all-pervading bias crops up in a number of areas,

including perceptions of the risk of illness (Raghubir and Menon 1998), and may creep into judgments of

financial loss or gain for oneself or others. The psychological antecedent for this bias stems from the

individual’s need to maintain positive self-esteem. It would imply that people believe that the financial

instruments that they are invested in have outperformed those that they are not invested in. Thus, decision-

makers do not account for alternative investment opportunities to the extent that they might, or maybe

benchmark against those that allow them to maintain a positive sense of well-being.

This also implies that perceptions of the risk of a financial instrument may be inversely related to

how psychologically "close" the investor is to the investment security--the closer you are, the lower you

perceive the risk to be. Closeness, in turn, may be a function of ownership, exposure, or familiarity.

DeBondt and Thaler (1994) describe this as the representativeness heuristic and employ it to explain the

manner in which the similarity of the existing decision period to a previous decision period leads to an over-

weighting of the most similar instance.

Illusion of Control. Another aspect impinging on decision making is the illusion of control, the

basic tendency to believe that one controls one's environment (Langer 1975). A large literature on

judgments of chance events shows that non-depressives over-estimate their degree of control over chance-

determined or random events (see Crocker 1982 for a review). This illusion when played out in the

financial markets suggests that people may over-trade. The literature on excess volume (Loewenstein

1988) is consistent with this effect. Another manifestation of this effect is the need to actively manage

one's portfolio. This has been used by Benartzi and Thaler (1995), who refer to it as myopic loss aversion

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to explain the seemingly puzzling result that equities have a substantially higher premium than bonds. The

authors offer a new explanation based on two behavioral concepts. First, investors are assumed to be 'loss

averse,' meaning that they are distinctly more sensitive to losses than to gains. Second, even long-term

investors are assumed to evaluate their portfolios frequently. The authors dub this combination 'myopic

loss aversion.' Using simulations, they find that the size of the equity premium is consistent with the

previously estimated parameters of prospect theory if investors evaluate their portfolios annually. Statman

and Tyebjee (1985) conducted experiments to elucidate a strong tendency to optimistically overstate sales

and understate costs in cashflow forecasts in capital budgeting.

Overconfidence effects: A robust finding in social psychology are that non-depressives are more

confident of their predictions than would be justified by objective reality (Vallone et al. 1990).

Overconfidence effects are an output of people believing they have control over (even chance) events.

These imply that decision-makers may underestimate risks. The Hubris hypothesis, proposed to explain

the mergers and acquisitions anomaly, is consistent with this effect. As per this hypothesis, an acquiring

firm's management has a higher opinion of its abilities given recent successes (cf. Roll 1986). Baradwaj,

Dubofsky, and Fraser, 1992, examined 108 bank takeovers (1981-87) and 18 defensive acquisitions and

found evidence for the hubris hypothesis. Daniel and Titman (1999) invoke the overconfidence effect to

explain momentum strategies, and Daniel, Hirshleifer, and Subrahmanyam (1998) have used it to explain

the book-to-market effect.

Other motivational biases may stem from the need to improve or maintain positive affective

states. Affect includes feelings and moods (e.g., happiness, uncertainty, sadness, guilt etc.). These are

discussed.

Optimism: The majority of humans are prone to over-optimism: the feeling that things will be

good in the future (Weinstein 1980). The finance literature captures this under the "market sentiment"

umbrella. DeLong, Shleifer, Summers and Waldmann (1990b) use a similar argument to explain why

closed-end funds have sold at premiums to net asset values when small-investor demand for funds and for

other securities has been high, and have sold at greater than average discounts to net asset values when

small-investor demand has been low. The authors interpret the closed-end fund discount as an index of

small-investor sentiment: a thermometer of the degree to which non-institutional investors are

overoptimistic or overpessimistic about the market.

Regret avoidance. A basic human tendency is the wish to avoid future regret (Simonson 1992).

This involves anticipating future outcomes under different alternative scenarios and imagining in which

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scenario regret will be minimized (simulation heuristic: Kahneman and Miller 1986). This could lead to

differential attention to upside versus downside potential when one is purchasing versus selling a stock.

The potential regret from making an investment mistake affects one's risk perception. It is of importance

when investors take steps to avoid regret (Bell 1982). The higher the regret level, the riskier the security is

perceived to be. DeBondt and Thaler (1994) argue that correcting this bias is possibly undertaken by

hiring an investment manager to make your decisions, so as to reduce risk perception by reducing regret,

since the possible loss may now be attributed to someone else. We summarize:

P6: Decision-making will be affected by motivational needs to improve to maintain a. positive self-image b. positive affective states

resulting in illusions of control, overconfidence, optimism, and counterfactual reasoning.

3.2.2 Indirect Effects (Moderators)

Many of the cognitive biases discussed earlier are likely to reduce under conditions of high

motivation when the importance of making the correct decision is high. These are discussed in terms of the

processes they moderate:

Perceptual biases primarily are due to inadequate attention paid to stimulus characteristics.

However, given that a stimulus may have multiple aspects (e.g., trend vs. noise information), motivational

factors can affect where attention is directed. Thus, goals of the decision maker -- whether they are a

decision to buy or one to sell may lead to differential attention to the likelihood of making a gain or a loss,

which may be in turn determined by the trend versus the noise (downturns) in the data. Thus, people may

be looking for trends in data where no trends exist. This is because trends imply greater predictability over

future outcomes, and thus, reduce uncertainty. As uncertainty is inherently a negatively valenced emotion,

uncertainty reduction tactics, including identification of patterns, serve an important motivational function--

of feeling in control. Such an effect has been categorized as “trend-chasing” (Jegadeesh and Titman 1993),

and causes prices to diverge from fundamentals.

Further, when data is ambiguous, prior expectations may themselves affect the manner in which

data is perceived--a classic demonstration of a hypothesis confirmation bias (Nisbett and Ross 1980, see

Taylor and Crocker 1981 for a review). Thus, if you are looking for high downside risk, you may see it in

the data; but if you are looking for high upside potential, the same data may reveal that to you. Note that

these biases while primarily due to attentional factors because decision-makers may not have either the

ability or the motivation to expend the cognitive resources required to make an accurate judgment, they

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may also be compounded by motivational factors exemplified by “We see what we want to see.” To the

extent decision makers can be motivated to make correct decisions (i.e., the additional effort required is

worth the accuracy desired to make a decision: Johnson and Payne 1985), then higher levels of motivation

should lead to an attenuation of the perceptual biases due to attention.

Thus, motivational factors such as goals and the desire to make an accurate decision, moderate

perceptual biases--that is, the size and type of a perceptual bias will be contingent on motivational states of

the decision maker.

P7: Motivational Moderators of Attention Deficits: Motivational factors such as goals, andfeelings, shall moderate perceptual biases by: a. Changing the quantity of information sampled. b. Changing the type of information sampled. c. Changing the amount of attention paid to the information sampled d. Directing attention to either trends or noise in the data patterns

Motivational antecedents of memory retrieval may work in the same direction as, or in the opposite

direction to, the cognitive accessibility effects. Thus, motivation to suppress negative feelings should lead

to a reduced likelihood that negative events will be more accessible than positive events. Similarly,

motivation to not feel future regret over missed opportunities may lead to greater focus on upsides rather

than downsides.

P8: Motivational Antecedents of Information Retrieval: Recall of past information frommemory may systematically diverge from actual information in the direction of: a. expectations, or b. goals

Finally, the motivation to be accurate is likely to moderate the presence of cognitive biases that are

controllable. Thus, the greater the individual's stake in the decision, the greater their ego-involvement, and,

accordingly, the lower the likelihood that they will draw a biased sample, or use heuristic processing etc.,

leading to less biased decision making.

P9: Accuracy Motivation attenuates information processing biases due to: a. incorrect perception, b. inadequate retrieval, c. inappropriate information integration, d. use of non-normative judgment criterion, and

e. employment of behavioral heuristics.

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3.2.3 Ability as a Moderator

Similar to the manner in which motivation for accuracy leads to more normative decision making,

ability affects the likelihood that people will behave in a biased manner. Given that most of the cognitive

biases are due to cognitive limitations, to the extent cognitive resources are increased and opportunities to

make normative decisions are presented, decision-makers should be able to make normatively superior

decisions. Ability as a moderator has been suggested in theories of attitude formation among others (e.g.,

Petty and Cacioppo 1986).

For example, prior experience allows human agents to better understand and encode information by

providing them with pre-existing cognitive structures formed during repeated encounters with types of

information and decision contexts (Alba and Hutchinson 1987). This better cognitive organization should

be able to help them in encoding data accurately, recalling it better, and integrating it as per normatively

appropriate rules. Thus, experience should reduce the likelihood that biased decision will be made due to

cognitive limitations. Note that while experience has most often been found to lead to less biased decision-

making, it may introduce biases of its own (e.g., a hypothesis confirmation bias), and lead to more biased

decision making under certain circumstances.

The finance literature has frequently, and variously, invoked the concept of the naïve investor to

explain anomalies ranging from excess returns from value investing (DeBondt and Thaler, 1985, Shefrin

and Statman 1993, 1995) to the equity premium puzzle (DeLong, Shleifer, Summers and Waldmann,

1990b). Specifically, DeBondt and Thaler (1985) argue that the contrarian investor hypothesis suggests

that value strategies work because they are contrarian to naïve investor strategies. The naïve investor

extrapolates recent bad information too much into the future, unlike the sophisticated investor, which leads

to overreacting to news (good or bad). On the other hand, Shefrin and Statman (1993, 1995) argue that

naïve investors equate a well-run company with a "good" investment, irrespective of price. Noise traders

overreact to this current information by extrapolating past performance too far into the future (ignoring that

present prices reflect future earnings expectations), leading to glamour stocks performing in a disappointing

way. On the other hand, DeLong et al. (1990b) use the Noise Trader Hypothesis to explain the equity

premium puzzle. The argument is as follows: noise trader risk drives down stock market prices and leads

to higher returns as noise traders are bullish: i.e., have a low market share when expected returns are high

(i.e., prices are low), and high shares when expected returns are low (i.e., prices are high). Note that in

these explanations, the noise traders lead to anomalous market behavior through their greater likelihood of

either perception, inferencing, or optimism--three very different aspects of information processing.

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Similarly, contextually available resources, including time, should also affect the likelihood that

cognitive ability will be available to make rational decisions. This applies to the genre of effects which are

intentional, consciously made, and controllable, and may less apply to those that are partially automatic

(Bargh 1989). Thus, under conditions of high time pressure, human agents may resort to effort-saving

heuristics, including "follow the leader" to make their judgments. A good exemplar of this effect in the

financial markets is the documented effects of volatility (Arrow 1982) and cascades and bubbles due to the

herding instinct (Shiller 1990) or hysteria (Seyhun 1990).

Thus, to summarize:

P10: Ability attenuates information processing biases, witha. experts are less prone to non-normative information processing as compared to novices.

b. more normative decisions possible with lower time pressure.

We now illustrate how experiments can be designed to tease out the individual causes of an effect.

The next section, while not a comprehensive investigation, is designed to provide a simple instance in which

experimental methods may be invoked in understanding financial psychology.

4. Illustrative Experimental Evidence

We present a series of 5 stylized mini-experiments to illustrate how the antecedents of an effect can

be isolated through systematically (a) manipulating underlying psychological constructs, (b) examining

alternative explanations, and (c) replicating results to examine robustness issues; and (d) using the

understanding of the antecedents to manage the size and direction of an effect. The specific effect that we

examine is the value investing anomaly. These experiments are presented purely for illustrative purposes to

show the novice experimenter how to design scenarios to uniquely understand causes behind behavior. As

the scenarios we present rely on verbal information, results may be sensitive to scenario-framing issues.

Some of the reasons for this effect that have been proposed in the literature include inferencing

(Shefrin and Statman 1993,1995) and the use of the representativeness heuristic (DeBondt and Thaler

1985, 1987). Based on the theoretical model proposed, we also examine whether motivational factors,

specifically justification motivation, could affect the decision to invest in value-stocks versus blue-chip

glamour stocks. Specifically, we examine a prediction that can be drawn from the contrarian literature

(e.g., Dreman 1993) that suggests that investors have a super-positive reaction to normal information

because prior information was negative. Given this, if investors have a choice between a glamour (over-

priced) and a value (under-priced) stock, and uncertain information about how they will perform relative to

26

each other in the future, positive information should be more informative for the stock with the initial

unfavorable prior (the value stock). A process of rational updating would, on the other hand, not indicate

such an effect.

Scenario 1. The following scenario was constructed.

Company X is a well-known company. It has a reputation for being well managed. It islarge, and considered a near "blue-chip" investment. It has a high price/earnings ratio and isbelieved to be overvalued in the market place. On the other hand Company Y is a new start up. It has a low price/earnings ratio, and is believed to be undervalued in the market place. A recentEEC decision is expected to have a positive effect on one of the companies and a negative impacton the other. Depending on market reaction it is unclear at this point which company will gainand which will lose. You own shares of both companies and need to sell some of these to raisemoney to make a short term speculative investment in another company. Which company'sshares will you sell:

Only 8 subjects (out of 49) were indifferent between the two options. The results show that a

significant majority (z = 5.31, p < .01), would raise money by selling shares of the blue-chip, an effect

counter to the "flight to quality" that one may expect in times of uncertainty.

Sell X (The Blue Chip) Sell Y (The Value Stock)

29 (70.7%) 12 (29.3%)

This is consistent with the argument that people have processed the uncertainty of the future in

favor of the underdog stock, a cognitive bias, rather than a motivational one. To counter the alternative

explanation that this effect may be due to motivational reasons, two variations of the same scenario were

administered. If motivational considerations affect the decision, then a manipulation aimed at changing

motives should lead to a different result.

Examining the moderating effect of justification. To assess whether the justifiability of the

decision was a factor leading to the effect noted, we asked 36 people the same question with one important

difference. Instead of trading on their own account, they were asked what decision they would make if they

were advising their client. This manipulation should increase the likelihood that the decision goal is one

that is easier to justify. Specifically, they were given the following agent task within the context of the

same company descriptions:

Scenario 2.

Assume you are an investment advisor. Your client owns shares of both companies andneeds to sell some of these to raise money to make a short term speculative investment in anothercompany.

27

The results parallel those of the original scenario. Six people (of a total of 36) were indifferent.

The others overwhelmingly preferred raising money through selling stock X, the "blue-chip" stock

(binomial z = 3.65, p < .01).

Sell X (The Blue Chip) Sell Y (The Value Stock)

20 (66.67%) 10 (33.3%)

Aggregating the results of the two experiments and analyzing them together should yield a null chi-

square to the extent the manipulation of own vs. agent task did not change choice. This analysis was as

expected (?2(1) = .14, p > .70), implying the effect is robust to motivational manipulations.

Examining the moderating effect of decision type. To the extent the systematic pattern of results

above suggests a greater weight to information as a function of priors, it suggests that the bias will be

ameliorated by manipulations aimed at altering the focus of attention. The type of decision--one to buy or

one to sell, is one such variable. While normatively it can be argued that these two decisions are the mirror

image of each other, to the extent one involves the realization of past profits and the other involves the

anticipation of future gains, the focus of attention on possible downsides versus upsides may differ. Thus,

by changing the decision of what stock to buy, rather than what stock to sell, people may be more likely to

focus on the likelihood that the price of "undervalued stock" may fall. This should attenuate the effect seen

in scenarios 1 and 2.

Scenario 3. Forty-nine participants were given the original scenario (#1), with the important

difference that they were asked which stock they would buy. The results show that the effect was nullified

as predicted (binomial z = .08, n.s.). Twelve subjects indicated indifference, and the others were equally

likely to choose the X (glamour) stock versus the Y (value) stock.

Buy X (The Blue Chip)Buy Y (The Value Stock)

18 (48.6%) 19 (51.4%)

An overall chisquare analysis, combining the results from scenarios 1 and 3, shows that decision

task moderated the choice decision (?2(1) = 3.09, p < .10).

Scenario 4. To replicate the null effect of justification motivation, the same scenario was repeated

with the difference that subjects had to make the decision in the role of investment advisors advising clients.

Again, this manipulation did not exert an effect (comparing across scenarios 3 and 4, ?2(1) = .06, p > .80),

increasing the confidence in the assertion that the effect is due to cognitive factors rather than motivational

28

factors. Specifically, of the 36 people who participated in this study, 7 expressed indifference, and the

others were equally divided in terms of their choice of the value stock versus the glamour stock.

Buy X (The Blue Chip)Buy Y (The Value Stock)

15 (51.7%) 14 (48.3%)

Moderating Effect of Negative versus Positive Information: Note that in the "buy" condition, there

is an indifference in the choice of the two stocks. We have proposed, and results of the above experiments

suggest, that this is due to a differential attention given to the potential upside versus downside of the

stocks that is differentially weighted given investors' prior expectations. If this is indeed so, then

manipulating the likelihood of downside versus upside should affect the decision to buy. Specifically, in all

the earlier scenarios, subjects were informed that the EEC decision would be unfavorable for one company

and favorable for the other. This allowed for the possibility for subjects to focus on a potential downside

risk and attenuated the value-investing effect. By manipulating the valence of the future outcome, one

should be able to reverse the effect and see the same pattern as in the case of when investors had to decide

which stock to sell.

This replication of an effect using different operational constructs that belong to the same

underlying mega-construct provides for nomological validity within the experimental paradigm where

results require constant replication, and knowledge is gained through a series of experiments, rather than

through the display of one-off effects.

Scenario 5.

To examine this prediction, we amended scenario 3. Subjects were instead told:

The recent EEC decision is expected to have a positive effect on both of the companies,but a more positive effect on one as compared to the other. Depending on market reaction it isunclear at this point which company will gain more.

Results show that the preference for value stocks was reversed and replicated results of scenario 1

(comparing across scenarios 1 and 5 after recoding that one is a buy decision and the other a sell decision,

?2(1) = .22, p > .64, and across scenarios 3 and 5, ?2(1) = 2.01, p < .10).

Buy X (The Blue Chip)Buy Y (The Value Stock)

11 (34.4%) 21 (65.6%)

Two-thirds of the subjects preferred to buy the value stock (7 people expressed indifference).

Summary of Experimental Results. A few simple experiments (with simple manipulations) enabled

29

us to understand the value versus glamour issue better. We learned the following: (i) That there is

experimental support for the hypothesis that when prior information is negative, people over react to good

news. (ii) Choices are robust to self- or agent- task factors, with results remaining unchanged when the

trade was for a client versus one’s own account. This suggests that the effect would probably not be

segmented by clientele in empirical work. (iii) However, the systematic preference for one stock versus

another vanished when the trade decision was a buy versus a sell. This indicates that empirical work might

produce different results in falling versus rising markets. (iv) And finally, when the main condition was

changed to being one where all the news was positive, the result showed that value stocks were significantly

preferred to glamour stocks. In a nutshell, our experimental exercise implies that psychological antecedents

moderate the glamour versus value debate. The observed biases appear to be cognitive, resulting in

different results when information conditions and decision tasks vary. These results are well summed up in

the words of Solt and Statman (1988), who, in writing about the lack of predictability in the Bearish

Sentiment Index, state – “The persistence of the belief in the usefulness of the index results from errors in

cognition that lead people to see patterns in random data and to neglect evidence that runs counter to their

beliefs.”

While the literature on value versus glamour stocks is wide-ranging, the experimental example

highlights an essential difference between empirical and experimental work. In empirical analyses, we

access many observations in a few situations, whereas with experiments we are able to generate many

sharply defined situations, albeit with fewer observations. Moreover, experimental results may lead to

improvements in the design of empirical tests. For example, it is clear that the glamour versus value

questions result depends critically on whether information is positive or negative around the time of

financial decision making. This suggests that empirical tests in different economic regimes may lead to

vastly varied outcomes.

The above set of experiments was presented to merely illustrate how experimental enquiry can be

used to complement the existing paradigms in behavioral finance. It has the advantage of being able to

isolate causes, which provide a diagnostic toolbox to control effects or leverage off them.

5. Summary and Concluding Comments

Experimental work offers a valuable complement to empirical methods in the understanding of

financial decisions. This paper offers a theoretical basis for experimental work that aims at isolating the

psychological antecedents of behavioral anomalies in finance. We proposed a simple classification of

30

common behavioral anomalies into four categories: price and return effects, volume and volatility effects,

time series patterns and others miscellaneous types. The theoretical framework comprises both cognitive

and motivational antecedents of potential bias in financial decision making. The model posits five types of

cognitive biases that may arise: perceptual, memory-related, information integration, judgment criterion

related, and behavioral. Motivational effects are either direct or indirectly moderate the extent to which a

cognitive bias may be seen. We illustratively examined financial decision making biases in the choice

between value and glamour stocks using a series of 5 mini-experiments. We found that this decision

embeds substantial cognitive bias, but few motivational effects. The choice between value and glamour

stocks appears to be based on the differential attention to the likelihood of positive versus negative

outcomes. The implication for empirical work is that the value-investing anomaly is moderated by the

information environment, and may be regime dependent.

Managerially, this article is an attempt to portray the many facets of human psychology that could

enter into a financial decision, with some leading to "non-normative" behavior. Managers need to be aware

of possible biases in behavior so that they may better control them, or if they are deep-seated and difficult

to control, then account for them in models of risk-management. While ability (via expertise and

experience) and opportunity (via resources available) should lead to an amelioration of many of the

cognitive biases that have been discussed here, some biases may prove to be of an "automatic" nature, in as

much as they may be uncontrollable through training, unintentional, and operate outside of conscious

awareness (Bargh 1989). There is tentative evidence that some of the perceptual biases may fall within this

category (e.g., Raghubir and Krishna 1996), while it is more likely that retrieval and information

integration biases are likely to be controllable. It is the latter type of bias for which trader training and

overall exposure may be most beneficial. For the former, other interventions (such as lower reliance on

human judgment with greater support from computer-based systems) may be more appropriate. Overall, a

mere recognition of the fallibility of human judgment should assist those practising in the financial industry

to institute controls and better manage their risks. Managers may also be well advised to account for the

manner in which people process financial information and make choices while designing and

communicating financial products (e.g., Shefrin and Statman 1993).

From a theoretical viewpoint, this article is a strong declaration of the advisability of incorporating

a theory-driven experimental paradigm at the micro-level to complement extant empirical macro- and

micro- level work in behavioral finance. We believe that the model presented may be a good first step

towards a more comprehensive theory of how financial decisions are made. The model suffers from some

31

obvious limitations, including the lack of attention paid to group decision making (and ensuing biases in

that process), and sensory inputs (e.g., noise, lighting etc.). The model needs empirical testing to refine its

propositions. However, it presents a starting point for behavioral finance researchers who either wish to

explain errant data patterns, or those who wish to identify systematic patterns in data that do not appear to

be derivable from the rational economic models of human behavior.

32

Table 1: Anomalies and Biases: A Bird’s Eye ViewThis table presents four categories of anomalies: price and return anomalies, volume anomalies, time series patterns and other miscellaneousanomalies. These representative anomalies are indicative of the literature and serve as an illustrative set for the psychological framework proposed inthis paper. The table is intended to illustrative rather than exhaustive. Some of the applicable literature has been cited. Some cites relate to critiquesof original papers.

I. Returns, Prices and Profit-Based AnomaliesAnomaly Psychological antecedent Literature1. Excess profits from contrarian trading Self-Positivity Bias,

Overconfidence, Illusion ofControl

DeBondt (1991), Conrad and Kaul (1993), Lo and Hirshleifer and Subrahmanyam (1998)

Representativeness Heuristic DeBondt & Thaler (1985, 1987) , Ball and Kothari (1989), Chan (1988)2. Excess returns from investingstrategies: value strategies, buy low P/Estocks, buy high B/M stocks, buy losers,size effects

Representativeness Heuristic Banz (1981), Basu (1977, 1983), Chan, Hamao and French (1992), DeBondt & Thaler (1985, 1987), Fama and French (1988), Chopra, Lakonishok and (1999)

Loss aversion Shefrin and Statman (1985), Ferris, Haugen and Harris (1988), Boot (1992)

Super-positive reaction to normalinformation because priorinformation was negative

Dreman (1993)

Inferencing and justification Shefrin & Statman (1993, 1995)3. Persistent deviation of stock pricesfrom fundamentals

Horizon effects/myopia Shleifer & Vishny (1990)

Sentiment, herding, noise traders,cascades and bubbles, positivefeedback

DeLong, et al (1990a,b), Figlewski (1978)

4. Return predictability: stock prices donot follow a random walk, overreaction,underreaction

Representativeness Heuristic DeBondt & Thaler (1985, 1987), Chan (1988), Ball and (1990), Jegadeesh and Titman (1991), Kaul & Nimalendran (1990), Daniel,Hirshleifer and Titman (1998), Barberis, Shleifer, and

Sentiment, herding, noise traders,cascades and bubbles

Froot, Scharfstein & Stein (1992), Shiller (1990), Lehmann (1990), Stein (1989), Klein (1990), Zarowin (1990),

33

Garber (1989), Michaely, Thaler and Womack (1995), Black (1986)5. Profitability of momentum strategies,and underreaction gains

Representativeness Heuristic DeBondt & Thaler (1994), Daniel, Hirshleifer and and Iitman (1999)

Sentiment, herding, noise traders,cascades and bubbles

DeLong, Shleifer, Summers and Waldmann (1990a,b), (1993)

6. Closed-end funds puzzle Cascades and bubbles Welch (1992), Bikhchandani, Hirshleifer & Welch (1992)Sentiment, herding and noisetraders, hot markets, mentalaccounting biases

Bodurtha, Kim and Lee (1995), Lee, Shleifer and (1973), Chen, Kan and Miller (1993)

7. Equity premium puzzle or risk-freerate puzzle

Habit persistence Mehra and Prescott (1985), Weil (1989), Constantinides (1990), Ferson (1991), Burge (1993), Braun, Constantinides and

Keeping up with the Jones’s Abel (1990)Noise trader hypothesis DeLong, Shleifer, Summers and Waldmann (1990b)Non-standard utility Epstein and Zin (1990)Myopia, Loss aversion Benartzi and Thaler (1995), Tversky and Kahneman (1979)

II. Volume and Volatility AnomaliesAnomaly Psychological Antecedent Literature1. Heavy trading volumes in excess ofnormal portfolio requirements

Overconfidence Odean (1997)

Illusion of control Heisler (1994)Need to maintain or change thestatus quo

Loewenstein (1988)

2. Excessively high volatility for noapparent reason

Sentiment, herding, noise traders,cascades and bubbles

Cutler, Poterba and Summers (1989)

Heuristic processing Arrow (1982) III. Pattern of Time-Series AnomaliesAnomaly Psychological Antecedent Literature1. Anomalies in the IPO market: short-term underpricing, long-termunderperformance, issuances in hot

Sentiment, herding, noise traders,cascades and bubbles

Rajan and Servaes (1994), Welch (1992), Bikhchandani, (1992), Ibbotson, Sindelar and Ritter (1988), Ritter (1984), and Jaffe (1975), Wang, Chan and Gau (1992),

34

markets Shleifer and Thaler (1991), Seyhun (1992), Michaely and Shaw 1994.Vested interest, sentimentalattachments, impressariohypothesis

Shiller (1990), Elton, Gruber and Rentzler (1989), (1993)

2. Mergers and acquisitions Hubris Agrawal, Jaffe and Madelkar (1992), Baradwaj, Hawawini and Swary (1990), Morck, Shleifer and

Overconfidence Roll (1986)IV. Other AnomaliesAnomaly Psychological Antecedent Literature1. Dividend puzzle Mental accounting and House Money

effect, etc.Miller, 1986, Shefrin and Statman (1984, 1995)

2. Transmission of sentiment acrossmarkets

Sentiment, herding, noise traders,cascades and bubbles

Lee, Shleifer and Thaler (1990, 1991),

35

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