does behavioral finance add to our understanding of financial markets? by per bjarte solibakke

Dissertation December 6th

2001 Side: 1

Does Behavioral Finance add to our

understanding of financial markets?

by

Per Bjarte Solibakke


2001 Side: 2

Overview

1. Behavioral Finance

2. Building Blocks of Behavioral Finance

i. Limits of Arbitrage

ii. Psychology Biases

3. Behavioral Finance and Financial Markets

i. Market Puzzles

ii. Cross Section of Average Asset Returns

iii. Individual Investor/Security analyst behavior

iv. Corporate Finance and Management Decision behavior

4. Summaries and Conclusions


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Behavioral Finance (BF)

Behavioral Finance (BF) argues that some financial phenomena can plausible be understood using

models in which some agents are not fully rational.

Hence, BF deals mostly with investor irrationality / bounded rationality / cognitive and decision biases.

Those biases create market ineffiencies in the shape of mispricings.


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1. Agents’ beliefs are correct.

2. Given these beliefs, agents make choices that are normative acceptable (SEU, Savage, 1964)

The traditional finance paradigm, the Efficient Market

Hypothesis (EMH), use models in which agents are rational,

implying that

In broad terms, BF argues that some financial phenomena can be better understood using

models in which some agents are not fully rational.


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Moreover, the efficient market hypothesis (EMH) appealingly simple, seems to show that its predictions

are not fully confirmed by available data.

Applying the EMH, basic facts are not easily understood in:

1. the aggregate stock market,

2. the cross section of average returns and

3. individual trading/security analyst behavior

4. classical corporate finance management decisions


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1. Failure to apply Bayes’ law properly

2. Agent hold correct belief but makes choices that are normative questionable (incompatible by

SEU)

Specifically, BF analyses what happens when we relax one or both, of the two tenets that underlie the

finance view of rationality


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Main Objection against BF (arbitrage argument):

Even if some agents are irrational, rational agents will prevent them from influencing security prices for

very long periods of time.

Recently a series of theoretical papers show that irrationality can have a substantial and long-lived

impact on security prices.

The literature on “limits of arbitrage” is the first of two building blocks of behavioral finance.


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Limits of Arbitrage

EMH suggests ”no free lunch” and security prices equal ”fundamental value”. The suggestion requires:

1. Deviation from fundamental value or simply mispricing, creates attractive investment opportunities

and that

2. Rational investors will immediately snap up the opportunity

BF disputes the first argument due to risk!


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Limits of Arbitrage

Sources of risk:

1. Fundamental Risk imperfect

substitute security

2. Noise Trader Risk mispricing

worsen in the short run

3. Implementation Costs difficult selling

securities short

4. Model Risk relying

100% on a model of fundamental value


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Limits of Arbitrage

Evidence: The ”joint hypothesis problems” make it difficult to

provide definite evidence of inefficiency. However, the

following financial market phenomena are almost certain

mispricing, and persistent ones:

Twin shares (Royal Dutch and Shell Transport)

ADR’s (New York price <> Home country price)

Index Inclusions (Yahoo increased by 24%)

Internet Carve-Outs (3Com 5% IPO of Palm Inc.)


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Arbitrage Risk (ex. US 1995-2000)

S&P 500 and NASDAQ indices seemed highly overvalued!

Few dared to act on their hunch, due to

Fundamental risk No effective substitute

security. Using Russel 2000 will make the position vulnerable to large stocks news.

Noise trader risk

Noise traders may push them up still further in the short run.

Model Risk

Is the index really mispriced?


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Scenario: Limited efficient Markets

Price and Value tend to converge, but markets can still move

far from reality (fundamental/intrinsic values) at times.

Hence, the raise and evolvement of market anomalies and deviations seem to suggest a need for

building behavioural models assuming specific form for irrationality.

This is the second building block in behavioral finance:

Psychology biases or

Investor irrationality /bounded rationality / cognitive and decision biases


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Psychology biases of particular interest

Two main groups of biases are found in the behavioral finance literature:

1. Beliefs

2. Preferences


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Psychology biases

1. Beliefs

Overconfidence Poor

calibrating, certain occurrences (80%) and impossible occurrences (20%).

Aggressive Trading.

Optimism and Mood effects, Wishful Thinking

Representativeness

Base Rate neglect and Sample size neglect Past performance

indicator for future performance

Conservatism

Base rate are over-emphasised relative to sample

evidence


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Psychology biases

1. Beliefs (cont.)

Confirmation Bias

Insufficient attention is paid to new data

Anchoring

Slow adjustment

Memory Biases

More recent events and more salient events will weight

more heavily and distort the estimate


2001 Side: 16

Psychology biases

2. Preferences

The vast majority of models of preference is represented by the expectation of a von Neumann-

Morgenstern utility function (EU).

Unfortunately, EU theory is systematically violated when choosing among risky gambles.

Several suggestions for improvements.

Prospect theory may be the most promising for financial applications (Kahneman and Tversky, 1979, 1992)


2001 Side: 17

Prospect Theory (Kahneman and Tversky, 1979; Tversky and Kahneman, 1992)

Allais Example “Fanning out” (1953) show inconsistence in expected utility theory!

Consider choosing between A1 and A2:

• A1 Sure gain of $1,000,000

$5,000,000 with probability 0.10

• A2 $1,000,000 with probability 0.89

$0 with probability 0.01

Now consider choosing between B1 and B2:

•B1 $5,000,000 with probability 0.10


•B2 $1,000,000 with probability 0.11


To be consistent with expected utility theory, A1 is preferred to A2, if and only if B2 is preferred to B1


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Consider choosing between C1 and C2:

•C1 Sure gain of $240,000

•C2 $1,000,000 with probability 0.25


Risk aversion makes most individuals to gravitate toward the sure gain.

Now consider choosing between D1 and D2:

•D1 Sure loss of $750,000

•D2 Loss $1,000,000 with probability 0.75


Choosing D2, which most individuals would do, makes the utility function “abnormally” convex because of the “certain loss aversion effect”, showing risk preference.

Individuals focus more on “prospects” –gains and losses- than on total wealth

Investors are reluctant to sell at loss.


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People are more sensitivity to differences in probabilities at higher probability levels:

Consider choosing between E1 and E2:

•E1 Sure gain of $3,000

•E2 Gain $4,000 with probability 0.8


where E1 is preferred to E2, and consider choosing between F1 and F2:

•F1 Gain $4,000 with probability 0.2


•F2 Gain $3,000 with probability 0.25


where F1 is preferred to F2. Violate EU theory.

People place much more weight on certain outcomes than merely probable outcomes: the certainty effect.

Non-linear probability transformation


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Contribute to a higher understanding of:

1. Framing: the way a problem is posed for the decision manager

2. Mental accounting in prospect theory is accounted for by the fact that the reference point from

which gains and losses are calculated can change over time

3. Narrow framing is the tendency to treat individual gambles separately from other portions of wealth.

4. Regret theory is a tendency for people to feel the pain of regret at having made errors, not putting such errors into

a larger perspective.


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Ambiguity Aversion (Ellsberg, 1961)

Probabilities are rarely objectively known. Savage (1964) developed a counterpart to EU known as SEU. Ellsberg

(1961) shows a violation of SEU.

Two urns, 1 and 2. Urn 2 contains a total of 100 balls, 50 red and 50 blue. Urn 1 also contains 100 balls, again a mix of red and blue, but the subject does not

know the proportion of each.

Subjects are then asked to choose one of following two gambles, each of which involves a possible payment of $10,000, depending on the colour of a ball drawn

at random from the relevant urn

g1 : a ball is drawn from Urn 1, $10.000 if red, $0 if blue

g2 : a ball is drawn from Urn 2, $10.000 if red, $0 if blue

Subjects are then asked to choose between following two gambles:

h1 : a ball is drawn from Urn 1, $10.000 if blue, $0 if red

h2 : a ball is drawn from Urn 2, $10.000 if blue, $0 if red

Typically, g2 is preferred to g1 and h2 is chosen over h1. Inconsistent with SEU.


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Ambiguity Aversion (Ellsberg, 1961)

People dislike subjective, or vague uncertainty more than they dislike objective uncertainty (see Camerer and Weber, 1992

for a review.)

“ambiguity aversion”.

Ambiguity can be defined as a situation where information that could be known, is not. Possibly, strengthen where

people feel their competence in assessing relevant probabilities is low (Heath and Tversky, 1991).


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Does BF add to our Understanding of Financial Markets?

Disclaimer:

Note that the conventional efficient market view is not abandoned. I could have, if it was the goal of this

presentation, found very many cases/results that suggest that the markets are impressively efficient.

Hence,

This is a behavioral oriented presentation attempting to understand phenomena applying psychological biases in

financial markets.


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BF: The Aggregate Stock Market

Three puzzles from the aggregate stock market:

1. The equity premium (Mehra & Prescott (1985))

2. Volatility (Name:Campbell (2000))

3. Predictability

The reference to as puzzles, is that they are hard to rationalize in a simple consumption based model.


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BF: The Equity Premium Puzzle

i) Prospect Theory (Benartzi and Thaler, 1995)

Suppose

where and are the probability weighting function and the value function from prospect theory, respectively. Rf,t and Rt+1 are gross returns on T-Bills and

the stock market from t to t+1, respectively. is the fraction of his financial wealth allocated to stocks.

, 1(1 ) 1f t tE R R

Additional Assumptions: Gains and Losses of prospect theory refer to changes in financial wealth and the relevant time interval is [t, t+1] for gains and

losses.

To make Rf and R equally attractive:

From prospect theory portfolio evaluation: Once a year.

More frequent evaluation myopic loss aversion.

The essence is that the 3.9% excess return for stocks cannot easily be explained by risk.


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i) Prospect Theory (Barberis, Huang and Santos, 2001)

Suppose preferences

Investors get utility from consumption + the holding of risky assets (X).

1

0 0 10

( )1

t tt t

t

CE b C X

They show that loss aversion can indeed provide a partial rationalization of the high Sharpe ratio on the

aggregate stock market.

The utility is determined by

where 2.25 is from Tversky and Kahneman (1992).

0ˆ( )

2.25 0

X for XX

X for X


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ii) Ambiguity Aversion

When faced with an ambiguity, people entertain a range of possible probability distributions and act to

maximize the minimum expected utility under any candidate distribution.

Epstein and Wang (1994) showed how such a approach could be incorporated into a dynamic asset

pricing model.

Maenhout (1999) applies state equations and non-linear objective functions to the equity premium.

However, to explain the whole 3,9% equity premium requires an unreasonable high concern about

misspecifications.

Only a partial explanation for the equity premium.


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BF: The Volatility Puzzle (Schiller, 1981 and LeRoy and Porter, 1981)

The essence is that the empirical volatility of the price-dividend ratio cannot easily be explained by

variation in expected dividend growth rate.

i) Beliefs (changing forecasts of future cash flows)

1. Investors believe that the mean dividend growth rate is more variable than it is.

The version of Representativeness known as the law of small numbers.

2. Overconfidence about private information.

Positive private information will push prices up too high relative to current dividend.


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i) Beliefs (cont.)

3. Investors extrapolate past returns too far into the future.

Again representativeness known as the law of small numbers.

4. Investors confuse real and nominal quantities when forecasting future cash flows (Ritter and Warr, 2000)

Incompetence


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ii) Preferences

A straightforward extension of the model presented under the equity premium puzzle can explain the

volatility puzzle

where zt is a state variable tracking past losses and gains.

1

0 0 10

( , )1

t tt t t

t

CE b C X z

The stock market is pushed up assuming good cashflow news. This create a cushion of prior gains (z) lower

risk aversion.

Discounting at a lower rate push prices further relative to current dividends.


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BF: The Cross-section of Average Returns

Anomalies:

The size premium (Fama and French, 1992)

Long-term Reversals (DeBondt and Thaler, 1985)

The Predictive power of Scaled-price Ratios (Fama and French, 1992)

Momentum (Jegadeesh and Titman, 1993)

Event studies of (e.g. Baker and Wurgler, 2000)

•Earnings announcements

•Dividend Announcements and Omissions

•Stock Repurchases

•Secondary Offerings


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BF: The Cross-section of Average Returns - Anomalies

i) Belief Based Models

1. The anomalies is the result of systematic errors of investors using public information (Barberis et al.

(1998))

Representativeness bias and the law of small numbers

Conservatism suggest that investors put too little weight on the latest piece of earnings news

relative to their prior beliefs.

The model generates post-earnings announcement drift, momentum, long-term reversal and cross-sectional

forecasting power for scaled-price ratios.


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i) Belief Based Models (cont.)

2. The anomalies is the result of systematic errors of investors using private information (Daniel et al.

(1998))

Overconfidence If

private information is positive the investor will push prices too far relative to fundamentals.

To generate momentum and post-earnings announcement effect, model is extended so that public

information change the private information asymmetrically (self-attribution bias).

Initial overconfidence is on average followed by even greater overconfidence, generating momentum.


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Chopra et al. (1992) and La Porta et al. (1997) provide compelling evidence that supports the idea that

investors make irrational forecasts of future cash flows.


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3. Momentum and reversals may also be positive feedback trading, when one group of investors buy more

of an asset which has recently gone up in value. (e.g. model in De Long et al. (1990).

Extrapolative expectations based on past returns due to representativeness and to the law

of small numbers.

4. Hong and Stein (1999) build a model where two boundedly rational groups of investors interacts (subset

of available information).

Hong et al. (1999) present supportive evidence for the view of Hong and Stein: the momentum

effect is high in small firms.


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ii) Belief Based Models with institutional frictions

1. A large class of investors, mutual funds, are not allowed to short stocks. Miller (1977) shows that

short sales constraints explain why high price-earnings ratio stocks earn lower returns. Scherbina

(2000) and Cheng et al. (2000) confirms.

2. The implications of short sales constraints and differences of opinion for higher order moments, lead to

skewness (Hong and Seng (1999))


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iii) Preferences

Barbereis and Huang (2000) show that applying prospect theory, narrow framing and a dynamic model of

loss aversion, individual stocks can generate evidence on long term reversals and on scaled-price

ratios.


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BF: Closed-end Funds (CeF)

The view predicts that closed-end funds should comove strongly, which is confirmed by Lee et al. (1991).

Noise trader risk must be systematic. Another group of assets primary owned by individuals are small

stocks. Consistent with the noise trader risk being systematic, Lee et al. (1991) find strong positive

correlation.

Why doesn’t CeF trade at the price of Net Asset Value (NAV)?

Lee et al. (1991) argue that some of the individual investors who are primary the primary owners are noise

traders, exhibiting irrational swings in their expectations about future fund returns (noise traders).


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BF: Co-movements / Cross-Correlations

Lee et al. (1991) assume a “habitat” view of comovement.

Many investors choose to trade only subset of available assets. As these investors’ risk or sentiment

changes, they alter exposure inducing a common factor in the returns.

Barberis and Scheifer (2000) argues categorizing as a comovement factor

Many investors group stocks into categories, and then allocate funds across these various categories. An

asset added to a category should therefore begin comovement with the category.


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BF: Investor Behaviour

Particular success in:

Explaining how groups of investors behave and

What kinds of portfolios investors choose to hold and trading

Growing Importance as:

Cost of entering the market has fallen dramatically


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Insufficient diversification

Investors diversify their portfolio holdings much less than recommended by normative models of portfolio

choice. French and Poterba (1991) show that investor are domestically biased (>90%). Grinblatt and

Keloharju (1999) show geographical local preferences in Finland.

Ambiguity and Familiarity offers a simple explanation; the degree of confidence in the probability distribution

is important.

Naive diversification

Investors diversify applying the 1/n heuristic, whatever option that exist.

Investor incompetence


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Excessive Trading

Overconfidence; investors believe they have information strong enough to justify a trade, while in fact it’s

too weak. Moreover, Odean (1999) suggest a worse situation: Misinterpreted valid information.

Evidence: Barber and Odean (2000)

2. Prospect Theory and Narrow Framing1. Irrational belief in mean reversion

The Selling Decision

Disposition effect suggest that investors are reluctant to sell assets trading at a loss relative to purchase

price. Odean (1998) show that investors are more willing to sell stocks that have gone up relative to

buying price than down.


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The Buying Decision

Odean (1999) shows that “Buys” are evenly split between prior winners and losers. Conditioning on the

stock being a prior winner (loser) though, the stock is a big winner (loser).

- Attention effect

- Good past performance (momentum)

- Poor prior performance (undervalued and will rebound)


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BF: Security Analysts biases

Analysts forecasts and recommendations are biased

•Stock recommendations are predominantly buys over sells, by a seven to one ratio (e.g. Womack

(1996))

•Optimistic forecasts at 12-month and longer horizon (e.g. Brown 2001)

Analysts forecast errors are predictable based upon past accruals, past forecast revisions and other

accounting value indicators.

•Past accounting accruals predict forecast errors (Teoh and Wong (2001))

•Analysts seem to underreact to unfavorable information and overreact to favorable information

(Easterwood and Nutt (1999))


2001 Side: 45

BF: Corporate Finance

1. Security Issuance, Capital structure and Investment

Results from actions taken by rational managers faced with irrational investors. Market timing suggest:

- security issuance and repurchase due to mis-pricing

Results from actions taken by managers that does not find mispricing irrational. Assuming Pecking-Order

financing:

- if stock prices go up, more attractive new projects eventually requiring new equity

Baker and Wurgler (2000) find supportive evidence for the market timing hypothesis.


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2. Why pay firms dividends?

Notion of self-control Consume the

dividend but don’t the portfolio capital (Shefrin and Statman, 1984)

Mental Accounting Firms

make it easier for investors to segregate gains from losses to increase their utility:

Gains:

Losses:

Avoiding Regret

Stronger for action they took than action they failed to take

(10) (2) (8) ( 10) (2) ( 12)


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3. Managerial Irrationality

Overconfidence

i. Hubris Hypothesis (Roll, 1986)

ii. Future performance is positive:

Can explain pecking order financing

Correlation in Cash flow and investments

Free cash flow should be minimized


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Summaries and Conclusions

persuasive evidence that investors make major systematic errors

persuasive evidence that psychological biases affect market prices

indications that there is substantial misallocation of resources

However, much of the BF work is narrow and partial. As progress is made, more than one or two strands are

incorporated into models.


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Summaries and Conclusions

Two predictions for the understanding of financial markets:

2. There will be better theories.

1. We will find that most of our current theories, rational and behavioural, are wrong.


2001 Side: 50

BF Fund in Operation

Fund -- Objective The Fund aims to provide long term capital appreciation through investments in listed Japanese

equities. The Fund's investments are based on insights from behavioural finance. In selecting companies for

investment, the Fund will focus on stocks that are currently undervalued because of emotional and behavioural patterns

present in stock markets. The selection of stocks is a systematic way.

Why Japan ?

Attractive valuation level: Nikkei 225 at its lowest in 15 years

Increased focus on shareholder value - beneficial for investors

Structural reforms heading in the right direction

Increased foreign investments in the Japanese equity market

does behavioral finance add to our understanding of financial markets? by per bjarte solibakke

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