Transcript Document

Does Behavioral Finance add to our
understanding of financial markets?
by
Per Bjarte Solibakke
Dissertation December 6th 2001
Side: 1
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
Dissertation December 6th 2001
Side: 2
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.
Dissertation December 6th 2001
Side: 3
Behavioral Finance (BF)
The traditional finance paradigm, the Efficient Market
Hypothesis (EMH), use models in which agents are rational,
implying that
1. Agents’ beliefs are correct.
2. Given these beliefs, agents make choices that are
normative acceptable (SEU, Savage, 1964)
 In broad terms, BF argues that some financial phenomena
can be better understood using models in which some
agents are not fully rational.
Dissertation December 6th 2001
Side: 4
Behavioral Finance (BF)
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
Dissertation December 6th 2001
Side: 5
Behavioral Finance (BF)
Specifically, BF analyses what happens when we relax one
or both, of the two tenets that underlie the finance view of
rationality
1. Failure to apply Bayes’ law properly
2. Agent hold correct belief but makes choices that are
normative questionable (incompatible by SEU)
Dissertation December 6th 2001
Side: 6
Behavioral Finance (BF)
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.
Dissertation December 6th 2001
Side: 7
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!
Dissertation December 6th 2001
Side: 8
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
Dissertation December 6th 2001
Side: 9
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.)
Dissertation December 6th 2001
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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?
Dissertation December 6th 2001
Side: 11
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
Dissertation December 6th 2001
Side: 12
Psychology biases of particular interest
Two main groups of biases are found in the behavioral
finance literature:
1. Beliefs
2. Preferences
Dissertation December 6th 2001
Side: 13
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
Dissertation December 6th 2001
Side: 14
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
Dissertation December 6th 2001
Side: 15
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)
Dissertation December 6th 2001
Side: 16
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
A2
Sure gain of $1,000,000
$5,000,000
with probability 0.10
$1,000,000
with probability 0.89
$0
with probability 0.01
Now consider choosing between B1 and B2:
•
•
B1
B2
$5,000,000
with probability 0.10
$0
with probability 0.90
$1,000,000
with probability 0.11
$0
with probability 0.89
To be consistent with expected utility theory, A1 is preferred to
A2, if and only if B2 is preferred to B1
Dissertation December 6th 2001
Side: 17
Prospect Theory
(Kahneman and Tversky, 1979; Tversky and Kahneman, 1992)
 Individuals focus more on “prospects” –gains and losses- than
on total wealth
Consider choosing between C1 and C2:
•
C1
Sure gain of $240,000
•
C2
$1,000,000
with probability 0.25
$0
with probability 0.75
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
$0
with probability 0.25
Choosing D2, which most individuals would do, makes the utility function “abnormally”
convex because of the “certain loss aversion effect”, showing risk preference.
 Investors are reluctant to sell at loss.
Dissertation December 6th 2001
Side: 18
Prospect Theory (Kahneman and Tversky, 1979; Tversky and Kahneman, 1992)
 Non-linear probability transformation
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
$0
with probability 0.2
where E1 is preferred to E2, and consider choosing between F1 and F2:
•
•
F1
F2
Gain $4,000
with probability 0.2
$0
with probability 0.8
Gain $3,000
with probability 0.25
$0
with probability 0.75
where F1 is preferred to F2.  Violate EU theory.
 People place much more weight on certain outcomes than
merely probable outcomes: the certainty effect.
Dissertation December 6th 2001
Side: 19
Prospect Theory
(Kahneman and Tversky, 1979; Tversky and Kahneman, 1992)
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.
Dissertation December 6th 2001
Side: 20
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.
Dissertation December 6th 2001
Side: 21
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).
Dissertation December 6th 2001
Side: 22
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.
Dissertation December 6th 2001
Side: 23
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.
Dissertation December 6th 2001
Side: 24
BF:
The Equity Premium Puzzle
The essence is that the 3.9% excess return for stocks
cannot easily be explained by risk.
i) Prospect Theory (Benartzi and Thaler, 1995)
Suppose
E  (1   ) R f ,t   Rt 1  1
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.
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.
Dissertation December 6th 2001
Side: 25
BF:
The Equity Premium Puzzle
i) Prospect Theory (Barberis, Huang and Santos, 2001)
 t Ct1


E0   
 b0Ct  ( X t 1 ) 
1 
t 0 


Suppose preferences
Investors get utility from consumption + the holding of risky assets (X).
The utility is determined by
 X
ˆ ( X )  
2.25 X
for X  0
for X  0
where 2.25 is from Tversky and Kahneman (1992).
They show that loss aversion can indeed provide a partial
rationalization of the high Sharpe ratio on the aggregate stock
market.
Dissertation December 6th 2001
Side: 26
BF:
The Equity Premium Puzzle
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.
Dissertation December 6th 2001
Side: 27
BF:
The Volatility Puzzle (Schiller, 1981 and LeRoy and Porter, 1981)
The essence is that the empirical volatility of the pricedividend 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.
Dissertation December 6th 2001
Side: 28
BF:
The Volatility Puzzle (Schiller, 1981 and LeRoy and Porter, 1981)
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
Dissertation December 6th 2001
Side: 29
BF:
The Volatility Puzzle (Schiller, 1981 and LeRoy and Porter, 1981)
ii) Preferences
A straightforward extension of the model presented under the
equity premium puzzle can explain the volatility puzzle
 t Ct1


E0   
 b0Ct  ( X t 1 , zt ) 
1 
t 0 


where zt is a state variable tracking past losses and gains.
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.
Dissertation December 6th 2001
Side: 30
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
Dissertation December 6th 2001
Side: 31
BF:
i)
The Cross-section of Average Returns - Anomalies
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.
Dissertation December 6th 2001
Side: 32
BF:
i)
The Cross-section of Average Returns - Anomalies
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.
Dissertation December 6th 2001
Side: 33
BF:
i)
The Cross-section of Average Returns - Anomalies
Belief Based Models (cont.)
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.
Dissertation December 6th 2001
Side: 34
BF:
i)
The Cross-section of Average Returns - Anomalies
Belief Based Models (cont.)
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.
Dissertation December 6th 2001
Side: 35
BF:
The Cross-section of Average Returns - Anomalies
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))
Dissertation December 6th 2001
Side: 36
BF:
The Cross-section of Average Returns - Anomalies
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.
Dissertation December 6th 2001
Side: 37
BF:
Closed-end Funds (CeF)
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).
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.
Dissertation December 6th 2001
Side: 38
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.
Dissertation December 6th 2001
Side: 39
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
Dissertation December 6th 2001
Side: 40
BF:
Investor Behaviour
 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
Dissertation December 6th 2001
Side: 41
BF:
Investor Behaviour
 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)
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.
2. Irrational
1.
Prospect belief
Theoryinand
mean
Narrow
reversion
Framing
Dissertation December 6th 2001
Side: 42
BF:
Investor Behaviour
 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)
Dissertation December 6th 2001
Side: 43
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))
Dissertation December 6th 2001
Side: 44
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.
Dissertation December 6th 2001
Side: 45
BF:
Corporate Finance
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:
 (10)  (2)   (8)
Gains:
Losses:  (10)  (2)   (12)
 Avoiding Regret
Stronger for action they took than action they
failed to take
Dissertation December 6th 2001
Side: 46
BF:
Corporate Finance
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
Dissertation December 6th 2001
Side: 47
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.
Dissertation December 6th 2001
Side: 48
Summaries and Conclusions
Two predictions for the understanding of financial markets:
1. We will find that most of our current theories, rational and
behavioural, are wrong.
2. There will be better theories.
Dissertation December 6th 2001
Side: 49
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
Dissertation December 6th 2001
Side: 50