Definition: Behavioral Finance is an approach
that attempts to explain anomalous behaviour
of security prices (i.e. deviations from efficient
markets theory and asset pricing models) by
the psychological biases and/or behavioral
regularities of market participants.
A Reminder of Theory
 Finance Theory basically suggests that the return of a
financial asset in period t should be the sum of expected
return (i.e. the compensation for holding the asset) and a
random error term (reflecting information surprises and any
temporary deviations).
Rt = E(Rt) + et
 E(Rt) is practically the sum of the protection against
inflation, reward for postponing consumption, and reward
for taking the risk associated with the asset
 A sufficiently long term average of Rt (realized returns)
should converge to E(Rt), such that E(RABN)=0.
 Further, efficient markets theory predicts that abnormal
returns (ei’s) should be unpredictable (follow a random
walk), so that it is impossible to persistently earn positive
abnormal returns. RABNt ~ iid(0,σi)
These examples do not constitute a violation of the theory, although
abnormal returns are not compensation for risk. But, they do suggest that
unexpected returns can be much larger than expected returns.
1.Chart: short period, unpredictable, should rather be assigned to the
error term. (manipulation in a small-cap stock in Turkish stock market).
2.Chart: The process of incorporation of new fundamental information into
price may seem like an anomaly, but does not necessarily violate theory.
(the rise of Crude Oil since 2002).
It is possible to obtain positive abnormal returns by exploiting superior
private information
(theory should allow for the effect of information surprises).
Maybe, the theory is to be revised as such:
Rt = E(Rt) + It + et
(Elton, 1999)
A true violation needs to be predictable cases of statistically and
economically significantly positive or negative average abnormal returns
over sufficiently long periods such that E(It)=0.
* Momentum Rules (Short-lag Positive Autocorrelation in Returns):
Create zero-cost arbitrage portfolios by buying most winners and
selling (short) most losers of the past 3-12 months, hold them for
the next 3-12 months.
Jegadeesh and Titman (1993) and Rauwenhorst (1998) report
around 1% monthly average excess returns to this strategy.
* Contrarian Rules (Long-lag Negative Autocorrelation in Returns):
Buy most losers and sell most winners of the past 3-5 years,
hold the portfolio for the next 3-5 years.
DeBondt and Thaler (1985) report significantly positive returns to
this strategy, however more recent studies suggest less
profitability (while profitability of momentum rules turns out to be
more robust over time).
* Excessive Volatility: Shiller (1981) finds that stocks are more
volatile than fundamentals require. Lo and MacKinlay (1990)
find that excessive volatility violates random walk.
So, there may be a human element adding to volatility.
* Trends, Trend Reversals and The Profitability of Technical
Analysis: It may be possible to make money by following trends
• In sum, short-lag positive and long-lag negative autocorrelation
in Rt series, which is a violation of weak form of efficiency.
* Sentiment Anomalies, Calendar Effects, etc.
• Underreaction due to Conservatism Bias: Human tend to be slow
in adapting to new information. Consequently, new information is
priced-in gradually (stepwise) rather than at a single step. This spurs
positive autocorrelation in returns and renders momentum rules
However, some of the profitability of momentum rules may result from
gradual diffusion of private information, and it’s difficult to distinguish
(semi-strong form of efficiency). Earnings momentum.
* Overreaction due to Representativeness Bias: A tendency to
overemphasize the most recent and the most salient may cause
overreaction, creating excessive volatility (continuing trends, then
* Overconfidence with Self-Attribution: Daniel et al. (1998) suggest it
may cause overreaction and subsequent reversals.
• Interaction between informed and momentum traders (Hong and
Stein, 1999): One of the most promising theories: The
insufficient number of informed traders results in underreaction,
which creates room for profitability for momentum traders, the
existence of which creates the potential for an overreaction and
subsequent reversal.
• Herd Behaviour: Herding is an instinctual defence mechanism
against unknown external danger. It may be rational as it
ensures to be not far worse than average. There is evidence
that some fund managers, analysts and even CEO’s mimic each
other. This may enhance existing trends.
• Disposition Effect: Tendency to hold losing stocks too long and
sell gaining stocks too early; may harm individual traders; has
little impact on market prices as informed traders dominate the
Statistical and economic significance of reported abnormal returns are not robust to
methodological issues (Fama, 1998).
Behaviourists: Especially momentum rules are robust.
Behavioural Factors are difficult to identify ex ante, and backward evaluations do
not constitute robust evidence against efficient markets (one may attach one of
a large number of behavioural theories to any anomaly).
Behaviourists: produced testable predictions and general models. Real-time tests
may overcome this problem.
Sophisticated market participants can learn not to commit behavioural biases. As
markets will be dominated by sophisticated traders as a result of “natural
selection”, markets may evolve toward efficient market conditions. Hence,
behavioural factors, even if they exist today, will be a temporary phenomenon.
Behaviourists: The causes of behavioural biases are instinctual, they are inherent
in human psychology, likely to repeat over next generations.

An Introduction to Behavioural Finance