1. A Challenge to Market Efficiency
Behavioral Economics
Dr. D. Kuebler, SS2003
Behavioral Finance –
A Challenge to Market Efficiency
Henry Fiebelkorn

2. Structure I II III IV V Introduction Behavioral Finance and Overview
Market Phenomena - Anomalies
III
Financial Asset Pricing Theory – Behavioral Models IV
Applying Behavioral Finance V
Summary and Outlook

3. Introduction
„Modern finance theorists have turned finance into a science,
but they forgot that it is a social science!“
BF:is the application of psychology to financial behavior –
the behavior of practioners.
Aim: recognize, understand and avoid mistakes.
State of Modern Finance:
Perfect Markets & Perfect People
Current State:
Imperfect Markets & Perfect People
Can we indeed say that investors behave rational?
Rational procedure of choices d.o. Alternatives, ecpectations, preferences
People act to reference points and perceive utility as value in changes relative to this
Example: Siamese-twin-Stocks: Shell / RoyalDutch:
shares are claimed to the same CF but trade in different locations:
60% CashFlows by RoyalDutch, 40 % by Shell ->
MV (Royal) / MV (Shel) = 1,5
But big deviation in Reality:more then 30 % mispricing despite even fundamental identical securities
offers Abitrage-possibilities, Abitrage doesn´t work very well, why? NoiseTrader risk
CAPM= E(Ri)= Rf+[b*(E(Rm) – Rf)] -> Grafik zeichnen, riskis measured in terms of variance
CAPM is not meant to explain investors behavior, you can´t draw any conclusion about that
Behavioral Finance:
Imperfect Markets & Imperfect People

4. Classification Behavioral Finance Heuristics Self-Concept
Framing Aspects
Representativeness
Availability
Anchoring
Ambiguity Aversion
Overconfidence
Self-Attribution
Cognitive Dissonanz
Self-Control
Confirmation Bias
Herding
Conservatism
Mental Accounting
Prospect Theory
Framing Aspects: The way people behave d.o. The way that their decesion problems are framed
Heuristics: Processes to find out things by yourselves, f.e. rules of thumb for a speedy decesion to safe time and effort
Heuristic driven Biases:
Representativness: reliance on stereotypes, future returns representated by historical average, follow the rules of association instaed of probability
Availability: people rely on information available and do only a little search expl.: trowing a coin, 5times head
Anchoring / Conservatism: DM based on irrelevant frames due to reference points, adapt earnings to slow, rely on regimes
Aversion to Ambiguity: people prefer the familiar to the unfamilar- certainty effect (100$ for 100% > 200$ for 55%)
Underlying Concepts:
Bounded Rationality
Emotions

5. Structure I II III IV V Introduction Behavioral Finance and Overview
Market Phenomena - Anomalies
III
Financial Asset Pricing Theory – Behavioral Models
Applying Behavioral Finance IV
Summary and Outlook V

6. Anomalies Volume Volatility Anomalies are consistent
2. Violate the Efficient Market Hypothesis
Dividends
Equity Premium Puzzle
Volume: in the sense of standard finance: market participants trade very little because trader are rational, but partly 700 mio shares a day traded at Nyse – which induces cognitive errors of noise trader, f.e. overconfidence etc.
Volatility: asset price vary too much to fundamentals according to rational efficient market theory: Behavioralists: changes in beliefs about demand curves of other investors but not due to change in fundamental values
Dividens: Modigliani / Miller: dividend policy is irrelevant in efficient markets, investors can do it by themselves, often share prices rise after announcement of dividends, following the Prospect Theory d. are framed in different mental accounts
EquityPremium Puzzle: abnormal returns on equity: difference between market index returns and return of a risk free security to high, risk premium of 7% per year emprically observed, much too high to be explained by risk alone
Book-2-market-ratio: B2M-ratio and size is priced in the markets contrary to the standard finance: f.e.: value-based stocks have higher returns than growth-based stocks
StockMarketcrash:
In 1987 stocks at Nyse fell 29 % in a single day in the absence of any significant fundamental news, liqidity and diversification as protections to reduce risk exposure failed, pesimistic investors failed to materialize as buyers and could be seen as very pesimistic relative to the prices by the market, therefore optimistic investors didinot by in as the market fell, price changes derives from changing beliefs about demand curve of other investors
Calendar effects:
f.e. Monday-effect: US-stock market has risen, but Friday-to-Monday 3 day return is negative, effect not large enough for profitable trading strategy, abitrage too risky
Book-to-Market Ratio

7. Structure I II III IV V Introduction Behavioral Finance and Overview
Market Phenomena - Anomalies
III
Financial Asset Pricing Theory – Behavioral Models IV
Applying Behavioral Finance V
Summary and Outlook

8. Model of Investor Sentiment
Existing Approaches
Model of Investor Sentiment
“Inefficient Markets” by Shleifer (2000)
Information Trader
Noise Trader
Abitrageurs:
Follow Standard CAPM: No cognitive errors
Utility expressed motives
Try to exploit Noise Trader
Outside CAPM (BAPM):
Cognitive errors: listen to gurus, follow rumors
Value expressed motives
= Investor Sentiment
Investors sentiment reflects common judgement errors from substantial number of investors

9. Model of Investor Sentiment
Existing Approaches
Model of Investor Sentiment
Abitrage is limited because:
Securities don´t have obvious substitutes
Abitrage is risky (Risk Aversion)
Noise Trader Risk
Uncertainty about beliefs of next period noise trader
No perfect substitute available because noise trader keeps u (unsafe asset) from driving down to fundamental values
Model Assumptions:
Overlapping generation structure: generates continuity
Fixed supply of unsafe asset: prevents arbitrageurs from money strategies (conversion safe asset into unsafe asset)
Systematic nature of noise trader risk: fundamental uncorrelated securities have correlated returns
Price changes in absence of fundamental news!
Belief about Noise Trader determines price

10. Model of Investor Sentiment
Existing Approaches
Model of Investor Sentiment
Price Determination: Two Earning Regimes:
News
News
Prior views
No revaluation due to
give up old model
Attach to new model due to
NoiseTrader: new information:
Conservatism
Representativeness
Underreaction
Overreaction

11. Investors: 2 Earning Regimes:
Existing Approaches
BSV - Model
Captures 2 Judgement
Biases:
Barberis, Shleifer, Vishny (1998)
Investors: 2 Earning Regimes:
Barberis, Shleifer, Vishny (1998)
(+)
Firms´ earnings are trending
Long-term-
Change
Representative-ness Bias
(-)
Conservatism
Earnings are meanreverting
Change temporarely
Overreaction of Stock Prices
Underreaction of Stock Prices

12. Captures 2 Judgement Biases: Special Prediction: Selective Items
Existing Approaches
DHS - Model
Captures 2 Judgement Biases:
Daniel, Hirshleifer, Subramanyam (1997)
Overconfidence
Self-Attribution
(-)
(+)
Exaggerate
Privat Information
Downweight Public
Information
stock prices are determined by the informed investors,
overconfidence: leads them to exaggerate the precision of their private signals about a stock´s value
self attribution: causes them to downweight public signals about value
overreaction to private information and underreaction to public information tend to produce
short term continuitation of stock returns but long term reversals as public information eventually overwhelms the behavioral biases
selective events: events that occur to take advantage of the misspricing of a firm´s stock
Special Prediction: Selective Items

13. Behavioral Asset Pricing Model (BAPM)
Existing Approaches
Behavioral Asset Pricing Model (BAPM)
BAPM
Enable user to identify value of products
value expressed characteristics
Utilitian characteristics
Rational Utility
Strong: Jewelry
Less: Automobiles
Absent: Laundry
Risk: Automobiles
: Laundry
“Behavioral Asset Pricing Theory” by Shefrin, Statmen (1994)
Utilitarian characteristics vs. Value expressed characteristics (Timex /Rolex example) 16

14. Model Requirements Behavioral Asset Pricing Model: Should include:
Identification of preferences of the buyers/sellers
Characteristics capturing value expressive (VEC) & utilitarian preferences (UC)
Behavioral Asset Pricing Model:
Should include:
What investors think
How they asses risk
How they forecast growth
What rules they follow
Conclude with equilibrium prices
New Model will be an old demand/ and Supply model, not as beatiful but more robust
Will teach us a lot about determinants of expected returns
Behavioral Portfolio Theory: BPT describes what is thouhgt and observed – i.O. to the CAPM – refers to menatal accounting: every segment has own values
PF from two parts: downside proetection and upside potential vs. CAMP: measures return over risk

15. Structure I II III IV V Introduction Behavioral Finance and Overview
Market Phenomena - Anomalies
III
Financial Asset Pricing Theory – Behavioral Models IV
Applying Behavioral Finance V
Summary and Outlook

16. Applying Behavioral Finance
Investors
Limitations
External
Biases information
Limitation of time &resources
Practical restrictions
Internal
Mental Accounts
Heuristics
Self-Deception

17. Applying Behavioral Finance
Emotional
Feelings
Performance Pressure
Overload of
Information
Biased
Information
Investor
Heuristics
Time & Resource Constraints
Practical Restrictions
Uncertainty

18. Coping with Limited Rationality
Applying Behavioral Finance
Coping with Limited Rationality
1. Identify / Framing
„People are
intendedly rational but limited to do so!“
2. Editing
3. Decomposition
1: identify, frame in different accounts
2: Simplifying
3. Divide in parts, single evaluation
4. Rules of thumb
Example: want to go into cinema:
A) bought 2 tickets for 40 € - lost! Will you buy new one?
B) Want to buy tickets, loose 40 € out of the pocket
According to Modigilani/ Miller: both is equal, but: A): transaction costs: effort for buying the tickets, tickets booked on leisure account, hurts more!
4. Heuristics

19. Applying Behavioral Finance
Investment Strategies
Value Investing
Mean Reversion Strategy
Momentum Strategy
Event Studies
Earning Revision Strategies
Combination Studies
Behavior:
Overreaction
Underreaction
Extrapolation
Herd Behavior
Two examples: Mean Reversion strategy (overreaction)
Momentum Strategy (Trending strategy: Herd Behavior)

20. Behavioral Finance leads to product development
Applying Behavioral Finance
Behavioral Finance leads to product development
Guaranteed products
RenteMaXX
Best of World Garant Fund
Loss Aversion
Overconfidence
Daytrading
Hedge Funds:
Event driven
Opportunistic
Relative value
Absolute Return

21. Structure I II III IV V Introduction Behavioral Finance and Overview
Market Phenomena - Anomalies
III
Financial Asset Pricing Theory – Behavioral Models IV
Applying Behavioral Finance V
Summary and Outlook

22. What lessons does Behavioral Finance teach us?
Summary
What lessons does Behavioral Finance teach us? ll
Investor
Market
Be aware of information biases:
seek and screen information actively
2. Avoid narrow framing, anchoring, overconfidence
3. Follow rules of decision making under uncertainty
Market and people are imperfect
There are systematic and recurring market inefficiencies
Anomalies are consistent and can´t be ignored
Sensible implementation of irrational human behavior into asset pricing models necessary
VEC as well as UC must be included
Thanks for your attention!