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Chapter 9
Behavioral Finance and
Technical Analysis
Behavioralism bias
• Motivation
Stock prices in the 1990s did not appear to match
“fundamentals,”
e.g., high price earnings ratios
Evidence of refusal to sell losers
Economics discipline is exploring behavioral aspects
of decision making
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Behavioralism
• Anchoring Bias & earnings
Many people become anchored to their ideas and will not
update their expectations when new information arrives.
This underreaction to news leads to momentum in stock
returns.
9-3
Behavioralism
• Framing errors
– Regret Avoidance
• Regret from losses is greater than joy from
gains.
• Regret is reduced with ‘shared pain.’
In order to induce investors to buy out of
favor stocks, stocks with poor recent
performance for example (value stocks),
these stocks have to pay a higher expected
return.
9-4
Behavioralism & Prospect Theory
Standard utility (satisfaction) theory versus prospect
theory
• Standard utility theory of investments:
– Investors desire more wealth and less risk
– Wealth provides diminishing marginal utility, thus
a gain of $1,000 provides less utility than the
utility loss from losing $1,000.
• This gives rise to risk aversion.
• Prospect theory:
An alternative behavioral theory suggesting that
investor utility depends on the change in wealth
from the start of the investment rather than on the
starting level of wealth.
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Why not arbitrage mispriced
stocks?
• If some investors are letting behavioral
biases affect prices, why don’t other better
trained investors engage in profitable
arbitrage?
•
Part of reason for growth in hedge funds.
Hedging in details; will be in Excel exam
9-6
Limits to arbitrage
• Fundamental Risk
Changes in fundamentals can wipe out any
arbitrage profits, making the strategy risky.
• Short sale constraints
Short sale constraints make it difficult to
arbitrage overpriced securities.
• Model Risk
How do you know when a security is truly
mispriced? Your model may be giving you
wrong signals.
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9.2Technical Analysis and Behavioral
Finance
9-8
Technical Trading Rules
1. Conceptual basis
– All technical analysis (TA) assumes that there are
recurring and predictable patterns in stock prices
which can be exploited to earn abnormal returns.
– Technical analysts believe:
• Market prices conform to new data only slowly,
giving rise to price trends
• Prices are affected by predictable behavioral or
psychological factors
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Point & Figure Charts
http://www.spreadsheetml.com/chart/freepointandfigure.shtml
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Basic Types of Technical
Analysis
2. Identifying trends using moving averages
Crossing the SMA from above is a bear signal;
from below is a bull signal.
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Basic Types of Technical
Analysis
3. Dow Theory
http://thedowtheory.com/Description&results.html
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Basic Types of Technical Analysis
4. Relative Strength
A simple relative strength ratio could be constructed as
ΔPi / ΔIndex
___________.
Increases in the relative strength ratio indicate the stock
is outperforming the index and could indicate a buy or
bullish signal.
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5.
Basic Types of Technical
Analysis
Breadth
– Measured as the difference between the number
of advancing and declining stocks
– Also used in industry indexes
• Cumulative breadth is found by adding the current
day’s net advances or declines to the previous
day’s total. The purpose is to gauge the trend.
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TA Sentiment Indicators
8. Short Interest
• Total number of shares of stock currently sold
short
• High short interest may indicate that a stock’s price
is expected to fall.
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TA Sentiment Indicators
8. Trin Statistic
Volume declining/Number declining
Trin =
Volume advancing/Number advancing
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TA Sentiment Indicators
• Confidence index
– Ratio of the average yield on 10 top-rated
corporate bonds divided by the average
yield on 10 intermediate-grade corporate
bonds
• Put/call ratio
– Call options give investors the right to buy
at a fixed exercise price and a put is the
right to sell at a fixed exercise price
– Change in ratio can be given a bullish or
bearish interpretation
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A Warning About Identifying
Trends
• Difficulty in identifying common price patterns
• Less than meets the eye
• Data mining
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