Chapter 13
Investor Behavior
and Capital Market
Efficiency
Copyright © 2011 Pearson Prentice Hall. All rights reserved.
13.1 Competition and Capital Markets (cont'd)
• Profiting from Non-Zero Alpha Stocks
 Investors can improve the performance of their
portfolios by buying stocks with positive alphas and by
selling stocks with negative alphas.
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Figure 13.1 An Inefficient Market Portfolio
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Figure 13.2 Deviations from the Security Market
Line
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13.1 Competition and Capital Markets
• Identifying a Stock’s Alpha
 To improve the performance of their portfolios,
investors will compare the expected return of a security
with its required return from the security market line.
rs  rf   s  (E[RMkt ]  rf )
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Alpha
The difference between a stock’s expected return
and its required return according to the security
market line is called the stock’s alpha.
 s  E[Rs ]  rs  E[Rs ]  (rf   s (E[RMkt ]  rf ))
 When the market portfolio is efficient, all stocks
are on the security market line and have an alpha
of zero.
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Textbook Example 13.1
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13.2 Information and Rational Expectations
(cont’d)
• Rational Expectations
 All investors correctly interpret and use their own
information, as well as information that can be inferred
from market prices or the trades of others.
 Regardless of how much information an investor has
access to, he can guarantee himself an alpha of zero
by holding the market portfolio.
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13.2 Information and Rational Expectations
(cont’d)
• Because the average portfolio of all investors is
the market portfolio, the average alpha of all
investors is zero.
• If no investor earns a negative alpha, then no
investor can earn a positive alpha, and the market
portfolio must be efficient.
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13.2 Information and Rational Expectations
(cont’d)
• The market portfolio can be inefficient only if a
significant number of investors either:
 Misinterpret information and believe they are earning a
positive alpha when they are actually earning a
negative alpha, or
 Care about aspects of their portfolios other than
expected return and volatility, and so are willing to hold
inefficient portfolios of securities.
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13.3 The Behavior of Individual Investors
• Underdiversification and Portfolio Biases
 There is much evidence that individual investors fail to
diversify their portfolios adequately.
 Familiarity Bias
• Investors favor investments in companies they are familiar
with
 Relative Wealth Concerns
• Investors care more about the performance of their
portfolios relative to their peers.
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13.3 The Behavior of Individual Investors
(cont’d)
• Excessive Trading and Overconfidence
 According to the CAPM, investors should hold risk-free
assets in combination with the market portfolio of all
risky securities.
 In reality, a tremendous amount of trading occurs each
day.
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Figure 13.3 NYSE Annual Share
Turnover, 1970–2008
Source:
www.nyxdata.com
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13.3 The Behavior of Individual Investors
(cont’d)
• Excessive Trading and Overconfidence
 Overconfidence Bias
• Investors believe they can pick winners and losers when, in
fact, they cannot; this leads them to trade too much.
 Sensation Seeking
• An individual’s desire for novel and intense risk-taking
experiences.
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Figure 13.4 Individual Investor Returns Versus
Portfolio Turnover
Source: B. Barber and T. Odean, “Trading Is Hazardous to Your Wealth: The Common
Stock Investment Performance of Individual Investors,” Journal of Finance 55 (2000) 773–
806.)
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13.3 The Behavior of Individual Investors
(cont’d)
• Individual Behavior and Market Prices
 If individuals depart from the CAPM in random ways,
then these departures will tend to cancel out.
 Individuals will hold the market portfolio in aggregate,
and there will be no effect on market prices or returns.
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13.4 Systematic Trading Biases
• Hanging on to Losers and the Disposition Effect
 Disposition Effect
• When an investor holds on to stocks that have lost their
value and sell stocks that have risen in value since the time
of purchase.
• Investor Attention, Mood, and Experience
 Studies show that individuals are more likely to buy
stocks that have recently been in the news, engaged in
advertising, experienced exceptionally high trading
volume, or have had extreme returns.
 Sunshine generally has a positive effect on mood, and
studies have found that stock returns tend to be higher
when it is a sunny day at the location of the stock
exchange.
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13.4 Systematic Trading Biases (cont’d)
• Investor Attention, Mood, and Experience
 Investors appear to put too much weight on their own
experience rather than considering all the historical
evidence.
 As a result, people who grew up and lived during a time
of high stock returns are more likely to invest in stocks
than people who experienced times when stocks
performed poorly.
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13.5 The Efficiency of the Market Portfolio
• If the market portfolio is efficient, securities should
not have alphas that are significantly different
from zero.
 For most stocks the standard errors of the alpha
estimates are large, so it is impossible to conclude that
the alphas are statistically different from zero.
 However, it is not difficult to find individual stocks that,
in the past, have not plotted on the SML.
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13.4 Systematic Trading Biases (cont’d)
• Herd Behavior
 When investors make similar trading errors because
they are actively trying to follow each other’s behavior
• Informational Cascade Effects
 Where traders ignore their own information hoping to
profit from the information of others
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13.4 Systematic Trading Biases (cont’d)
• Implications of Behavioral Biases
 If individual investors are engaging in strategies that
earn negative alphas, it may be possible for more
sophisticated investors to take advantage of this
behavior and earn positive alphas
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13.5 The Efficiency of the Market Portfolio
• Trading on News or Recommendations
 Takeover Offers
• If you could predict whether the firm would ultimately be
acquired or not, you could earn profits trading on that
information
10-22
Figure 13.5 Returns to Holding Target Stocks
Subsequent to Takeover Announcements
Source: Adapted from M. Bradley, A. Desai, and E. H. Kim, “The Rationale Behind Interfirm
Tender Offers: Information or Synergy?” Journal of Financial Economics 11 (1983) 183–206.
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13.5 The Efficiency of the Market Portfolio
(cont’d)
• Trading on News or Recommendations
 Stock Recommendations
• Jim Cramer makes numerous stock recommendations on
his television show, Mad Money


For stocks with news, it appears that the stock price correctly
reflects this information the next day, and stays flat (relative to the
market) subsequently
On the other hand, for the stocks without news, there appears to
be a significant jump in the stock price the next day, but the stock
price then tends to fall relative to the market, generating a
negative alpha, over the next several weeks
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Figure 13.6 Stock Price Reactions
to Recommendations on Mad Money
Source: Adapted from J. Engelberg, C. Sasseville, J. Williams, “Market Madness? The Case of
Mad Money,” SSRN working paper, 2009.
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13.5 The Efficiency of the Market Portfolio
(cont’d)
• The Performance of Fund Managers
 Numerous studies report that the actual returns to
investors of the average mutual fund have a negative
alpha
 Superior past performance is not a good predictor of a
fund’s future ability to outperform the market
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13.6 Style-Based Anomalies and the
Market Efficiency Debate
• Researchers have studied whether portfolios of
stocks plot on this line and have searched for
portfolios that would be most likely to have
nonzero alphas.
 Researchers have identified a number of characteristics
that can be used to pick portfolios that produce high
average returns.
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The Size Effect
• Size Effect
 Stocks with lower market capitalizations have been
found to have higher average returns.
• Portfolios based on size were formed.
• Portfolios consisting of small stocks had higher average
excess returns than those consisting of large stocks.
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Figure 13.9 Excess Return of Size Portfolios,
1926–2008
Source: Data courtesy of Kenneth French.
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Figure 13.10 Excess Return of Book-to-Market
Portfolios, 1926–2008
Source: Data courtesy of Kenneth French.
10-30
The Size Effect (cont'd)
• Data Snooping Bias
 The idea that given enough characteristics, it will
always be possible to find some characteristic that by
pure chance happens to be correlated with the
estimation error of a regression
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The Size Effect (cont'd)
However,
1. The size effect did not seem to disappear
2. This is evidence against the efficiency of the
market portfolio (Berk, 1995)
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Example
• Problem
 Suppose two firms, ABC and XYZ, are both expected to
pay a dividend stream $2.2 million per year in
perpetuity.
 ABC’s cost of capital is 12% per year and XYZ’s cost of
capital is 16%.
 Which firm has the higher market value?
 Which firm has the higher expected return?
10-33
Example
• Problem
 Now assume both stocks have the same estimated
beta, either because of estimation error or because the
market portfolio is not efficient.
 Based on this beta, the CAPM would assign an
expected return of 15% to both stocks.
 Which firm has the higher alpha?
 How do the market values of the firms relate to
their alphas?
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Past Returns
• Momentum Strategy
 Buying stocks that have had past high returns (and
shorting stocks that have had past low returns)
• When the market portfolio is efficient, past returns should
not predict alphas.
• However, researchers found that the best performing stocks
had positive alphas over the next six months.

This is evidence against the efficiency of the market portfolio.
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Implications of Positive Alphas
• If the CAPM correctly computes the risk premium,
an investment opportunity with a positive alpha is
a positive-NPV investment opportunity, and
investors should flock to invest in such strategies.
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Implications of Positive Alphas
If the CAPM correctly computes risk premiums, but
investors are ignoring opportunities to earn extra
returns without bearing any extra risk, it is because
1. Investors are systematically ignoring positive-NPV
investment opportunities.
2. The positive-alpha trading strategies contain risk that
investors are unwilling to bear but the CAPM does not
capture. This would suggest that the market portfolio
is not efficient.
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Three main reasons why the CAPM fails
• Proxy Error
• Investors do not care about mean-variance
• Non-tradeable Wealth
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13.7 Multifactor Models of Risk
• The expected return of any marketable security is:
E[Rs ]  rf  seff  (E[Reff ]  rf )
 When the market portfolio is not efficient, we have to
find a method to identify an efficient portfolio before we
can use the above equation. However, it is not actually
necessary to identify the efficient portfolio itself.
 All that is required is to identify a collection of portfolios
from which the efficient portfolio can be constructed.
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Factor Portfolios
•
Assume that there are two portfolios that can be combined to form an efficient
portfolio.
1. RF1 and RF2 are factor portfolios. The efficient portfolio
consists of some (unknown) combination of these two
factor portfolios, represented by portfolio weights x1 and
x2: Reff  x1 RF 1  x2 RF 2
2. To find βs of stock s, use multiple regression:
Rs  rf   s  sF1 (RF1  rf )  sF 2 (RF 2  rf )   s
3. This means that if we form a portfolio where we buy stock
s, short each of the factor portfolio according to its
respective β, and invest in the risk free asset. We will get
RP  rf   s   s , which translates to αs risk
premium and ε s risk.
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Factor Portfolios
 Setting αs equal to zero and taking expectations of both
sides (step 2), the result is the following two-factor
model of expected returns:
E[Rs ]  rf  sF1 (E[RF1 ]  rf )  sF 2 (E[RF 2 ]  rf )
• Factor Beta
 The sensitivity of the stock’s excess returns to the
excess return of a factor portfolio.
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Using Factor Portfolios (cont'd)
• Single-Factor versus Multi-Factor Model
 A singe-factor model uses one portfolio while a multifactor model uses more than one portfolio in the model.
 The CAPM is an example of a single-factor model while
the Arbitrage Pricing Theory (APT) is an example of
a multifactor model.
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Building a Multifactor Model
• Given N factor portfolios with returns RF1, . . . ,
RFN, the expected return of asset s is defined as:
E[Rs ]  rf   sF 1 (E[RF 1 ]  rf )   sF 2 (E[RF 2 ]  rf ) 
 rf 
N

n 1
FN
s
  sFN (E[RFN ]  rf )
(E[RFN ]  rf )
 β1…. βN are the factor betas.
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Building a Multifactor Model (cont'd)
• If all factor portfolios are self-financing then:
E[Rs ]  rf   sF 1E[RF 1 ]   sF 2 E[RF 2 ] 
 rf 
  sFN E[RFN ]
N
FN

 s (E[RFN ])
n 1
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Selecting the Portfolios
• A trading strategy that each year buys a portfolio
of small stocks and finances this position by short
selling a portfolio of big stocks has historically
produced positive risk-adjusted returns.
 This self-financing portfolio is widely known as the
small-minus-big (SMB) portfolio.
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Selecting the Portfolios (cont'd)
• A trading strategy that each year buys an equallyweighted portfolio of stocks with a book-to-market
ratio less than the 30th percentile of NYSE firms
and finances this position by short selling an
equally-weighted portfolio of stocks with a bookto-market ratio greater than the 70th percentile of
NYSE stocks has historically produced positive
risk-adjusted returns.
 This self-financing portfolio is widely known as the
high-minus-low (HML) portfolio.
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Selecting the Portfolios (cont'd)
• Each year, after ranking stocks by their return over the last
one year, a trading strategy that buys the top 30% of
stocks and finances this position by short selling bottom
30% of stocks has historically produced positive riskadjusted returns.
 This self-financing portfolio is widely known as the prior one-year
momentum (PR1YR) portfolio.
• This trading strategy requires holding the portfolio for a year and
the process is repeated annually.
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Selecting the Portfolios (cont'd)
• Currently the most popular choice for the
multifactor model uses the excess return of the
market, SMB, HML, and PR1YR portfolios.
 Fama-French-Carhart (FFC) Factor Specifications
E[Rs ]  rf   sMkt (E[RMkt ]  rf )   sSMB E[RSMB ]
  sHML E[RHML ]   sPR1YR E[RPR1YR ]
10-48
Back to the basic
Why is the inefficiency of the CAPM important?
1. For someone who wants to construct a portfolio
2. For a corporate manager trying to assess the
NPV of a project.
10-49
Calculating the Cost of Capital Using the
Fama-French-Carhart Factor Specification
10-50
Problem
You are considering making an investment in a project in
the semiconductor industry.
 The project has the same level of non-diversifiable risk
as investing in Intel stock.
 Assume you have calculated the following factor betas
for Intel stock:  Mkt  0.171
INTC
SMB
 INTC
 0.432
HML
 INTC
 0.419
PR1YR
 INTC
 0.121
 Determine the cost of capital by using the FFC
factor specification if the monthly risk-free rate
is 0.5%.
4-factors.xls
10-51
Calculating the Cost of Capital Using the FamaFrench-Carhart Factor Specification (cont'd)
• Although it is widely used in research to measure
risk, there is much debate about whether the FFC
factor specification is really a significant
improvement over the CAPM.
 One area where researchers have found that the FFC
factor specification does appear to do better than the
CAPM is measuring the risk of actively managed
mutual funds.
• Researchers have found that funds with high returns in the
past have positive alphas under the CAPM. When the same
tests were repeated using the FFC factor specification to
compute alphas, no evidence was found that mutual funds
with high past returns had future positive alphas.
10-52
So, why is it hard to calculate cost of
capital based on multi-factor models?
• Calculating the cost of capital using the the
multifactor model relies on accurate estimates of
risk premiums and betas.
 Accurately estimating these quantities is difficult. Both
risk premiums and betas may not remain stable over
time because..
1. Statistical errors – especially in multifactor models.
2. Economic reason – firms change over time
10-53
Variation of CAPM Beta in Time
10-54