chapter 13 investments empirical evidence on security returns slides by richard d. johnson copyright...
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CHAPTER 13CHAPTER 13
InvestmentsInvestments
Empirical Evidence Empirical Evidence on Security Returnson Security Returns
Slides bySlides by
Richard D. JohnsonRichard D. Johnson
Copyright © 2008 by The McGraw-Hill Companies, Inc. All rights reservedCopyright © 2008 by The McGraw-Hill Companies, Inc. All rights reserved McGraw-Hill/IrwinMcGraw-Hill/Irwin
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Overview of Investigation
Tests of the single factor CAPM or APT Model
Tests of the Multifactor APT Model– Results are difficult to interpret
Studies on volatility of returns over time
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Tests of the Single Factor Model
Tests of the expected return beta relationship: First Pass Regression
– Estimate beta, average risk premiums and unsystematic risk.
Second Pass: Using estimates from the first pass to determine if model is supported by the data.
Most tests do not generally support the single factor model.
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Single Factor Test Results
Return %
Beta
Predicted
Actual
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Roll’s Criticism
The only testable hypothesis is on the efficiency of the market portfolio.
Benchmark error
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Table 13.1 Summary of Fama and MacBeth (1973) Study (All Rates in Basis Points per Month)
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Measurement Error in Beta
Statistical propertyIf beta is measured with error in the first
stage, second stage results will be biased in the direction the tests have supported.
Test results could result from measurement error.
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Jaganathan and Wang Study
Included factors for cyclical behavior of betas and human capital.
When these factors were included the results showed returns were a function of beta.
Size is not an important factor when cyclical behavior and human capital are included.
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Table 13.2 Evaluation of Various CAPM Specifications
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Table 13.3 Portfolio Shares Relative to Total Assets by Age and Net Worth
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Table 13.4 Determinants of Stockholdings
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Tests of the Multifactor Model
Chen, Roll and Ross 1986 StudyFactors
Growth rate in industrial production
Changes in expected inflation
Unexpected inflation
Changes in risk premiums on bonds
Unexpected changes in term premium on bonds
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Study Structure & Results
Method: Two -stage regression with portfolios constructed by size based on market value of equity.
Findings Significant factors: industrial production, risk
premium on bonds and unanticipated inflation.
Market index returns were not statistically significant in the multifactor model.
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Table 13.5 Economic Variables and Pricing (Percentage per Month 3, 10), Multivariate Approach
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Fama-French Three Factor Model
Size and book-to-market ratios explain returns on securities
Smaller firms experience higher returnsHigh book to market firms experience
higher returnsReturns are explained by size, book to
market and by beta
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Table 13.6 Three Factor Regressions for Portfolios Formed from Sorts on Size and Book-to-Market Ratios (B/M)
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Interpretation of Three-Factor Model
Size is a proxy for risk that is not captured CAPM Beta
Premiums are due to investor irrationality or behavioral biases
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Risk-Based Interpretations
Liew and VassalouPetkova and Zhang
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Figure 13.1 Difference in Return to Factor Portfolios in Year Prior to Above-Average versus Below-Average GDP Growth
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Figure 13.2 HML Beta in Different Economic States
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Behavioral Explanations
Market participants are overly optimistic – Analysts extrapolate recent performance
too far into the future– Prices on these glamour stocks are overly
optimistic – Lower book-to-market on these glamour
firms leads to underperformance compared to value stocks
Chan, Karceski and Lakonishok LaPort, Lakonishok, Shleifer and Vishny
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Figure 13.3 The Book-to-Market Ratio Reflects Past Growth, but Not Future Growth Prospects
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Figure 13.4 Value minus Glamour Returns Surrounding Earnings Announcements
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Liquidity and Asset Pricing
Acharya and Pedersen– Premiums observed in the three-factor
model may be illiquidity premiums
– Liquidity may explain the size premium but not the book-to-market premium
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Table 13.7 Characteristics of Portfolios Sorted by Liquidity
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Time-Varying Volatility
Stock prices change primarily in reaction to information.
New information arrival is time varying.Volatility is therefore not constant
through time.
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Stock Volatility Studies and Techniques
Volatility is not constant through time. Improved modeling techniques should
improve results of tests of the risk-return relationship.
ARCH and GARCH models incorporate time varying volatility.
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Figure 13.5 Estimates of the Monthly Stock Return Variance 1835 - 1987
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Figure 13.6 Implied Volatility versus Estimated Volatility
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Figure 13.7A Implied Volatility of the Nasdaq 100 Portfolio (VXN) and Historical Volatility of the Nasdaq Composite Portfolio
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Figure 13.7B Implied Volatility of the Standard and Poor’s 100 Portfolio (VIX) and the Nasdaq 100 Portfolio (VXN)
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Figure 13.8A Historical Volatility of the CRSP Nasdaq Large Capitalization (Decile 10) and Small Capitalization (Decile 1)
Portfolios
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Figure 13.8B Historical Volatility of the CRSP NYSE Large Capitalization (Decile 10) and Small Capitalization (Decile 1)
Portfolios
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Equity Premium Puzzle
Rewards for bearing risk appear to excessive.
Possible Causes– CAPM doesn’t consider the impact of
consumption
– Predicting returns from realized returns
Survivorship bias also creates the appearance of abnormal returns in market efficiency studies.
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Figure 13.9 Real Returns on Global Stock Markets