chapter 13 investments empirical evidence on security returns slides by richard d. johnson copyright...

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

chapter 13 investments empirical evidence on security returns slides by richard d. johnson copyright...

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