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Chapter 10

Chapter 10. Empirical Evidence on Security Returns. Chapter Summary. Objective: To discuss the empirical evidence in support of equilibrium models. Tests of the Single Factor Model Tests of the Multifactor Model Other Studies. Overview of Investigation.

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Chapter 10

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  1. Chapter 10 Empirical Evidence on Security Returns

  2. Chapter Summary • Objective: To discuss the empirical evidence in support of equilibrium models. • Tests of the Single Factor Model • Tests of the Multifactor Model • Other Studies

  3. 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

  4. 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

  5. Thin Trading • Many Canadian securities do not trade very frequently • This may cause biases in the statistical estimates • Several techniques exist to correct these biases

  6. Return % Predicted Actual Beta Single Factor Test Results

  7. Roll’s Criticism on the Tests • The only testable hypothesis: the mean-variance efficiency of the market portfolio • All other implications are not independently testable • CAPM is not testable unless we use the true market portfolio • The benchmark error

  8. Measurement Error in Beta Statistical property: • If 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

  9. Conclusions on the Tests’ Results • Tests proved that CAPM seems qualitatively correct • Rates of return are linear and increase with beta • Returns are not affected by nonsystematic risk • But they do not entirely validate its quantitative predictions • The expected return-beta relationship is not fully consistent with empirical observation.

  10. Summary Reminder • Objective: To discuss the empirical evidence in support of equilibrium models. • Tests of the Single Factor Model • Tests of the Multifactor Model • Other Studies

  11. Tests of the Multifactor Model Factors identified by Chen, Roll and Ross in their 1986 study: • Growth rate in industrial production • Changes in expected inflation • Unexpected inflation • Changes in risk premiums on bonds • Unexpected changes in term premium on bonds

  12. 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

  13. Anomalies Literature Is the CAPM or APT Model Valid? • Numerous studies show the approach is not valid • Why do the studies show this result • Other factors influence returns on securities • Statistical problems prohibit a good test of the model

  14. Summary Reminder • Objective: To discuss the empirical evidence in support of equilibrium models. • Tests of the Single Factor Model • Tests of the Multifactor Model • Other Studies

  15. Fama and French Study (1992) • Size and book-to-market ratios explain returns on securities • Beta is not a significant variable when other variables are included • Study results show no support for the CAPM or APT

  16. Researchers’ Responses to Fama and French • Utilize better econometric techniques • Improve estimates of beta • Reconsider the theoretical sources and implications of the Fama and French-type results • Return to the single-index model, accounting for non-traded assets and cyclical behavior of betas

  17. Jaganathan and Wang Study (1996) • 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

  18. Stochastic Volatility • Stock prices change primarily in reaction to information • New information arrival is time varying • Volatility is therefore not constant through time

  19. Stock Volatility Studies and Techniques • Pagan and Schwert Study • Study of 150 years of volatility on NYSE stocks • Volatility is not constant through time • Improved modeling techniques should improve results of tests of the risk-return relationship • GARCH Models to incorporate time varying volatility

  20. Equity Premium Puzzle • Rewards for bearing risk appear too excessive • Possible causes: • Unanticipated capital gains • Survivorship bias • Survivorship bias also creates the appearance of abnormal returns in market efficiency studies

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