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Testing linear factor models on individual stocks using the average F -test

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  • Soosung Hwang
  • Stephen E. Satchell
Abstract
In this paper, we propose the average F -statistic for testing linear asset pricing models. The average pricing error, captured in the statistic, is of more interest than the ex post maximum pricing error of the multivariate F -statistic that is associated with extreme long and short positions and excessively sensitive to small perturbations in the estimates of asset means and covariances. The average F -test can be applied to thousands of individual stocks and thus is free from the information loss or the data-snooping biases from grouping. This test is robust to ellipticity, and more importantly, our simulation and bootstrapping results show that the power of the average F -test continues to increase as the number of stocks increases. Empirical tests using individual stocks from 1967 to 2006 demonstrate that the popular four-factor model (i.e. Fama-French three factors and momentum) is rejected in two sub-periods from 1967 to 1971 and from 1982 to 1986.

Suggested Citation

  • Soosung Hwang & Stephen E. Satchell, 2014. "Testing linear factor models on individual stocks using the average F -test," The European Journal of Finance, Taylor & Francis Journals, vol. 20(5), pages 463-498, May.
    • Handle: RePEc:taf:eurjfi:v:20:y:2014:i:5:p:463-498
    DOI: 10.1080/1351847X.2012.717097
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    Cited by:

    1. M Hashem Pesaran & Takashi Yamagata, 2024. "Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities," Journal of Financial Econometrics, Oxford University Press, vol. 22(2), pages 407-460.
    2. Shafiqur Rahman & Matthew J. Schneider, 2019. "Tests of Alternative Asset Pricing Models Using Individual Security Returns and a New Multivariate F-Test," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-34, March.

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