Skip to main content
Log in

Are There Sectoral Anomalies Too? The Pitfalls of Unreported Multiple Hypothesis Testing and a Simple Solution

  • Published:
Review of Quantitative Finance and Accounting Aims and scope Submit manuscript

Abstract

The recent emphasis on sector-specific investment strategies has led to the emergence of industry-specific calendar anomalies, notably the technology sector “summer swoon”. A standard t-test implies that these price movements provide arbitrage opportunities. However, this test fails to account for the many tests that may have preceded the swoon’s discovery. We propose the use of the Bonferroni correction to account for this unreported testing. Its application reverses the conclusions about the summer swoon and finds no evidence of calendar-based price patterns in any other sector. We also use the Bonferroni correction to revisit previously documented, market-wide, anomalies. Conclusions about the most widely cited anomalies (e.g., the January effect) are unchanged, but evidence for some other “anomalies” is substantially weakened. Our results emphasize that in evaluating a proposed anomaly, sectoral in nature or otherwise, it is crucial to account for the hypotheses that were likely to have been tested but not reported.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Ahmed, S.W., “Issues Arising in the Application of Bonferroni Procedures in Federal Surveys.” American Statistical Association Proceedings Survey Research Methods Section 344-349, (1991).

  • Ariel, R.A., “A Monthly Effect in Stock Returns.” Journal of Financial Economics 18, 161-174, (1987).

    Google Scholar 

  • Black, F., Estimating Expected Returns, New York: Goldman Sachs, 1992.

    Google Scholar 

  • Black, “Noise.” Journal of Finance 41, 529-543, (1986).

    Google Scholar 

  • Dimson, E. and Marsh P., “Murphy's Law and Market Anomalies.” The Journal of Portfolio Management 25, 53-69, (1999).

    Google Scholar 

  • Fama, E.F, “Market Ef®ciency, Long-term Returns, and Behavioral Finance.” Journal of Financial Economics 49, 283-306, (1998).

    Google Scholar 

  • French, K., “Stock Returns and the Weekend Effect.” Journal of Financial Economics 8, 55-69, (1980).

    Google Scholar 

  • Keim, D.B., “Size-related Anomalies and Stock Return Seasonality: Further Empirical Evidence.” Journal of Financial Economics 12, 13-32, (1983).

    Google Scholar 

  • Lo, AW. and A.C. McKinley, “Data-Snooping Biases in Tests of Financial Asset Pricing Models.” Review of Financial Studies 3, 431-467, (1990).

    Google Scholar 

  • Marcus, A.J., “The Magellan Fund and Market Efficiency.” The Journal of Portfolio Management 17, 85-88, (1990).

    Google Scholar 

  • Miller, R.G. Jr, Simultaneous Statistical Inference, New York: Springer-Verlag, 1981.

    Google Scholar 

  • Richardson, M., “Temporary Components of Stock Prices: A Skeptic's View.” Journal of Business and Economic Statistics 11, 199-207, (1993).

    Google Scholar 

  • Shaffer, J.P., “Multiple Hypothesis Testing.” Annual Review of Psychology 46, 561-584, (1995).

    Google Scholar 

  • Sullivan, R., A. Timmermann, and H. White, “Dangers of Data-Driven Inference: The Case of Calendar Effects in Stock Returns.” University of California. San Diego Department of Economics Discussion Paper 98-16, (1998).

  • Sullivan, R., A. Timmermann, and H. White, “Data-Snooping, Technical Trading Rule Performance, and the Bootstrap.” Journal of Finance 54, 1647-1691, (1999).

    Google Scholar 

  • Thaler, R.H., “Calendar Effects in the Stock Market” in Richard H. Thaler, The Winner's Curse, Princeton: Princeton University Press, (1992).

    Google Scholar 

  • Wang, K., Y. Li, and J. Erickson, “A New Look at the Monday Effect.” Journal of Finance 52, 2171-2186, (1997).

    Google Scholar 

  • White, H., “A Reality Check for Data Snooping.” Working paper, 1997.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Greenstone, M., Oyer, P. Are There Sectoral Anomalies Too? The Pitfalls of Unreported Multiple Hypothesis Testing and a Simple Solution. Review of Quantitative Finance and Accounting 15, 37–55 (2000). https://doi.org/10.1023/A:1008313703909

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008313703909

Navigation