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The dangers of data-driven inference: the case of calender effects in stock returns

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  • Sullivan, Ryan
  • Timmermann, Allan
  • White, Halbert
Abstract
Economics is primarily a non-experimental science. Typically, we cannot generate new data sets on which to test hypotheses independently of the data that may have led to a particular theory. The common practice of using the same data set to formulate and test hypotheses introduces data-snooping biases that, if not accounted for, invalidate the assumptions underlying classical statistical inference. A striking example of a data-driven discovery is the presence of calendar effects in stock returns. There appears to be very substantial evidence of systematic abnormal stock returns related to the day of the week, the week of the month, the month of the year, the turn of the month, holidays, and so forth. However, this evidence has largely been considered without accounting for the intensive search preceding it. In this paper we use 100 years of daily data and a new bootstrap procedure that allows us to explicitly measure the distortions in statistical inference induced by data-snooping. We find that although nominal P-values of individual calendar rules are extremely significant, once evaluated in the context of the full universe from which such rules were drawn, calendar effects no longer remain significant.

Suggested Citation

  • Sullivan, Ryan & Timmermann, Allan & White, Halbert, 1998. "The dangers of data-driven inference: the case of calender effects in stock returns," LSE Research Online Documents on Economics 119142, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:119142
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    Cited by:

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    2. Hendry, David F., 2001. "Achievements and challenges in econometric methodology," Journal of Econometrics, Elsevier, vol. 100(1), pages 7-10, January.
    3. Goran Karanovic & Bisera Karanovic, 2018. "The Day-of-the-Week Effect: Evidence from Selected Balkan Markets," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 65(1), pages 1-11, March.
    4. Kunkel, Robert A. & Compton, William S. & Beyer, Scott, 2003. "The turn-of-the-month effect still lives: the international evidence," International Review of Financial Analysis, Elsevier, vol. 12(2), pages 207-221.
    5. Clements, Michael P. & Franses, Philip Hans & Swanson, Norman R., 2004. "Forecasting economic and financial time-series with non-linear models," International Journal of Forecasting, Elsevier, vol. 20(2), pages 169-183.
    6. David McMillan & Alan Speight, 2004. "Intra-day periodicity, temporal aggregation and time-to-maturity in FTSE-100 index futures volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 14(4), pages 253-263.
    7. Edwin D. Maberly & Daniel F. Waggoner, 2000. "Closing the question on the continuation of turn-of-the-month effects: evidence from the S&P 500 Index futures contract," FRB Atlanta Working Paper 2000-11, Federal Reserve Bank of Atlanta.
    8. Sasidharan, Anand, 2009. "Does seasonality persists in Indian stock markets?," MPRA Paper 24185, University Library of Munich, Germany, revised Aug 2010.
    9. Philip Kostov & Seamus McErlean, 2004. "Estimating the probability of large negative stock market," Finance 0409011, University Library of Munich, Germany.
    10. Lee, Tae-Hwy & Saltoglu, Burak, 2002. "Assessing the risk forecasts for Japanese stock market," Japan and the World Economy, Elsevier, vol. 14(1), pages 63-85, January.

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    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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