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Dangers of Data-Driven Inference: The Case of Calendar Effects in Stock Returns (1998)
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| Venue: | Journal of Finance |
| Citations: | 37 - 3 self |
BibTeX
@ARTICLE{Sullivan98dangersof,
author = {Ryan Sullivan and Allan Timmermann and Halbert White},
title = {Dangers of Data-Driven Inference: The Case of Calendar Effects in Stock Returns},
journal = {Journal of Finance},
year = {1998},
volume = {54},
pages = {1647--1691}
}
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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 datadriven 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.
Keyphrases
calendar effect stock return data-driven inference generate new data set classical statistical inference nominal p-values systematic abnormal stock return individual calendar rule data-snooping bias particular theory data set new bootstrap procedure striking example daily data full universe statistical inference common practice substantial evidence datadriven discovery non-experimental science intensive search
