Library Header Image
LSE Research Online LSE Library Services

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

Sullivan, Ryan, Timmermann, Allan and White, Halbert (1998) The dangers of data-driven inference: the case of calender effects in stock returns. Financial Markets Group Discussion Papers (304). Financial Markets Group, The London School of Economics and Political Science, London, UK.

[img] Text (dp304) - Published Version
Download (181kB)


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.

Item Type: Monograph (Discussion Paper)
Official URL:
Additional Information: © 1998 The Authors
Divisions: Financial Markets Group
Subjects: H Social Sciences > HC Economic History and Conditions
H Social Sciences > HG Finance
JEL classification: G - Financial Economics > G1 - General Financial Markets > G10 - General
G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice; Investment Decisions
G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing; Trading volume; Bond Interest Rates
C - Mathematical and Quantitative Methods > C6 - Mathematical Methods and Programming > C63 - Computational Techniques
Date Deposited: 05 Jul 2023 08:48
Last Modified: 16 Sep 2023 00:03

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics