Stock market anomalies: An extreme bounds analysis

Title: Stock market anomalies: An extreme bounds analysis
Creator: Kim, Jae H.; Shamsuddin, Abul
Relation: International Review of Financial Analysis Vol. 90, Issue November 2023, no. 102841
Publisher Link: http://dx.doi.org/10.1016/j.irfa.2023.102841
Publisher: Elsevier
Resource Type: journal article
Date: 2023
Description: We conduct the extreme bounds analysis (EBA) to evaluate the robustness or fragility of a range of stock market anomalies, using U.S. daily data from 1960 to 2023. The EBA is a large-scale sensitivity analysis, able to isolate the effects of potential data-mining or p-hacking under model uncertainty. The anomalies covered include the effects of Halloween, sports event, seasonal affective disorder, weather, political cycle, daylight saving, and lunar phase. We find that the empirical evidence for the anomalies is highly fragile, in terms of effect size estimates and their statistical significance.
Subject: data-mining; market efficiency; model uncertainty; extreme bounds analysis (EBA)
Identifier: http://hdl.handle.net/1959.13/1486859
Identifier: uon:51976
Identifier: ISSN:1057-5219
Language: eng
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