PT  - JOURNAL ARTICLE
AU  - Marcos López de Prado
TI  - A Data Science Solution to the Multiple-Testing Crisis in Financial Research
AID  - 10.3905/jfds.2019.1.099
DP  - 2019 Jan 18
TA  - The Journal of Financial Data Science
PG  - jfds.2019.1.099
4099  - https://pm-research.com/content/early/2019/01/23/jfds.2019.1.099.short
4100  - https://pm-research.com/content/early/2019/01/23/jfds.2019.1.099.full
AB  - Most discoveries in empirical finance are false, as a consequence of selection bias under multiple testing. Although many researchers are aware of this problem, the solutions proposed in the literature tend to be complex and hard to implement. In this article, the author reduces the problem of selection bias in the context of investment strategy development to two sub-problems: determining the number of essentially independent trials and determining the variance across those trials. The author explains what data researchers need to report to allow others to evaluate the effect that multiple testing has had on reported performance. He applies his method to a real case of strategy development and estimates the probability that a discovered strategy is false.