Detection of false investment strategies using unsupervised learning methods

Marcos López de Prado and Michael J. Lewis

Quantitative Finance, 2019, vol. 19, issue 9, 1555-1565

Abstract: In this paper we address the problem of selection bias under multiple testing in the context of investment strategies. We introduce an unsupervised learning algorithm that determines the number of effectively uncorrelated trials carried out in the context of a discovery. This estimate is critical for computing the familywise false positive probability, and for filtering out false investment strategies.

Date: 2019
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DOI: 10.1080/14697688.2019.1622311

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