Detection of false investment strategies using unsupervised learning methods

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.

Keywords:

• Backtest overfitting
• Selection bias
• Multiple testing
• Quantitative investments
• Machine learning

JEL Classification:

• G0
• G1
• G2
• G15
• G24
• E44

AMS Classification:

• 91G10
• 91G60
• 91G70
• 62C
• 60E

Acknowledgements

We wish to thank Prof. Germán G. Creamer and two anonymous referees for their help and useful comments.

Disclosure statement

No potential conflict of interest was reported by the authors.