Detection of False Investment Strategies Using Unsupervised Learning Methods

26 Pages Posted: 23 Apr 2018 Last revised: 20 May 2018

Marcos Lopez de Prado

Lawrence Berkeley National Laboratory; True Positive Technologies; RCC - Harvard University

Michael J. Lewis

True Positive Technologies; New York University (NYU) - Courant Institute of Mathematical Sciences

Date Written: April 22, 2018

Abstract

Most investment strategies uncovered by practitioners and academics are false. This partially explains the high rate of failure, especially among quantitative hedge funds (smart beta, factor investing, stat-arb, CTAs, etc.) In this paper we examine why false positives are so prevalent in finance, why researchers fail (in many cases purposely) to detect them, and why firms are able to monetize their scheme. Beyond merely pointing to this industrywide problem, we offer a practical solution. We hope that the machine learning tools presented in this paper will help financial academic journals filter out false positives, and bring up the retraction rate to reasonable levels. The SEC, FINRA and other regulatory agencies worldwide could use these tools to take a more active role in curving this rampant financial fraud.

A presentation based on this paper can be found at https://ssrn.com/abstract=3173146

Keywords: Backtest overfitting, selection bias, multiple testing, quantitative investments, machine learning, financial fraud

JEL Classification: G0, G1, G2, G15, G24, E44

Suggested Citation

Lopez de Prado, Marcos and Lewis, Michael J., Detection of False Investment Strategies Using Unsupervised Learning Methods (April 22, 2018). Available at SSRN: https://ssrn.com/abstract=3167017 or http://dx.doi.org/10.2139/ssrn.3167017

Marcos Lopez de Prado (Contact Author)

Lawrence Berkeley National Laboratory ( email )

1 Cyclotron Road
Berkeley, CA 94720
United States

HOME PAGE: http://www.lbl.gov

True Positive Technologies ( email )

12 East 49th Street, Floor 37
New York, NY 10017
United States

HOME PAGE: http://www.truepositive.com

RCC - Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

HOME PAGE: http://www.rcc.harvard.edu

Michael J. Lewis

New York University (NYU) - Courant Institute of Mathematical Sciences ( email )

New York University
New York, NY 10012
United States

True Positive Technologies ( email )

12 East 49th Street, Floor 37
New York, NY 10017
United States

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