Detection of False Investment Strategies Using Unsupervised Learning Methods

24 Pages Posted: 23 Apr 2018 Last revised: 19 Aug 2018

See all articles by Marcos Lopez de Prado

Marcos Lopez de Prado

AQR Capital Management, LLC; Cornell University - Operations Research & Industrial Engineering; RCC - Harvard University

Michael J. Lewis

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

Date Written: August 18, 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

López de Prado, Marcos and Lewis, Michael J., Detection of False Investment Strategies Using Unsupervised Learning Methods (August 18, 2018). Available at SSRN: https://ssrn.com/abstract=3167017 or http://dx.doi.org/10.2139/ssrn.3167017

Marcos López de Prado (Contact Author)

AQR Capital Management, LLC ( email )

One Greenwich Plaza
Greenwich, CT 06830
United States

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

Cornell University - Operations Research & Industrial Engineering ( email )

237 Rhodes Hall
Ithaca, NY 14853
United States

HOME PAGE: http://www.orie.cornell.edu

RCC - Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

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

Michael J. Lewis

True Positive Technologies ( email )

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

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

New York University
New York, NY 10012
United States

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