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The 7 Reasons Most Machine Learning Funds Fail (Presentation Slides)

45 Pages Posted: 6 Sep 2017  

Marcos Lopez de Prado

Guggenheim Partners, LLC; Lawrence Berkeley National Laboratory; Harvard University - RCC

Date Written: September 2, 2017

Abstract

The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of assets, and deliver consistently exceptional performance to their investors. However, that is a rare outcome, for reasons that will become apparent in this presentation. Over the past two decades, I have seen many faces come and go, firms started and shut down. In my experience, there are 7 critical mistakes underlying most of those failures.

Keywords: Machine learning, investment strategies, quantamental investing, backtest overfitting

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

Suggested Citation

Lopez de Prado, Marcos, The 7 Reasons Most Machine Learning Funds Fail (Presentation Slides) (September 2, 2017). Available at SSRN: https://ssrn.com/abstract=3031282

Marcos Lopez de Prado (Contact Author)

Guggenheim Partners, LLC ( email )

330 Madison Avenue
New York, NY 10017
United States

HOME PAGE: http://www.QuantResearch.org

Lawrence Berkeley National Laboratory ( email )

1 Cyclotron Road
Berkeley, CA 94720
United States

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

Harvard University - RCC ( email )

26 Trowbridge Street
Cambridge, MA 02138
United States

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

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