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

45 Pages Posted: 6 Sep 2017  

Marcos Lopez de Prado

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

Date Written: September 2, 2017


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.

This paper is partly based on the book Advances in Financial Machine Learning (Wiley, 2018). A full paper can be downloaded at:

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: or

Marcos Lopez de Prado (Contact Author)

Lawrence Berkeley National Laboratory ( email )

1 Cyclotron Road
Berkeley, CA 94720
United States


True Positive Technologies ( email )

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


RCC - Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States


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