The 10 Reasons Most Machine Learning Funds Fail

21 Pages Posted: 18 Jan 2018 Last revised: 1 Jul 2018

See all articles by Marcos Lopez de Prado

Marcos Lopez de Prado

Cornell University - Operations Research & Industrial Engineering; Abu Dhabi Investment Authority; True Positive Technologies

Date Written: January 27, 2018

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 article. Over the past two decades, I have seen many faces come and go, firms started and shut down. In my experience, there are ten critical mistakes underlying most of those failures.

This paper is partly based on the book Advances in Financial Machine Learning (Wiley, 2018). The first chapter of this book is available at http://ssrn.com/abstract=3104847.

A presentation can be found at http://ssrn.com/abstract=3031282.

Keywords: Big Data, Machine Learning, High Performance Computing, Investment Strategies, Quantamental Investing, Backtest Overfitting

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

Suggested Citation

López de Prado, Marcos, The 10 Reasons Most Machine Learning Funds Fail (January 27, 2018). Journalof Portfolio Management, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3104816 or http://dx.doi.org/10.2139/ssrn.3104816

Marcos López de Prado (Contact Author)

Cornell University - Operations Research & Industrial Engineering ( email )

237 Rhodes Hall
Ithaca, NY 14853
United States

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

Abu Dhabi Investment Authority ( email )

211 Corniche Road
Abu Dhabi, Abu Dhabi PO Box3600
United Arab Emirates

HOME PAGE: http://www.adia.ae

True Positive Technologies ( email )

NY
United States

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

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