The 7 Reasons Most Machine Learning Funds Fail (Presentation Slides)
45 Pages Posted: 6 Sep 2017 Last revised: 19 Jan 2018
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
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