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

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.

This paper is partly based on the book Advances in Financial Machine Learning (Wiley, 2018). A full paper can be downloaded at: http://ssrn.com/abstract=3104816.

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

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