The 10 Reasons Most Machine Learning Funds Fail

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