Machine Learning for Visual Risk Analysis and Hedge Fund Selection

Abstract

One of the main principles to build portfolios of financial assets is to achieve stable long-term performance and avoid large drawdowns. This article describes how a method of Machine Learning, Kohonen’s Self-Organising Maps (SOM), can be applied to visualise risk and to build robust portfolios of hedge fund managers. Essentially, it documents a feasibility study that was conducted to gauge whether Machine Learning can add any value to the investment process of an investor in hedge funds. We suggest a simple method to exploit the SOM feature of identifying similarities in high-dimensional data: managers are selected from the 4 most remote parts of the SOM, i.e., the units in the lower left, lower right, upper left and upper right corners. Hedge Fund portfolios based on this method achieve more favourable risk/return ratios and lower drawdowns than benchmarks. In discussions with clients it has turned out that the way SOMs work as well as the method to pick managers from remote areas of the SOM can be intuitively explained and understood, which increases acceptance by practitioners.