Dr. Claus Huber is the founder and Managing Director of Rodex Risk Advisers LLC, based in Altendorf (Switzerland), where he works within the area of Market and Operational Risk Management. Prior to that, he was Head of Risk Management for Alternative Investments at Swiss Re, Zurich, where he was responsible for the development of stress testing and value-at-risk modeling, the development of illiquid asset hedging approaches, and risk-return analysis. profiles. Prior to that, he had extensive financial market experience as Chief Risk Officer of a hedge fund, credit strategist, hedge fund analyst and government bond trader.
Machine Learning for Hedge Fund Selection
This article describes the application of Kohonen’s Self-Organising Maps (SOM), a method of Machine Learning, to the problem of selecting hedge funds to achieve stable portfolio performance. SOM can help to identify similarities in return structures of hedge fund managers and hence to avoid concentrations in a portfolio. The core question is if SOM can add any value for manager selection. 2 novel yet simple methods to select hedge funds based on the specific properties of SOM are proposed that both target to identify unique investment strategies. To evaluate their performance relative to other, simpler benchmark methods of portfolio selection, a simulation study finds both SOM-based methods proposed enhance risk/return profiles and drawdown patterns.