Previsão de retornos em Hedge Funds e seleção de fundos: Uma abordagem com Machine Learning2017 Jan 14
Apesar dos bons resultados o maior diferencial desse artigo é a metodologia em que os autores dividiram os fundos em quatro categorias que são equity, event-driven, macro, e relative value e realizaram análises do tipo cross-sectional para mensuração de performance. Sem dúvidas um bom artigo para quem queira trabalhar com esse tipo de fundo, ou mesmo ter o próprio fundo particular usando Machine Learning.
Hedge Fund Return Prediction and Fund Selection: A Machine-Learning Approach - Jiaqi Chen, Wenbo Wu, and Michael L. Tindall - Federal Reserve Bank of Dallas
Abstract: A machine-learning approach is employed to forecast hedge fund returns and perform individual hedge fund selection within major hedge fund style categories. Hedge fund selection is treated as a cross-sectional supervised learning process based on direct forecasts of future returns. The inputs to the machine-learning models are observed hedge fund characteristics. Various learning processes including the lasso, random forest methods, gradient boosting methods, and deep neural networks are applied to predict fund performance. They all outperform the corresponding style index as well as a benchmark model, which forecasts hedge fund returns using macroeconomic variables. The best results are obtained from machine-learning processes that utilize model averaging, model shrinkage, and nonlinear interactions among the factors.
Conclusions: We propose a supervised machine-learning approach to forecast hedge fund returns and select hedge funds quantitatively. The framework is based on cross-sectional forecasts of hedge fund returns utilizing a set of 17 factors. The approach allows the investor to identify funds that are likely to perform well and to construct the corresponding portfolios. We find that our method is applicable across hedge fund style categories. Focusing on factors constructed from characteristics idiosyncratic to individual funds, our models offer distinctive perspectives when compared to models that are driven by macroeconomic variables. Retrospectively, when benchmarked against a traditional factor model, our machine-learning approach generates portfolios with large alphas. The relatively low explanatory power of the regressions indicates that most of the performance of the algorithm-generated portfolios is due to success in identifying funds likely to deliver good performance. Our approach is flexible enough to incorporate new developments both in risk-factor research field and in the machine-learning field.