Hedge fund performance, classification with machine learning, and managerial implications

Prior academic research on hedge funds focuses predominately on fund strategies in relation to market timing, stock picking, and performance persistence, among others. However, the hedge fund industry lacks a universal classification scheme for strategies, leading to subjective fund classifications and inaccurate expectations of hedge fund performance.

This study from researchers at the Universities of Bath, Reading and New York uses machine learning techniques to address this issue. First, it examines whether the reported fund strategies are consistent with their performance. Second, it examines the potential impact of hedge fund classification on managerial decision making.

The results suggest that for most reported strategies there is no alignment with fund performance. Classification matters in terms of abnormal returns and risk exposures, although the market factor remains the most important exposure for hedge funds. An important policy implication of the study is that the classification of hedge funds affects asset and portfolio allocation decisions, and the construction of the benchmarks against which performance is judged.

Read the full study

Related Posts

Previous Post
OCC stock loan volumes up 13.9% yoy to $136.2bn in July
Next Post
Interview: ISLA experts on navigating the ESG regulatory grey zone

Fill out this field
Fill out this field
Please enter a valid email address.


Reset password

Create an account