A Heuristic Approach to Asian Hedge Fund Allocation

Abstract

The authors first note that, unlike traditional investment vehicles, hedge funds seem to produce return distributions with significantly non-normal skewness and kurtosis. Hedge fund managers that apply the mean-variance optimization approach to form optimal portfolios may find that approach no longer appropriate. Moreover, utilizing a portfolio optimizer to perform portfolio allocations will cause what is known as the “butterfly effect”—that is, small changes in inputs, especially mean returns, can cause large changes in the optimal asset weightings. This phenomenon, coupled with the illiquidity of hedge funds, may prompt hedge fund managers to consider alternative approaches to portfolio allocation. In this study, the authors introduce a practical heuristic approach using the semi-variance (that better accounts for non-normality in hedge fund returns) as a measure for downside risk. This heuristic approach is able to provide better forecasts, stable portfolio allocations, and more diversification than the optimization approach. The authors find the “butterfly effect” in their sample of Asian hedge funds when using portfolio optimizers resulting in dramatic changes in optimal weights over time. They also find that their risk-return approach recommends portfolio with higher returns when compared with optimizers. In risk-reward comparisons against the optimizers, the heuristic approach yields the highest return to standard deviation and return to semi-deviation ratio (trade-offs).