The empirical results suggest that monthly hedge fund returns are forecastable by means of multivariate regression models which rely on economic predictors such as changes in interest rates or changes in business outlooks. Accounting for the fact that hedge fund returns are non-normally distributed, heteroscedastic and time-varying in their exposure to pervasive risk factors, the devised econometric models are found to deliver significant out-ofsample predictive power. The thesis at hand also documents that the interdependencies between the monthly changes of envisaged risk factors and the subsequent hedge fund returns remain remarkably stable throughout time. In essence, the performance of hedge funds appears to be sensitive to common business cycle movements.
Altogether, the results are relevant to researchers in search of a description and application of contemporary return prediction methods as well as to investors in need of a better understanding of the drivers of hedge fund returns.
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