Performance Measurement of Hedge Funds Using Data Envelopment Analysis

Data envelopment analysis (DEA) is a nonparametric method from the area of operationsresearch that measures the relationship of produced outputs to assignedinputs and determines an efficiency score. This efficiency score can be interpretedas a performance measure in investment analysis. Recent literature contains intensivediscussion of using DEA to measure the performance of hedge funds, as thisapproach yields some advantages compared to classic performance measures. Thispaper extends the current discussion in three aspects. First, we present differentDEA models and analyze their suitability for hedge fund performance measurement.Second, we systematize possible inputs and outputs for DEA and again examinetheir suitability for hedge fund performance measurement. Third, two rulesare developed to select inputs and outputs in DEA of hedge funds. Using thisframework, we find a completely new ranking of hedge funds compared to classicperformance measures and compared to previously proposed DEA applications.Thus, we propose that classic performance measures should be supplemented withDEA based on the suggested rules to fully capture hedge fund risk and return characteristics.