Performance measurement of hedge funds using data envelopment analysis

Data envelopment analysis (DEA) is a nonparametric method from the area of operations research that measures the relationship of produced outputs to assigned inputs and determines an efficiency score. This efficiency score can be interpreted as a performance measure in investment analysis. Recent literature contains intensive discussion of using DEA to measure the performance of hedge funds, as this approach yields some advantages compared to classic performance measures. This paper extends the current discussion in three aspects. First, we present different DEA models and analyze their suitability for hedge fund performance measurement. Second, we systematize possible inputs and outputs for DEA and again examine their suitability for hedge fund performance measurement. Third, two rules are developed to select inputs and outputs in DEA of hedge funds. Using this framework, we find a completely new ranking of hedge funds compared to classic performance measures and compared to previously proposed DEA applications. Thus, we propose that classic performance measures should be supplemented with DEA based on the suggested rules to fully capture hedge fund risk and return characteristics.      

An application of data envelopment analysis to the evaluation of audit risk: a reinterpretation

This paper, which reinterprets previous work by Bradbury and Rouse (2002 ), addresses the risk quantification issue at an intuitive level. The insights provided by such quantification are discussed. Risk factors are associated with the risk-return concept. This allows measuring whether risks taken on are appropriately rewarded. The paper gives a non-technical exposition of DEA and outlines possible applications to accounting and finance. Using data for a large multinational, it shows how DEA analysis can be combined with internal audit procedures. It explains how the results obtained can be used to improve risk management.