Evaluation of mutual funds using multi-dimensional information

Abstract

To make better use of mutual fund information for decision-making we propose a coned-context, data envelopment analysis (DEA) model with expected shortfall (ES) modeled under an asymmetric Laplace distribution in order to measure risk when evaluating performance of mutual funds. Unlike traditional models, this model not only measures the attractiveness of mutual funds relative to the performance of other funds, but also takes the decision makers’ preferences and expert knowledge/judgment into full consideration. The model avoids unsatisfying and impractical outcomes that sometimes occur with traditional measures and it also provides more management information for decision-making. Determining input and output variables is obviously very important in DEA evaluation. Using statistical tests and theoretical analysis, we demonstrate that ES under an asymmetric Laplace distribution is reliable and we therefore propose the model as a major risk measure for mutual funds. At the same time, we consider a fund’s performance over different time horizons (e.g., one, three and five years) in order to determine the persistence of fund performance. Using the coned-context DEA model with ES value under an asymmetric Laplace distribution, we also present the results of an empirical study of mutual funds in China, which provides significant insights into management of mutual funds. This analysis suggests that the coned context measure will help investors to select the best fund and fund managers in order to identify the funds with the most potential.

Keywords

mutual fund / data envelopment analysis (DEA) / performance evaluation / expected shortfall (ES) / cone-ratio

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant No. 70801006) and Chinese Universities Scientific Fund. The authors would also like to thank Prof. Zhimin Huang of School of Business, Adelphi University for his valuable suggestions on cone-context modeling, and Prof. Shouyang Wang of Chinese Academy of Sciences for his discussions and valuable suggestions on the empirical work in this research.

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg