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.
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References
Markowitz H. Portfolio selection. Journal of Finance, 1952, 7(1): 77–91
Sharpe W. Capital asset prices: a theory of market equilibrium under condition of risk. Journal of Finance, 1964, 19(3): 425–442
Sharpe W. Mutual fund performance. Journal of Business, 1966, 39(S1): 119–138
Treynor J L. How to rate management of investment funds. Harvard Business Review, 1966, 43(1): 63–75
Jensen M. The performance of mutual funds in the period 1945–1964. Journal of Finance, 1968, 23(2): 389–416
Jensen, M.R. Risk, the pricing of capital assets, and the evaluation of investment portfolios. Journal of Finance, 1969, 42(2): 167–247
Roll R. Ambiguity, when performance is measured by the security market line. Journal of Finance, 1978, 33(4): 1051–1069
Murthi B, Choi Y, Desai P. Efficiency of mutual funds and portfolio performance measurement: a non-parametric approach. European Journal of Operational Research, 1997, 98(2): 408–418
Choi Y, Murthi B. Relative performance evaluation of mutual funds: a nonparametric approach. Journal of Business Finance & Accounting, 2001, 28(7–8): 853–876
McMullen P, Strong R. Selection of mutual fund using data envelopment analysis. Journal of Business and Economic Studies, 1998, 4: 1–12
Sedzro K, Sardano D. Mutual Fund Performance Evaluation Using Data Envelopment Analysis. Working Paper, School of Business, University of Quebec at Montreal, Canada, 1999
Morey M, Morey R. Mutual fund performance appraisals: a multihorizon perspective with endogenous benchmarking. International Journal of Management Science, 1999, 27: 241–258
Galagedera D, Silvapulle P. Australian mutual fund performance appraisal using data envelopment analysis. Managerial Finance, 2002, 28(9): 60–73
Wilkens K, Zhu J. Portfolio evaluation and benchmark selection: a mathematical programming approach. Journal of Alternative Investments, 2001, 4(1): 9–19
Basso A, Funari S. A data envelopment analysis approach to measure the mutual fund performance. European Journal of Operational Research, 2001, 135(3): 477–492
Basso A, Funari S. Measuring the performance of ethical mutual funds: a DEA approach. Journal of the Operational Research Society, 2003, 54(5): 521–531
Chang K P. Evaluating mutual fund performance: an application of minimum convex input requirement set approach. Computers & Operations Research, 2004, 31(6): 929–940
Zhao X J, Zhang H S, Lai K K, Wang S Y. A Method for evaluating mutual funds’ performance based on asymmetric Laplace distribution and DEA approach. Systems Engineering: Theory and Practice, 2007, 27(10): 1–10
Zhao X J, Ma C Q. Excavate mutual funds’ management information based on DEA model. Systems Engineering: Theory and Practice, 2008, 28(8): 190–196
Zhao X, Wang S. Empirical study on Chinese mutual funds’ performance. Systems Engineering - Theory & Practice, 2007, 27(3): 1–11
Zhao X J, Wang S Y. Studies on Mutual Funds Evaluation System in China, Beijing: Science Press, 2007 (in Chinese)
Seiford L M, Zhu J. Context-dependent data envelopment analysismeasuring attractive and progress. International Journal of Management Science, 2003, 31: 397–408
Halme M, Joro T, Korhonen P, Salo S, Wallenius J. A value efficiency approach to incorporating preference information in data envelopment analysis. Management Science, 1999, 45(1): 103–115
Taylor S. Modelling Financial Time Series, World Scientific Publishing Company, 2007
Simonson I, Tversky A. Choice in context: tradeoff contrast and extremeness aversion. Journal of Marketing Research, 1992, 29(3): 281–295
Charnes A, Cooper W W, Wei Q L, Huang Z M. Cone ratio data envelopment analysis and multi-objective programming. International Journal of Systems Science, 1989, 20(7): 1099–1118
Charnes A, Cooper W W, Huang Z M. Polyhedral cone-ratio DEA models with an illustrative application to large commercial banks. Journal of Econometrics, 1990, 46(1–2): 73–91
Thompson R G, Langemeier L N, Lee C, Lee E, Thrall R. The role of multiplier bounds in efficiency analysis with applications to Kansas farming. Journal of Econometrics, 1990, 46(1–2): 93–108
Thompson R G, Dharmapala P S, Thrall R M. Linked-cone DEA profit ratios and technical efficiency with application to illinois coal mines. International Journal of Production Economics, 1995, 39(1–2): 99–115
Thompson R G, Dharmapala P S, Louis J. DEA/AR efficiency and profitability of 14 major oil companies in U.S. Exploration and Production. Computers & Operations Research, 1996, 23(4): 357–373
Artzner P, Delbaen F, Eber J, Heath D. Thinking coherently. Risk (Concord, NH), 1997, 10(11): 68–71
Artzner P, Delbaen F, Eber J, Heath D. Coherent measures of risk. mathematical finance, 1999, 9(3): 203–228
Embrechts P, McNeil A, Straumann D. Correlation and Dependency in Risk Management: Properties and Pitfalls. In: Risk Management: Value at Risk and Beyond. Cambridge University Press, 2002, 176–223
Zhu H Q, Lu Z D, Wang S Y. The core estimation theory of valueat-risk. Systems Sciences and Mathematics, 2002, 22(3): 365–374
Huang H. Modeling and Forecasting of Value-at-Risk in Management: New Method Based on asymmetric Laplace distribution. Master Thesis, Graduate University, Chinese Academy of Sciences, 2003
Shi M, Wang S Y, Xu S Y. Amendatory sharp index and its application in funds’ performance evaluation. Systems Engineering Theory and Application, 2006, 7: 1–10
Charnes A, Cooper W W, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research, 1978, 2(6): 429–444
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Zhao, X., Shi, J. Evaluation of mutual funds using multi-dimensional information. Front. Comput. Sci. China 4, 237–253 (2010). https://doi.org/10.1007/s11704-010-0503-7
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DOI: https://doi.org/10.1007/s11704-010-0503-7