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A New Direction of Fund Rating Based on the Finite Normal Mixture Model

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Author Info
Zhangpeng Gao (Nanyang Technological University, Singapore)
Shahidur Rahman (Division of Economics,School of Humanities and Social Sciences, Nanyang Technological University, Singapore)
Abstract

In this paper we try to develop a theoretical framework for fund rating under the assumption that superior funds could have a higher expected return than that of inferior funds, which could arise from the segmented market information or the differentiated ability of mangers to acquire and analyze the information. Under this setting, the funds are rated based on the cross-sectional distribution of all the funds instead of the presetpercentiles as Morningstar. We use the finite normal mixture for rating fund performance with the number of performance groups determined by likelihood ratio test using parametric bootstrap procedures, and we estimate the model with EM algorithm by treating the group information of funds as missing information.

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Publisher Info
Paper provided by Nanyang Technolgical University, School of Humanities and Social Sciences, Economic Growth centre in its series Economic Growth centre Working Paper Series with number 0603.

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Length: 20 pages
Date of creation: Mar 2006
Date of revision:
Handle: RePEc:nan:wpaper:0603

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Related research
Keywords: Fund Rating; Fund Performance; Finite Normal Mixture; Bootstrap; EM Algorithm;

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Find related papers by JEL classification:
G0 - Financial Economics - - General
G1 - Financial Economics - - General Financial Markets
C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General
D4 - Microeconomics - - Market Structure and Pricing

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  1. Carhart, Mark M, 1997. " On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March. [Downloadable!] (restricted)
  2. John H. Cochrane, 1996. "A Cross-Sectional Test of a Production-Based Asset Pricing Model," NBER Working Papers 4025, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  3. Woodward, Wayne A. & Sain, Stephan R., 2003. "Testing for outliers from a mixture distribution when some data are missing," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 193-210, October. [Downloadable!] (restricted)
  4. Tao, Jian & Shi, Ning-Zhong & Lee, S.-Y.Sik-Yum, 2004. "Drug risk assessment with determining the number of sub-populations under finite mixture normal models," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 661-676, July. [Downloadable!] (restricted)
  5. Robert Kosowski & Allan Timmermann & Russ Wermers & Hal White, 2006. "Can Mutual Fund "Stars" Really Pick Stocks? New Evidence from a Bootstrap Analysis," Journal of Finance, American Finance Association, vol. 61(6), pages 2551-2595, December. [Downloadable!] (restricted)
  6. Glosten, L. R. & Jagannathan, R., 1994. "A contingent claim approach to performance evaluation," Journal of Empirical Finance, Elsevier, vol. 1(2), pages 133-160, January. [Downloadable!] (restricted)
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  7. Cai, Jun & Chan, K C & Yamada, Takeshi, 1997. "The Performance of Japanese Mutual Funds," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 10(2), pages 237-73.
  8. J. Hartigan & Surya Mohanty, 1992. "The runt test for multimodality," Journal of Classification, Springer, vol. 9(1), pages 63-70, January. [Downloadable!] (restricted)
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