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Asset Performance Evaluation with the Mean-Variance Ratio


Zhidong Bai


Northeast Normal University

Keyan Wang


Northeast Normal University

Wing-Keung Wong


Hong Kong Baptist University (HKBU)

Kok Fai Phoon


Singapore Management University

September 13, 2011


Abstract:     
Bai, et al. (2011c) have developed the mean-variance-ratio (MVR) statistics to test the performance among assets for small samples. They have also provided theoretical reasoning to use MVR and proved that their proposed statistic is uniformly most powerful unbiased. In this paper, we illustrate the superiority of the MVR test over the Sharpe ratio (SR) test by applying both tests to analyze the performance of Commodity Trading Advisors (CTAs). Our findings show that while the SR test conclude that most of the CTA funds being analyzed are indistinguishable in their performance, the MVR statistics show that some funds outperformed others. In addition, the SR statistic indicate that one fund significantly outperformed another even when the difference between the two funds becomes insignificant or even changes directions over sub-periods. Moreover, the MVR statistic can detect the changes in the sub-periods.

Number of Pages in PDF File: 31

Keywords: Sharpe ratio, performance hypothesis testing, Normal distribution, uniformly most powerful unbiased test, CTA funds

JEL Classification: G11, C13

working papers series


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Date posted: May 8, 2006 ; Last revised: September 13, 2011

Suggested Citation

Bai, Zhidong, Wang, Keyan, Wong, Wing-Keung and Phoon, Kok Fai, Asset Performance Evaluation with the Mean-Variance Ratio (September 13, 2011). Available at SSRN: http://ssrn.com/abstract=900973 or http://dx.doi.org/10.2139/ssrn.900973

Contact Information

Zhidong Bai (Contact Author)
Northeast Normal University ( email )
Changchun
China
Keyan Wang
Northeast Normal University ( email )
Changchun
China
Wing-Keung Wong
Hong Kong Baptist University (HKBU) ( email )
Kowloon
Hong Kong
Kok Fai Phoon
Singapore Management University ( email )
Li Ka Shing Library
70 Stamford Road
Singapore 178901, 178899
Singapore
Feedback to SSRN (Beta)


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