Asset Performance Evaluation with Mean-Variance Ratio

Bai, et al. (2011c) develop the mean-variance-ratio (MVR) statistic to test the performance among assets for small samples. They provide theoretical reasoning to use MVR and prove that our proposed statistic is uniformly most powerful unbiased. In this paper we illustrate the superiority of our prop...

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Bibliographic Details
Main Authors: Bai, Zhidong, PHOON, Kok Fai, Wang, Keyan, Wong, Wing-Keung
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2011
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Online Access:https://ink.library.smu.edu.sg/lkcsb_research/3156
https://ink.library.smu.edu.sg/context/lkcsb_research/article/4155/viewcontent/2011PBFEAM_115.pdf
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Institution: Singapore Management University
Language: English
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Summary:Bai, et al. (2011c) develop the mean-variance-ratio (MVR) statistic to test the performance among assets for small samples. They provide theoretical reasoning to use MVR and prove that our proposed statistic is uniformly most powerful unbiased. In this paper we illustrate the superiority of our proposed 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 concludes most of the CTA funds being analyzed as being indistinguishable in their performance, our proposed statistics show that some funds outperform the others. On the other hand, when we apply the SR statistic on some other funds in which the recent difference between the two funds is insignificant and even changes directions, the SR statistic indicates that one fund is significantly outperforming another fund whereas the MVR statistic could detect the change.