Prospect performance evaluation: Making a case for a non-asymptotic UMPU test

Zhidong Bai, Yongchang Hui, Wing Keung WONG*, Ričardas Zitikis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


We propose and develop mean-variance-ratio (MVR) statistics for comparing the performance of prospects (e.g., investment portfolios, assets, etc.) after the effect of the background risk has been mitigated. We investigate the performance of the statistics in large and small samples and show that in the non-asymptotic framework, the MVR statistic produces a uniformly most powerful unbiased (UMPU) test. We discuss the applicability of the MVR test in the case of large samples and illustrate its superiority in the case of small samples by analyzing Korea and Singapore stock returns after the impact of the American stock returns (which we view as the background risk) has been deducted. We find, in particular, that when samples are small, the MVR statistic can detect differences in asset performances while the Sharpe ratio test, which is the mean-standard-deviation-ratio statistic, may not be able to do so.

Original languageEnglish
Article numbernbr020
Pages (from-to)703-732
Number of pages30
JournalJournal of Financial Econometrics
Issue number4
Publication statusPublished - Sep 2012

Scopus Subject Areas

  • Finance
  • Economics and Econometrics

User-Defined Keywords

  • Asset
  • Fund management
  • Hypothesis test
  • Investment portfolio
  • Mean-variance ratio
  • Sharpe ratio
  • Uniformly most powerful unbiased test


Dive into the research topics of 'Prospect performance evaluation: Making a case for a non-asymptotic UMPU test'. Together they form a unique fingerprint.

Cite this