Bayesian evaluation of mutual fund performance with non-random missing data: Application to the Chinese market

This paper studies mutual fund performance evaluation, where non-random missing data is an important issue. Ignoring sample selection issues can lead to biased parameter estimates and invalid performance evaluation. Therefore, we propose a novel Beta selection model to characterize this non-random missingness mechanism. We can significantly reduce the bias in alpha estimates, by introducing our Beta selection mechanism into the Noise-Reduced Alpha (NRA) model of Harvey and Liu (2018). A computationally attractive Bayesian estimation procedure is also provided. Simulation results show that our proposed method achieves superior finite-sample performance compared to classical fund-by-fund ordinary least squares (OLS) and the baseline NRA model. Finally, we apply our method to Chinese mutual fund data, and find that: (i) our out-of-sample alpha forecasts exhibit greater predictive accuracy across all tested missingness rates; (ii) approximately 18% of funds exhibit statistically significant positive alphas, suggesting market-beating skill; and (iii) portfolios constructed using our method deliver significantly higher returns than those using alternative approaches.