Type of Document Dissertation Author Loon, Yee Cheng Author's Email Address yeechengloon@comcast.net URN etd-08142007-130418 Title Model uncertainty and mutual fund investing Degree Ph.D. Department Finance Advisory Committee
Advisor Name Title Dr. Vikas Agarwal Committee Chair Dr. Ajay Subramanian Committee Member Dr. Jason Greene Committee Member Dr. Jayant Kale Committee Member Keywords
- mutual fund investing
- financial models
Date of Defense 2007-07-31 Availability unrestricted Abstract Yee Cheng Loon’s dissertation abstract
Model uncertainty exists in the mutual fund literature. Researchers employ a variety of models to estimate risk-adjusted return, suggesting a lack of consensus as to which model is correct. Model uncertainty makes it difficult to draw clear inference about mutual fund performance persistence. We explicitly account for model uncertainty by using Bayesian model averaging techniques to estimate a fund’s risk-adjusted return. Our approach produces the Bayesian model averaged (BMA) alpha, which is a weighted combination of alphas from individual models. Using BMA alphas, we find evidence of performance persistence in a large sample of US equity, bond and balanced mutual funds. Funds with high BMA alphas subsequently generate higher risk-adjusted returns than funds with low BMA alphas, and the magnitude of outperformance is economically and statistically significant. We also find that mutual fund investors respond to the information content of BMA alphas. High BMA alpha funds receive subsequent cash inflows while low BMA alpha funds experience subsequent cash outflows.
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