单位：Essex University Business School
讲座主题：On the Selection of the Optimal Number of Subgroups for Fund Performance Evaluation
摘要：Can the Gaussian Mixture Distributions Plug-in Approach via Traditional Procedures Select the Right Number of Fund Subgroups? Probably not. This is due to the fact that according to our in-sample/out-of-sample likelihood score analysis, within both U.S. mutual funds and hedge funds, the actual locations of fund subgroups, in real data, are too close to each other. The information loss incurred by parameter uncertainty outweigh the one incurred by mis-specification. An arbitrary choice of two subgroups only causes affordable information loss relative to more fund subgroups. These findings challenge the reliability of the Gaussian Mixture Distributions (GMD) plug-in approach via traditional procedures (e.g., BIC, Likelihood Ratio and Chi-square statistics) in selecting the correct number of subgroups.