Bootstrap analysis of mutual fund performance
共同基金绩效的自助法分析AI 解读 ↓
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共同基金绩效的自助法分析AI 解读 ↓
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We study bootstrap methods for fund performance evaluation. We first show that two prominent bootstrap tests have biased test sizes in a large cross-section with short time series and lack test power to detect skilled funds when a substantial number of unskilled funds are present. We then develop the theory for a valid bootstrap Hotelling’s T-squared test for zero alpha. We apply the proposed bootstrap test in a practical two-step procedure to identify skilled funds. Our empirical analysis finds that skilled funds are more engaged in active management and hold stocks with higher expected anomalous returns.
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