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unige:110006 
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The Cross-Sectional Distribution of Fund Skill Measures |
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| Authors | ||
| Year | 2018 | |
| Abstract | We develop a simple, non-parametric approach for estimating the entire distribution of skill. Our approach avoids the challenge of correctly specifying the distribution, and allows for a joint analysis of multiple measures–a key requirement for examining skill. Our results show that more than 85% of the funds are skilled at detecting profitable trades, but unskilled at overriding capacity constraints. Aggregating both skill dimensions using the value added, we find that around 70% of the funds are able to generate profits. The average value added after funds have reached their long-term size equals 7.1 mio. per year, which represents two thirds of the optimal value predicted by neoclassical theory. For all skill measures, the distribution is highly non-normal and reveals a strong heterogeneity across funds. | |
| Keywords | Mutual fund skill — Non-parametric density estimation — Large panel | |
| Full text | ||
| Structures | ||
| Research group | Geneva Finance Research Institute (GFRI) | |
| Citation (ISO format) | BARRAS, Laurent, GAGLIARDINI, Patrick, SCAILLET, Olivier. The Cross-Sectional Distribution of Fund Skill Measures. 2018 https://archive-ouverte.unige.ch/unige:110006 |
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