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Title

The Cross-Sectional Distribution of Fund Skill Measures

Authors
Barras, Laurent
Gagliardini, Patrick
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 skillNon-parametric density estimationLarge panel
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Research group Geneva Finance Research Institute (GFRI)
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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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Deposited on : 2018-10-29

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