The Cross-Sectional Distribution of Fund Skill Measures

Laurent Barras, Patrick Gagliardini and Olivier Scaillet

No unige:110006, Working Papers from University of Geneva, Geneva School of Economics and Management

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 (search for similar items in EconPapers)
JEL-codes: C14 C33 C58 G11 G12 (search for similar items in EconPapers)
Date: 2018
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Working Paper: The Cross-Sectional Distribution of Fund Skill Measures (2018)
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