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Mutual Fund Flows, Performance Persistence, and Manager Skill

机译:共同资金流动,绩效持久性和经理技能

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This paper adapts the model in Berk and Green (2004) to explain with reasonable success the data on mutual fund returns and flows. Using a Bayesian measure of fund-manager skill, I find that posterior estimates of skill vary substantially in the cross section and that perceived differences in ability persist through time. Consistent with the model, investor fund flows respond in a convex manner to posterior updates of manager skill scaled by functions of the expense ratio, and this result is robust to controlling for a convex function of past performance. While cross-sectional vari- ation in posterior fund manager skill estimates (which control for fund flows) has predictive power for out-of-sample subsequent fund performance, such predictability is present only in the short run. Beyond one year, high-skilled managers do not consistently out-perform low-skilled managers as skill-chasing fund flows equalize the realized abnormal net fund returns across managers. Overall, my empirical evidence is consistent with some managers possessing high ability, investors rationally chasing returns generated by those managers, and lack of long-run persistence in fund returns due to fund flows and diseconomies of scale in assets under management. Outside of the model, I show that the cross-sectional distribution of managerial ability is related to fund style and fund-manager compensation in a way that is consistent with matching marginal managerial productivity to the nature of the assets in the underlying portfolio.
机译:本文采用Berk and Green(2004)中的模型来合理成功地解释了共同基金收益和流量数据。通过贝叶斯对基金经理技能的测度,我发现技能的后验估计在横截面上有很大不同,并且能力的感知差异会随着时间的推移持续存在。与该模型一致,投资者资金流以凸出的方式响应由费用比率函数决定的经理技能的后更新,并且该结果对于控制过去绩效的凸出函数是鲁棒的。尽管后代基金经理技能估计的横断面变化(控制资金流向)对样本外的后续基金业绩具有预测能力,但这种可预测性仅在短期内存在。超过一年后,高技能经理人的表现并不会一直优于低技能经理人,因为技能追赶资金流量等于各经理人已实现的异常净基金收益。总的来说,我的经验证据与一些具有高能力的管理者,投资者合理地追逐那些经理所产生的收益,以及由于资金流向和所管理资产规模不经济而导致基金收益缺乏长期持续性相一致。在模型之外,我证明了管理能力的横截面分布与基金风格和基金经理薪酬有关,其方式与使边际管理生产率与基础投资组合中资产的性质相匹配。

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