Learning about Mutual Fund Managers

Abstract

We study capital allocations to managers with two mutual funds, and show that investors learn about managers from their performance records. Flows into a fund are predicted by the manager's performance in his other fund, especially when he outperforms and when signals from the other fund are more useful. In equilibrium, capital should be allocated such that there is no cross-fund predictability. However, we find positive predictability, particularly among underperforming funds. Our results are consistent with incomplete learning: while investors move capital in the right direction, they do not withdraw enough capital when the manager underperforms in his other fund. * Darwin Choi and Abhiroop Mukherjee are at Hong Kong University of Science and Technology, and Bige Kahraman is at Saïd Business School, University of Oxford. We thank Kenneth Singleton (the Editor), an Associate Editor, and two anonymous referees for many helpful suggestions. We are also grateful for comments received from

Mutual Fund Flows and Cross‐Fund Learning within Families

“…19 For evidence of cross-sectional variation in mutual fund manager characteristics and performance, see Chevalier and Ellison (1999). Choi, Kahraman, and Mukherjee (2014) find that alphas of funds managed by the same managers are significantly correlated even after excluding common holdings. Within and Rho4 Within are average correlations of idiosyncratic returns within families, estimated using the Fama-French three-factor model and the Carhart four-factor model, respectively.…”

“…For evidence of cross‐sectional variation in mutual fund manager characteristics and performance, see Chevalier and Ellison (). Choi, Kahraman, and Mukherjee () find that alphas of funds managed by the same managers are significantly correlated even after excluding common holdings.…”

“…Sialm and Tham (2015) find that the prior stock price performance of a fund management company predicts flows into its affiliated funds. Choi, Kahraman, and Mukherjee (2014) examine pairs of funds managed by the same manager, and find that flows into one fund respond positively to the performance of the other fund. Our model of cross-fund learning provides a rational explanation for these findings.…”

Network Influence Analysis

“…where T A it is the total net asset of fund i at time t and r it is the fund return at time t. To avoid the impact of outliers induced by cash flow, we remove the top 2.5% observations, i.e., 11 funds, from the data set so there remain 409 observations in our study, and the resulting network density for these 409 funds is 20.9%. Removing the top percentage of observations is not uncommon in finance applications; for instance, Choi et al (2016) proposed removing the top 2.5% mutual funds by cash flow and Li and Schürhoff (2019) suggested eliminating the top percentage of observations in studying financial networks. In addition, after removing those observations, the distribution of the remaining cash flow is not heavy-tailed.…”

How active management survives

“…For example, Pastor and Stambaugh (2012) present a model where rational investors fail to learn that their active fund manager is expected to underperform going forward. Gruber (1996) also argues that investment in active mutual funds can be rational despite evidence of underperformance, as does Glode (2011) andChoie, Kahraman, andMukherjee (2016). All of these papers must stretch hard theoretically to justify persistent rational investment in active equity management despite its underperformance.…”