Summary:
Public mutual funds not only fulfill the function of value discovery and creation in the secondary market, but also serve as an important vehicle for Chinese residents to achieve common prosperity by obtaining property income through capital market participation. However, in recent years, both academia and industry have found a significant performance gap between funds and fund investors in the fund market. Explaining the performance gap of fund investors is of great significance for further optimizing the capital market's institutional mechanisms and achieving healthy accumulation of national wealth. Existing research mainly explains the fund-investor performance gap from the perspective of investors' own behavioral biases, arguing that investors' performance-chasing behavior of buying high and selling low is the main reason for their lower performance. This paper argues that fund managers' trading behavior is also an important cause of this phenomenon. Even if investors possess sufficient rationality, the performance comovement formed by funds imitating each other's holdings will weaken investors' learning ability and reduce their performance. Based on this idea, this paper constructs a multi-period game model that includes heterogeneous fund abilities and rational investors, starting from fund managers' trading behavior under the assumption of investor rationality. In the model, low-ability fund managers may adopt imitation strategies, following the positions of high-ability funds, thereby generating holding concentration and performance comovement. Although fund investors can rationally learn from funds' historical performance and infer fund ability, such imitation-induced performance comovement dilutes the information content embedded in historical performance. This leads to a mismatch between fund capital flows and fund ability, ultimately creating a performance gap between funds and investors. To test the above logic, this paper uses daily historical performance data and quarterly holdings data of actively managed Chinese funds from 2007 to 2022 from CSMAR, and constructs performance-comovement networks and portfolio-holdings networks based on Jaccard distance between funds. Based on funds' positions in the holding network and performance comovement network, combined with fund capital flow and investor performance data, the paper finds: First, the holding network between funds creates performance comovement, and for funds with lower ability, the performance comovement caused by holding imitation is more intense when the market transitions to a stable period. Second, the performance comovement formed through the holdings network reduces fund investors' learning ability, manifested as a diminished convexity in the flow-performance relationship. Finally, due to the weakening of investors' learning ability, low-ability funds obtain more future capital inflows through performance comovement, causing a mismatch between fund capital flows and ability, ultimately widening the performance gap between funds and investors. This evidence effectively supports the paper's argument. In addition, the paper also conducts an in-depth analysis of the heterogeneous characteristics and transmission mechanisms of performance comovement from the perspectives of risk-return characteristics, network diffusion effects, and sources of holding concentration. The potential marginal contributions of this paper are: First, in terms of research perspective, this paper starts from fund trading on the market supply side and provides a new explanation for the fund-investor performance gap, providing a complementary micro-mechanism explanation to existing research. Second, asset return comovement is a core issue in securities portfolio theory, but existing research mainly focuses on stock and bond markets in developed countries such as Europe and the United States. This paper supplements the understanding of asset return comovement from a fund perspective. Third, this paper has important policy implications for strengthening financial regulation and building a high-quality capital market.
Antón, M. and C. Polk, 2014. “Connected Stocks”, The Journal of Finance, 69(3), pp.1099~1127.
[10]
Barber B M, X Huang, and T. Odean, 2016. “Which Factors Matter to Investors? Evidence From Mutual Fund Flows”, The Review of Financial Studies, 29(10), pp.2600~2642.
[11]
Barberis, N., A. Shleifer, and J. Wurgler, 2005. “Comovement”, Journal of Financial Economics, 75(2), pp.283~317.
[12]
Ben-David, I., J. Li, A. Rossi, and Y. Song, 2022. “What Do Mutual Fund Investors Really Care About?”, The Review of Financial Studies, 35(4), pp.1723~1774.
[13]
Berk, J.B. and R.C. Green, 2004. “Mutual Fund Flows and Performance in Rational Markets”, Journal of Political Economy, 112(6), pp.1269~1295.
[14]
Berk, J.B. and J.H. Van Binsbergen, 2016. “Assessing Asset Pricing Models Using Revealed Preference”, Journal of Financial Economics, 119(1), pp.1~23.
[15]
Chen, H., V. Singal, and R.F. Whitelaw, 2016. “Comovement Revisited”, Journal of Financial Economics, 121(3), pp.624~644.
[16]
Fricke, D. and H. Wilke, 2023. “Connected Funds”, The Review of Financial Studies, 36(11), pp.4546~4587.
[17]
Friesen, G.C. and T.R.A. Sapp, 2007. “Mutual Fund Flows and Investor Returns: An Empirical Examination of Fund Investor Timing Ability”, Journal of Banking & Finance, 31(9), pp.2796~2816.
[18]
Irvine, P., J.H. (John) Kim, and J. Ren, 2024. “The Beta Anomaly and Mutual Fund Performance”, Management Science, 70(1), pp.143~163.
[19]
Song Y., 2020. “The Mismatch Between Mutual Fund Scale and Skill”, The Journal of Finance, 75(5), pp.2555~2589.