Summary:
Enhancing investors' welfare is a long-standing imperative for promoting the high-quality development of the mutual fund industry. With the rapid advance of digital technologies, fund distribution channels are undergoing a profound digital transformation. Yet, mutual funds differ fundamentally from ordinary consumer goods: they entail substantial cognitive demands and uncertain returns, making investor education and guidance by distribution platforms essential. This paper studies a major online fund distributor's introduction of a “Recommended Funds” advisory service. The service selects a small set of high-quality funds from a universe of thousands to reduce investors' search costs and provides continuous monitoring, interpretation, and follow-up guidance aimed at facilitating long-term investment. Although these features are designed from an investment advisory perspective to help investors select funds and allocate assets more systematically, advisory services inherently exhibit negative network externalities. On a large-traffic digital platform, uniform advisory signals can unintentionally generate diminishing returns to scale and reduced managerial efficiency. We examine this issue using an entropy balancing procedure to assign weights to treated (recommended) and matched control funds to ensure comparability on observable fund characteristics and prior performance. Taking July 2020, the launch date of the recommended-fund list, as the treatment event, we implement a difference-in-differences design to compare net flows and fund performance between recommended funds and matched controls over the two years before and after the service rollout. Our main findings are as follows. First, the introduction of the recommendation list leads to a sharp increase in flows into recommended funds. On average, recommended funds experience quarterly asset-growth rates 11.3% higher than matched funds, indicating that unified digital sales guidance exerts a substantial effect on investor fund choices and persistently attracts inflows. Second, recommended funds exhibit significantly lower subsequent abnormal performance. Their quarterly Jensen alphas decline by 1.2% relative to matched funds. Further, when we sort recommended funds by the magnitude of inflows, the performance deterioration is concentrated among funds experiencing larger inflows. This pattern confirms that investor crowding triggered by the advisory service is the primary mechanism driving performance decline. Third, we study the underlying channels from the perspective of fund managers. Larger assets under management can erode performance through multiple channels. We examine adjustments in portfolio rebalancing ability, managerial activeness, and trading impact costs. The evidence shows that reduced short-term rebalancing flexibility and increased passive holdings are the dominant channels through which performance deteriorates for recommended funds. From an academic perspective, this paper uses the mutual fund distribution setting to uncover a distinctive challenge in the digitalization of investment advisory services. The classic advisory literature primarily focuses on how agency conflicts and behavioral biases affect client outcomes (e.g., Hackethal et al., 2012; Hoechles et al., 2017; Linnainmaa et al., 2021). A growing strand of literature examines how technology-enabled advisory tools can improve investor welfare (e.g., D’Acunto et al., 2019; Hao et al., 2022; Rossi & Utkus, 2024; Bianchi & Brière, 2024). However, it has not recognized a key distinction between advisory services and ordinary products: investment advice exhibits negative network externalities. Unlike standard goods and services, the utility of an advisory service declines as more clients follow the same recommendation. Consequently, uniform investment guidance delivered through large digital platforms can impair the performance and value creation of the advised products, representing an inefficient form of advisory digitalization. This paper also provides causal evidence on how increases in fund scale reduce performance. A large strand of literature studies the relationship between fund size and returns. Chen et al. (2004) first documented a negative relation between fund performance and lagged fund size. Subsequent studies have largely followed the same setting and employed more sophisticated empirical techniques, yet they have not fully addressed endogeneity concerns (Pástor et al., 2015; Zhu, 2018). Reuter & Zitzewitz (2021) exploited a plausibly exogenous setting but failed to find consistent negative scale effects. Leveraging the release of a recommended-fund list by a major distribution platform as an exogenous shock, this paper provides clean evidence that scale expansion not only depresses fund performance but also undermines the fund's value creation. From a practical standpoint, the results deepen our understanding of individual investors' fund selection behavior and offer important implications for regulating fund distribution on the digital platform. Financial regulation in the digital era must guard not only against the moral hazard of distributors but also against the unintended consequences of well-intentioned advisory practices. On large-traffic digital platforms, centralized advisory guidance can accelerate diminishing returns to scale, ultimately lowering investors' perceived service quality and damaging the reputation of financial institutions.
胡聪慧, 赵佳文, 彭锐, 王琳. 数字时代的投顾服务与基金价值创造——基于网络外部性的视角[J]. 金融研究, 2026, 547(1): 189-206.
HU Conghui, ZHAO Jiawen, PENG Rui, WANG Lin. Investment Advisory Services and Fund Value Creation in the Digital Era: A Network Externalities Perspective. Journal of Financial Research, 2026, 547(1): 189-206.
Barras, L., P. Gagliardini and O. Scaillet, 2022, “Skill, Scale and Value Creation in the Mutual Fund Industry”, Journal of Finance, 77(1), pp.601～638.
[10]
Bianchi, M. and M. Brière, 2024, “Human-Robot Interactions in Investment Decisions”,Management Science, Forthcoming.
[11]
Ben-David, I., J. Li, A. Rossi, et al, 2022, “What Do Mutual Fund Investors Really Care About？”,The Review of Financial Studies, 35(4), pp.1723～1774.
[12]
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.
[13]
Berk, J. B., and J. H. Van Binsbergen, 2015 “Measuring Skill in the Mutual Fund Industry”, Journal of Financial Economics,118(1),pp. 1～20.
[14]
Chen, J., H. Hong, M. Huang, et al, 2004, “Does Fund Size Erode Mutual Fund Performance？ The Role of Liquidity and Organization”,American Economic Review, 94(5), pp.1276～1302.
[15]
Cremers, K. J. M. and A. Petajisto, 2009, “How Active Is Your Fund Manager？ A New Measure That Predicts Performance”,The Review of Financial Studies, 22(9), pp.3329～3365.
[16]
D’Acunto, F., N. Prabhala and A. G. Rossi, 2019, “The Promises and Pitfalls of Robo-Advising”,Review of Financial Studies, 32(5), pp.1983～2020.
[17]
Hackethal, A., M. Haliassos and T. Jappelli, 2012, “Financial Advisors: A Case of Babysitters？”, Journal of Banking ＆ Finance, 36(2), pp.509～524.
[18]
Hao, R., C. Hu, X. Xu and Y. Zhang, 2022, “Beyond Performance: The Financial Education Role of Robo-Advising”,Available at SSRN 4230191.
[19]
Hoechle, D., S. Ruenzi, N. Schaub and M. Schmid, 2017, “The Impact of Financial Advice on Trade Performance and Behavioral Biases”, Review of Finance, 21(2), pp.871～910.
[20]
Hong, C. Y., X. Lu, and J. Pan, 2025, “Fintech Platforms and Mutual Fund Distribution”,Management Science, 71(1).
[21]
Kacperczyk, M., C. Sialm and L. Zheng, 2008, “Unobserved Actions of Mutual Funds”,The Review of Financial Studies, 21(6), pp.2379～2416.
[22]
Kaniel, R. and R. Parham, 2017, “WSJ Category Kings: The Impact of Media Attention on Consumer and Mutual Fund Investment Decisions”,Journal of Financial Economics, 123(2), pp.337～356.
[23]
Katz, M. L. and C. Shapiro, 1985, “Network Externalities, Competition and Compatibility”,The American Economic Review, 75(3), pp.424～440.
[24]
Liebowitz, S. J. and S. E. Margolis, 1994, “Network Externality: An Uncommon Tragedy”,Journal of Economic Perspectives, 8(2), pp.133～150.
[25]
Linnainmaa, J. T., B. T. Melzer and A. Previtero, 2021, “The Misguided Beliefs of Financial Advisors”,The Journal of Finance, 76(2), pp.587～621.
[26]
Pástor, L'., R. F. Stambaugh and L. A. Taylor, 2020, “Fund Tradeoffs”,Journal of Financial Economics, 138(3), pp.614～634.
[27]
Pástor, L'., R. F. Stambaugh and L. A. Taylor, 2015, “Scale and Skill in Active Management”,Journal of Financial Economics, 116(1), pp.23～45.
[28]
Reuter, J. and E. Zitzewitz, 2006, “Do Ads Influence Editors？ Advertising and Bias in the Financial Media”,The Quarterly Journal of Economics, 121(1), pp.197～227.
[29]
Reuter, J. and E. Zitzewitz, 2021, “How Much Does Size Erode Mutual Fund Performance？ A Regression Discontinuity Approach”, Review of Finance, 25(5), pp.1395～1432.
[30]
Rohlfs, J, 1974, “A Theory of Interdependent Demand for a Communications Service”,The Bell Journal of Economics and Management Science, pp.16～37.
[31]
Rossi, A. G. and S. Utkus, 2024, “The Diversification and Welfare Effects of Robo-Advising”,Journal of Financial Economics, 157, pp.103869.
[32]
Roussanov, N., H. Ruan and Y. Wei, 2021, “Marketing Mutual Funds”, The Review of Financial Studies, 34(6), pp.3045～3094.
[33]
Sirri, E. R. and P. Tufano, 1998, “Costly Search and Mutual Fund Flows” ,The Journal of Finance, 53(5), pp.1589～1622.
[34]
Zhu, M, 2018, “Informative Fund Size, Managerial Skill and Investor Rationality”, Journal of Financial Economics, 130(1), pp.114～134.
