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Network externalities in mutual funds

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  • Blocher, Jesse

Abstract

The literature on mutual fund flows documents surprisingly large return effects given that mutual fund flows are uninformed (i.e., not related to fundamentals). I provide evidence that network externalities generate the necessary amplification mechanism to support these results. Network externalities are generated by mutual funds with common holdings and return-chasing investors. Economically, I show that the fund flow network externality is 32–92% as large as the typical explanatory effects (e.g., lagged flows). Network externalities generate a 1.5% quarterly excess return that reverses in the subsequent year, and are independent of style investing and robust to multiple specifications of holdings similarity.

Suggested Citation

  • Blocher, Jesse, 2016. "Network externalities in mutual funds," Journal of Financial Markets, Elsevier, vol. 30(C), pages 1-26.
  • Handle: RePEc:eee:finmar:v:30:y:2016:i:c:p:1-26
    DOI: 10.1016/j.finmar.2016.04.001
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    Cited by:

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    3. Xusen Cheng & Danya Huang & Jin Chen & Xiangsong Meng & Chengyao Li, 2019. "An Investigation on Factors Affecting Stock Valuation Using Text Mining for Automated Trading," Sustainability, MDPI, vol. 11(7), pages 1-17, April.
    4. Liyun Zhou & Chunpeng Yang, 2020. "Investor sentiment, investor crowded-trade behavior, and limited arbitrage in the cross section of stock returns," Empirical Economics, Springer, vol. 59(1), pages 437-460, July.
    5. Kosenko, Konstantin & Michelson, Noam, 2022. "It takes more than two to tango: Multiple bank lending, asset commonality and risk," Journal of Financial Stability, Elsevier, vol. 61(C).
    6. Martin Hodula & Zlatuše Komárková & Lukáš Pfeifer, 2021. "The relationship between capital and liquidity prudential instruments," Journal of Regulatory Economics, Springer, vol. 59(1), pages 47-70, February.
    7. Sophie Béreau & Nicolas Debarsy & Cyrille Dossougoin & Jean-Yves Gnabo, 2022. "Contagion in the Banking Industry: a Robust-to-Endogeneity Analysis," Working Papers halshs-03513049, HAL.
    8. Liyun Zhou & Chunpeng Yang, 2019. "Differences in the effects of seller-initiated versus buyer-initiated crowded trades in stock markets," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(4), pages 859-890, December.
    9. Tanja Artiga Gonzalez & Teodor Dyakov & Justus Inhoffen & Evert Wipplinger, 2021. "Crowding of International Mutual Funds," Discussion Papers of DIW Berlin 1937, DIW Berlin, German Institute for Economic Research.
    10. Gong, Xiao-Li & Liu, Jia, 2023. "Institutional investor information network, analyst forecasting and stock price crash risk," Research in International Business and Finance, Elsevier, vol. 65(C).
    11. Liu, Junbin & Liu, Xiaoxing & Shi, Guangping, 2019. "What influences portfolio contagion among open-end mutual funds?," Finance Research Letters, Elsevier, vol. 30(C), pages 145-152.
    12. Zhou, Liyun & Yang, Chunpeng, 2019. "Stochastic investor sentiment, crowdedness and deviation of asset prices from fundamentals," Economic Modelling, Elsevier, vol. 79(C), pages 130-140.
    13. Bing Zhu & René-Ojas Woltering, 2021. "Is fund performance driven by flows into connected funds? spillover effects in the mutual fund industry," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(3), pages 544-571, July.

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    More about this item

    JEL classification:

    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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