Fund Flows and Stock Returns: Uncovering the Transmission Mechanisms

Authors

  • Sinong Xiao

DOI:

https://doi.org/10.54097/rqtd7103

Keywords:

Fund Flows, Stock Returns, Investor Sentiment, Market Liquidity, Stock Price Non-synchronicity.

Abstract

Fund flows, directly reflecting capital movements in financial markets, are central to asset pricing research on their relationship with stock returns. Existing studies have predominantly examined either their direct price pressure effect or indirect influences from isolated perspectives, such as behavioral finance or liquidity, lacking a systematic integration of multiple transmission channels. To address this gap, this paper develops a comprehensive "quadruple parallel mediation model" to fully unveil the complex mechanisms through which fund flows affect stock returns. Based on China's A-share market data from 2008 to 2024, this study employs a standardized empirical approach. First, a standardized fund flow measure is constructed following Bennett and Sias [1]. Second, intermediary variables: investor sentiment, market illiquidity, and stock price non-synchronicity are developed using text analysis and market microstructure data. Finally, a parallel multiple mediation model is established to simultaneously test both the direct effect of fund flows on stock returns and their transmission through three key indirect channels. The empirical findings reveal that: (1) Fund flows exhibit a robust positive contemporaneous total effect on stock returns. (2) Fund flows affect returns simultaneously through three parallel channels: boosting investor sentiment, inducing a liquidity premium, and promoting information incorporation. These multiple transmission mechanisms coexist. (3) The mechanisms show significant market state heterogeneity, with their modes of action dynamically changing across bull and bear markets. This study transcends single-theory explanations by integrating price pressure, investor sentiment, liquidity premium, and information hypotheses into one analytical framework, offering comprehensive empirical evidence for investors and regulators.

Downloads

Download data is not yet available.

References

[1] Bennett J A, Sias R W. Can money flows predict stock returns? [J]. The Journal of Financial Analysts, 2001, 57 (6): 53 - 65.

[2] Lee B S, Paek M, Ha Y, et al. The dynamics of market volatility, market return, and equity fund flow: international evidence [J]. International Review of Economics and Finance, 2015, 35 (1): 214 - 227.

[3] Baker M, Wurgler J. Investor sentiment and the cross-section of stock returns [J]. The Journal of Finance, 2006, 61 (4): 1645 - 1680.

[4] Acharya V, Pedersen L H. Asset pricing with liquidity risk [J]. Journal of Financial Economics, 2005, 77 (2): 375 - 410.

[5] Grossman S, Stiglitz J. On the impossibility of informationally efficient markets [J]. American Economic Review, 1980, 70 (3): 393 - 408.

[6] Campbell J Y, Ramadorai T, Schwartz A. Caught on tape: Institutional trading, stock returns, and earnings announcements [J]. Journal of Financial Economics, 2009, 92 (1): 66 - 91.

[7] Hou L, Xie C, Dai J, et al. Application analysis of capital flow indicators in A-share market [J]. Securities Market Herald, 2010 (11): 72 - 77.

[8] Yang M. ETF fund flows, market returns and investor sentiment: Evidence from A-share market [J]. Journal of Financial Research, 2013 (4): 156 - 169.

[9] Baker M, Wurgler J. Investor sentiment and the cross-section of stock returns [J]. The Journal of Finance, 2006, 61 (4): 1645 - 1680.

[10] Wang C. Impact of investor sentiment on stock market returns and volatility: Empirical study based on net inflows of open-end equity funds [J]. Chinese Journal of Management Science, 2014, 22 (9): 49 - 56.

[11] Wan Y, Zhao Y, Zhao L. Investor sentiment and securities investment returns: Text analysis of online social media based on machine learning [J]. Financial Markets Study, 2024 (5): 65 - 77.

[12] Admati A R, Pfleiderer P. A theory of intraday patterns: Volume and price variability [J]. The Review of Financial Studies, 1988, 1 (1): 3 - 40.

[13] Pan N, et al. Correlation between stock liquidity and asset liquidity: Theory and empirical analysis [J]. Systems Engineering-Theory & Practice, 2011, 31 (4): 710 - 720.

[14] Ma F, He X, Lu X. Modeling and forecasting of volatility in China's financial market: Based on a new decomposition method [J]. Systems Engineering-Theory & Practice, 2023, 43 (10): 2827 - 2845.

[15] Grossman S, Stiglitz J. On the impossibility of informationally efficient markets [J]. American Economic Review, 1980, 70 (3): 393 - 408.

[16] Roll R. R-squared [J]. Journal of Finance, 1988, 43 (3): 541 - 566.

[17] Shen B, Ran G, Sheng J. Research on informed trading behavior based on institutional investors' fund flows [J]. Issues in Financial Economy, 2012 (4): 10 - 17.

[18] Guo W, Lu L, Zhong Y. How does investor sentiment affect stock market bubbles? [J]. Financial Regulation Research, 2024 (1): 61 - 78.

[19] Wen Z, Ye B. Analyses of mediating effects: The development of methods and models [J]. Advances in Psychological Science, 2014, 22 (5): 731 - 745.

[20] Amihud Y. Illiquidity and stock returns: Cross-section and time-series effects [J]. Journal of Financial Markets, 2002, 5 (1): 31 - 56.

Downloads

Published

08-06-2026

How to Cite

Xiao, S. (2026). Fund Flows and Stock Returns: Uncovering the Transmission Mechanisms. Highlights in Business, Economics and Management, 67, 26-43. https://doi.org/10.54097/rqtd7103