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Modeling the Spatial-temporal Characteristics of Mutual Funds’ Herd Behavior

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Abstract

Herd behavior in financial markets often leads to unjustified macroscopic phenomena. However, despite existing studies on modeling herd behavior, how it varies across individual agents and over time remains unclear. We show that herd behavior in mutual fund companies can be understood from the functional networks representing interactions inferred from investment similarities. Specifically, in this paper, the spatial characteristics of herd behavior stand for the topology relationships of observations in networks. We analyze the collective dynamics of mutual fund investment from 2003 to 2019 in China using the language of network science and show that herding behavior accompanies this industry’s development but dwindles after the 2015 Chinese market crash. By integrating community detection analysis, we found an increased degree of coherence in the collective herding behavior of the system, even though the localization of herding behavior decreases for clusters of mutual fund companies when the systemic risk builds up. Further analysis showed that herding behavior impacts the payoff of individual fund companies differently across years. The spatial-temporal changes of herding behavior between mutual funds presented in this paper shed light on the debate of individual versus systemic risk and, thus, could interest regulators and investors.

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Acknowledgments

This work has been supported in part by National Natural Science Foundation of China (Grant Nos. 71873012, 11971504, 72001222), the Program for Innovation Research, the Disciplinary Funding and the Emerging Interdisciplinary Project of Central University of Finance and Economic. Shan Lu is the corresponding author of this paper. We thank referees for their help to improve the quality of the paper.

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Authors and Affiliations

  1. School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, 100081, China

    Rong Guan, Hongjia Chen & Shan Lu

Authors
  1. Rong Guan
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  2. Hongjia Chen
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  3. Shan Lu
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Corresponding author

Correspondence to Shan Lu.

Additional information

Rong Guan is an associate professor on statistics at Central University of Finance and Economics, China. She received her doctoral degree on management science and engineering from Beihang University in 2013. She specializes in statistical analysis of complex data, such as interval data and compositional data. She has published over twenty articles on the topic of complex data analysis. She has been a PI or Co-PI for several research programs supported by National Natural Science Funding of China. Her recent research interests focus on network data and its applications with financial market.

Hongjia Chen is currently a postgraduate of statistics and mathematics, Central University of Finance and Economics, China. Her main research interest is network science.

Shan Lu received her Ph.D. degree in statistics in 2019 and B.S. degree in industrial engineering in 2014 from Beihang University. She is currently an assistant professor at School of Statistics and Mathematics, Central University of Finance and Economics, Beijing China. Her main research interests are complex data analysis, machine learning and network science.

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Guan, R., Chen, H. & Lu, S. Modeling the Spatial-temporal Characteristics of Mutual Funds’ Herd Behavior. J. Syst. Sci. Syst. Eng. 30, 748–776 (2021). https://doi.org/10.1007/s11518-021-5514-4

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