Mutual fund herding and return comovement in Chinese equities

Caglayan, Mustafa; Hu, Yu; Xue, Wenjun

doi:10.1016/j.pacfin.2021.101599

Cited by 18 publications

(4 citation statements)

References 47 publications

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“…Similarly, Bhattacharya and Sonaer (2018) and found relationship between herding and returns earned in mutual fund. Also, in China, Caglayan et al (2021) found mutual fund herding to have stronger effect on return co-movement. Drakos and Ballis (2020) found herding in cryptocurrency markets.…”

Section: Literature Reviewmentioning

confidence: 97%

Herding to Market Consensus: An Empirical Investigation of its Presence in Nepal Stock Exchange

Rijal

2022

J. Econ. Concerns

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The study examines herd formation in Nepalese equity market. The study uses linear regression models including quadratic variant to model the relationship between return dispersion of individual scrips and aggregate market return for finding the herding behavior. The study does not find presence of herding. On the contrary, when presence of herding is tested for bearish and bullish trend, herd formation is detected in bullish trend. The study supports the notion that Nepalese equity market is prone to behavioral biases leading to pricing inefficiencies. The findings of the study can be helpful to make policies to protect retail investors from downside risk arising from these behavioral biases.

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Section: Literature Reviewmentioning

confidence: 97%

Herding to Market Consensus: An Empirical Investigation of its Presence in Nepal Stock Exchange

Rijal

2022

J. Econ. Concerns

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“…The model measures herding behavior by comparing the actual shares of investors buying and selling decisions over the same period with the expected values under the independent trading assumption. For example, Caglayan et al (2021) explored the herding of mutual funds in China using the LSV model and found that the herding effect of mutual funds significantly increases the convergence of return volatility across stocks. Guo et al (2020) used the model to conduct an empirical study and found an intentional herding effect among institutional investors in the US.…”

Section: Related Studies On Herding Behavior Measurement Models (I) D...mentioning

confidence: 99%

The influence of the social networks of fund managers on the herding behavior of SIFs in China

Wang

2023

Humanit Soc Sci Commun

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This paper explores the influence of fund managers’ social networks on the herding behavior of securities investment funds (SIFs) in China. Using the data of Chinese SIFs in recent years as a sample, it adopts the CSAD model to measure SIFs’ herding behavior. Combining indicators such as social network centrality, constraint, and size, it constructs a regression model of the influence of fund managers’ social networks on the herding behavior of SIFs and conducts an empirical study. The heterogeneity of this influence is investigated in four dimensions, degree of herding behavior, gender, diploma, and region. The results show that: (i) The larger the social network size of fund managers, the lower the degree of herding behavior of fund managers, but the effect of the network constraint on the degree of herding behavior is not significant. (ii) When the degree of herding behavior of SIFs is high, the effect of network size on herding behavior is heterogeneous. (iii) The relationship between the social network of male fund managers and the degree of herding behavior is more significant than that of female fund managers, as network size is significant for male fund managers, while the regression results of social network centrality, network constraint, network size, and the degree of herding behavior of female fund managers are all not significant. (iv) The social network of the fund manager with a master’s degree has a significant impact on the herding behavior of SIFs, which is not significant in fund managers with Ph.D.

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“…Mutual fund herding Hung et al (2010) revealed that mutual funds in the Taiwan stock market exhibit herding. Caglayan et al (2021) found that mutual fund herding considerably reduces return comovement in Chinese equities. revealed that Wang et al (2021) documented that herding is evident in all types of funds other than income funds, and that non-fundamental factors primarily influence herding behavior.…”

Section: Effect Of Institutional Herding On the Stock Pricementioning

confidence: 99%

Behavioral Biases and their Influence on Investment Decision-Making: A Systematic Literature Review and Future Research Agenda

Paúl¹,

Natarajan²

2023

J. of Law and Sust. Develop.

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Objective: This study aims to systematically review the existing literature published over the past 42 years on behavioral biases in investment decision-making. Therefore, the current study concentrates on two biases: overconfidence and herding bias of individual and institutional investors. Theoretical framework: Kahneman and Tversky (1979) formulated prospect theory and concluded that investors make decisions based on possible gains and losses, rather than on final outcomes. Therefore, this study used this theory to understand the influence of behavioral biases on investment decisions. Methods: This study used a systematic literature review (SLR) and 109 selected articles for review published between 1980 and 2022. Most importantly, this study conducted a content analysis of behavioral biases in investment decision-making. Result and Conclusion: The volume of research on behavioral bias has increased over the past two decades. The existing literature shows limited research on institutional investors’ overconfidence bias, limited studies on individual herding behavior, ineffective direct measures of overconfidence, the majority of research showed that institutional investors are more inclined to herd than individual investors, and the maximum number of studies showed that individual investors are more susceptible to overconfidence bias than institutional investors. Implications of the research: This study highlights the influence of behavioral biases in investment decision-making and suggests a future research agenda for future researchers, as well as helpful to investment advisors enabling them to manage biases and select purposeful stocks for their clientele. Originality/value: This study delineates the behavioral biases of individual and institutional investors. This first study encompasses relevant studies conducted between 1980 and 2022 and lucidly summarizes the behavioral biases, including the ‘overconfidence’ and ‘herding’ tendencies of these different kinds of investors.

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Mutual fund herding and return comovement in Chinese equities

Cited by 18 publications

References 47 publications

Herding to Market Consensus: An Empirical Investigation of its Presence in Nepal Stock Exchange

Herding to Market Consensus: An Empirical Investigation of its Presence in Nepal Stock Exchange

The influence of the social networks of fund managers on the herding behavior of SIFs in China

Behavioral Biases and their Influence on Investment Decision-Making: A Systematic Literature Review and Future Research Agenda

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