Abstract

 


 



Positive Trading Effects and Herding Behavior in Asian Markets: Evidence from Mutual Funds


Sandra Hsieh


National Taichung Institute of Technology

Tzu-Yi Yang


Hwa Hsia Institute of Technology

Yang Yu Tai


Chung Yuan Christian University

2010

The International Journal of Business and Finance Research, Vol. 4, No. 2, pp. 177-188, 2010

Abstract:     
Many studies on mutual funds have demonstrated the existence of herding behavior and positive feedback trading. However, most research has not examined the characteristics of herding behavior, but simply attempted to determine if herding behavior exists. These studies fail to probe into the actual causes behind herding behavior. The current study fills this gap in the literature. The study is based on the herding definition of Bikhchndani and Sarma (2001) and examines Asian country mutual funds with a six-year sample period. We examine if there are “Buy high, sell low”, “Buy previous winners, sell previous losers”, “positive feedback trading,” and “herding behavior” in global mutual funds. We also explore the possible factors behind these phenomena.

Number of Pages in PDF File: 12

Keywords: Mutual fund, positive feedback effect, herding, financial crisis, behavioral finance

JEL Classification: C30, G01, G15

Accepted Paper Series


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Date posted: September 9, 2010  

Suggested Citation

Hsieh, Sandra, Yang, Tzu-Yi and Tai, Yang Yu, Positive Trading Effects and Herding Behavior in Asian Markets: Evidence from Mutual Funds (2010). The International Journal of Business and Finance Research, Vol. 4, No. 2, pp. 177-188, 2010. Available at SSRN: http://ssrn.com/abstract=1667139

Contact Information

Sandra Hsieh (Contact Author)
National Taichung Institute of Technology ( email )
Department of Finance
National Taichung Institute of Technology
Taiwan
Tzu-Yi Yang
Hwa Hsia Institute of Technology ( email )
Zhonghe, Taipei County
Taiwan
Yang Yu Tai
Chung Yuan Christian University ( email )
22 Pu-Jen, Pu-chung Li
Chung-Li, 32023
Taiwan
Feedback to SSRN (Beta)


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