An application of the information-adjusted noise model to the Shenzhen stock market
International Journal of Managerial Finance
ISSN: 1743-9132
Article publication date: 1 February 2016
Abstract
Purpose
The purpose of this paper is to: first, test if information-adjusted noise model (IANM) can be applied in China; second, quantify noise trader risk, overreaction, underreaction and information pricing errors in that market; and third, explain the relationship between noise trader risk and return.
Design/methodology/approach
The authors use a behavioural asset pricing model (BAPM), CAPM, the information-adjusted noise model and model proposed by Ramiah and Davidson (2010).
Findings
The findings show that noise traders are active 99.7 per cent of the time on the Shenzhen A-share market. Furthermore, our results suggest that the Shenzhen market overreacts 41 per cent of the time, underreacts 18 per cent of the time and information pricing errors occur 40 per cent of the time.
Originality/value
Various methods have been applied to the Chinese stock market in an effort to measure noise trading activities and all of them failed to account for information arrival. Our study uses a superior and alternative model to detect noise trader risk, overreaction and underreaction in China.
Keywords
Acknowledgements
The authors wish to acknowledge the invaluable research assistance of Binesh Seetanah and Yilang Zhao in gathering the data. An earlier version of this paper was presented at the European Financial Management Association 2011 Annual Meetings and at the UNISA seminar series. The authors thank Petko Kalev, Douglas Foster, Dave Michayluk and the conference participants for their helpful comments. Any remaining errors, however, are of the authors’.
Citation
Xu, X., Ramiah, V., Moosa, I. and Davidson, S. (2016), "An application of the information-adjusted noise model to the Shenzhen stock market", International Journal of Managerial Finance, Vol. 12 No. 1, pp. 71-91. https://doi.org/10.1108/IJMF-01-2015-0010
Publisher
:Emerald Group Publishing Limited
Copyright © 2016, Emerald Group Publishing Limited