Issue Information - Pricing

2016 ◽  
Vol 37 (1) ◽  
pp. 3-3
Keyword(s):  
Information Pricing

Information Pricing: The Evidence From Equity Mutual Funds

1996 ◽  
Vol 31 (2) ◽  
pp. 365-380 ◽  
Author(s):  
Conrad S. Ciccotello ◽  
C. Terry Grant
Keyword(s):  
Mutual Funds ◽  
Equity Mutual Funds ◽  
Information Pricing

scholarly journals Business Link information pricing policies – the way ahead

1996 ◽  
Vol 13 (4) ◽  
pp. 251-257
Author(s):  
Maria E. Burke ◽  
Joanne Rigby
Keyword(s):  
Pricing Policies ◽  
Link Information ◽  
The Way ◽  
Information Pricing

scholarly journals Information pricing based on trusted system

2018 ◽  
Author(s):  
Zehua Liu ◽  
Nan Zhang ◽  
Hongfeng Han
Keyword(s):  
Information Pricing

An application of the information-adjusted noise model to the Shenzhen stock market

2016 ◽  
Vol 12 (1) ◽  
pp. 71-91 ◽  
Author(s):  
Xiaoming Xu ◽  
Vikash Ramiah ◽  
Imad Moosa ◽  
Sinclair Davidson
Keyword(s):  
Stock Market ◽  
Noise Model ◽  
Chinese Stock Market ◽  
Content Type ◽  
Asset Pricing Model ◽  
Noise Trader ◽  
Pricing Errors ◽  
Trading Activities ◽  
The Relationship ◽  
Information Pricing

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.

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