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Public information content and market information efficiency: A comparison between China and the U.S

Bin Liu, XiangYang Xia and Wen Xiao

China Economic Review, 2020, vol. 60, issue C

Abstract: Based on the concept that the presence of public information shocks can increase the synchronicity of stock returns, we develop an extended version of the mixture of distribution hypothesis model (MDH) along the lines of Andersen (1996) to measure the public information content embedded in the stock prices. The results show that although we add an additional information structure, the significance of the MDH is still higher than that of Andersen's (1996) model, which indicates that our setting is reasonable. We measure and compare the market effectiveness between China and the United States from 1998 to 2016 by using the public information content calculated by the developed MDH model. The calculation results show that the average public information in the Chinese market is 0.336, which means the market efficiency is slightly lower than 0.317 in the United States.11Based on the theoretical context of this article, the public information content is negatively related to market efficiency, so “0.336 is lower than 0.317”. This setting also applies to Fig. 1 and 2, where a large number represents low market efficiency and vice versa. Further analysis shows that market efficiency has increased steadily between 2002 and 2014 and has smaller volatility in the Chinese market. The slow increasing in market efficiency indicate that the securities laws enacted by China in the 1990s and 2000s aimed at promoting the development of the stock market are effective.

Keywords: PMDH; CU-GMM; Market efficiency; Return synchronicity (search for similar items in EconPapers)
JEL-codes: C51 C52 G12 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chieco:v:60:y:2020:i:c:s1043951x2030002x

DOI: 10.1016/j.chieco.2020.101405

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China Economic Review is currently edited by B.M. Fleisher, K. X. D. Huang, M.E. Lovely, Y. Wen, X. Zhang and X. Zhu

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