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Title: Aggregate accounting earnings and security returns : China evidence and the replication of US results
Authors: Du, Z
Tang, F
Zhang, WS
Issue Date: 2014
Source: China accounting and finance review (中國會計與財務硏究), June 2014, v. 16, no. 2, p. 107-127
Abstract: This paper examines the earnings-return association over long return intervals. The research design is built upon one important accounting intuition: as earnings are aggregated over longer intervals, the effect of earnings measurement error and the time lag between earnings recognition and market reaction slowly dwindles. Therefore, over time, we should observe an improving association between (aggregate) earnings and stock return. In this study, we first replicate the results of Easton, Harris, and Ohlson (1992) for the same period of 1968-1986 and find very similar results under refined correlation metrics. Second, we expand coverage to test US data from 1962 to 2011 and find that their prediction holds for the past 50 years in the US market. Third, our post-1992 China and US data generate the same pattern of rising earnings-return correlation as the return interval expands, despite China’s immature stock market. Further comparison indicates that the earnings-return correlation in China is lower than that in the US market in the same period of 1992-2011. Finally, to make our results comparable to the original ones in Easton, Harris, and Ohlson (1992), we also report the 1992-2011 results under the original metrics, such as R2 and concordant pair percentage; we still reach the same conclusions. Overall, the empirical results support Easton, Harris, and Ohlson’s theory in both the US market and the emerging China market, extending the external validity of their theory to the international capital market.
Keywords: Aggregation
Long intervals
Returns-earnings association
Journal: China accounting and finance review (中國會計與財務硏究) 
ISSN: 1029-807X
EISSN: 2307-3055
DOI: 10.7603/s40570-014-0009-z
Rights: © The Author(s) 2014. This article is published with open access by The Hong Kong Polytechnic University.
Open Access. This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/) which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
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