Aggregate Accounting Earnings and Security Returns: China Evidence and the Replication of US Results
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".