The Relation between Aggregate Earnings and Security Returns over Long Intervals*
Bibliographic record
Abstract
Abstract This paper provides a theoretical explanation and consistent empirical evidence for the increase in the contemporaneous correlation between returns and aggregate earnings as the return interval is lengthened. Consistent with intuition and with Easton, Harris, and Ohlson 1992, the analysis shows that aggregation over time renders the lag in accounting recognition relatively less important and thus improves the returns‐earnings R2. Interestingly, the analysis also reveals that aggregating earnings over longer periods increases the positive covariance between aggregate earnings and the accounting lag, which may further increase the R2. This positive covariance can lead to an earnings coefficient greater than one over some range of aggregation, which is consistent with the findings of Easton et al. that over the 10‐year interval the returns‐earnings regression slope coefficient is greater than one (1.7). The empirical results highlight the fact that the slope coefficient, which is greater than one and increasing with the interval, accounts for much of the increment to the returns‐earnings R2. In fact, constraining the slope coefficient to be one results in an R2 of 11 percent for the 10‐year interval, which is considerably lower than the R2 of 47 percent when the regression is unconstrained. Hence, the positive covariance between current earnings and the accounting lag, rather than the diminishing effect of the accounting lag, appears to be the dominant explanation for the observed high R2 over long intervals.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".