Association Between Accounting Conservatism and Analysts’ Forecast Inefficiency*
Bibliographic record
Abstract
Abstract We find that analysts’ earnings forecasts do not fully impound the implications of accounting conservatism. Forecast optimism is negatively associated with the magnitude of beginning‐of‐year balance sheet reserves (BSR), which are associated with conservative accounting in prior years. However, this result vanishes once we allow for the negative association, documented in several prior studies, between BSR and Basu’s asymmetric timeliness measure of conservatism [Journal of Accounting and Economics 24 (1997) 3] . After controlling for this association, we find that forecasters’ under‐reaction to bad versus good news is negatively associated with the magnitude of BSR. We obtain similar results after allowing for the positive association between asymmetric timeliness and Khan and Watts’ C_Score [Journal of Accounting and Economics 48 (2009) 132] . Therefore, our results are consistent with a subtle form of inefficiency of forecasts with respect to accounting conservatism; that is, analysts do not fully appreciate that the earnings of companies with lower BSR or higher C_Scores are likely to be both: (i) lower relative to forecast; and (ii) more asymmetrically timely than the earnings of companies exhibiting higher BSR or lower C_Scores.
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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 | 0.005 | 0.056 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".