The Accrual Anomaly: Accrual Originations, Accrual Reversals, and Resolution of Uncertainty<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT We combine a fundamental property of accruals and a behavioral phenomenon to provide an explanation for the accrual anomaly. The fundamental property is accruals that originate must subsequently reverse. The behavioral phenomenon is individuals tend to underestimate the variance of noisy signals in various domains of decision making. We argue that originating accruals represents a noisier signal than reversing accruals because the uncertainty of whether originating accruals will eventually convert into cash is high, while there is no uncertainty regarding reversing accruals. If investors underestimate the variance of originating accruals but understand reversing accruals, then originating accruals will be mispriced while reversing accruals will not. To test this prediction, we first develop and empirically validate a novel method for ex ante detecting accrual originations and their reversals. Then we document that investors face increased uncertainty when accruals originate and decreased uncertainty when accruals reverse, and we provide evidence that only originating accruals are mispriced. We further demonstrate that our findings can be useful for improving the accrual‐based trading strategy by ex ante detecting and removing accrual reversals from extreme accrual portfolios. Overall, we provide a behavioral explanation for the accrual anomaly that is consistent with the mispricing of originating accruals only.
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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 | 0.003 | 0.041 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".