The Accrual Volatility Anomaly
Bibliographic record
Abstract
We find that quarterly cash flow shocks are more likely to be offset by contemporaneous accruals than to be reported as earnings. We examine the pricing implications of a consistent deviation of earnings from cash flow. Measuring the consistent deviation by accrual volatility, we find a strong and long-lasting negative association between accrual volatility and future stock returns. In decile portfolios that rank accrual volatility, a hedge portfolio that goes long in the lowest decile and short in the highest decile generates an annual, risk-adjusted return in the order of 10% from one-month to five-year horizon. These results are robust to common risk factors and return-informative variables, extend to both operating accruals and discretionary accruals, are distinct from the accrual anomaly, and are not subsumed by transaction costs and short-sale constraints. In addition, an accrual-volatility mimicking portfolio provides additional explanatory power to returns on the Fama-French 25 size/book to market portfolio. The accrual volatility effect is consistent with the information uncertainty effect where higher historical information uncertainty leads to lower future returns, and is also consistent with the earnings fixation hypothesis in that investors overprice the transitory accruals component of earnings in high accrual volatility stocks.
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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 | 0.001 | 0.018 |
| 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) | 0.006 | 0.001 |
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".