Accruals Quality, Stock Returns, and Macroeconomic Conditions
Bibliographic record
Abstract
ABSTRACT: This study examines whether and how earnings quality, measured as accruals quality (AQ), affects the cost of equity capital. Using two-stage cross-sectional regression tests, we find that the AQ risk factor is significantly priced, after controlling for low-priced stocks. This result is robust in tests using individual stocks, various portfolio formations, and different beta estimations. Furthermore, we show that AQ and its pricing effect systematically vary with business cycles and macroeconomic variables. In particular, this pricing effect is prominent in total AQ and innate AQ but not in discretionary AQ. The risk premium associated with AQ exists only in economic expansion but not in recession periods. Poorer AQ firms are more vulnerable to macroeconomic shocks. The risk premium and the dispersion of AQ are also related to future economic activity. Overall, our results suggest that AQ contributes to the cost of equity capital and that its pricing effect is associated with fundamental risk.
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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 | 0.013 |
| 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 | 0.000 |
| 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".