Accruals Quality, Stock Return Seasonality, and the Cost of Equity Capital: International Evidence
Bibliographic record
Abstract
ABSTRACT Mashruwala and Mashruwala (2011) argue that inconsistent earlier findings regarding whether accruals quality (AQ) is priced in equity markets (Core, Guay, and Verdi 2008; Kim and Qi 2010) may be explained by seasonality in returns deriving from tax‐loss selling. Finding no evidence of annual AQ premia for U.S. firms, Mashruwala and Mashruwala report that significant monthly premia concentrate in January, with the remainder of the year demonstrating negative or insignificant returns to AQ and attribute this strong seasonality to tax‐loss selling by investors, rather than information risk. However, the end of the tax year for U.S. investors coincides with the calendar year and the financial year for the majority of firms, which may suggest alternative explanations for seasonal variation in returns. We extend Mashruwala and Mashruwala's study, using an international sample including countries where incentives for tax‐loss selling exist, but in which the standard tax and financial years differ (Japan and the United Kingdom), and where the tax and financial years conclude in a month other than December (Australia), as well as employing a longer U.S. sample. We find some evidence of an AQ premium in the United States, which although dominated by January returns, remains significant annually. However, these findings are sensitive to the inclusion of low price stocks and the choice of asset pricing test. In Japan, the United Kingdom, and Australia we document consistent evidence that an AQ premium exists on average throughout the year, and in samples excluding the first month of the tax year. The sensitivity of our U.S. results to the January period may reflect the conflation of numerous seasonal influences on returns, not all of which necessarily reflect mispricing.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".