Accrual-Based Earnings Management and the COVID-19 Pandemic
Bibliographic record
Abstract
In this study, we document the accrual-based earnings management of Old Economy firms and New Economy firms (firms in the technology industry) and loss-making firms (firms with negative earnings in the pre-pandemic year, 2019) and profit firms in each economy, respectively, before, during, and in the recovery year of the COVID-19 pandemic. Using both univariate and difference-in-difference regression analyses, we find that old and new economy firms adopt different accrual-based earnings management, and Old Economy Loss firms changed their accrual-based earnings management the most during and in the recovery of the pandemic. During the 2020 pandemic, Old Economy Loss reported the lowest amount of accrual-based discretionary accruals. This suggests that Old Economy Loss firms are engaged in the most conservative approach to reporting their earnings, consistent with the big bath proposition. In the recovery year of the pandemic, 2021, we find that accrual-based earnings management reversed, with the old economy losing firms reporting the highest amounts of discretionary accruals. However, we do not find that the explanatory power of earnings on the variance of stock prices for the old economy loss firms is affected by their discretionary change in accounting accruals.
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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 | 0.006 |
| 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) | 0.002 | 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".