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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".