U.S. Oil Companies' Earnings Management in Response to Hurricanes Katrina and Rita
Bibliographic record
Abstract
This study examines earnings management by U.S.-based oil companies in the period immediately after the impact of hurricanes Katrina and Rita. We show that large petroleum refining firms - but not the smaller crude oil and gas production companies - recorded significant abnormal income-decreasing accruals in the fiscal quarter immediately after the impact of hurricanes Katrina and Rita (Q4 of 2005). In addition, we show that these results are driven by abnormal current accruals. Prior studies show that some firms respond to periods of heightened political scrutiny by recording abnormal income-decreasing accruals (e.g., Cahan, 1992; Han and Wang, 1998). Our results add to this stream of research by examining a political cost-increasing event that occurred after the passage of the Sarbanes-Oxley Act (SOX) of 2002. The results suggest that in the post-SOX period managers continue to engage in income-decreasing earnings management during periods of heightened political cost sensitivity, at least in the case of large petroleum refining firms.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".