Real earnings management in the motion picture industry: strengthening the inferences from academic research
Bibliographic record
Abstract
Abstract The Gong, Young, and Zhou (GYZ) (Gong et al. 2023) paper examines potential earnings management by movie studio companies. Using a large sample of 3094 US-produced English-language movies released between 1997 and 2019, they find that movie studio companies, when faced with a below expected US box office revenue yield from their movies in a specific quarter, move up the release dates of movies with high expected revenues. This “move-up” release is an example of what the accounting research literature calls real earnings management. This commentary on GYZ (Gong et al. 2023) adds more structure to the decision-making context in which movie release dates are set, placing greater emphasis on the role of movie screening companies, which have the final say on the release dates for the movies they show on their screens. It also highlights the rich information setting that exists in the motion picture industry, which can be further exploited to probe the reliability of the earnings management findings reported by GYZ (Gong et al. 2023). This rich information includes security analyst reports and screen days available to quarter-end for each movie released. The commentary has relevance for other research in specific industries where the institutional domain has the potential to provide insight into real earnings management.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".