Investors' Interpretations of Imprecise Standards and Their Perceptions of Earnings Management by Reputable Companies
Bibliographic record
Abstract
ABSTRACT Standards with imprecise guidelines require interpretation by users. In this study we investigate how investors' perceptions of earnings management vary with their interpretations of imprecise standards and the type of company reputation. We design a quasi-experiment that exploits the role of the press as a “watchdog” of corporate activities to focus the attention of investors on the financial reporting practices of companies. The results show that both factors interact to influence investors' perceptions. Investors, whose interpretations of the imprecise standard are inconsistent with that of the company, are more likely to suspect earnings management when the company has a financial rather than non-financial reputation. Investors in the inconsistent/financial reputation condition are also more likely to sell their investments than those in the inconsistent/non-financial reputation condition. The type of reputation does not show a significant effect on investors' perceptions when investors' interpretations are consistent with that of the company. JEL Classifications: M40; M41.
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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 | 0.010 | 0.060 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".