How Changes in Expectations of Earnings Affect the Associations of Earnings Overstatements and Audit Effort with Audit Risk and Market Price*
Bibliographic record
Abstract
ABSTRACT In this study, we provide theoretical guidance for both analytical research and empirical research by considering how changing expectations of earnings affect a dishonest manager's strategy to overstate earnings and an auditor's strategy to exert effort in a two‐period setting. We expect our study's insights on changing economic conditions to help shape future research. We model the manager type as either honest or dishonest, which allows us to differentiate audit risk from audit effort. The key takeaways for future research are the insights on how changes in payoffs and expected earnings affect the associations that involve earnings overstatements and audit effort with audit risk and market price. For instance, researchers typically assume audit effort and audit risk are negatively associated, but we find the association can be positive when, for example, the auditor chooses a period 2 strategy based on the changes in period 1 game parameters. The results of our study provide two additional key insights on the design of future empirical tests. First, by dichotomizing, we show the importance of estimating the intercept in the market pricing equation when studying earnings quality, because market price also adjusts for expected bias through changes in the intercept. Second, our multiperiod setting demonstrates that the effects from a change in the manager's or auditor's incentives in period 1 may reverse in period 2. Empirical studies typically examine the contemporaneous effects of these changes on market price and/or audit risk but fail to identify the cross‐temporal effects we document in our study.
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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 | 0.006 | 0.060 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".