The association between management earnings forecasts, earnings management, and stock market valuation: Evidence from French IPOs
Bibliographic record
Abstract
This study investigates managers' motivations to engage in earnings management through purposeful interventions in the setting of discretionary accruals, in the context of initial public offerings (IPOs) in France. Firms issuing forecasts in their prospectuses are expected to differ from nonforecasters in the level of earnings management during the year following the public offering. Within the context of contracting theory, four research questions are addressed. First, are IPO firms issuing forecasts more inclined to manage earnings 1 year after an IPO compared to nonforecasting firms? Second, is a forecasting firm's level of earnings management conditioned by earnings-forecast deviation? Third, is earnings management by IPO forecasting firms affected by contractual and governance environments? Fourth, how do investors see through earnings management following IPO earnings forecasts, i.e., how do stock market participants value earnings components (i.e., nondiscretionary and discretionary accruals)? Our findings document that in the year following an IPO, the magnitude of earnings management is much higher for forecasters than for nonforecasters. Results also show that a firm's accrual behavior is affected by earnings-forecast deviation, but the relationship is moderated by contractual and governance constraints. Finally, it would appear that French investors do not adequately readjust the relationship between reported earnings and a firm's market value for the year in which earnings are subject to manipulations.
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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 | 0.002 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".