Unequal Impact of Conditional Conservatism on Components Accruals: Evidence from French Capital Market
Bibliographic record
Abstract
Applied to the French context, this study examines the unequal impact of conditional conservatism on accrual components. The study’s sample is an unbalanced panel of 331 French companies listed on Euronext Paris during the period time going from 2000 till 2015. First, this work aims at attributing empirical evidence to conditional conservatism using Basu (1997) and Khan and Watts (2009) models to detect this accounting practice. Then, it analyses differential implications of conditional conservatism on accrual components.Actually, French companies are known to be conservative firms as they implement conditional conservatism through an accrual component of earning, two accruals drivers (Revenue and receivables) and the non-discretionary accrual. According to Richardson, Sloan, Soliman & Tuna (2005), the working capital component is the preferred tool, among accrual components, for the transmission of conditional conservatism.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".