What Determines Cumulative Abnormal Returns? An Empirical Validation in the French Market
Bibliographic record
Abstract
<p>This paper test the factors explaining of cumulative abnormal returns. To this end, we examined a sample of 137 firms in 2007. We tested event study methodology to measure the cumulative abnormal returns. An event window spans from-10 days to 10 days. In our study, we considered an estimation period from -20 days to -10 days. For the dependent variable, and after the announcement date (date of the general meeting), we try to estimate the cumulative abnormal returns of 1 day, 2 days, 6 days and 8 days. The empirical results of the cross sectional model show that the market reacts negatively because of an increase in profitability, firm size and managerial ownership. The opposite effect is observed for leverage. However, the effect of spending on research and development is not statistically significant.<span style="font-size: 10px;"> </span></p>
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".