From Optimism to Recalibration: The Temporal Dynamics of Market Reactions to Women’s Board Appointments in Saudi Arabia
Bibliographic record
Abstract
This study examines stock market reactions to female board appointments among 34 publicly listed companies in Saudi Arabia between 2021 and 2024. We employ a multi-method approach covering 36 announcements. Our primary methodology is an event study, which we complement with two distinct robustness checks: the Local Projections (LP) method to capture the evolving nature of market responses and the Quantile-on-Quantile analysis to investigate how market conditions interact with the three phases surrounding the event—the anticipation period before the appointment, the appointment event itself, and the post-appointment adjustment period. This comprehensive methodological framework allows us to capture the immediate market response to appointment announcements and the longer-term implications for firm performance while accounting for various econometric challenges inherent in financial market data. Our findings reveal a negative market reaction that gradually intensifies, becoming marginally significant by the tenth trading day. This pattern suggests that investors in the Saudi market may initially view female board appointments with skepticism, potentially reflecting uncertainty about the impact of gender diversity in a traditionally male-dominated business environment. Furthermore, the evolution from 2021 to 2024 suggests a market that is progressively developing more sophisticated frameworks for evaluating female board appointments. Rather than exhibiting a monotonic trend toward either increasingly positive or negative reactions, the market appears to be engaging in a learning process characterized by periodic reassessments. Moreover, our results indicate that while the immediate event and anticipation phases yield mixed impacts across the return distribution, the adjustment period exhibits a robust and significantly negative interaction with market returns. These findings suggest that market overreactions, particularly during bullish periods, contribute to a pronounced correction effect following female board appointments.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".