The impact of shareholder perk abolition on stock prices in Japan: Evidence from signaling, investor composition, perk convertibility, and industry type
Bibliographic record
Abstract
Japanese firms commonly use shareholder perk programs to attract and retain individual investors. However, the market implications of discontinuing such programs remain underexplored. This study investigates the impact of shareholder perk discontinuation announcements on stock prices in Japan, drawing on the signaling theory and behavioral finance, particularly the concept of loss aversion. Using a combination of event study methodology and cross-sectional regression analysis, the study reveals that perk discontinuation leads to significantly negative stock price reactions. This negative reaction is especially pronounced for firms with highly convertible perks (e.g., gift cards), those with a high proportion of individual shareholders, and firms operating in B2C industries. Furthermore, the magnitude of this negative reaction was found to be amplified by growth opportunities and mitigated by a simultaneous dividend increase, indicating firm-specific heterogeneity in the market’s response to perk discontinuation. By integrating these and other relevant firm attributes as explanatory and control variables (e.g., firm age) within a unified framework, this study offers a comprehensive evaluation of how various attributes influence investor responses. The findings contribute both theoretically and empirically to the literature on shareholder perks and provide practical implications for firms considering revisions to their shareholder perk policies.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".