The Determinants of the Stock Price Performance of Analyst Recommendations
Bibliographic record
Abstract
This study examines the cross-sectional determinants of the price reaction to analysts’ recommendations disseminated through various type of media and for firms listed in Taiwan stock markets. We measure abnormal returns using the market model of event study. Based on the type of media (traditional media/social media) and the type of exchange (Taiwan Stock Exchange (TWSE)/Taipei Exchange (TPEx)), we classify the combined sample observations into four samples and run quantile regressions to investigate whether the relation will be uniform across various quantile levels. Our results show that the relation between firm characteristics and cumulative abnormal returns is not homogeneous across various quantiles of abnormal returns. Our evidence indicates that in general the relation tends to be stronger for firms at higher performance quantile levels and tends to be more pronounced for TWSE firms. The strongest relation is found for the Traditional/TWSE sample, where the abnormal returns are positively related to insider ownership and prior-period earnings, and negatively related to institutional shareholding and price-to-book ratio for firms in the highest abnormal performance quantile.
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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 | 0.026 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".