Information transparency and stock sentiment beta: Evidence from China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Stock returns demonstrate different levels of sensitivity to marketwide sentiment fluctuations. Previous studies argue that stock sentiment risk is caused by information opacity and that companies lacking information transparency tend to be young, small, paying no dividend, volatile, and fast growing. However, little direct evidence exists regarding the impact of information transparency on stock sentiment sensitivity/beta. This paper contributes to fill this gap by employing proximate measures of information transparency: quality of accruals and earnings, and accuracy of analyst forecast. Empirical results validate that information transparency indeed helps curb stock sentiment beta. Such an impact is more pronounced during periods of low market sentiment when irrational investors are mostly sidelined. Two mediating factors are identified: noise trading and stable institutional shareholding. Additionally, improving information transparency on corporate governance also constrains stock sentiment sensitivity. Our results are robust to alternative measures and the endogeneity concern.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.002 |
| Open science | 0.000 | 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 it