INVESTOR ATTENTION AND STOCK RETURNS UNDER NEGATIVE SHOCKS: AN EMPIRICAL ANALYSIS BASED ON “DRAGON AND TIGER” LIST IN 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
Using the “Dragon and Tiger” list, we construct a clean indicator that directly measures investor attention, empirically test the effect of investor attention on stock return under negative shocks and whether the effect is affected by the bull or bear market, the industry, firm size, age and state ownership, institutional shareholder holding percentage. The results show that i) an increase in investor attention negatively predicts stock returns when cumulative daily return of a stock listed on “Dragon and Tiger” list on listing day is negative; ii) Investor attention is negatively correlated with stock returns when the stock entered in “Dragon and Tiger” list experienced current cumulative monthly return is negative; iii) Investor attention is negatively correlated with stock returns when monthly cumulative net purchase amount of top 10 institution to the stock listed in “Dragon and Tiger” list is negative; iv) Investor attention is negatively correlated with stock returns when the stock listed in “Dragon and Tiger” list, the ratio of monthly cumulative trading amount of the top 10 institutional traders to total trading amount of the secondary market is in the bottom 30 percentile. These findings not only contribute to the academic research about the relationship between investor attention and stock return, but also provide some guidance to the financial regulatory agencies as to the capital market stability.
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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 | 0.000 |
| 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