Relationship between investor sentiment and earnings news in high‐ and low‐sentiment periods
Bibliographic record
Abstract
Abstract Using the Chinese equity market as the testing venue, this study explores how investor sentiment affects the immediate reaction of stock prices to earnings news in high‐ and low‐sentiment periods. Our key finding is that the sentiment‐driven pricing of earnings will differ between the two periods. Specifically, during high‐sentiment (low‐sentiment) periods, the stock price sensitivity to good (bad) earnings news increases (decreases) with investor sentiment, whereas the stock price sensitivity to bad (good) earnings news is unrelated to investor sentiment. Additionally, we find that the effect of sentiment is more pronounced for young, high volatility, growth and distressed stocks. However, contrary to the U.S. evidence, our results show that small stocks are not always more exposed to sentiment. Further analysis of the role of short‐sales constraints in the sensitivity of stock prices to bad earnings news implies that short sales can enhance informational efficiency.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".