Investor Sentiment and Stock Returns during the COVID-19 Pandemic: Evidence from Chinese Stock Market
Bibliographic record
Abstract
After decades of development, China's stock market is now at a critical stage of scale expansion. However, China's stock market is dominated by retail investors. Such an immature market is vulnerable to investor sentiment. Based on the current realistic background, this paper studies the relationship between investor sentiment and stock returns in The Chinese market and forecasts the development trend of the future market. This paper firstly combs the literature on investor sentiment at home and abroad and adopts the classical research method of investor sentiment index & principal component analysis. The investor sentiment index was constructed using weekly data of the Shanghai composite index from 2020 to 2021. Select the turnover rate, China SSE 50ETF turnover ratio, closed-end fund discount, Teng Fall Index (TFL) and financing net buying turnover ratio of these five variables to construct. Finally, the multiple linear regression is used to analyze the relationship between investor sentiment and returns. We find that the yield of Shanghai composite index is positively correlated with investor sentiment, which will affect the return of stock market. The significance of this conclusion lies in that it can help to understand the interaction between investor behavior and market activities after the COVID-19 and help to test whether the use of investor sentiment indicators can provide guidance for investors' decisions.
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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 | 0.002 |
| 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 | 0.000 | 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".