Effect of social media rumors on stock market volatility: A case of data mining in China
Bibliographic record
Abstract
The Stock Market is a typical complex network composed of investors, stocks, and market information. The abnormal fluctuation of the Stock Market has a strong effect on the economy of a country and even that of the world. Fueled by the herd effect of the increasingly abundant social media, Internet rumors, as an important source of market information and an exogenous financial risk, can lead to the collapse of investor confidence and the further propagation of financial risks, which can damage the financial system and even lead to social unrest. With additional availability of computing techniques, we attempt to uncover the media information effects in the stock market and seek to provide researchers with 1) a theoretical reference for a comprehensive understanding of such a complex network, 2) accurate prediction of future data, and 3) design of efficient and reliable risk intervention models. Based on the data of China’s Stock Market, this study uses machine learning to investigate social media rumors to reveal the interplay of social media rumors and stock market volatility. In this work, we find patterns from social media rumors from financial forums using machine learning, quantify social media rumors based on statistics, and analyze the mechanism of propagation and influence of social media rumors on stock market volatility using econometric models. The empirical results show that rumors play an important information transmission effect on stock market volatility and the constructed Internet Financial Forum Rumor Index is helpful to sense the potential impact of rumors, i.e., a significant lagged negative effect. These findings are of guidance for the optimization of the information environment, and can serve to promote the healthy and stable development of the stock market.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".