Effect of Digitalized Rumor Clarification on Stock Markets
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 volatility is influenced by the release, dissemination, and acceptance of information. Rumor clarification is expected to reduce asymmetric information and abnormal stock returns by increasing information transparency. However, investors are irrational, and modern behavioral finance studies attribute non-random stock movements to investors’ cognitive and emotional biases. The verification of rumor authenticity may cause fluctuations in investor sentiment, which increases impulsive investing behaviors and stock movements. Due to the widespread and fast accessibility of social media, many electronic information platforms have been established to clarify rumors. It is critical to understand the effects of digitalized rumor clarification on stock markets. In this study, we extracted 12,663 rumor-clarification pairs from 1,804,520 social media posts. We quantified the language used in these messages via sentiment analysis, along with online firm behaviors, to study the effect of clarifications on stock markets. Our findings are as follows: (1) Digitalized rumor-clarification messages affect the abnormal returns of relevant stocks. (2) This influence can be quantified and measured by the emotion polarity of rumor clarification. (3) Firms’ online clarification behaviors, including information disclosure frequency, response time, and wording, have limited to no influence on abnormal returns.
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 | 0.001 | 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