The Effect of Twitter Messages and Tone on Stock Return: The Case of Saudi Stock Market “Tadawul”
Why this work is in the frame
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Bibliographic record
Abstract
This research aims to examine whether corporate Twitter messages and tone have an effect on corporate stock return (RET) for the Saudi Stock Exchange “Tadawul”. The study also investigates whether the association differs across large- and small-sized firms. We used a sample of 11,099 firm-daily observations for non-financial firms that were traded on the Saudi Stock Exchange “Tadawul” across the period 1 April 2020 to 31 December 2020. Using panel ordinary least square (OLS) and two-stage least square (2SLS), we found that corporate Twitter (currently renamed ‘X’) messages is positively and significantly associated with stock return (RET). The findings also suggest that the message tone increases the stock returns. Furthermore, our results show different effects of Twitter messages and tone on stock return across small- and large-sized firms. In addition, our findings show that Twitter tone is positively associated with RET when the firm is large in size. However, when the firm is small, Twitter messages has a stronger effect on RET. Our findings provide policy implications for regulators and investors. Regulators might monitor the information in accurate ways. Also, investors might start to show interest in Twitter channels to follow the firm’s news.
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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 | 0.017 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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