Impact of Negative Tweets on Diverse Assets during Stressful Events: An Investigation through Time-Varying Connectedness
Bibliographic record
Abstract
Tweets seem to impact diverse assets, especially during stressful periods. However, their interrelations during stressful events may change. Cryptos are apparently more sensitive to the sentiment spread by tweets. Therefore, a construct could be formed to study such complex interrelation during stressful events. This study found an interesting outcome while investigating three major asset classes (namely, Equity, Gold and Bond) alongside negative sentiment (derived from tweets of Elon Musk) and Dogecoin (an emerging asset class) from 1 June 2015 to 20 February 2022. Negative sentiment emerged as the significant risk transmitter, while Gold emerged as the significant net recipient of shocks (risk). Interestingly, Dogecoin was found to be less impacted and not impactful (not transmitting shock and receiving tiny shocks) at the same time. In fact, the interconnectedness between negative sentiment (percolated through Twitter) and Dogecoin prices was found to be rather feeble. Further, the study showed that the COVID-19 breakout and Brexit referendum in 2016 were less stressful events compared to the Greek debt crisis back in 2015.
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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 | 0.001 | 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 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".