Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps?
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909068496470016 |
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| author | Lefort, Baptiste Benhamou, Eric Ohana, Jean-Jacques Saltiel, David Guez, Beatrice Challet, Damien |
| author_facet | Lefort, Baptiste Benhamou, Eric Ohana, Jean-Jacques Saltiel, David Guez, Beatrice Challet, Damien |
| contents | We used a dataset of daily Bloomberg Financial Market Summaries from 2010 to 2023, reposted on large financial media, to determine how global news headlines may affect stock market movements using ChatGPT and a two-stage prompt approach. We document a statistically significant positive correlation between the sentiment score and future equity market returns over short to medium term, which reverts to a negative correlation over longer horizons. Validation of this correlation pattern across multiple equity markets indicates its robustness across equity regions and resilience to non-linearity, evidenced by comparison of Pearson and Spearman correlations. Finally, we provide an estimate of the optimal horizon that strikes a balance between reactivity to new information and correlation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05447 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps? Lefort, Baptiste Benhamou, Eric Ohana, Jean-Jacques Saltiel, David Guez, Beatrice Challet, Damien Statistical Finance Artificial Intelligence We used a dataset of daily Bloomberg Financial Market Summaries from 2010 to 2023, reposted on large financial media, to determine how global news headlines may affect stock market movements using ChatGPT and a two-stage prompt approach. We document a statistically significant positive correlation between the sentiment score and future equity market returns over short to medium term, which reverts to a negative correlation over longer horizons. Validation of this correlation pattern across multiple equity markets indicates its robustness across equity regions and resilience to non-linearity, evidenced by comparison of Pearson and Spearman correlations. Finally, we provide an estimate of the optimal horizon that strikes a balance between reactivity to new information and correlation. |
| title | Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps? |
| topic | Statistical Finance Artificial Intelligence |
| url | https://arxiv.org/abs/2401.05447 |