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Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps?
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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.
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MATES: Multi-view Aggregated Two-Sample Test
A multi-view graph-based two-sample test aggregates moment-specific Manhattan distances and has a chi-square null limit that enables fast p-values.
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