Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps?
This provides a method for financial analysts and investors to leverage AI for market prediction, though it is incremental as it applies an existing AI model to a new financial dataset.
The study tackled the problem of using ChatGPT to compute sentiment scores from Bloomberg market summaries to predict stock market movements, finding a statistically significant positive correlation with future returns in the short to medium term that reverts to negative over longer horizons, with validation across multiple equity markets.
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.