mlsanityData to 7 Oct 2026

Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps?

arXiv 2401.05447 ↗First posted 9 Jan 2024q-fin.ST cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 2 of its 36 references that are in the map. First posted on arXiv 9 Jan 2024.

Builds on

2 of 36 references.

References · 2

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challengescited by 59

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2 papers.