Paper: arXiv 2401.05447

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper employs basic statistical formulas and correlation analysis but lacks advanced mathematical derivations, while its strong empirical rigor is evidenced by a long-term dataset, cross-market validation, and implementation-heavy testing with real-world financial news.

Research Flowchart

  flowchart TD
  A["Research Goal<br/>Can ChatGPT compute trustworthy<br/>sentiment scores from Bloomberg Market Wraps?"] --> B
  subgraph B ["Data & Methodology"]
      B1["Bloomberg Market Summaries<br/>2010-2023"] --> B2["Two-Stage Prompt Approach<br/>(ChatGPT Sentiment Scoring)"]
      B2 --> B3["Correlation Analysis<br/>Pearson & Spearman"]
  end
  B3 --> C{"Key Findings"}
  C --> C1["Significant Positive Correlation<br/>Sentiment → Short/Medium-term Returns"]
  C --> C2["Correlation Reverts<br/>Negative over Long Horizons"]
  C --> C3["Robust Across Markets<br/>Resilient to Non-linearity"]
  C --> C4["Optimal Horizon Estimation<br/>Balances Reactivity & Correlation"]