Paper: arXiv 2510.16503

Authors: Domenica Mino, Cillian Williamson

Abstract

Artificial intelligence techniques have increasingly been applied to understand the complex relationship between public sentiment and financial market behaviour. This study explores the relationship between the sentiment of news related to the Russia-Ukraine war and the volatility of the stock market. A comprehensive dataset of news articles from major US platforms, published between January 1 and July 17, 2024, was analysed using a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model adapted for financial language. We extracted sentiment scores and applied a Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model, enhanced with a Student-t distribution to capture the heavy-tailed nature of financial returns data. The results reveal a statistically significant negative relationship between negative news sentiment and market stability, suggesting that pessimistic war coverage is associated with increased volatility in the S&P 500 index. This research demonstrates how artificial intelligence and natural language processing can be integrated with econometric modelling to assess real-time market dynamics, offering valuable tools for financial risk analysis during geopolitical crises.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 6.0/10 (capped from 8.5: no out-of-sample test; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematical concepts like BERT embeddings, GARCH modeling with Student-t distributions, and statistical hypothesis testing, while demonstrating strong empirical rigor through a comprehensive dataset from a defined period (Jan-July 2024), specific S&P 500 index analysis, and statistically significant results.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Analyze Sentiment-Volatility Link<br>during Geopolitical Crises"] --> B
  subgraph B ["Data Collection & Preparation"]
      B1["News Articles<br>Jan-Jul 2024"]
      B2["S&P 500 Market Data"]
  end
  B --> C["Sentiment Analysis:<br>Fine-tuned Financial BERT Model"]
  C --> D["Volatility Modeling:<br>GARCH with Student-t Distribution"]
  D --> E{"Statistical Analysis"}
  E --> F["Key Finding:<br>Negative Sentiment<br>Significantly Increases<br>Market Volatility"]

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0 of 41 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.