Narratives from GPT-derived Networks of News, and a link to Financial Markets Dislocations

Paper: arXiv 2311.14419

Abstract

Starting from a corpus of economic articles from The Wall Street Journal, we present a novel systematic way to analyse news content that evolves over time. We leverage on state-of-the-art natural language processing techniques (i.e. GPT3.5) to extract the most important entities of each article available, and aggregate co-occurrence of entities in a related graph at the weekly level. Network analysis techniques and fuzzy community detection are tested on the proposed set of graphs, and a framework is introduced that allows systematic but interpretable detection of topics and narratives. In parallel, we propose to consider the sentiment around main entities of an article as a more accurate proxy for the overall sentiment of such piece of text, and describe a case-study to motivate this choice. Finally, we design features that characterise the type and structure of news within each week, and map them to moments of financial markets dislocations. The latter are identified as dates with unusually high volatility across asset classes, and we find quantitative evidence that they relate to instances of high entropy in the high-dimensional space of interconnected news. This result further motivates the pursued efforts to provide a novel framework for the systematic analysis of narratives within news.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses standard NLP and network analysis techniques (GPT extraction, graph theory, fuzzy clustering) with accessible formulas, lacking deep mathematical derivations. However, it demonstrates high empirical rigor with a substantial real-world dataset (Wall Street Journal, 4 years), multi-asset volatility indices, rolling z-score calculations, and a clear backtesting-ready framework for mapping news features to market dislocations.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Linking News Narratives to<br>Financial Market Dislocations"] --> B["Data Input:<br>Wall Street Journal<br>Economic Articles"]
  B --> C["Method: GPT3.5 Entity Extraction"]
  C --> D["Process: Weekly Co-occurrence<br>Graph Construction"]
  D --> E["Method: Fuzzy Community<br>Detection & Sentiment Analysis"]
  E --> F["Process: Feature Design<br>High Entropy & Topic Structure"]
  F --> G["Outcome: Quantitative Evidence<br>High News Entropy -><br>Market Volatility Dislocations"]

Cited References (30)

1. BERTopic: Neural topic modeling with a class-based TF-IDF procedure (2022) — link
2. Company Similarity using Large Language Models (2023) — link
3. Fund2Vec: Mutual Funds Similarity Using Graph Learning (2022) — link
4. Fast unfolding of communities in large networks (2008) — link
5. Full-Text or Abstract? Examining Topic Coherence Scores Using Latent Dirichlet Allocation (2017) — link
6. Uncovering fuzzy community structure in complex networks (2007)
7. Distributed Representations of Words and Phrases and their Compositionality (2013) — link
8. Fund2Vec: Mutual Funds Similarity using Graph Learning (2021) — link

0 of 30 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.