A Novel Framework for Analyzing Economic News Narratives Using GPT-3.5: Conclusions and References

Table of Links

Abstract and Intro

Data

Framework

Results

Conclusions, Acknowledgements, and References

5 Conclusions

Starting from a curated selection of economic articles sourced from The Wall Street Journal, our research introduces an innovative and dynamic approach to dissecting news content. We leverage on GPT3.5 to sift out the most salient entities within each article, which become the building blocks of a proposed series of graphs. The graphs track indeed the co-occurrence of such entities among news on a weekly basis, and allow investigations on the inter-relations of topics discussed over time. Network analysis techniques and fuzzy community detection are then used to design a comprehensive framework, which systematically unveils interpretable topics and surrounding narratives within news.

The importance of the proposed investigations is highlighted by the results of the logistic regression models. Indeed, we test whether there is a statistically significant connection between the features and structure of news, and moments of dislocation within financial markets. As expected (and desired), lower sentiment within news is more likely to be associated with weeks of market dislocation. However, multiple features computed from our graph construct are found to be also significant, especially from the entropy of discussions and consequent likelihood of contagion of sentiment, both in the contemporaneous and predictive scenarios. This suggests that the interconnectedness of news’ topics and structure therein are meaningful aspect to further analyse within financial research, for which our proposed study desires to serve as a first baseline. Improving entity recognition, extending the corpus of news, and designing generalisation studies are examples of possible advances to pursue in this research branch.

As a final remark, we desire to point to the problem of network alignment, which is especially important in network biology [26]. Many related studies try to find a measure of protein similarity between proteins in different species, since similar protein structures often imply the same biological results. With a parallel approach, one could investigate more deeply whether equivalent structures among news (but that do not account for the actual “label” of the topic) result indeed in similar market reactions.

Acknowledgements

Deborah Miori’s research was supported by the EPSRC CDT in Mathematics of Random Systems (EPSRC Grant EP/S023925/1).

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