Cross-Sector Market Regime Forecasting with LLM-Augmented News Analysis
Mudarisov, T., State, R. V., Kraussl, Z. 
ORCID: 0000-0001-8718-4874 , Yakubov, A. & Petrova, T. (2024).
Cross-Sector Market Regime Forecasting with LLM-Augmented News Analysis.
In:
Proceedings of the 5th ACM International Conference on AI in Finance.
ICAIF '24: 5th ACM International Conference on AI in Finance, 14-17 Nov 2024, Brooklyn, NY, USA.
doi: 10.1145/3677052.3698642
Abstract
This paper investigates the utilization of news in predicting market regimes. The findings illustrate that employing an ensemble of multiple FinBERT models can outperform straightforward time-series prediction by 73% in accuracy and 110% in F1 score. The NLP models demonstrate strong performance across two different market-regime scenarios and show the ability to detect market shifts.
| Publication Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | Copyright © 2024 Owner/Author. This work is licensed under a Creative Commons Attribution International 4.0 License. |
| Publisher Keywords: | Large Language Models, efficient market hypothesis, market-regimes |
| Subjects: | H Social Sciences > HG Finance Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Departments: | Bayes Business School Bayes Business School > Faculty of Finance |
| SWORD Depositor: |
Available under License Creative Commons Attribution.
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