City Research Online

Cross-Sector Market Regime Forecasting with LLM-Augmented News Analysis

Mudarisov, T., State, R. V., Kraussl, Z. ORCID: 0000-0001-8718-4874 (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 > Finance
SWORD Depositor:
[thumbnail of 3677052.3698642.pdf]
Preview
Text - Published Version
Available under License Creative Commons Attribution.

Download (925kB) | Preview

Export

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Downloads

Downloads per month over past year

Loading...

View more statistics

Actions (login required)

Admin Login Admin Login