Abstract
Applying machine learning to financial time series has been an active area of industrial research enabling innovation in market insights, risk management, strategic decision-making, and policy formation. This paper explores the novel use of Large Language Models (LLMs) for explainable financial time series forecasting, addressing challenges in cross-sequence reasoning, multi-modal data integration, and result interpretation that are inherent in traditional approaches. Focusing on NASDAQ-100 stocks, we utilize public historical stock data, company metadata, and economic/financial news. Our experiments employ GPT-4 for zero-shot/few-shot inference and Open LLaMA for instruction-based fine-tuning. The study demonstrates LLMs’ ability to generate well-reasoned decisions by leveraging cross-sequence information and extracting insights from text and price time series. We show that our LLM-based approach outperforms classic ARMA-GARCH and gradient-boosting tree models. Furthermore, fine-tuned public LLMs, such as Open-LLaMA, can generate reasonable and explainable forecasts, although they underperform compared to GPT-4.- Anthology ID:
- 2023.emnlp-industry.69
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Mingxuan Wang, Imed Zitouni
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 739–753
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2023.emnlp-industry.69/
- DOI:
- 10.18653/v1/2023.emnlp-industry.69
- Cite (ACL):
- Xinli Yu, Zheng Chen, and Yanbin Lu. 2023. Harnessing LLMs for Temporal Data - A Study on Explainable Financial Time Series Forecasting. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 739–753, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Harnessing LLMs for Temporal Data - A Study on Explainable Financial Time Series Forecasting (Yu et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2023.emnlp-industry.69.pdf