LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction

Meiyun Wang, Kiyoshi Izumi, Hiroki Sakaji

Abstract
Recently, Large Language Models (LLMs) have attracted significant attention for their exceptional performance across a broad range of tasks, particularly in text analysis. However, the finance sector presents a distinct challenge due to its dependence on time-series data for complex forecasting tasks. In this study, we introduce a novel framework called LLMFactor, which employs Sequential Knowledge-Guided Prompting (SKGP) to identify factors that influence stock movements using LLMs. Unlike previous methods that relied on keyphrases or sentiment analysis, this approach focuses on extracting factors more directly related to stock market dynamics, providing clear explanations for complex temporal changes. Our framework directs the LLMs to create background knowledge through a fill-in-the-blank strategy and then discerns potential factors affecting stock prices from related news. Guided by background knowledge and identified factors, we leverage historical stock prices in textual format to predict stock movement. An extensive evaluation of the LLMFactor framework across four benchmark datasets from both the U.S. and Chinese stock markets demonstrates its superiority over existing state-of-the-art methods and its effectiveness in financial time-series forecasting.
Anthology ID:
2024.findings-acl.185
Volume:
Findings of the Association for Computational Linguistics ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand and virtual meeting
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
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Publisher:
Association for Computational Linguistics
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Pages:
3120–3131
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URL:
https://aclanthology.org/2024.findings-acl.185
DOI:
Bibkey:
Cite (ACL):
Meiyun Wang, Kiyoshi Izumi, and Hiroki Sakaji. 2024. LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction. In Findings of the Association for Computational Linguistics ACL 2024, pages 3120–3131, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
Cite (Informal):
LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction (Wang et al., Findings 2024)
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