FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs

TL;DR

This paper introduces a novel LLM-based method for stock movement prediction that incorporates news dissemination, context, and explicit instructions, significantly improving accuracy over existing approaches.

Contribution

It presents a data-driven approach that enhances sentiment analysis by integrating dissemination and contextual information into LLM prompts and fine-tuning.

Findings

Prediction accuracy improved by 8% over existing methods.

Incorporating dissemination breadth enhances model understanding.

Explicit instructions in prompts lead to better stock movement predictions.

Abstract

Financial sentiment analysis is crucial for understanding the influence of news on stock prices. Recently, large language models (LLMs) have been widely adopted for this purpose due to their advanced text analysis capabilities. However, these models often only consider the news content itself, ignoring its dissemination, which hampers accurate prediction of short-term stock movements. Additionally, current methods often lack sufficient contextual data and explicit instructions in their prompts, limiting LLMs' ability to interpret news. In this paper, we propose a data-driven approach that enhances LLM-powered sentiment-based stock movement predictions by incorporating news dissemination breadth, contextual data, and explicit instructions. We cluster recent company-related news to assess its reach and influence, enriching prompts with more specific data and precise instructions. This data is used to construct an instruction tuning dataset to fine-tune an LLM for predicting short-term stock price movements. Our experimental results show that our approach improves prediction accuracy by 8\% compared to existing methods.