Exploring financial sentiment analysis via fine-tuning large language model and attributed graph neural network.

Abstract

Financial sentiment analysis (FSA) refers to the task of classifying textual content into predefined sentiment categories to analyze their potential impacts on financial market fluctuations. However, directly applying these pre-trained LLMs to FSA still poses significant challenges. Existing approaches fail to align with domain-specific objectives and struggle to adapt to customized financial data schemas. Moreover, these LLMs predict the stock change primarily depending on its own information, failing to take into account cross-impact among relevant stocks. In this paper, we propose a novel framework that synergizes an LLM with a Graph Neural Network (GNN) to model stock price dynamics, leveraging stock sentiment signals extracted from financial news. Specifically, we employ the open-source Llama-3-8B model as the backbone, then enhance its sensitivity to financial sentiment patterns through supervised fine-tuning (SFT) and direct preference optimization (DPO) techniques. Leveraging the sentiment outputs from the fine-tuned LLM, we design a GNN to enhance stock representations and model cross-asset dependencies via two types of text-attributed graphs, which dynamically encode time-varying price correlations. Experiments on the Chinese A-share market demonstrate that financial sentiment significantly influences stock price variations. Our framework outperforms previous baselines and exhibits an average improvement of 50% in Sharpe ratio.