Keywords
Large Language Models
Abstract
Stock movement prediction has traditionally been approached through statistical time series models and, more recently, deep learning architectures. However, existing models face two fundamental challenges: first, they struggle to integrate the vast corpus of unstructured financial text—including earnings calls, analyst reports, regulatory filings, and social media—into a coherent predictive framework; second, they lack the broad world knowledge and reasoning capabilities that are necessary to interpret complex financial events in context. Large Language Models (LLMs) offer a promising solution to both problems, as they encode extensive semantic knowledge and can perform multi-step reasoning. However, applying LLMs directly to stock prediction is challenging because their parametric knowledge may be outdated, they tend to hallucinate factual claims, and they lack real-time access to market data. In this paper, we propose FinRAG-LM (Financial Retrieval-Augmented Generation with Language Model), a novel framework that combines retrieval-augmented generation (RAG) with a fine-tuned financial LLM for stock movement prediction. FinRAG-LM constructs a Financial Knowledge Corpus (FKC) from diverse sources including SEC filings, earnings transcripts, analyst reports, financial news, and social media, and uses a dense retriever to retrieve the most relevant passages for a given stock and time window. A Sentiment-Grounded Reasoning Module (SGRM) then processes the retrieved passages together with numerical price features through a cross-attention mechanism to generate predictions and interpretable reasoning chains. We conduct extensive experiments on three major markets (S&P 500, ASX 200, and Euro Stoxx 50), showing that FinRAG-LM outperforms ten competitive baselines including S3G (Lu, Hu, and Zhang, 2026), the state-of-the-art stock state space graph model, with an average improvement of 6.1% in directional accuracy and 10.4% in Matthews Correlation Coefficient (MCC). Notably, FinRAG-LM achieves particularly strong performance during earnings announcement periods, where textual information is most informative, outperforming S3G by 8.3 percentage points in directional accuracy during Q2 2023 earnings season.
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