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DOI: Retrieval-Augmented Generation (RAG) for Real-Time Financial Market Analysis
Priyank Tailor , Data Scientist / AI Researcher Jersey City, NJ, USAAbstract
The rapid growth of unstructured financial data—ranging from earnings calls and SEC filings to real-time social me- dia and global news—has outpaced the ability of traditional analysis tools to provide timely, contextual insights. Most natural language models are trained on static data and lack the capacity to integrate dynamic, real-world updates. Retrieval- Augmented Generation (RAG) bridges this gap by combining document retrieval with generative capabilities, creating a more grounded and up-to-date understanding of user queries. This paper presents a domain-adapted RAG-based framework for real-time financial analysis, using vector databases and domain-specific language models. The framework demon- strates improved contextual accuracy, reduced hallucination, and greater interpretability compared to traditional NLP mod- els. Our findings indicate that RAG has the potential to become a core component in next-generation financial intelli- gence systems.
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Copyright (c) 2025 Priyank Tailor
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