Retrieval-Augmented Generation (RAG) for Real-Time Financial Market Analysis

Аннотация

The rapid growth of unstructured financial OPEN ACCESS data—ranging from earnings calls and SEC filings to real- SUBMITED 14 June 2025 time social me- dia and global news—has outpaced the ACCEPTED 26 June 2025 ability of traditional analysis tools to provide timely, PUBLISHED 29 July 2025 VOLUME Vol.07 Issue 07 2025 contextual insights. Most natural language models are CITATION trained on static data and lack the capacity to integrate Priyank Tailor. (2025). Retrieval-Augmented Generation (RAG) for Real- dynamic, real-world updates. Retrieval- Augmented Time Financial Market Analysis. The American Journal of Interdisciplinary Generation (RAG) bridges this gap by combining Innovations and Research, 7(07), 137–144. https://doi.org/10.37547/tajiir/Volume07Issue07-12 document retrieval with generative capabilities, creating a more grounded and up-to-date understanding COPYRIGHT © 2025 Original content from this work may be used under the terms of user queries. This paper presents a domain-adapted of the creative commons attributes 4.0 License. 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. 1. Introduction The financial sector is inherently dynamic, characterized by rapid market fluctuations, evolving policies, and real- time events that significantly influence investment decisions. Re- cent events such as sudden stock market crashes and eco- nomic downturns underscore the need for advanced, real-time financial analysis systems. Traditional methods lag behind rapidly unfolding market realities, necessitating intelligent, context-aware AI models like RAG to drive timely decision- making. Traditional financial analysis often relies on historical data and static models, which struggle to keep pace with the The American Journal of Interdisciplinary Innovations and Research 137 https://www.theamericanjournals.com/index.php/tajiir The American Journal of Interdisciplinary Innovations and Research sheer volume, velocity, and variety of modern financial 1 We present a complete, end-to-end RAG framework information. The proliferation of unstructured data from tai- lored specifically for the high-velocity, high- diverse sources, including earnings call transcripts, SEC stakes do- main of real-time financial analysis. filings, real-time news feeds, and social media, presents 2 We detail a robust data ingestion and preprocessing both an opportunity and a challenge. While these data pipeline that integrates diverse, unstructured data sources contain invaluable insights, extracting and sources, from regulatory filings to social media, synthesizing them in a timely and accurate manner is into a unified knowledge base. beyond the capabilities of conventional tools. This 3 We provide a comprehensive empirical evaluation limitation can lead to delayed decision-making, missed of the system, using a combination of quantitative opportunities, and increased exposure to market risks. metrics and qualitative assessments from financial Furthermore, traditional Natural Language Processing experts, demon- strating significant improvements (NLP) models, often trained on static datasets, tend to over baseline models. hallucinate or provide outdated information when confronted with rapidly evolving financial landscapes. We discuss the practical implementation details, ethical This gap between the static nature of trained models considerations, and limitations, offering a blueprint for and the dynamic reality of financial markets highlights a the responsible deployment of generative AI in financial critical need for more adaptive and context-aware AI markets. solutions. 2. Related Work Retrieval-Augmented Generation (RAG) models have emerged as a promising solution by combining neural There has been considerable research on applying re- trieval mechanisms with generative transformers. Natural Language Processing (NLP) to finance, These mod- els enable queries to be grounded in the particularly in sentiment analysis, event detection, and most relevant and up-to-date information from summarization of fi- nancial texts. Early efforts focused external data sources, thereby mitigating the issues of on finetuning generalpurpose language models on hallucination and outdated knowl- edge inherent in financial corpora to improve their understanding of purely generative models. The core idea behind RAG is domainspecific terminology and nuances. For instance, to augment the generative capabilities of large language FinBERT stands out as a foundational work in this area. models (LLMs) by providing them with access to an It is a BERT-based model pretrained on a large financial external, continuously updated knowledge