Enhancing Stock Market Information Retrieval with Retrieval-Augmented Generation

Huan Phong Thai, Thu Anh Ngoc Le, Nhu Huynh Thi Le, Nhan Huu Tran, Lenh Cong Phan Pham, Hong Nhung Nguyen

Enhancing Stock Market Information Retrieval with Retrieval-Augmented Generation: 1 upvotes on Hugging Face Daily Papers, #42 of 46 papers on 2026-11-04. Day-by-day upvote history.

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It had 1 upvotes by its first day, 1 upvotes by the next day, 1 upvotes by day 3, 1 upvotes by day 7, and has 1 now. It ranked #122 of 130 papers in its week and #122 of 130 in its month. Its code repository has 6 GitHub stars.

Natural Language Processing (NLP) has been widely applied in financial domains, enabling automated analysis and retrieval of stock market information. However, traditional NLP-based systems often struggle with maintaining contextual accuracy and generating relevant responses in dynamic financial environments. To address these challenges, Retrieval-Augmented Generation (RAG) models have emerged as a powerful solution, combining retrieval-based methods with generative capabilities to enhance response quality. In this study, we present a Retrieval-Augmented Generation (RAG) system designed to generate responses related to stock market information. The system integrates retrieval-based and generative approaches to enhance the accuracy and relevance of financial text generation. To evaluate its effectiveness, we employ the ROUGE metric, specifically ROUGE-1, ROUGE-2, and ROUGE-L, to measure lexical and structural similarity between generated and reference texts. Experimental results on a dataset of 100 stock market-related queries demonstrate high ROUGE scores, indicating strong alignment with reference documents. Additionally, we assess latency to evaluate the system responsiveness, achieving an average response time of 1.16 seconds. These findings confirm that our RAG model is capable of providing accurate and timely stock market insights, making it a promising approach for financial information retrieval.

Paper page on Hugging Face · arXiv

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