Benchmarking Retrieval Augmented Generation in Quantitative Finance

This paper presents a benchmarking study of Retrieval Augmented Generation (RAG) systems within the applications of quantitative finance. Building and utilizing an AI multi-modality application for integrating RAG with advanced language models and financial data APIs, our study evaluates these systems’ capability in processing, interpreting, and analyzing vast financial datasets. The focus of this study is to assess the accuracy, efficiency, scalability, and adaptability of RAG systems in practical financial market scenarios. By analyzing the performance of RAG systems, we aim to provide insights into their effectiveness in delivering valuable financial analytics. This study contributes to understanding the potential role and limitations of RAG systems in modern financial analysis, offering a foundation for future research in AI-driven financial technologies.