Equity Research Chatbot Using LLM: A Responsive Agent for Investment Research

Our research paper includes the combination of chatbot development and research on various companies' fundamentals (P&L, Balance sheet, and Cash Flow) for giving end users precise investment advice.The literature on technologies like machine learning (ML), neural networks, and Generative AI in financial analysis is surveyed, emphasizing the growing body of research using Large Language Models (LLMs) and their applications using chatbot assistance for investment opportunities. Additionally, previous studies on chatbots in finance are reviewed to contextualize their role in equity research.The methodology outlines the research approach, data collection method, and usage of LLMs in this study. Here the data used are the pdf files containing company financial reports which are then fed into the chatbot for further processing. The data regarding current market trends and overall fundamental analysis provides a clear idea to chatbot for suggesting precise investments. Here NLP processes include text chunking, building semantics, and knowledge base for proceeding to further steps. Further, our results include categories of questions for which we have also showcased its graph that mentions the response time and accuracy versus the type of question asked to the chatbot. This analysis gives an efficient understanding of the chatbot. Overall, this research contributes to the knowledge of how chatbots can transform equity research, paving the way for more efficient and accessible financial analysis in the digital age.