GPTQuant's Conversational AI: Simplifying Investment Research for All

Abstract

This paper presents GPTQuant, a conversational AI chatbot for developing and evaluating investment strategies. GPTQuant leverages prompt templates and LangChain's integration to activate few-shot learning capabilities of GPT3 for generating Python code instantly, which can be executed using the Python interpreter. Our case studies demonstrate GPTQuant's efficacy in investment research, highlighting its potential to reduce the workload of human agents and democratize investment research. GPTQuant's contributions to fintech, including its ability to generate Python code via natural language commands and its intuitive interface for evaluating investment strategies, make it a valuable tool for investors. Our case studies demonstrate the practical applications of the chatbot and compare its performance with Open AI's ChatGPT. We highlight the chatbot's potential to overcome limitations in GPT variants and to open new doors in domain-specific applications in finance.

Keywords: finance and technology, investment, Conversational AI, ChatGPT, OpenAI, GPT, machine learning, investment research, cross sectional equities, style factor, backtesting