The new frontier of personalized portfolio management: quantitative methods with LangChain - Cheam, Caleb Zhong Wei (Nanyang Technological University, 2024)
This paper explores the integration of advanced computational techniques and Large Language Models (LLMs) in portfolio management, aiming to overcome the limitations of traditional robo-advisors and mean-variance optimization (MVO). We present a novel framework that incorporates Monte Carlo simulations, Geometric Brownian Motion, and machine learning methods like clustering algorithms and differential evolution to enhance portfolio optimization. Our methodology leverages the power of LLMs to process unstructured data and provide personalized investment advice, reflecting a shift from conventional financial advisory methods toward more adaptive and investor-centric models. The research demonstrates how combining modern computational tools and AI can address specific investor preferences, improve risk management, and increase the transparency of investment strategies. We use a series of experiments to validate the effectiveness of our proposed solutions in achieving superior portfolio allocations compared to traditional methods. The findings suggest that our integrated approach not only aligns more closely with individual investor profiles but also enhances the robustness and efficiency of portfolio management in dynamic market conditions.