LLM-based Personalized Portfolio Recommender: Integrating Large Language Models and Reinforcement Learning for Intelligent Investment Strategy Optimization
This addresses the need for personalized investment strategies in financial markets, though it appears incremental as it combines existing methods like LLMs and RL.
The paper tackled the problem of creating personalized and adaptive portfolio strategies for investors by introducing an integrated framework combining Large Language Models, reinforcement learning, and risk preference modeling, resulting in a system for intelligent investment decision-making.
In modern financial markets, investors increasingly seek personalized and adaptive portfolio strategies that reflect their individual risk preferences and respond to dynamic market conditions. Traditional rule-based or static optimization approaches often fail to capture the nonlinear interactions among investor behavior, market volatility, and evolving financial objectives. To address these limitations, this paper introduces the LLM-based Personalized Portfolio Recommender , an integrated framework that combines Large Language Models, reinforcement learning, and individualized risk preference modeling to support intelligent investment decision-making.