LLM-based Personalized Portfolio Recommender: Integrating Large Language Models and Reinforcement Learning for Intelligent Investment Strategy Optimization

TL;DR

This paper presents an innovative framework that combines large language models and reinforcement learning to create personalized, adaptive investment strategies tailored to individual risk preferences and market dynamics.

Contribution

It introduces a novel integrated approach leveraging LLMs and reinforcement learning for personalized portfolio recommendation, addressing limitations of traditional static methods.

Findings

Demonstrates improved personalization in portfolio recommendations.

Shows adaptability to changing market conditions.

Enhances decision-making with integrated risk modeling.

Abstract

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.