用大模型+强化学习，为投资者定制动态投资组合。
LLM-based Personalized Portfolio Recommender: Integrating Large Language Models and Reinforcement Learning for Intelligent Investment Strategy Optimization
- 融合大模型与强化学习，理解用户风险偏好
- 动态调整投资策略，适应市场变化
- 适合追求个性化理财的普通投资者
在现代金融市场中，投资者越来越需要反映个人风险偏好的个性化、自适应投资策略，并能响应动态市场环境。传统的基于规则或静态优化的方法往往难以捕捉投资者行为、市场波动和不断变化的财务目标之间的非线性关系。为此，本文提出一种基于大语言模型的个性化投资组合推荐框架（LLM-based Personalized Portfolio Recommender），该框架整合了大语言模型、强化学习与个体化风险偏好建模，以支持智能化的投资决策制定。
原文摘要 · Abstract (English)
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.
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