用AI代理自动筛选投资组合，提升收益风险比。
Designing Agentic AI-Based Screening for Portfolio Investment
- 双AI代理分别分析公司基本面和新闻情绪，协同决策
- 在标普500上实现更高夏普比率，优于人工与传统方法
- 提出‘合理筛选’概念，误差下仍能准确估计目标收益
我们提出一种用于投资组合管理的新型智能体式人工智能平台。架构分为三层：首先，两个大语言模型代理分别承担筛选优质基本面公司与有利新闻公司的任务；其次，这些代理通过协商生成并确认买卖信号，大幅缩小候选资产池；最后，采用高维精确度矩阵估计法确定最优投资权重。基于信息获取理论，我们证明智能体筛选相比人类可带来效用提升。引入‘合理筛选’概念，表明在轻微筛选误差下，筛选后投资组合的平方夏普比率可一致估计其目标值。实证结果显示，该方法在标普500数据上，无论短期还是中期，均显著优于未筛选基准组合及传统筛选方法。
原文摘要 · Abstract (English)
We introduce a new agentic artificial intelligence (AI) platform for portfolio management. Our architecture consists of three layers. First, two large language model (LLM) agents are assigned specialized tasks: one agent screens for firms with desirable fundamentals, while a sentiment analysis agent screens for firms with desirable news. Second, these agents deliberate to generate and agree upon buy and sell signals from a large portfolio, substantially narrowing the pool of candidate assets. Finally, we apply a high-dimensional precision matrix estimation procedure to determine optimal portfolio weights. We show, through information acquisition theory, that screening with agentic AI can bring utility gains in screening compared with humans. We introduce the concept of \emph{sensible screening} and establish that, under mild screening errors, the squared Sharpe ratio of the screened portfolio consistently estimates its target. Empirically, our method achieves superior Sharpe ratios relative to an unscreened baseline portfolio and to conventional screening approaches, evaluated on S\&P~500 data over both short and medium terms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。