用大模型预测收益和不确定性，优化投资组合表现。
LLM-Enhanced Black-Litterman Portfolio Optimization
- 将大模型的预测结果转化为投资观点与信心度
- 在标普500成分股上回测，表现优于传统方法
- 不同大模型有固定投资风格，选型即选风格
Black-Litterman模型通过引入投资者观点缓解了传统均值-方差优化对输入敏感的问题，但系统生成这些观点仍是关键挑战。本文提出并验证了一套系统框架，将大型语言模型（LLMs）的收益预测和预测不确定性转化为Black-Litterman模型的核心输入：投资者观点及其置信度。通过对标普500成分股进行回测，我们证明由表现最优的LLM驱动的投资组合在绝对收益和风险调整后收益上显著优于传统基准。关键发现是，每个LLM表现出独特且一致的投资风格，这是绩效的主要驱动力。因此，选择LLM并非寻找单一最优预测器，而是战略性地选择一种与其所处市场环境相匹配的投资风格。代码与数据见 https://github.com/youngandbin/LLM-BLM。
原文摘要 · Abstract (English)
The Black-Litterman model addresses the sensitivity issues of tra- ditional mean-variance optimization by incorporating investor views, but systematically generating these views remains a key challenge. This study proposes and validates a systematic frame- work that translates return forecasts and predictive uncertainty from Large Language Models (LLMs) into the core inputs for the Black-Litterman model: investor views and their confidence lev- els. Through a backtest on S&P 500 constituents, we demonstrate that portfolios driven by top-performing LLMs significantly out- perform traditional baselines in both absolute and risk-adjusted terms. Crucially, our analysis reveals that each LLM exhibits a dis- tinct and consistent investment style which is the primary driver of performance. We found that the selection of an LLM is therefore not a search for a single best forecaster, but a strategic choice of an investment style whose success is contingent on its alignment with the prevailing market regime. The source code and data are available at https://github.com/youngandbin/LLM-BLM.
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