Abstract
This paper investigates how Large Language Models (LLMs) from leading providers (OpenAI, Google, Anthropic, DeepSeek, and xAI) can be applied to quantitative sector-based portfolio construction. We use LLMs to identify investable universes of stocks within S&P 500 sector indices and evaluate how their selections perform when combined with classical portfolio optimization methods. Each model was prompted to select and weight 20 stocks per sector, and the resulting portfolios were compared with their respective sector indices across two distinct out-of-sample periods: a stable market phase (January-March 2025) and a volatile phase (April-June 2025). Our results reveal a strong temporal dependence in LLM portfolio performance. During stable market conditions, LLM-weighted portfolios frequently outperformed sector indices on both cumulative return and risk-adjusted (Sharpe ratio) measures. However, during the volatile period, many LLM portfolios underperformed, suggesting that current models may struggle to adapt to regime shifts or high-volatility environments underrepresented in their training data. Importantly, when LLM-based stock selection is combined with traditional optimization techniques, portfolio outcomes improve in both performance and consistency. This study contributes one of the first multi-model, cross-provider evaluations of generative AI algorithms in investment management. It highlights that while LLMs can effectively complement quantitative finance by enhancing stock selection and interpretability, their reliability remains market-dependent. The findings underscore the potential of hybrid AI-quantitative frameworks, integrating LLM reasoning with established optimization techniques, to produce more robust and adaptive investment strategies.
Abstract (translated)
本文研究了领先供应商（OpenAI、Google、Anthropic、DeepSeek 和 xAI）提供的大型语言模型（LLMs）在基于量化行业的投资组合构建中的应用。我们使用这些模型来识别标普500指数中各行业成分股的投资范围，并评估它们的选择与经典投资组合理论方法结合后的表现。每个模型被提示选择并加权各行业中20只股票，然后我们将生成的投资组合与其他同类市场指数在两个不同的样本外时间段进行了比较：一个稳定的市场时期（2025年1月至3月）和一个动荡的市场时期（2025年4月至6月）。我们的研究结果揭示了LLM投资组合绩效具有明显的时变特性。在稳定市场的条件下，通过累积回报和风险调整后收益（夏普比率）衡量，LLM加权的投资组合常常优于行业指数表现。然而，在动荡的市场期间，许多由LLM构建的投资组合的表现不佳，这表明当前模型可能难以适应其训练数据中代表性不足的制度转变或高波动性环境。值得注意的是，当基于LLM的选择与传统优化技术相结合时，投资组合在性能和一致性方面都有所提升。 这项研究提供了对生成式AI算法在资产管理中的多模态、跨供应商评估的一个早期示例。研究表明，虽然LLMs可以通过增强选股能力和可解释性有效地补充量化金融，但其可靠性仍然依赖于市场条件。这些发现强调了混合AI-量化框架的潜力，即结合LLM推理和成熟的优化技术来生成更加稳健且适应性强的投资策略。
URL
https://arxiv.org/abs/2512.24526
