生成式AI用于股票选择
Generative AI for Stock Selection
AI总结：
生成式AI通过检索增强生成技术，从股票数据中自动发现经济动机特征，提升短期收益预测的夏普比率，展现可解释且高效的特征发现能力。
摘要：
我们研究生成式AI是否能自动化美国股票的特征发现。使用具有检索增强生成和结构化/程序化提示的大型语言模型，我们从分析师、期权和价格成交量数据中合成经济动机的特征。这些特征随后作为输入用于表格机器学习模型，以预测短期收益。在多个数据集中，AI生成的特征在基准测试中表现一致，夏普比率提升范围从14%到91%，具体取决于数据集和配置。检索质量至关重要：更好的知识库显著改善结果。AI生成的信号与传统特征弱相关，支持组合。总体而言，当检索质量受控时，生成式AI可以有意义地增强特征发现，产生可解释的信号同时减少手动工程工作量。
英文摘要：
We study whether generative AI can automate feature discovery in U.S. equities. Using large language models with retrieval-augmented generation and structured/programmatic prompting, we synthesize economically motivated features from analyst, options, and price-volume data. These features are then used as inputs to a tabular machine-learning model to forecast short-horizon returns. Across multiple datasets, AI-generated features are consistently competitive with baselines, with Sharpe improvements ranging from 14% to 91% depending on dataset and configuration. Retrieval quality is pivotal: better knowledge bases materially improve outcomes. The AI-generated signals are weakly correlated with traditional features, supporting combination. Overall, generative AI can meaningfully augment feature discovery when retrieval quality is controlled, producing interpretable signals while reducing manual engineering effort.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
