FactorBench：面向自动化因子挖掘的投资组合感知基准
FactorBench: A Portfolio-Aware Benchmark for Automated Factor Mining
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- King’s College London（伦敦国王学院）
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中文总结 AI 辅助
针对自动化因子挖掘领域缺乏统一评估的问题，本文提出投资组合感知基准FactorBench，系统比较九种方法在五个市场挖掘的约五千个因子，发现无单一范式持续占优。
中文摘要 AI 辅助
因子挖掘旨在从金融数据中发现能预测未来资产收益并指导投资组合构建的信号。自动化因子挖掘现已涵盖遗传编程、强化学习、生成模型和大语言模型智能体等多种范式。然而，目前尚不清楚这些范式的进展是否能够产生更具泛化性、独特性和经济价值的金融信号。我们提出了FactorBench，一个投资组合感知基准，用于比较来自五个股票市场中九种自动化挖掘方法的大约五千个挖掘因子。一个共享的数据和评估契约同时支持符号表达式和可执行的Python因子，将异构的发现算法连接到通用的信号组合和投资组合构建流程。FactorBench在三个层面追踪挖掘系统的输出：因子有效性、时间泛化性以及超越已测风险与风格暴露的预测能力；方法内和方法间的池内独特性，包括与基准Alpha101的相似性；以及复合信号质量和扣除成本后的多头及多空投资组合表现。通过系统评估因子挖掘的进展是否能转化为信号质量和投资组合表现，FactorBench发现没有任何一种范式能够持续占据主导地位。
英文摘要
Factor mining seeks to discover signals from financial data that predict future asset returns and guide portfolio construction. Automated factor mining now spans genetic programming, reinforcement learning, generative models, and large language model agents. Yet it remains unclear whether advances across these paradigms yield more generalizable, distinct, and economically useful financial signals. We introduce FactorBench, a portfolio-aware benchmark comparing roughly five thousand mined factors from nine automated mining methods across five equity markets. A shared data and evaluation contract supports both symbolic expressions and executable Python factors, connecting heterogeneous discovery algorithms to common signal combination and portfolio construction procedures. FactorBench traces the outputs of mining systems across three levels: factor validity, temporal generalization, and predictiveness beyond measured risk and style exposures; within- and across-method pool distinctness, including similarity to the benchmark Alpha101; and composite-signal quality and after-cost long-only and long--short portfolio performance. After systematically assessing whether advances in factor mining translate into signal quality and portfolio performance, FactorBench finds that no paradigm consistently dominates.
