FactorGCL：一种基于超图的因子模型，结合时序残差对比学习用于股票收益预测
FactorGCL: A Hypergraph-Based Factor Model with Temporal Residual Contrastive Learning for Stock Returns Prediction
AI总结：
FactorGCL提出基于超图的因子模型，通过级联残差超图架构和时序残差对比学习，从残差信息中挖掘隐藏因子，以捕捉股票收益与因子间的高阶非线性关系，并在真实市场数据上验证其优于现有方法。
摘要：
作为经济学和金融学中的一种基础方法，因子模型在量化投资中得到了广泛应用。近年来，该领域出现了从具有专家设计因子的传统线性模型向更灵活的、具有数据驱动因子的非线性机器学习模型转变的范式迁移，旨在提升这些因子模型的有效性。然而，由于市场数据中信噪比较低，在数据驱动模型中挖掘有效因子仍然具有挑战性。在本工作中，我们提出了一种基于超图的因子模型，并引入时序残差对比学习（FactorGCL），该模型利用超图结构更好地捕捉股票收益与因子之间的高阶非线性关系。为了挖掘能够补充人工设计先验因子以预测股票收益的隐藏因子，我们设计了一种级联残差超图架构，其中隐藏因子是从去除先验因子影响后的残差信息中提取的。此外，我们提出了一种时序残差对比学习方法，通过对比不同时间段内股票特定的残差信息，引导提取有效且全面的隐藏因子。我们在真实股票市场数据上的大量实验表明，FactorGCL不仅优于现有的最先进方法，而且能够挖掘出有效的隐藏因子用于预测股票收益。
英文摘要：
As a fundamental method in economics and finance, the factor model has been extensively utilized in quantitative investment. In recent years, there has been a paradigm shift from traditional linear models with expert-designed factors to more flexible nonlinear machine learning-based models with data-driven factors, aiming to enhance the effectiveness of these factor models. However, due to the low signal-to-noise ratio in market data, mining effective factors in data-driven models remains challenging. In this work, we propose a hypergraph-based factor model with temporal residual contrastive learning (FactorGCL) that employs a hypergraph structure to better capture high-order nonlinear relationships among stock returns and factors. To mine hidden factors that supplement human-designed prior factors for predicting stock returns, we design a cascading residual hypergraph architecture, in which the hidden factors are extracted from the residual information after removing the influence of prior factors. Additionally, we propose a temporal residual contrastive learning method to guide the extraction of effective and comprehensive hidden factors by contrasting stock-specific residual information over different time periods. Our extensive experiments on real stock market data demonstrate that FactorGCL not only outperforms existing state-of-the-art methods but also mines effective hidden factors for predicting stock returns.
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。
