Modeling and characterizing multiple factors is perhaps the most important step in achieving excess returns over market benchmarks. Both academia and industry are striving to find new factors that have good explanatory power for future stock returns and good stability of their predictive power. In practice, factor investing is still largely based on linear multi-factor models, although many deep learning methods show promising results compared to traditional methods in stock trend prediction and portfolio risk management. However, the existing non-linear methods have two drawbacks: 1) there is a lack of interpretation of the newly discovered factors, 2) the financial insights behind the mining process are unclear, making practitioners reluctant to apply the existing methods to factor investing. To address these two shortcomings, we develop a novel deep multi-factor model that adopts industry neutralization and market neutralization modules with clear financial insights, which help us easily build a dynamic and multi-relational stock graph in a hierarchical structure to learn the graph representation of stock relationships at different levels, e.g., industry level and universal level. Subsequently, graph attention modules are adopted to estimate a series of deep factors that maximize the cumulative factor returns. And a factor-attention module is developed to approximately compose the estimated deep factors from the input factors, as a way to interpret the deep factors explicitly. Extensive experiments on real-world stock market data demonstrate the effectiveness of our deep multi-factor model in the task of factor investing.
翻译:模拟和定性多种因素也许是实现超额市场基准收益的最重要步骤。学术界和产业界都在努力寻找对未来股票回报和预测力稳定具有良好解释力的新因素。在实践中,要素投资在很大程度上仍然以线性多因素模型为基础,尽管许多深层次的学习方法与股票趋势预测和投资组合风险管理的传统方法相比,显示出有希望的结果。然而,现有的非线性方法有两个缺点:(1)对新发现因素缺乏解释,(2)采矿过程背后的金融洞察力不明确,使从业者不愿运用现有方法来进行要素投资。为了解决这两个缺陷,我们开发了一个新的深层次多因素模型,采用具有明确财务洞察力的工业中和市场中性模块,这有助于我们在等级结构中建立一个动态和多关系股票图表,以了解不同层次(例如工业水平和普遍水平)股票关系的图表说明。随后,采用图表关注模块来估计一系列深层因素,使累积要素回报最大化。为了解决这两个缺陷,我们开发了一个全新的多因素模型模型。我们开发了一个采用新的多因素模型,将深度数据输入到深度的市场要素,以精确地解释一个深度的模型。将数据分析从深度的模型,将数据推算出一个从深度数据到深层数据分析到深层的模型。