  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.
