Asset pricing via deep graph learning to incorporate heterogeneous predictors

Abstract

Tradition financial studies on asset pricing focused on the economic indicators and media information of a stock. Recent financial studies found that the momentum spillovers of relevant firms are salient as well for measuring asset risk. However, previous studies on asset pricing via machine learning only relied on partial of these market information types. In this study, a deep learning framework is proposed to combine these three market information types with different data structures, that is, numerical economic indicators represented as scalars, media represented as textual vectors, and the influences of related firms captured by graphs. More importantly, the unique data characteristics brought by such data fusion are well addressed in the proposed learning framework. Specifically, a matrix-based module is first proposed to fuse numerical economic data and textual media, which specifically considers the interactions of the fused features. Such fused information, along with the firm relevance represented in graphs, is further integrated by a novel self-adaptive graph neural network that can address the dynamic merging of multilinked listed firms. Experiments performed on real market data demonstrate the effectiveness of the proposed approach over state-of-the-art algorithms, including eLSTM, RGCN, and TGC.