Model-Guided Stock Movements Prediction with Homogeneous–Heterogeneous pattern learning

Abstract

Stock movement prediction is a difficult task in the field of financial technology due to non-stationary dynamics and complex market interdependencies. Most of the existing research is based on deep neural networks, which lack interpretability. An interpretable prediction method helps uncover the mystery of the underlying operating mechanism of the securities market. In this work, we propose a model-guided method with interpretable homogeneous–heterogeneous processing for stock movement prediction. Specifically, based on that the correlations among the entities in the market are homogeneous within a short period, we unroll the iterative algorithm for solving the tensor robust principal component analysis (TRPCA) to separate the homogeneous and heterogeneous patterns from multiview data. Then, a specialized tensor-based attention for homogeneous and heterogeneous feature extraction is designed, and embedded in long short-term memory (LSTM) for better prediction. Experiments on real datasets show our model’s superiority over state-of-the-art stock forecast methods.