Essential tensor learning for multimodal information-driven stock movement prediction

In the literature, an increasing amount of information from various sources related to the stock market is being considered for stock movement prediction. However, previous studies usually modeled market information as a vector, failing to effectively utilize the inner structure in terms of multimodal and multitemporal characteristics. Moreover, the release, dissemination, and absorption of information causing spillover effects from stocks related to the target stock should not be neglected in today’s information society. Thus, this study proposes a general tensor representation and fusion framework to capture the intrinsic interactions of multimodal and multitemporal stock market information based on the invariant correlations among stocks within a short period. Specifically, we construct a general correlation matrix to represent the correlation between the stocks with respect to a given mode of information for a single day. Then, for a short period, with multimodal information, the matrices are concatenated into a tensor, which is highly inner correlated. A tensor robust principal component analysis (TRPCA) model is then employed to fuse the multimodal and multitemporal information, adaptively infer essential interactions, and faithfully enhance the inner correlation of the constructed tensor. Experiments on real datasets show that the proposed tensor representation and fusion framework can efficiently improve the performance of stock movement prediction. The performance of the investment simulation further illustrates the superiority of the proposed method in terms of the return rate (26.73%) for a full year.