Market Abnormality Period Detection via Co-movement Attention Model

Yue Wang, Chenwei Zhang, Shen Wang, Philip S Yu, Lu Bai, Lixin Cui

Abstract: The financial contagion describes a widespread phenomenon of the interdependency for pairs of stock time series during the market abnormality periods. Since the interdependency rule between stocks varies in different periods, it is difficult to capture the interdependency rule for stocks related to the market status effectively. We define this interdependency rule as, the co-movement pattern, a high-dimensional co-varying relationship between pairs of stock time series and propose a Co-movement Attention Model (CAM) to discover the co-movement patterns for the stocks related to the market status. With the discovered patterns, CAM focuses on the stock-level abnormality periods by the co-movement attention automatically. CAM is trained under the supervision of the stock sector label information. CAM has the ability to model financial contagion and detect global market abnormality periods, by modeling co-movement patterns on every pair-wise stocks. We verify our methods on the real-world stock data and compare it with state of the art methods. The experimental result shows that our method not only captures the co-movement attentions with better quantitative metric values but also covers more real market abnormalities than the other alternatives.