Multi-scale Time Based Stock Appreciation Ranking Prediction via Price Co-movement Discrimination

The prediction of the stock market trends is an important problem and has attracted tremendous research interest. However, previous methods often consider modeling each stock separately and rarely leverage the information between different stocks to jointly train a model. In this paper, we address the problem of predicting the stock market trends and bring two key insights. First, we show that a better prediction model can be trained by simultaneously considering the features of correlated stocks. Unlike previous methods, our model does not rely on any prior manual input knowledge. Second, we observe that stock trend information on a single time scale is confined and not sufficient because the holding period can be different among investors. We thus design an encoder with multiple time scales to capture features for different time granularity. On top of these, we present a novel stock trend prediction framework called MPS. Extensive experiments are conducted on both the China A-Shares and NASDAQ markets, and results show that MPS outperforms baselines on different holding periods.