SSTP: Stock Sector Trend Prediction with Temporal-Spatial Network

Abstract

In financial big data field, most existing work of stock prediction has focused on the prediction of a single stock trend. However, it is challenging to predict a stock price series due to its drastic volatility. While the stock sector is a group of stocks belonging to the same sector, and the stock sector index is the weighted sum of the prices of all the stocks in the sector. Therefore the trend of stock sector is more stable and more feasible to predict than that of a single stock. In this paper, we propose a new method named Stock Sector Trend Prediction (SSTP) to solve the problem of predicting stock sector trend. In SSTP method, we adopt the Relative Price Strength (RPS) to describe the trend of the stock sector, which is the relative rank of stock sector trend. In order to learn the intrinsic probability distribution of the stock sector index series, we construct the multi-scale RPS time series and build multiple independent fully-connected stock sector relation graphs based on the real relationship among stock sectors. Then, we propose a Temporal-spatial Network (TSN) to extract the temporal features from the multi-scale RPS series and the spatial features from the stock sector relation graphs. Finally, the TSN predicts and ranks the trends of the stock sector trend with the temporal-spatial features. The experimental results on the real-world dataset validate the effectiveness of the proposed SSTP method for the stock sector trend prediction.