Multi-Channel Temporal Graph Convolutional Network for Stock Return Prediction

Abstract

Stock return prediction can help investors make better investment decisions and trends of country's economics. However, most of methods for stock return prediction are based on time-series models, treating the stocks as independent from each other. Inter-relations among stocks' time series are out of consideration. In this work, a Multi-Channel Temporal Graph Convolutional Neural Network (MCT-GCN) is proposed to optimize stock movement prediction. Experiments show that its performance is greater than benchmark algorithms, LSTM in the S&P 500.