Relational Temporal Graph Convolutional Networks for Ranking-Based Stock Prediction

Stock prediction is an attractive topic in fintech. However, traditional solutions for stock prediction have two drawbacks: (1) Some focus on the temporal patterns of stocks and model each stock as an independent individual but neglect their relations. Some models consider the relations among stocks, but work in a two-step format (i.e., capturing the temporal patterns first and then considering the relation dependency), which makes them complex and inefficient; (2) They model the stock prediction as a regression (predicting stock price) or classification task (predicting stock trend), which cannot optimize the target of investment, i.e., selecting the best stocks from the exchange market with the highest expected revenue in the future. To fully utilize the relations among stocks and achieve the highest revenue, a relation-temporal graph convolutional network (RT-GCN) is proposed. We first model the relations among stocks and their daily features into a relation-temporal graph. Then, we apply RT-GCN and three relation-aware strategies to realize the relation-temporal feature extraction for each stock. Finally, the features are fed for score calculation in a learning-to-rank way, and the stock with the highest score represents the highest investment revenue in the future. Extensive experiments demonstrate the effectiveness and efficiency of our method.