Chart GCN: Learning chart information with a graph convolutional network for stock movement prediction

Abstract

Advanced deep learning methods have been widely adopted in stock movement prediction with technical analysis (TA), while researchers prefer technical indicators to technical charts due to the divergence in quantification difficulty. In traditional TA, researchers usually utilize chart similarity to solve the quantifying problem, while chart similarity is often limited to specific charts as the templates for comparison, resulting in massive inadequate use of information. Accordingly, we propose a novel similarity framework to overcome the limitation of chart similarity to specific charts. Specifically, after extracting the key point sequence (i.e., the draft chart) from the stock price series, we transform it into a graph and ultimately employ an arbitrary graph kernel such as the Weisfeiler-Lehman graph kernel and graph convolutional network (GCN) to sufficiently mine the information in the chart for stock movement prediction. Our similarity framework is more robust than the chart similarity measures commonly used in traditional TA. Additionally, we further evaluate the effectiveness of our framework on real-world stock data and show that our framework achieves the best performance compared to several state-of-the-art baselines in stock movement prediction and obtains the highest average net values in a trading simulation. Our results complement the existing application of the chart similarity method in deep learning and provide support for the investing application of financial market decisions.