A novel graph convolutional feature based convolutional neural network for stock trend prediction

Stock trend prediction is one of the most widely investigated and challenging problems for investors and researchers. Since the convolutional neural network (CNN) was introduced to analyze financial data, many researchers have dedicated to predicting stock trend by transforming stock market data into images. However, most of the existing studies just focused on individual stock information, and ignored stock market information, such as the existing correlations between stocks. In fact, the price volatility of a stock may be affected by those of other stocks, thus, taking the stock market information into the stock trend prediction can further improve the prediction performance. In this paper, we propose a novel method for stock trend prediction using graph convolutional feature based convolutional neural network (GC-CNN) model, in which both stock market information and individual stock information are considered. Specifically, an improved graph convolutional network (IGCN) and a Dual-CNN are designed to construct GC-CNN, which can simultaneously capture stock market features and individual stock features. Six randomly selected Chinese stocks are used to demonstrate the superior performance of the proposed GC-CNN based method. The experimental analysis demonstrates that the proposed GC-CNN based method outperforms several stock trend prediction methods and stock trading strategies. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

The paper introduces a novel method for stock trend prediction using GC-CNN model, which considers both stock market information and individual stock information. Experimental analysis demonstrates that the proposed method outperforms several stock trend prediction methods and stock trading strategies.