Forecasting Price Movements of Global Financial Indexes Using Complex Quantitative Financial Networks.

As predicting trends in the financial market becomes more important, and artificial intelligence technology advances, there is active research on predicting stock movements by analyzing historical prices. However, few attempts have been made to utilize complex financial networks to predict price movements. Most studies focus only on the target financial index, and only a few studies examine both the target financial index and influential financial indexes. To fill the research gap, we propose a novel deep learning algorithm based on quantitative complex financial networks to forecast the price movement of global financial indexes using technical analysis. In other words, we propose a method that analyses the causal relationship between financial indexes in a cleaned correlation network, which considers the causal impact in this quantitatively constructed network. We use the random matrix theory to construct the financial network first, and we use transfer entropy to find directional impact within the network. Based on a daily historical dataset for global indexes and out-of-sample tests, the results show that the proposed method outperforms past state-of-the-art algorithms. Our findings reveal that identifying and using proper financial networks are important in predicting problems. Our study suggests that it is important to develop deep learning algorithms and to consider the financial network based on complex system theory when solving prediction problems in the financial market.