Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets
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Stock prices are influenced by numerous factors. We present a method to combine these factors and we validate the method by taking the international stock market as a case study. In today's increasingly international economy, return and volatility spillover effects across international equity markets are major macroeconomic drivers of stock dynamics. Thus, foreign market information is one of the most important factors in forecasting domestic stock prices. However, the cross-correlation between domestic and foreign markets is so complex that it would be extremely difficult to express it explicitly with a dynamical equation. In this study, we develop stock return prediction models that can jointly consider international markets, using multimodal deep learning. Our contributions are three-fold: (1) we visualize the transfer information between South Korea and US stock markets using scatter plots; (2) we incorporate the information into stock prediction using multimodal deep learning; (3) we conclusively show that both early and late fusion models achieve a significant performance boost in comparison with single modality models. Our study indicates that considering international stock markets jointly can improve prediction accuracy, and deep neural networks are very effective for such tasks.
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