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In today's increasingly international economy, return and volatilityspillover effects across international equity markets are major macroeconomicdrivers of stock dynamics. Thus, information regarding foreign markets is oneof the most important factors in forecasting domestic stock prices. However,the cross-correlation between domestic and foreign markets is highly complex.Hence, it is extremely difficult to explicitly express this cross-correlationwith a dynamical equation. In this study, we develop stock return predictionmodels that can jointly consider international markets, using multimodal deeplearning. Our contributions are three-fold: (1) we visualize the transferinformation between South Korea and US stock markets by using scatter plots;(2) we incorporate the information into the stock prediction models with thehelp of multimodal deep learning; (3) we conclusively demonstrate that theearly and intermediate fusion models achieve a significant performance boost incomparison with the late fusion and single modality models. Our study indicatesthat jointly considering international stock markets can improve the predictionaccuracy and deep neural networks are highly effective for such tasks.

Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets