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 complexthat 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.
 In today's increasingly international economy, return and volatility spillover effects across international equity markets are major macroeconomic drivers of stock dynamics. Thus, information regarding foreign markets is one of 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-correlation 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 by using scatter plots; (2) we incorporate the information into the stock prediction models with the help of multimodal deep learning; (3) we conclusively demonstrate that the early and intermediate fusion models achieve a significant performance boost in comparison with the late fusion and single modality models. Our study indicates that jointly considering international stock markets can improve the prediction accuracy and deep neural networks are highly effective for such tasks.
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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.
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foreign market information
information regarding foreign markets
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so complexthat it would be
highly complex. Hence, it is
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express it explicitly
explicitly express this cross-correlation
A
by
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stock prediction using
the stock prediction models with the help of
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show that both early and late
demonstrate that the early and intermediate
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the late fusion and
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jointly
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jointly can improve prediction accuracy,
can improve the prediction accuracy
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very
highly