Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Markets
This addresses the problem of complex cross-correlations in international stock markets for financial analysts and investors, but it is incremental as it applies existing multimodal deep learning techniques to a specific domain.
The study tackled forecasting domestic stock prices by integrating information from international markets using multimodal deep learning, resulting in significant performance boosts for early and intermediate fusion models compared to late fusion and single modality models.
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.