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

## TL;DR

This paper develops multimodal deep learning models to improve stock return forecasting by integrating international market data, demonstrating significant performance gains over traditional methods.

## Contribution

It introduces a multimodal deep learning approach that effectively combines domestic and foreign market information for stock prediction, with visualizations and fusion strategies.

## Key findings

- Early and intermediate fusion models outperform late fusion and single modality models.
- Joint consideration of international markets enhances prediction accuracy.
- Deep neural networks are highly effective for international stock market forecasting.

## Abstract

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.