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- Title
Multimodal deep learning for finance: integrating and forecasting international stock markets.
- Authors
Lee, Sang Il; Yoo, Seong Joon
- 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 threefold: (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.
- Subjects
SOUTH Korea; INTERNATIONAL markets; STOCK exchanges; MULTIMODAL user interfaces; DEEP learning; STOCK price forecasting; INTERNATIONAL competition; FINANCE; DATA fusion (Statistics)
- Publication
Journal of Supercomputing, 2020, Vol 76, Issue 10, p8294
- ISSN
0920-8542
- Publication type
Article
- DOI
10.1007/s11227-019-03101-3