英文标题：
《Multimodal Deep Learning for Finance: Integrating and Forecasting
  International Stock Markets》
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作者：
Sang Il Lee and Seong Joon Yoo
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最新提交年份：
2019
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英文摘要：
  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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中文摘要：
在当今日益国际化的经济中，国际股票市场的回报和波动溢出效应是股票动态的主要宏观经济驱动力。因此，有关外国市场的信息是预测国内股票价格的最重要因素之一。然而，国内和国外市场之间的相互关系非常复杂。因此，很难用动力学方程明确表示这种互相关。在这项研究中，我们利用多模式深度学习开发了可以联合考虑国际市场的股票收益预测模型。我们的贡献有三个方面：（1）利用散点图可视化了韩国和美国股市之间的传递信息；（2） 借助多模态深度学习，我们将信息纳入股票预测模型；（3） 我们最终证明，与后期融合和单模态模型相比，早期和中期融合模型实现了显著的性能提升。我们的研究表明，联合考虑国际股票市场可以提高预测精度，而深度神经网络对于此类任务非常有效。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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