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- W2896421130 abstract "Over recent decades, globalization has resulted in a steady increase in cross-border financial flows around the world. To build an abstract representation of a real-world financial market situation, we structure the fundamental influences among homogeneous and heterogeneous markets with three types of correlations: the inner-domain correlation between homogeneous markets in various countries, the cross-domain correlation between heterogeneous markets, and the time-series correlation between current and past markets. Such types of correlations in global finance challenge traditional machine learning approaches due to model complexity and nonlinearity. In this paper, we propose a novel cross-domain deep learning approach (Cd-DLA) to learn real-world complex correlations for multiple financial market prediction. Based on recurrent neural networks, which capture the time-series interactions in financial data, our model utilizes the attention mechanism to analyze the inner-domain and cross-domain correlations, and then aggregates all of them for financial forecasting. Experiment results on ten-year financial data on currency and stock markets from three countries prove the performance of our approach over other baselines." @default.
- W2896421130 created "2018-10-26" @default.
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- W2896421130 date "2018-07-01" @default.
- W2896421130 modified "2023-09-24" @default.
- W2896421130 title "Cross-Domain Deep Learning Approach For Multiple Financial Market Prediction" @default.
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- W2896421130 doi "https://doi.org/10.1109/ijcnn.2018.8489360" @default.
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