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.