Abstract
Forecasting foreign exchange (FX) rate and optimizing FX portfolio with the help of Artificial Intelligence has aroused wide interest among global capital market. As far as we know, this is the first paper which, from the perspective of institutional and individual investors, proposes a complete quantitative analysis decision system based on Deep Learning and NSGA-II to forecast FX rate and select FX portfolio successively. To be specific, we provide a whole procedure from data collection to FX forecasting with Stacked Autoencoders and further to optimal FX portfolio selection with NSGA-II. Furthermore, an empirical analysis has been conducted with 28 FX currency pairs, in which our algorithm has been compared with two other machine learning algorithms. Ultimately, our system provides optimized FX portfolio solutions for investors with diverse preference.
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Acknowledgements
The work was supported by the National Natural Science Foundation of China (No. 71531012), and the Natural Science Foundation of Beijing (No. 4172032).
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Shen, H., Liang, X. (2018). A Quantitative Analysis Decision System Based on Deep Learning and NSGA-II for FX Portfolio Prediction. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham. https://doi.org/10.1007/978-3-319-92058-0_55
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DOI: https://doi.org/10.1007/978-3-319-92058-0_55
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