Exchange Rate Forecasting Based on Deep Learning and NSGA-II Models. (22nd September 2021)

• Record Type: Journal Article
• Authors: Chen, Jun; Zhao, Chenyang; Liu, Kaikai; Liang, Jingjing; Wu, Huan; Xu, Shiyan
• Other Names: Ahmed Syed Hassan Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2021(2021)
• Publication Date: 2021-09-22
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2021/2993870
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : Today, the global exchange market has been the world's largest trading market, whose volume could reach nearly 5.345 trillion US dollars, attracting a large number of investors. Based on the perspective of investors and investment institutions, this paper combines theory with practice and creatively puts forward an innovative model of double objective optimization measurement of exchange forecast analysis portfolio. To be more specific, this paper proposes two algorithms to predict the volatility of exchange, which are deep learning and NSGA-II-based dual-objective measurement optimization algorithms for the exchange investment portfolio. Compared with typical traditional exchange rate prediction algorithms, the deep learning model has more accurate results and the NSGA-II-based model further optimizes the selection of investment portfolios and finally gives investors a more reasonable investment portfolio plan. In summary, the proposal of this article can effectively help investors make better investments and decision-making in the exchange market.