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.
Highlights
No matter for the purpose of exchange investment or exchange hedging, accurate exchange analysis and forecasting and exchange derivatives trading strategy have become the research contents of institutions and individuals in various countries [1]
Deep learning has not been widely used in time series prediction, but it has been explored; at present, scholars mainly focus on the time series prediction based on the DBN algorithm, while the time series prediction based on the SRU deep network model is rarely completed [5]
The significance of the two core algorithms proposed by the model is as follows: the exchange rate prediction algorithm based on SRU deep learning obtains more accurate results than typical traditional exchange rate prediction algorithms; the dual-objective measurement optimization algorithm of the investment portfolio further uses the results predicted by the above model to optimize the selection of investment portfolios and gives investors a more reasonable investment portfolio plan
Summary
No matter for the purpose of exchange investment or exchange hedging, accurate exchange analysis and forecasting and exchange derivatives trading strategy have become the research contents of institutions and individuals in various countries [1]. Traditional methods often fail to capture the complex characteristics of discontinuous, nonlinear, and high frequency in exchange financial time series dataset [3]. In this context, from the perspective of individual or institutional exchange traders, this paper constructs a dual-objective optimization econometric model of exchange forecast analysis portfolio from the whole process of exchange trading platform selection data collection exchange rate analysis exchange portfolio optimization selection, in order to achieve more accurate forecast portfolio selection through technical analysis, the maximization of income and the minimization of portfolio risk [4].
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