[1]李章晓,宋微,田野.基于深度学习和进化计算的外汇预测与投资组合优化[J].郑州大学学报(工学版),2019,40(01):92.
 Exchange Rate Forecasting and Portfolio Optimization Based on Deep Learning and Evolutionary Computation[J].Journal of Zhengzhou University (Engineering Science),2019,40(01):92.
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基于深度学习和进化计算的外汇预测与投资组合优化()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年01期
页码:
92
栏目:
出版日期:
2019-01-10

文章信息/Info

Title:
Exchange Rate Forecasting and Portfolio Optimization Based on Deep Learning and Evolutionary Computation
作者:
李章晓宋微田野
文献标志码:
A
摘要:
利用深度学习和进化计算技术来分别实现对外汇价格的预测与投资组合优化。首先,利用循环神经网络建立汇率预测模型,用来预测外汇产品的价格并计算期望收益率。接着建立了一个双目标的投资组合模型,即最大化期望收益率与最小化风险。为了更接近真实的外汇交易市场,该模型中允许买空与卖空,并考虑了点差对收益的影响。基于多个外汇产品的期望收益率与投资组合模型,利用多目标进化算法来搜索出最优的投资组合。在多个外汇产品的真实历史数据上的结果表明,该方法能够实现在外汇交易市场中的盈利。
Abstract:
The techniques in deep learning and evolutionary computation were adopted to forecast the exchange rate and to optimize the portfolio respectively. Firstly, recurrent neural network is used to build an exchange rate forecasting model, to forecasting the price of instrument and to calculate its expected yield. Then, a bi-objective portfolio model, i.e., maximizing the expected yield and minimizing the risk. For approximating the real market, the proposed model could allow long and short selling, and could also consider the influence of spread. Based on the expected yields of multiple instruments and the proposed portfolio model,a multi-objective evolutionary algorithm was adopted to search for the optimal portfolio. According to the back test on the historical data of multiple instruments, it was verified that the proposed approach could make profit in the exchange market
更新日期/Last Update: 2019-03-02