Neural Network for Forecasting High Price and Low Price on Foreign Exchange Market

Krin Chinprasatsak, N. Niparnan, A. Sudsang
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引用次数: 1

Abstract

This research compares 4 neural networks from the original researches (I. Backpropagation Neural Network II. Bayesian Regularized Neural Network III. Empirical Mode Decomposition Stochastic Time Strength Neural Network IV. Random Data-time Effective Radial Basis Function Neural Network) and 2 proposed neural networks (I. Empirical Mode Decomposition Random Data-time Effective Radial Basis Function Neural Network II. Empirical Mode Decomposition Random Data-time Effective Bayesian Regularized Neural Network) for predicting the exchange rate of EUR/USD currency pairs using input as a technical indicator and evaluating the networks with trading simulations consisting of investment strategies, risk management methods and financial management principles. The experiments show that the proposed neural networks yield higher returns than the original researches.
基于神经网络的外汇市场高价和低价预测
本研究比较了原始研究的4种神经网络(1 .反向传播神经网络;贝叶斯正则化神经网络III。经验模态分解随机时间强度神经网络IV.随机数据-时间有效径向基函数神经网络)和2个提出的神经网络(I.经验模态分解随机数据-时间有效径向基函数神经网络II.随机数据-时间有效径向基函数神经网络)。经验模式分解随机数据-时间有效贝叶斯正则化神经网络)用于预测欧元/美元货币对的汇率,使用输入作为技术指标,并通过由投资策略,风险管理方法和财务管理原则组成的交易模拟评估网络。实验结果表明,本文提出的神经网络比原来的研究方法获得了更高的收益。
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