Exchange rate forecasting: comparison of various architectures of neural networks
Exchange rate forecasting: comparison of various architectures of neural networks
复制标题
DOI：
10.1007/s00521-010-0385-5
复制
发表时间：
2011-04
影响因子：
6
通讯作者：
A. K. Dhamija;V. K. Bhalla
中科院分区：
文献类型：
--
作者：
A. K. Dhamija;V. K. Bhalla
本文评价了神经网络在汇率预测中的预测精度。采用不同结构的多层感知器（MLP）和径向基函数（RBF）网络对5个汇率时间序列进行预测。根据所使用的网络和架构，对每个预测的结果进行评估和比较。研究发现，神经网络可以有效地用于预测汇率，从而在设计交易策略。在我们的仿真实验中，RBF网络的性能优于MLP网络。这个实验表明，有可能提取隐藏在汇率中的信息并预测未来。
This paper evaluates the predictive accuracy of neural networks in forecasting exchange rate. The multi-layer perceptron (MLP) and radial basis function (RBF) networks with different architectures are used to forecast five exchange rate time series. The results of each prediction are evaluated and compared according to the networks and architectures used. It is found that neural networks can be effectively used in forecasting exchange rate and hence in designing trading strategies. RBF networks performed better than MLP networks in our simulation experiment. This experiment suggests that it is possible to extract information hidden in the exchange rate and predict it into future.