Exchange rate prediction using hybrid neural networks and trading indicators
Exchange rate prediction using hybrid neural networks and trading indicators
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DOI：
10.1016/j.neucom.2008.09.023
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发表时间：
2009-08
期刊：
影响因子：
6
通讯作者：
He Ni;Hujun Yin
中科院分区：
文献类型：
--
作者：
He Ni;Hujun Yin
本文描述了一种混合模型，由各种回归神经网络模型，如时间自组织映射和支持向量回归，外汇汇率时间序列的建模和预测的混合。一组选定的有影响力的交易指标，包括移动平均收敛/发散和相对强度指数，也利用在所提出的方法。采用遗传算法对混合回归模型和经济指标的信息进行融合。实验结果和比较表明，所提出的方法优于全球建模技术，如广义自回归条件异方差的利润回报。一个虚拟交易系统的建立，以检查所研究的方法的性能。
This paper describes a hybrid model formed by a mixture of various regressive neural network models, such as temporal self-organising maps and support vector regressions, for modelling and prediction of foreign exchange rate time series. A selected set of influential trading indicators, including the moving average convergence/divergence and relative strength index, are also utilised in the proposed method. A genetic algorithm is applied to fuse all the information from the mixture regression models and the economical indicators. Experimental results and comparisons show that the proposed method outperforms the global modelling techniques such as generalised autoregressive conditional heteroscedasticity in terms of profit returns. A virtual trading system is built to examine the performance of the methods under study.