Recurrent neural network and a hybrid model for prediction of stock returns
Recurrent neural network and a hybrid model for prediction of stock returns
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DOI：
10.1016/j.eswa.2014.12.003
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发表时间：
2015-04-15
影响因子：
8.5
通讯作者：
Sastry, V. N.
中科院分区：
文献类型：
--
作者：
Rather, Akhter Mohiuddin;Agarwal, Arun;Sastry, V. N.
在本文中，我们提出了一种稳健的、新颖的股票收益预测混合模型。该模型由两个线性模型组成：自回归滑动平均模型、指数平滑模型和一个非线性模型：递归神经网络。用一种新的回归模型生成递归神经网络的训练数据。与线性模型相比，递归神经网络产生了令人满意的预测。为了进一步提高预测的精度，提出的混合预测模型融合了从这三个基于预测的模型获得的预测。提出了一种为模型生成最优权值的优化模型，并用遗传算法对模型进行了求解。结果证实了回归神经网络预测性能的准确性。正如预期的那样，所提出的混合预测模型的预测性能优于递归神经网络。该模型在基于预测的模型领域是一种很有前途的方法，这些模型的数据是非线性的，其模式很难被传统模型捕获。(C)2014爱思唯尔有限公司。保留所有权利。
In this paper, we propose a robust and novel hybrid model for prediction of stock returns. The proposed model is constituted of two linear models: autoregressive moving average model, exponential smoothing model and a non-linear model: recurrent neural network. Training data for recurrent neural network is generated by a new regression model. Recurrent neural network produces satisfactory predictions as compared to linear models. With the goal to further improve the accuracy of predictions, the proposed hybrid prediction model merges predictions obtained from these three prediction based models. An optimization model is introduced which generates optimal weights for proposed model; the model is solved using genetic algorithms. The results confirm about the accuracy of the prediction performance of recurrent neural network. As expected, an outstanding prediction performance has been obtained from proposed hybrid prediction model as it outperforms recurrent neural network. The proposed model is certainly expected to be a promising approach in the field of prediction based models where data is non-linear, whose patterns are difficult to be captured by traditional models. (C) 2014 Elsevier Ltd. All rights reserved.