Abstract: Stock index data has the characteristics of multiple types, high dimensions, and multiple collinearity. In order to reduce the dimension of the data, eliminate the multiple collinearity, and forecast the stock price. Firstly, the deep autoencoder is constructed  based on the Restricted Boltzmann machine to encode the high-dimensional data to low dimensional space. Then the regression model is established between low-dimensional coding sequence and stock price based on BP neural network. The experimental results show that the ability of the deep autoencoder to extract the feature is better than that of the principal component analysis and factor analysis. Comparing with using original high-dimensional data, the model can reduce the computational cost and achieve higher prediction accuracy by using the coded data.

Key words: deep autoencoder, restricted Boltzmann machine, BP neural network, stock prediction

摘要： 股票指标数据种类多、维度高，且指标之间存在多重共线性。为了降低数据的维度、消除指标间的多重共线性和预测股票价格，首先构建了基于受限布尔兹曼机的深度自编码器，实现了高维数据向低维空间的压缩编码。然后基于BP神经网络建立了低维编码序列与股票价格之间的回归模型。实验结果表明，深度自编码器提取特征的能力优于主成分分析法和因子分析法；相比较使用降维前的数据，使用编码后的数据用预测股票价格，模型可以减少计算开销，并且获得更高的预测精度。

关键词: 深度自编码器, 受限布尔兹曼机, BP神经网络, 股票预测