一种股票市场的深度学习复合预测模型  

Deep Learning Hybrid Forecasting Model for Stock Market

作　　者：张永安[1] 颜斌斌 ZHANG Yong-an;YAN Bin-bin(School of Economics and Management,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学经济与管理学院,北京100124

出　　处：《计算机科学》2020年第11期255-267,共13页Computer Science

基　　金：国家自然科学基金(61571052,70972115)。

摘　　要：深度学习能够从大量原始数据中提取高级抽象特征而不依赖于先验知识,对于金融市场预测具有潜在的吸引力。基于"分解—重构—综合"的思想,提出了一种全新的深度学习预测方法论,并在此基础上构建了一种股票市场单步向前的深度学习复合预测模型——CEEMD-LSTM。在此模型中,序列平稳化分解模块的CEEMD能将时间序列中不同尺度的波动或趋势逐级分解出来,产生一系列不同特征尺度的本征模态函数(Intrinsic Mode Function,IMF);采用深度学习中适合处理时间序列的长短期记忆网络(Long-Short Term Memory,LSTM)分别对每个IMF与趋势项提取高级、深度特征,并预测下一交易日收盘价的收益率;最后,综合各个IMF分量以及趋势项的预测值,得到最终的预测值。基于3类不同发达程度股票市场的股票指数的实证结果表明,此模型在预测的两个维度即预测误差与预测命中率上均要优于其他参照模型。Without relying on prior knowledge,deep learning can extract high-level abstract features from a large amount of raw data,which is potentially attractive for financial market forecasting.Based on the idea of "Decomposition-Reconstruction-Integration",this paper proposes a new method of deep learning prediction methodology,and constructs a deep learning hybrid prediction model-CEEMD-LSTM-for forecasting one-step-ahead closing price of stock market.In this model,CEEMD,as a sequence smoothing decomposition module,can decompose the fluctuations or trends of different scales in time series step by step,producing a series of Intrinsic Mode functions(IMFs) with different feature scales.Long and short-term memory network(LSTM),which is suitable for processing time series in deep learning,is adopted to extract advanced and deep features of each IMF and residual term and to predict the return of closing price of the next trading day.Finally,the predicted values of each IMF components and residual term are integrated to obtain the final predicted value.The empirical results of the stock indices from three stock markets of different developed types demonstrates that the proposed model is superior to other benchmark models in two dimensions-predictive error(RMSE,MAE,NMSE) and predicted directional symmetry(DS).

关 键 词：深度学习 深度学习预测方法论 股票市场预测 长短期记忆网络 互补集成经验模态分解 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]