工业工程

工业工程 ›› 2011, Vol. 14 ›› Issue (6): 133-137.

• 实践与应用 • 上一篇    下一篇

基于小波多尺度分析的股票价格组合预测方法

  

  1. 北京航空航天大学 经济管理学院， 北京 100191
  • 出版日期:2011-12-31 发布日期:2011-12-23

Combined Prediction Method of Stock Price Based on Wavelet Multi-Scale Analysis

  1.  School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
  • Online:2011-12-31 Published:2011-12-23

摘要： 股票价格是众多因素影响的综合结果，波动规律异常复杂，属于典型的非平稳时间序列。为了对股价进行更有效的预测，提出一种基于小波分析、灰色残差GM(1，1)模型和AR模型的组合预测方法。运用小波分解算法，将股价序列分解成不同尺度上的逼近信号和细节信号，分别重构成低频序列和高频序列，即股价的趋势项和随机项。根据低频序列和高频序列的不同特性，分别采用灰色残差模型和自回归模型对未来股价进行预测，重新组合生成预测价格。实证研究结果表明，该方法比传统的股价预测方法具有更高的预测精度。  

关键词: 小波分析, 灰色残差模型, 自回归模型, 预测

Abstract:  Stock price is affected by a large number of factors and is a typical nonstationary time series. In order to predict the stock price more accurately, a combined prediction method is proposed by combining the wavelet analysis, remanet GM (1, 1) model, and autoregressive (AR) model. By using the wavelet decomposing algorithm, the stock price is approximately decomposed into a number of signals of different scales. Then, these signals are reconstructed to form a number of low and high frequency time serials called the tendency part and random part of the stock price data. These serials are used for stock price prediction by using remanet GM (1, 1) and AR models, respectively, with respect to their different features. The predicted results of all serials are combined into the final prediction price. As shown in the experimental result obtained from an example, by the proposed method, the prediction accuracy is higher than that obtained by the traditional ones.

Key words: wavelet analysis, remanet GM (1, 1) model, autoregressive (AR) model, prediction