[1]WU Zhao-yang.Forecasting stock indexes based on a revised grey model and the ARMA model[J].CAAI Transactions on Intelligent Systems,2010,5(3):277-281.
Copy

Forecasting stock indexes based on a revised grey model and the ARMA model

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 3 Page number: 277-281 Column: 学术论文—智能系统 Public date: 2010-06-25
Title:
Forecasting stock indexes based on a revised grey model and the ARMA model
Author(s):
WU Zhao-yang
The Department of Mathematics and Statistics, Concordia University, Montreal H3G 2H9, Canada
Keywords:
grey model;  GM (1;  1) model;  ARMA model;  GMARMA model;  stock prediction
CLC:
TP18
DOI:
-
Abstract:
A hybrid grey model—autoregressive moving average (GMARMA) model, constructed by combing the GM (1, 1) model and the ARMA model, has two drawbacks. One drawback is that the GMARMA model may not be optimal since the traditional GM (1, 1) model is not optimal. The other is that the GMARMA model does not combine two submodels properly; this may also cause the GMARMA model to be suboptimal. This paper tries to first modify the GM (1, 1) model by introducing 2 parameters, the grey dimension degree and white background value. A revised GMARMA model was constructed by optimizing all parameters in the GM (1, 1) model and the ARMA model simultaneously. For convenience, we called this revised GMARMA model the RGMARMA model. Experimental results showed that the RGMARMA model has fewer prediction errors than the ARMA model or the GMARMA model and gives a new solution for construction of hybrid models. 
References:
［1］朱宁,徐标,仝殿波. 上证指数的时间序列预测模型［J］. 桂林电子工业学院学报, 2006, 26(2): 124128.
ZHU Ning, XU Biao, TONG Dianbo. Timeseries prediction model of Shanghai composite index［J］. Journal of Guilin University of Electronic Technology, 2006, 26(2): 124128.
［2］POTERBA J M, SUMMERS L H. The persistence of volatility and stock market fluctuations［J］. American Economic Review, 1986, 76: 11431151.
 ［3］郭宁，向凤红.灰色理论和神经元网络在证券市场中的应用［J］.自动化技术与应用， 2008, 27(10): 13.
GUO Ning, XIANG Fenghong.Application of grey model and neural network in the stockmarket［J］.Techniques of Automation and Applications, 2008, 27(10): 13. 
［4］FRENCH K R, SCHWERT G W, STAMHAUGH R F. Expected stock returns and volatility［J］. Journal of Financial Economics, 1987, 19: 329.
［5］李国平,于广青,陈森发. 中国股票价格灰色预测研究综述［J］. 东南大学学报: 哲学社会科学版, 2005, 7(2): 2830, 126.
LI Guoping, YU Guangqing, CHEN Senfa. A review of research on stock price gray forecast in China［J］. Journal of Southeast University: Philosophy and Social Science, 2005, 7(2): 2830, 126.
［6］吴庚申,梁平,龙新峰. 基于GMARMA的年电力负荷组合模型［J］. 湖北电力, 2005, 29(2): 2123.
WU Gengshen, LIANG Ping, 〖KG*1/3〗 LONG Xinfeng. 〖KG*1/3〗An 〖KG*1/3〗annual  electric consumption combined model based on GMARMA［J］. Hubei Electric Power, 2005, 29(2): 2123.
［7］郝永红,王学萌. 灰色动态模型及其在人口预测中的应用［J］. 数学的实践与认识, 2002, 32(5): 813820.
HAO Yonghong, WANG Xuemeng. The dynamic model ofgray system and its application to population forecasting ［J］. Mathematics in Practice and Theory, 2002, 32(5): 813820.
［8］邓聚龙.灰色系统基本方法［M］.武汉:华中理工大学出版社, 1988.
［9］袁嘉祖.灰色系统理论及其应用［M］.北京:科学出版社, 1991.
［10］李国平,林敬松.一种改进的灰色模型在股票价格中的应用［J］.商场现代化, 2005(10): 188189.
［11］刘虹,张岐山. 基于微粒群算法的GM(1,1,λ)模型的机械产品寿命预测［J］. 机械设计, 2007, 24(10): 45, 61.
 LIU Hong, ZHANG Qishan. Lifespan prediction on mechanical products of GM (1,1,λ) model based on particle swarm algorithm［J］. Journal of Machine Design, 2007, 24(10): 45, 61.
［12］谢开贵,李春燕,周家启. 基于遗传算法的GM(1,1,λ)模型［J］. 系统工程学报, 2000, 15(2): 168172.
XIE Kaigui, LI Chunyan, ZHOU Jiaqi. Gray model (GM(1,1,λ)) based on genetic algorithm［J］. Journal of Systems Engineering, 2000, 15(2): 168172.
［13］陈举华,史岩彬,沈学会. GM优化方法在机械系统寿命预测中的应用［J］. 山东大学学报:工学版, 2003, 33(4): 379381.
CHEN Juhua, SHI Yanbin, SHEN Xuehui. Application of GM( 1,1, ω*) methods in the life prediction of mechanical systems［J］. Journal of Shandong University: Engineering Science, 2003, 33(4): 379381.
Similar References:

Memo

-

Last Update: 2010-08-27

Copyright © CAAI Transactions on Intelligent Systems