Using A Trainable Neural Network Ensemble for Trend Prediction of Tehran Stock Exchange

被引：0

作者：

Nikoo, Hossein ^{[1
]}

Azarpeikan, Mahdi ^{[3
]}

Yousefi, Mohammad Reza ^{[3
,5
]}

Ebrahimpour, Reza ^{[4
,5
]}

Shahrabadi, Abolfazl ^{[2
,6
]}

机构：

[1] Qazvin Islamic Azad Univ, Fac Management, Finance, Qazvin, Iran

[2] Qazvin Islamic Azad Univ, Fac Management, Qazvin, Iran

[3] Shahid Rajaee Univ, Dept Elect Engn, Elect Engn, Tehran, Iran

[4] Shahid Rajaee Univ, Dept Elect Engn, Tehran, Iran

[5] Sch Cognit Sci, Inst Studies Theoret Phys & Math, Tehran, Iran

[6] Zanjan Islamic Azad Univ, Dept Human Sci, Zanjan, Iran

来源：

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY | 2007年 / 7卷 / 12期

关键词：

Trainable neural network ensemble; Stock price trend prediction; Tehran Stock Exchange; Iran;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper represents a study of neural network ensembles for stock price trend prediction. The historical data available in this case study are from Kharg petrochemical company in TSE (Tehran stock Exchange). This company is a big producer of petrochemicals, including methanol, in Iran and its stock price is very much dependent on world methanol price. The results show how neural network ensembles can overcome just a Multilayered Perceptrons (MLPs), as a Non-parametric combinatorial forecasting method. This study also demonstrates how we can bit the market without the use of extensive market data or knowledge.

引用

页码：287 / 293

页数：7

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