A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

被引:111
作者
Hafezi, Reza [1 ]
Shahrabi, Jamal [2 ]
Hadavandi, Esmaeil [2 ]
机构
[1] Amirkabir Univ Technol, Technol Foresight Grp, Dept Management Sci & Technol, Tehran, Iran
[2] Amirkabir Univ Technol, Dept Ind Engn, Tehran, Iran
关键词
Stock price prediction; Bat algorithm; Artificial neural network; Multi-agent system; Fundamental analysis; DAX stock price; DATA MINING SYSTEM; AGENT-BASED MODEL; FRAMEWORK; MARKET; INFERENCE;
D O I
10.1016/j.asoc.2014.12.028
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:196 / 210
页数:15
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