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A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price
Affiliation:1. Technology Foresight Group, Department of Management, Science and Technology, Amirkabir University of Technology, P.O. Box 15875-4413, Tehran, Iran,;2. Department of Industrial Engineering, Amirkabir University of Technology, P.O. Box 15875-4413, Tehran, Iran;3. Member of Futures Studies Research Institute;1. Département génie électrique, Ecole Mohamamdia d’Ingénieurs (EMI), Université Mohammed V Agdal, Rabat, Morocco;2. Laboratoire de Recherche en Economie de l’Energie, Environnement et Ressources, Département d’Economie, University Caddy Ayyad, Marrakech, Morocco;1. Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan University, Khandagiri, Bhubaneswar, OD 751030, India;2. Department of Computer Science & Engineering, Veer Surendra Sai University of Technology, Burla, Sambalpur, OD 768018, India;1. Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran;2. Technology Foresight Group, Department of Management, Science and Technology, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran;3. Member of Futures Studies Research Institute, Tehran, Iran;4. Dept. of Engineering Mgmt., Portland State University, Portland, OR, USA;1. Department of Information Engineering, I-Shou University, Kaohsiung 84001, Taiwan;2. Department of Electrical Engineering, I-Shou University, Kaohsiung 84001, Taiwan;1. Institute of Business Intelligence and Knowledge Discovery, Guangdong University of Foreign Studies, Sun Yat-sen University, Guangzhou 510006, PR China;2. School of Management, Guangdong University of Foreign Studies, Higher Education Mega Center, Guangzhou 510006, PR China;3. School of Business, Sun Yat-sen University, No. 135, Xingang Xi Road, Guangzhou 510275, PR China;4. Department of Management and Marketing, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China;5. University of Kansas Medical Center, Kansas City, KS 66160, USA
Abstract: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.
Keywords:Stock price prediction  Bat algorithm  Artificial neural network  Multi-agent system  Fundamental analysis  DAX stock price
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