Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction |
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| Affiliation: | 1. Adana Science and Technology University, Industrial Engineering Department, Yeşiloba Yerleşkesi, 01180, Adana, Turkey;2. Kilis 7 Aralık University, Business and Administration Department, Kilis, Turkey;3. Gaziantep University, Industrial Engineering Department, Gaziantep, Turkey;1. Sustainable Energy Technologies Center, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, KSA;2. Department of Electrical Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, KSA;1. Polytechnic University of Bari, via E. Orabona, 4 –Bari 70125, Italy;2. Yahoo! Inc., 701 First Avenue –Sunnyvale,CA94089, USA;3. HP Labs, 1501 Page Mill rd. –Palo Alto,CA94304, USA;1. School of Life and Environmental Sciences & Sydney Institute of Agriculture, The University of Sydney, NSW, Australia;2. National Institute of Agricultural Sciences, Rural Development Administration, Wanju 54875, Republic of Korea;1. Computer Science and Engineering, Siksha ‘O’ Anusandhan University, Bhubaneswar, India\n;2. Multidisciplinary Research Cell, Siksha O Anusandhan University, Bhubaneswar, India |
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| Abstract: | Stock market price is one of the most important indicators of a country's economic growth. That's why determining the exact movements of stock market price is considerably regarded. However, complex and uncertain behaviors of stock market make exact determination impossible and hence strong forecasting models are deeply desirable for investors' financial decision making process. This study aims at evaluating the effectiveness of using technical indicators, such as simple moving average of close price, momentum close price, etc. in Turkish stock market. To capture the relationship between the technical indicators and the stock market for the period under investigation, hybrid Artificial Neural Network (ANN) models, which consist in exploiting capabilities of Harmony Search (HS) and Genetic Algorithm (GA), are used for selecting the most relevant technical indicators. In addition, this study simultaneously searches the most appropriate number of hidden neurons in hidden layer and in this respect; proposed models mitigate well-known problem of overfitting/underfitting of ANN. The comparison for each proposed model is done in four viewpoints: loss functions, return from investment analysis, buy and hold analysis, and graphical analysis. According to the statistical and financial performance of these models, HS based ANN model is found as a dominant model for stock market forecasting. |
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