Volume 18, Issue 1 (3-2021)                   jor 2021, 18(1): 101-124 | Back to browse issues page

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Yazdani Khodashahri M B, Nasl Mosavi S H, Hosseini Shirvani M. Optimal Stock Portfolio Selection Using Hybrid Meta-Heuristic Algorithms. jor 2021; 18 (1) :101-124
URL: http://jamlu.liau.ac.ir/article-1-1988-en.html
Optimal Stock Portfolio Selection Using Hybrid Meta-Heuristic Algorithms
M. B. Yazdani Khodashahri , S. H. Nasl Mosavi * , M. Hosseini Shirvani
Department of Accounting, Qaemshahr Branch, Islamic Azad University, Qaemshahr, Iran
Abstract:   (1819 Views)
Choosing a stock portfolio is always one of the most important issues for investors. Theoretically, selecting a stock portfolio can be solved by minimizing risk assumptions with the help of mathematical relationships, but with the variety of choices in the capital market, mathematical relationships alone are not an effective solution. The variety of investment tools and the differences in the functionality of investors’complexity have complicated the selection process. Now the expansion of financial and capital markets, the use of rule-based systems for quick decisions, with minimal risk and away from human error, design, development, or improvement of these systems can be a competitive advantage. In the present study, neural network algorithms and genetic programming algorithms have been used to identify effective features and the decision tree to improve id3 has been proposed as a method for predicting price and trend of stock price change to select the optimal basket. The research results show that in addition to reducing computational and memory overhead, the proposed method is able to accurately predict severe fluctuations with nonlinear patterns and compared to modern methods such as nearest neighbor search, linear regression, autoregressive integrated moving average, and time series prophet algorithm will do better.
Keywords: The Stock Optimal Portfolio, Enhanced Decision Trees, Genetic Algorithm, Neural Networks.
Full-Text [PDF 1107 kb]   (678 Downloads)    
Type of Study: Research | Subject: Special
Received: 2020/01/25 | Accepted: 2020/08/23
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