RT - Journal Article T1 - Optimal Stock Portfolio Selection Using Hybrid Meta-Heuristic Algorithms JF - JAMLU YR - 2021 JO - JAMLU VO - 18 IS - 1 UR - http://jamlu.liau.ac.ir/article-1-1988-en.html SP - 101 EP - 124 K1 - The Stock Optimal Portfolio K1 - Enhanced Decision Trees K1 - Genetic Algorithm K1 - Neural Networks. AB - 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. LA eng UL http://jamlu.liau.ac.ir/article-1-1988-en.html M3 10.52547/jamlu.18.1.101 ER -