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Shahvaroughi Farahani, milad, Nejad Falatouri Moghaddam, Mohammadreza, Ramezani, Ali. (1401). Forecasting Tehran Price Index (TEPIX) Using Novel Meta-Heuristic Algorithms. , 8(28), 185-216. doi: 10.30495/ijfma.2022.60589.1640
milad Shahvaroughi Farahani; Mohammadreza Nejad Falatouri Moghaddam; Ali Ramezani. "Forecasting Tehran Price Index (TEPIX) Using Novel Meta-Heuristic Algorithms". , 8, 28, 1401, 185-216. doi: 10.30495/ijfma.2022.60589.1640
Shahvaroughi Farahani, milad, Nejad Falatouri Moghaddam, Mohammadreza, Ramezani, Ali. (1401). 'Forecasting Tehran Price Index (TEPIX) Using Novel Meta-Heuristic Algorithms', , 8(28), pp. 185-216. doi: 10.30495/ijfma.2022.60589.1640
Shahvaroughi Farahani, milad, Nejad Falatouri Moghaddam, Mohammadreza, Ramezani, Ali. Forecasting Tehran Price Index (TEPIX) Using Novel Meta-Heuristic Algorithms. , 1401; 8(28): 185-216. doi: 10.30495/ijfma.2022.60589.1640

Forecasting Tehran Price Index (TEPIX) Using Novel Meta-Heuristic Algorithms

International Journal of Finance & Managerial Accounting
دوره 8، شماره 28، فروردین 2023، صفحه 185-216  XML اصل مقاله (1.04 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.30495/ijfma.2022.60589.1640
نویسندگان
milad Shahvaroughi Farahani email orcid 1؛ Mohammadreza Nejad Falatouri Moghaddamorcid 2؛ Ali Ramezani3
1Department of Finance, Faculty of Management, Khatam University, Tehran, Iran.
2Ph.d Candidate, Department of Financial Management, Faculty of Management and
3Ph.d Candidate, Department of Financial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran
چکیده
The stock market involves risks and returns that, if forecasted correctly, can lead to profitability, and for this forecasting, appropriate methods are needed. It is affected by various parameters and needs a way to identify these parameters well and have a dynamic nature. The main goal of this article is forecasting Tehran Price Index (TEPIX) by using hybrid Artificial Neural Network (ANN) based on Genetic Algorithm (GA), Harmony Search (HS) particle Swarm Optimization algorithm (PSO) Moth Flame Optimization (MFO) and Whale Optimization algorithms. GA is used as feature selection. So, PSO, HS MFO and WOA are used to determine the number of input and hidden layers. We use the daily values of the stock price index of the Tehran Stock Exchange from 2013 to 2018 in order to forecasting price and test it. The accuracy of ANN, hybrid Artificial Neural Network with HS, PSO MFO and WOA is evaluated based on different loss functions such as MSE, MAE and etc. the results show that the predictability of Meta-heuristic algorithms in testing period is higher than normal ANN. Also, the predictability of hybrid WOA is higher than hybrid PSO and HS algorithms and MFO.
کلیدواژه‌ها
Whale Optimization Algorithm؛ Genetic Algorithm؛ Harmony Search؛ particle Swarm Optimization algorithm؛ Moth Flame Optimization Algorithm
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