Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange
During the recent decades, neural network models have been focused upon by researchers due to their more real performance and on this basis, different types of these models have been used in forecasting. Now, there is a question that which kind of these models has more explanatory power in forecasti...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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University of Sistan and Baluchestan
2016-10-01
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| Series: | International Journal of Business and Development Studies |
| Subjects: | |
| Online Access: | https://ijbds.usb.ac.ir/article_2635_6ec4858c51fc98ddca15dcf14a32b695.pdf |
| _version_ | 1797803306280550400 |
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| author | Abbas Ali Abounoori Esmaeil Naderi Nadiya Gandali Alikhani Hanieh Mohammadali |
| author_facet | Abbas Ali Abounoori Esmaeil Naderi Nadiya Gandali Alikhani Hanieh Mohammadali |
| author_sort | Abbas Ali Abounoori |
| collection | DOAJ |
| description | During the recent decades, neural network models have been focused upon by researchers due to their more real performance and on this basis, different types of these models have been used in forecasting. Now, there is a question that which kind of these models has more explanatory power in forecasting the future processes of the stock. In line with this, the present paper made a comparison between static and dynamic neural network models in forecasting (uninvariable) the return of Tehran Stock Exchange (TSE) index in order to find the best model to be used for forecasting this series. The data were collected daily from 26/11/2009 to 17/10/2014. The models examined in this study included two static models (Adaptive Neuro-Fuzzy Inference Systems "ANFIS" and Multi-layer Feed-forward Neural Network "MFNN") and a dynamic model (nonlinear neural network autoregressive model "NNAR"). The findings showed that based on the Mean Square Error and Root Mean Square Error criteria, ANFIS model had a much higher forecasting ability compared to other models. |
| first_indexed | 2024-03-13T05:18:51Z |
| format | Article |
| id | doaj.art-12be427d9dbe419fb479ea20377f15f8 |
| institution | Directory Open Access Journal |
| issn | 2538-3302 2538-3310 |
| language | English |
| last_indexed | 2024-03-13T05:18:51Z |
| publishDate | 2016-10-01 |
| publisher | University of Sistan and Baluchestan |
| record_format | Article |
| series | International Journal of Business and Development Studies |
| spelling | doaj.art-12be427d9dbe419fb479ea20377f15f82023-06-15T16:55:33ZengUniversity of Sistan and BaluchestanInternational Journal of Business and Development Studies2538-33022538-33102016-10-0181435910.22111/ijbds.2016.26352635Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock ExchangeAbbas Ali AbounooriEsmaeil NaderiNadiya Gandali AlikhaniHanieh MohammadaliDuring the recent decades, neural network models have been focused upon by researchers due to their more real performance and on this basis, different types of these models have been used in forecasting. Now, there is a question that which kind of these models has more explanatory power in forecasting the future processes of the stock. In line with this, the present paper made a comparison between static and dynamic neural network models in forecasting (uninvariable) the return of Tehran Stock Exchange (TSE) index in order to find the best model to be used for forecasting this series. The data were collected daily from 26/11/2009 to 17/10/2014. The models examined in this study included two static models (Adaptive Neuro-Fuzzy Inference Systems "ANFIS" and Multi-layer Feed-forward Neural Network "MFNN") and a dynamic model (nonlinear neural network autoregressive model "NNAR"). The findings showed that based on the Mean Square Error and Root Mean Square Error criteria, ANFIS model had a much higher forecasting ability compared to other models.https://ijbds.usb.ac.ir/article_2635_6ec4858c51fc98ddca15dcf14a32b695.pdfforecastingstock marketdynamic neural networkstatic neural network |
| spellingShingle | Abbas Ali Abounoori Esmaeil Naderi Nadiya Gandali Alikhani Hanieh Mohammadali Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange International Journal of Business and Development Studies forecasting stock market dynamic neural network static neural network |
| title | Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange |
| title_full | Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange |
| title_fullStr | Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange |
| title_full_unstemmed | Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange |
| title_short | Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange |
| title_sort | comparative study of static and dynamic artificial neural network models in forecasting of tehran stock exchange |
| topic | forecasting stock market dynamic neural network static neural network |
| url | https://ijbds.usb.ac.ir/article_2635_6ec4858c51fc98ddca15dcf14a32b695.pdf |
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