Stock Price Forecasting in Iran Stock Market: A Comparative Analysis of Deep-learning Approaches
The capital market plays a crucial role within a country's financial structure and is instrumental in funding significant, long-term projects. Investments in the railway transport industry are vital for boosting other economic areas and have a profound impact on macroeconomic dynamics. Nonethel...
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University of science and culture
2023-07-01
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| Series: | International Journal of Web Research |
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| Online Access: | https://ijwr.usc.ac.ir/article_193977_e8d1fe67714618805fdbf2e9e2a64fb9.pdf |
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| author | Faraz Bodaghi Amin Owhadi Arash Khalili Nasr Melody Khadem Sameni |
| author_facet | Faraz Bodaghi Amin Owhadi Arash Khalili Nasr Melody Khadem Sameni |
| author_sort | Faraz Bodaghi |
| collection | DOAJ |
| description | The capital market plays a crucial role within a country's financial structure and is instrumental in funding significant, long-term projects. Investments in the railway transport industry are vital for boosting other economic areas and have a profound impact on macroeconomic dynamics. Nonetheless, the potential for delayed or uncertain returns may deter investors. Accurate predictions of rail company stock prices on exchanges are therefore vital for making informed investment choices and securing sustained investment. This study employs deep learning techniques to forecast the closing prices of MAPNA and Toucaril shares on the Tehran Stock Exchange. It utilizes deep neural networks, specifically One-dimensional Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM) networks, and a combined CNN-LSTM model, for stock price prediction. The effectiveness of these models is measured using various metrics, including MAE, MSE, RMSE, MAPE, and R2. Findings indicate that deep learning methods can predict stock prices effectively, with the CNN-LSTM model outperforming others in this research. According to the results, The CNN-LSTM model reached the highest R2 of 0.992. Also, based on criteria such as MAE, MSE, RMSE, and MAPE the best results belong to LSTM (Kaggle-modified) with 521.715, 651119.194, 806.920, and 0.028, respectively. |
| format | Article |
| id | doaj-art-650287d3ec7e4b14ad5d18f43f16a15b |
| institution | Kabale University |
| issn | 2645-4343 |
| language | English |
| publishDate | 2023-07-01 |
| publisher | University of science and culture |
| record_format | Article |
| series | International Journal of Web Research |
| spelling | doaj-art-650287d3ec7e4b14ad5d18f43f16a15b2024-12-26T05:54:38ZengUniversity of science and cultureInternational Journal of Web Research2645-43432023-07-0162294210.22133/ijwr.2024.407077.1170Stock Price Forecasting in Iran Stock Market: A Comparative Analysis of Deep-learning ApproachesFaraz Bodaghi0https://orcid.org/0009-0000-5241-1934Amin Owhadi1https://orcid.org/0000-0003-4829-088XArash Khalili Nasr 2https://orcid.org/0000-0001-6111-5168Melody Khadem Sameni3https://orcid.org/0000-0001-9096-7263Graduate School of Management and Economics, Sharif University of Technology, Tehran, IranSchool of Railway Engineering, Iran University of Science and Technology, Tehran, IranGraduate School of Management and Economics, Sharif University of Technology, Tehran, IranSchool of Railway Engineering, Iran University of Science and Technology, Tehran, IranThe capital market plays a crucial role within a country's financial structure and is instrumental in funding significant, long-term projects. Investments in the railway transport industry are vital for boosting other economic areas and have a profound impact on macroeconomic dynamics. Nonetheless, the potential for delayed or uncertain returns may deter investors. Accurate predictions of rail company stock prices on exchanges are therefore vital for making informed investment choices and securing sustained investment. This study employs deep learning techniques to forecast the closing prices of MAPNA and Toucaril shares on the Tehran Stock Exchange. It utilizes deep neural networks, specifically One-dimensional Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM) networks, and a combined CNN-LSTM model, for stock price prediction. The effectiveness of these models is measured using various metrics, including MAE, MSE, RMSE, MAPE, and R2. Findings indicate that deep learning methods can predict stock prices effectively, with the CNN-LSTM model outperforming others in this research. According to the results, The CNN-LSTM model reached the highest R2 of 0.992. Also, based on criteria such as MAE, MSE, RMSE, and MAPE the best results belong to LSTM (Kaggle-modified) with 521.715, 651119.194, 806.920, and 0.028, respectively.https://ijwr.usc.ac.ir/article_193977_e8d1fe67714618805fdbf2e9e2a64fb9.pdftime series predictioniran stock marketrailway stockdeep learningwavelet transformation |
| spellingShingle | Faraz Bodaghi Amin Owhadi Arash Khalili Nasr Melody Khadem Sameni Stock Price Forecasting in Iran Stock Market: A Comparative Analysis of Deep-learning Approaches International Journal of Web Research time series prediction iran stock market railway stock deep learning wavelet transformation |
| title | Stock Price Forecasting in Iran Stock Market: A Comparative Analysis of Deep-learning Approaches |
| title_full | Stock Price Forecasting in Iran Stock Market: A Comparative Analysis of Deep-learning Approaches |
| title_fullStr | Stock Price Forecasting in Iran Stock Market: A Comparative Analysis of Deep-learning Approaches |
| title_full_unstemmed | Stock Price Forecasting in Iran Stock Market: A Comparative Analysis of Deep-learning Approaches |
| title_short | Stock Price Forecasting in Iran Stock Market: A Comparative Analysis of Deep-learning Approaches |
| title_sort | stock price forecasting in iran stock market a comparative analysis of deep learning approaches |
| topic | time series prediction iran stock market railway stock deep learning wavelet transformation |
| url | https://ijwr.usc.ac.ir/article_193977_e8d1fe67714618805fdbf2e9e2a64fb9.pdf |
| work_keys_str_mv | AT farazbodaghi stockpriceforecastinginiranstockmarketacomparativeanalysisofdeeplearningapproaches AT aminowhadi stockpriceforecastinginiranstockmarketacomparativeanalysisofdeeplearningapproaches AT arashkhalilinasr stockpriceforecastinginiranstockmarketacomparativeanalysisofdeeplearningapproaches AT melodykhademsameni stockpriceforecastinginiranstockmarketacomparativeanalysisofdeeplearningapproaches |