| تعداد نشریات | 63 |
| تعداد شمارهها | 2,257 |
| تعداد مقالات | 18,555 |
| تعداد مشاهده مقاله | 57,400,903 |
| تعداد دریافت فایل اصل مقاله | 29,417,374 |
Application of Deep-Learning-Based Models for Prediction of Stock Price in the Iranian Stock Market | ||
| Journal of Mathematics and Modeling in Finance | ||
| مقاله 8، دوره 2، شماره 1، مهر 2022، صفحه 151-166 اصل مقاله (532.96 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2022.14570 | ||
| نویسندگان | ||
| Abdulrashid Jamnia* 1؛ Mohammad Reza Sasouli2؛ Emambakhsh Heidouzahi2؛ Mohsen Dahmarde Ghaleno3 | ||
| 1Department of Economics, Higher Education Complex of Saravan, Saravan, Sistan and Baluchestan province (IRAN) | ||
| 2Department of Economics, Higher Education Complex of Saravan, (Saravan, Sistan and Baluchestan province), IRAN | ||
| 3Department of Accounting, Higher Education Complex of Saravan, Saravan, (Saravan, Sistan and Baluchestan province), IRAN | ||
| چکیده | ||
| The capital or stock market along with the money market is one of the most important parts of financial sector of the nation’s economy, providing long-term financing required for efficient production and service activities. The total stock price index as reflector of stock market fluctuation is important for finance practitioners and policy-makers. Therefore, in this research, a comparative investigation was presented on two superior deep-learning-based models, including long short-term memory (LSTM), and convolutional neural network long short-term memory (CNN)-LSTM, applied for analysing prediction of the total stock price index of Tehran stock exchange (TSE) market. The complete dataset utilized in the current analysis covered the period from September 23, 2011 to June 22, 2021 with a total of 3,739 trading days in the TSE market. Forecasting accuracy and performance of the two proposed models were appraised using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) criteria. Based on the results, the CNN-LSTM showed the lowest values of the aforementioned metrics compared to the LSTM model, and it was found that the CNN-LSTM model could be effective in providing the best prediction performance of the total stock price index on the TSE market. Eventually, graphically and numerically, various prediction results obtained from the proposed models were analysed for more comprehensive analysis. | ||
| کلیدواژهها | ||
| LSTM؛ CNN-LSTM؛ Stock Market؛ Prediction | ||
| مراجع | ||
|
[1] D. Anaconda, Anaconda Navigator 2.0.3, 2021, https://docs.anaconda.com. [2] A. Cura, H.Kucuk, E.Ergen, and I. B.Oksuzoglu, Driver Pro ling Using Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) Methods, IEEE. T. Intell. Transp., (2020), 1-11, [3] F. Gao, R. Zhang, and X. Yang, The Application of Stock Index Price Prediction with Neural Network, Comput. Appl., (2020), 25(3), 53, [4] A. E. Hassanien, and A. Darwish, Machine Learning and Big Data Analytics Paradigms, Analysis, Applications and Challenges, Springer Nature, Vol. 77. [5] S. Hochreiter, and J. F. Schmidhuber, Long Short-Term Memory, Neural. Comput., 9 (1997), pp.1735-1780, [6] Z. Hu, Y. Zhao, and M. Khushi, A Survey of Forex and Stock Price Prediction Using Deep Learning, Appl. Syst. Innov., 9 (2021), https://doi.org/10.3390/asi4010009. [7] D. Jiang, G. Li, Y. Sun, J. Kong, and B. Tao, Gesture recognition based on skeletonization algorithm and CNN with ASL database, Multimed. Tools Appl., 78 (2019), pp.29953-29970, [8] J. D.Kelleher, B. Mac Namee, and A. D'arcy, Fundamentals of machine learning for predictive data analytics, algorithms, worked examples, and case studies, MIT press, 2020. [9] Keras, Keras 2.3.1, 2021, [10] T. Kim, and H. Y. Kim, Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data, PLOS ONE, 14 (2019), [11] C. R. Ko, and H. T. Chang, LSTM-based sentiment analysis for stock price forecast, PeerJ Comput. Sci., 7 (2021). [12] F. Lazzeri, Machine Learning for Time Series Forecasting with Python, John Wiley and Sons, 2020. [13] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86 (1998), pp.2278-2324, [14] I. E. Livieris, E. Pintelas, and P. Pintelas, A CNNLSTM model for gold price time-series forecasting, Neural Comput. Appl., 32 (2020), pp.17351-17360, [15] W. Lu, J. Li, Y. Li, A. Sun, and J. Wang, A CNN-LSTM-Based Model to Forecast Stock Prices, Complexity, (2020), [16] Python, Python programming language 3.7.9, 2021, [17] F. Radenovic, G.Tolias, and O.Chum, Fine-Tuning CNN Image Retrieval with No Human Annotation, IEEE. T. Pattern. Anal., 41 (2019), pp.1655-1668,TPAMI.2018.2846566. [18] M. Sarkar, and A. De Bruyn, LSTM Response Models for Direct Marketing Analytics:Replacing Feature Engineering with Deep Learning, J. Interact. Mark., 53 (2021), pp.80-95. [19] Spyder, Spyder application 4.1.4, 2021, https://www.spyder-ide.org. [20] Tensorflow, Tensor ow 2.1.0, 2021, https://www.tensorflow.org. [21] A.Thakkar, and K. Chaudhari, A comprehensive survey on deep neural networks for stock market: The need, challenges, Expert. Syst. Appl., 177 (2021). [22] J. M. T. Wu, Z. Li, N. Herencsar, B. Vo, and J. C. W. Lin, A graph-based CNN-LSTM stock price prediction algorithm with leading indicators, Multimed. Syst., (2021). [23] G. Xu, T. Ren, Y. Chen, and W. Che, A One-Dimensional CNN-LSTM Model for Epileptic Seizure Recognition Using EEG Signal Analysis, Front. Neurosci., 14 (2020). [24] Y. Yu, X. Si, C. Hu, and J. Zhang, A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures, Neural. Comput., 31 (2019), pp.1235-1270. | ||
|
آمار تعداد مشاهده مقاله: 836 تعداد دریافت فایل اصل مقاله: 711 |
||