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Deep Learning Approach for Stock Price Prediction: Comparing Single and Hybrid Models | ||
| Advances in Industrial Engineering | ||
| دوره 58، شماره 1، شهریور 2024، صفحه 237-249 اصل مقاله (513.81 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/aie.2024.372470.1889 | ||
| نویسندگان | ||
| Asgar Noorbakhsh* 1؛ Mostafa Shaygani2 | ||
| 1Assistant Professor, Department of Financial Management, Faculty of Management and Accounting, Farabi Campus of University of Tehran, Qom, Iran. | ||
| 2M.Sc., Department of Financial Management, Faculty of Management and Accounting, Farabi Campus of University of Tehran, Qom, Iran. | ||
| چکیده | ||
| This study utilizes deep-learning models for stock price prediction, focusing on data from five companies listed on the Tehran Stock Exchange over the period 2001 to 2022. Five models are employed, including two hybrid models and three single models. The hybrid CNN-LSTM model serves as the primary model, with its predictive accuracy compared against the other four models. Results indicate that the CNN-LSTM model demonstrates superior performance relative to the others, although the CNN-GRU hybrid model also yields satisfactory results. Interestingly, among the single models, the CNN model surpasses both the LSTM and GRU models, defying initial expectations. The accuracy of the models is notably impacted by factors such as volatility, which increases uncertainty. This research, which exclusively relies on technical indicators, suggests that achieving optimal results hinges not only on selecting the right neural network but also on determining the appropriate number of layers in each model. Overall, the CNN-LSTM model delivers the best performance across four of the five stocks, with the CNN-GRU model slightly outperforming it for one stock. Among the single models, the CNN model consistently outperforms the others. | ||
| کلیدواژهها | ||
| Deep Learning؛ Convolutional Neural Network (CNN)؛ Long Short-Term Memory Neural Network (LSTM)؛ Gated Recurrent Unit (GRU)؛ Stock Price Prediction | ||
| مراجع | ||
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