روشی ترکیبی جهت پیشبینی قیمت سهام در بازار بورس ایران با بهره گیری از یادگیری عمیق بهینه شده
الموضوعات : electrical and computer engineering
محسن مهدوی اصل
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مارال کلاه کج
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1 - دانشكده مهندسي كامپيوتر، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران
2 - گروه مهندسی كامپيوتر، واحد سوسنگرد، دانشگاه آزاد اسلامی، سوسنگرد، ایران
الکلمات المفتاحية: بهینهسازی, پیشبینی, شبکه عصبی کانولوشن, یادگیری عمیق,
ملخص المقالة :
پیشبینی دقیق قیمت سهام همواره یکی از چالشهای اساسی در بازارهای مالی بوده است و توسعهی مدلهای هوشمند میتواند نقش مؤثری در تصمیمگیری سرمایهگذاران ایفا کند. در این پژوهش، یک چارچوب ترکیبی مبتنی بر یادگیری عمیق برای پیشبینی قیمت سهام در بازار سرمایه ایران ارائه شده است. روش پیشنهادی از شبکه عصبی کانولوشنی بهعنوان هستهی اصلی یادگیری و از الگوریتم فراابتکاری شاهین هریس برای بهینهسازی وزنها و پارامترهای مدل بهره میگیرد. هدف از این ترکیب، افزایش دقت پیشبینی و کاهش پیچیدگی محاسباتی از طریق یادگیری خودکار ویژگیها در لایههای میانی شبکه است. دادههای مورداستفاده شامل اطلاعات روزانهی سهام شرکت بهمن خودرو از تاریخ ۱۸/۰۱/۱۳۸۰ تا ۲۳/۱۲/۱۳۹۹ بوده و متغیرهایی مانند تعداد تراکنشها، حجم معاملات، ارزش، و قیمتهای دیروز، اولین، آخرین، پایانی، کمترین و بیشترین را در بر میگیرد. نتایج شبیهسازیها نشان میدهد که مدل پیشنهادیCNN-HHO نسبت به روشهای متداول مبتنی بر شبکههای عصبی و الگوریتمهای فراابتکاری، عملکرد بهتری داشته و مقدار خطای MSE آن به طور محسوسی کمتر است. بهطورکلی، نتایج این تحقیق نشان میدهد که ترکیب CNN با الگوریتم HHO میتواند بهعنوان یک رویکرد هوشمند، دقیق و کارا برای پیشبینی سریهای زمانی مالی مورداستفاده قرار گیرد و راهکار مؤثری برای تصمیمگیری آگاهانهتر در بازار بورس فراهم سازد.
[1] E. D. O. Carosia, A. E. A. da Silva, and G. P. Coelho, "Predicting the Brazilian stock market with sentiment analysis, technical indicators and stock prices: A deep learning approach," Computational Economics, vol. 65, pp. 23512378, 2025.
[2] M. Kolahkaj, A. Harounabadi, and M. Sadeghzade, "A recommender system for web mining using neural network and fuzzy algorithm," International Journal of Computer Applications, vol. 78, no. 8, pp. 20-24, Sept 2013.
[3] A. Hemmati, H. M. Arzanagh, and A. M. Rahmani, "A taxonomy and survey of big data in social media," Concurrency and Computation: Practice and Experience, vol. 36, no. 1, Article ID: e7875, Jan. 2024.
[4] M. J. Awan, et al., "Social media and stock market prediction: A big data approach," Computers, Materials & Continua, vol. 67, no. 2, pp. 2569-2583, 2021.
[5] W. Landis, S. Cha, and M. Shaalan, "On optimization of stock market prediction methods," in Proc. IEEE Int. Conf. Big Data, pp. 6116-6118, Los Angeles, CA, USA,9 -12 Dec. 2019.
[6] M. Nabipour, P. Nayyeri, H. Jabani, A. Mosavi, and E. Salwana, "Deep learning for stock market prediction," Entropy, vol. 22, no. 8, Article ID: 840, Aug 2021.
[7] D. Selvamuthu, V. Kumar, and A. Mishra, "Indian stock market prediction using artificial neural networks on tick data," Financial Innovation, vol. 5, no. 16, 12 pp., 2019.
[8] X. Pang, Y. Zhou, P. Wang, W. Lin, and V. Chang, "An innovative neural network approach for stock market prediction," The Journal of Supercomputing, vol. 76, pp. 2098-2118, 2020.
[9] M. N. Ashtiani and B. Raahmei, "News-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review," Expert Systems with Applications, vol. 217, Article ID: 119509, May 2023.
[10] S. T. Z. de Pauli, M. Kleina, and W. H. Bonat, "Comparing artificial neural network architectures for Brazilian stock market prediction," Annals of Data Science, vol. 7, 613-628, 2020.
[11] S. Deng, Y. Zhu, Y. Yu, and X. Huang, "An integrated approach of ensemble learning methods for stock index prediction using investor sentiments," Expert Systems with Applications, pt. A, vol. 238, Article ID: 121710, Mar. 2024.
[12] X. Li, P. Wu, and W. Wang, "Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong," Information Processing & Management, vol. 57, no. 5, Article ID: 102212, Sept. 2020.
[13] T. J. Strader, J. J. Rozycki, T. H. Root, and Y. H. J. Huang, "Machine learning stock market prediction studies: Review and research directions," Journal of International Technology and Information Management, vol. 28, no. 4, pp. 63-83, 2020.
[14] A. Moghar and M. Hamiche, "Stock market prediction using LSTM recurrent neural network," Procedia Computer Science, vol. 170, pp. 1168-1173, 2020.
[15] P. Balasubramanian, P. Chinthan, S. Badarudeen, and H. Sriramanm "A systematic literature survey on recent trends in stock market prediction." Peer J Computer Science, vol. 10, Article ID: e1700, 2024.
[16] M. Kolahkaj, "An image retrieval approach based on feature extraction and self-supervised learning," in Proc. 2nd. Int. Conf. on Distributed Computing and High Performance Computing, pp. 46-51, Qom, Iran, 2-3 Mar 2022.
[17] D. P. Goel, K. Mahajan, N. D. Nguyen, N. Srinivasan, and C. P. Lim, "Towards an efficient backbone for preserving features in speech emotion recognition: deep-shallow convolution with recurrent neural network," Neural Computing and Applications, vol. 35, no. 3, pp. 2457-2469, 2023.
[18] C. Szegedy, et al., "Going deeper with convolutions," in Proc. IEEE Conf. Comput. Vis. Pattern Recognition, Boston, MA, USA, 7-12 Jun. 2015.
[19] I. K. Nti, A. F. Adekoya, and B. A. Weyori, "A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction," Journal of Big Data, vol. 8, Article ID: 17, 28 pp., 2021.
[20] S. Bhoite, et al., "Stock market prediction using recurrent neural network and long short-term memory," in Proc. ICT Infrastructure and Computing: of ICT4SD, pp. 635-643, Goa, India, 29–30 Jul. 2022.
[21] C. Deng, Y. Huang, N. Hasan, and Y. Bao, "Multi-step-ahead stock price index forecasting using long short-term memory model with multivariate empirical mode decomposition," Information Sciences, vol. 607, pp. 297-321, Aug. 2022.
[22] P. Chhajer, M. Shah, and A. Kshirsagar, "The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction," Decision Analytics Journal, vol. 2, Article ID: 100015, Mar. 2022.
[23] A. Kelotra and P. Pandey, "Stock market prediction using optimized deep-ConvLSTM model," Big Data, vol. 8, no. 1, pp. 5-24, Feb. 2020.
[24] M. Kolahkaj, "A hybrid approach for intrusion detection in computer systems using optimized deep neural networks," Journal of Innovations in Computer Science and Engineering, vol. 2, Special Issues 4, pp. 70-78, 2025.
[25] M. A. Mohammad, M. Kolahkaj, "Detecting Network Anomalies Using the Rain Optimization Algorithm and Hoeffding Tree-Based Autoencoder," in Proc. 10th Int. Conf. on Web Research, pp. 137-141, Tehran, Iran, 24-25 Apr. 2024.
[26] A. A. Heidari, et al., "Harris hawks optimization: Algorithm and applications," Future Generation Computer Systems, vol. 97, pp. 849-872, Aug. 2019.
[27] P. R. Patil, D. Parasar, and S. Charhate, "Optimisation-enabled transfer learning framework for stock market prediction," Journal of Information & Knowledge Management, vol. 23, no. 2, Article ID: 2450013, 2024.
[28] A. Thakkar and K. Chaudhari, "Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions," Information Fusion, vol. 65, pp. 95-107, Jan. 2021.
[29] A. Kurani, P. Doshi, A. Vakharia, and M. Shah, "A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting," Annals of Data Science, vol. 10, no. 1, pp. 183-208, 2023.
[30] Y. Zhao and G. Yang, "Deep learning-based integrated framework for stock price movement prediction," Applied Soft Computing, vol. 133, Article ID: 109921, Jan. 2023.
[31] R. Chiong, Z. Fan, Z. Hu, and S. Dhakal, "A novel ensemble learning approach for stock market prediction based on sentiment analysis and the sliding window method," IEEE Trans. on Computational Social Systems, vol. 10, pp. 1-12, Oct. 2022.
[32] A. Ghasemieh and R. Kashef, "An enhanced Wasserstein generative adversarial network with Gramian angular fields for efficient stock market prediction during market crash periods," Applied Intelligence, vol. 1, no. 22, pp. 1-22, 2023.
[33] A. Ponmalar and V. Dhanakoti, "An intrusion detection approach using ensemble support vector machine-based chaos game optimization algorithm in big data platform," Applied Soft Computing, vol. 116, Article ID: 108295, Feb. 2022.
[34] X. Li, J. Wang, J. Tan, S. Ji, and H. Jia, "A graph neural network-based stock forecasting method utilizing multi-source heterogeneous data fusion," Multimedia Tools and Applications, vol. 81, no. 30, pp. 43753-43775, 2022.
[35] S. Meng, et. al., Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models, arXiv preprint arXiv:2410.19241, 2024.
[36] K. Yoosin, S. R. Jung, and I. Ghani, "Text opinion mining to analyze news for stock market prediction," International Journal of Advanced Soft Computing Applications, vol. 6, no. 1, pp. 1-13, Mar. 2014.
[37] K. Nti, A. F. Adekoya, and B. A. Weyori, "Efficient stock-market prediction using ensemble support vector machine," Open Computer Science, vol. 10, no. 1, pp. 153-163, 2020.
[38] H. C. Bhanujyothi and I. J. Jacob, "A hybrid CNN-LSTM attention-based deep learning model for stock price prediction using technical indicators," Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 28012-28017, Oct. 2025.
[39] W. Huang, J. Zhao, and X. Wang, "Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price," Energy Economics, vol. 132, pp. 107459, Apr. 2024.
[40] J. Dong and S. Liang, "Hybrid CNN-LSTM-GNN neural network for A-share stock prediction," Entropy, vol. 27, no. 8, pp. 881, Aug. 2025.
[41] http://old.tsetmc.com/Site.aspx?ParTree=111C, accessed on Oct. 15, 2025.
[42] https://www.nseindia.com/, accessed on Oct. 15, 2025.
[43] S. Mehtab and J. Sen, Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series, arXiv preprint arXiv:2001.09769, 2020.
[44] W. Chen, H. Zhang, M. K. Mehlawat, and L. Jia, "Mean–variance portfolio optimization using machine learning-based stock price prediction," Applied Soft Computing, vol. 100, Article ID: 106943, Mar. 2021.
[45] P. Yan, "Research on stock price prediction based on the GWO-LSTM-ATT model," Frontiers in Computing and Intelligent Systems, vol. 3, pp. 107-114, 2024.
