Stock Price Prediction Using Deep Learning Algorithms Based on Technical Indicators
DOI:
https://doi.org/10.31181/jopi21202427Keywords:
Artificial neural networks, Stock price prediction, Technical IndicatorsAbstract
Accurately forecasting stock prices helps investors decide when and where to invest. However, the dynamic, non-linear, complex and chaotic nature of the stock market makes price forecasting difficult. Market movements are influenced by many macroeconomic factors such as political events, corporate policies, economic conditions, commodity prices and bank interest rates. In addition, advances in technology and communication systems allow these events to be processed quickly, causing stock prices to fluctuate rapidly. Banks, financial institutions and large investors are therefore forced to act quickly, and the complexity of the market makes accurate forecasting even more difficult. Therefore, new and effective methods must be developed for accurate stock price predictions. In this study, technical indicators were utilized to reduce the noise in raw data, obtain meaningful results, and enhance prediction accuracy. Artificial Neural Network (ANN) models demonstrate significant efficiency in analyzing financial time series data. The most appropriate parameters for predictions made with ANN were selected using the correlation coefficient method. The objective was to predict stock prices using technical indicators obtained from websites such as Yahoo Finance. Following the data preprocessing process, various methods were applied, including Single Layer Long Short Term Memory (LSTM), 3-Layer LSTM, 3-Layer Bidirectional Long Short Term Memory (BiLSTM), and Hybrid Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). This model aimed not only to find realistic price estimates but also to reduce the features that influence stock price estimates through technical indicators. The results indicate that the Single Layer LSTM method provides more realistic estimates than other deep learning (DL) techniques.
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Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia computer science, 167, 599-606. https://doi.org/10.1016/j.procs.2020.03.326
Lu, W., Li, J., Li, Y., Sun, A., & Wang, J. (2020). A CNN‐LSTM‐based model to forecast stock prices. Complexity, 2020(1), 6622927. https://doi.org/10.1155/2020/6622927
Lu, W., Li, J., Wang, J., & Qin, L. (2021). A CNN-BiLSTM-AM method for stock price prediction. Neural Computing and Applications, 33(10), 4741-4753. https://doi.org/10.1007/s00521-020-05532-z
Şakrak, K. (2022). Hisse senedi gruplandırma duyurularının hisse senedi piyasa fiyatına etkisi Borsa İstanbul üzerine bir uygulama (Master's thesis, Hitit Üniversitesi Lisansüstü Eğitim Enstitüsü). http://earsiv.hitit.edu.tr/xmlui/bitstream/handle/11491/8613/kemal-sakrak2022.pdf?sequence=1
Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Comparison of ARIMA and artificial neural networks models for stock price prediction. Journal of Applied Mathematics, 2014(1), 614342. https://doi.org/10.1155/2014/614342
Anand, C. (2021). Comparison of stock price prediction models using pre-trained neural networks. Journal of Ubiquitous Computing and Communication Technologies, 3(2), 122-134. https://irojournals.com/jucct/article/view/3/2/5
Nikou, M., Mansourfar, G., & Bagherzadeh, J. (2019). Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management, 26(4), 164-174. https://doi.org/10.1002/isaf.1459
Çetinyokuş, T., & Gökçen, H. (2002). Borsada göstergelerle teknik analiz için bir karar destek sistemi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 17(1), 43-58. https://dergipark.org.tr/en/pub/gazimmfd/issue/6651/89300
Birgili, M. E. (2013). Teknik analiz yöntemini kullanan yatırımcıların davranışsal finans modelleri ile açıklanması: Türkiye'de bir araştırma (Master's thesis, Adnan Menderes Üniversitesi). http://hdl.handle.net/11607/535
Uyar, U., Kelten, G. S., & Moralı, T. (2020). YATIRIMCILAR İÇİN TEKNİK ANALİZ: BITCOIN VE ETHEREUM UYGULAMALARI. Finansal Araştırmalar ve Çalışmalar Dergisi, 12(23), 653-671. https://doi.org/10.14784/marufacd.785878
Göçken, M., Özçalıcı, M., Boru, A., & Dosdoğru, A. T. (2016). Integrating metaheuristics and artificial neural networks for improved stock price prediction. Expert Systems with Applications, 44, 320-331. https://doi.org/10.1016/j.eswa.2015.09.029
Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017, September). Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 international conference on advances in computing, communications and informatics (icacci) (pp. 1643-1647). IEEE. 10.1109/ICACCI.2017.8126078
Zahedi, J., & Rounaghi, M. M. (2015). Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange. Physica A: Statistical Mechanics and its Applications, 438, 178-187. https://doi.org/10.1016/j.physa.2015.06.033
Laboissiere, L. A., Fernandes, R. A., & Lage, G. G. (2015). Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks. Applied Soft Computing, 35, 66-74. https://doi.org/10.1016/j.asoc.2015.06.005
Nikou, M., Mansourfar, G., & Bagherzadeh, J. (2019). Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management, 26(4), 164-174. https://doi.org/10.1002/isaf.1459
Çınaroğlu, E., & Avcı, T. (2020). THY hisse senedi değerinin yapay sinir ağları ile tahmini. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 34(1), 1-19. https://doi.org/10.16951/atauniiibd.530322
Islam, M. R., & Nguyen, N. (2020). Comparison of financial models for stock price prediction. Journal of Risk and Financial Management, 13(8), 181. https://doi.org/10.3390/jrfm13080181
Nguyen, T. T., & Yoon, S. (2019). A novel approach to short-term stock price movement prediction using transfer learning. Applied Sciences, 9(22), 4745. https://doi.org/10.3390/app9224745
Shah, D., Campbell, W., & Zulkernine, F. H. (2018, December). A comparative study of LSTM and DNN for stock market forecasting. In 2018 IEEE international conference on big data (big data) (pp. 4148-4155). IEEE. 10.1109/BigData.2018.8622462
Kehinde, T. O., Chan, F. T., & Chung, S. H. (2023). Scientometric review and analysis of recent approaches to stock market forecasting: Two decades survey. Expert Systems with Applications, 213, 119299. 10.1016/j.eswa.2022.119299
Gülmez, B. (2023). Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm. Expert Systems with Applications, 227, 120346. https://doi.org/10.1016/j.eswa.2023.120346
Nasiri, H., & Ebadzadeh, M. M. (2023). Multi-step-ahead stock price prediction using recurrent fuzzy neural network and variational mode decomposition. Applied Soft Computing, 148, 110867.
https://doi.org/10.48550/arXiv.2212.14687
Zhang, J., Ye, L., & Lai, Y. (2023). Stock price prediction using CNN-BiLSTM-Attention model. Mathematics, 11(9), 1985. https://doi.org/10.3390/math11091985
Li, M., Zhu, Y., Shen, Y., & Angelova, M. (2023). Clustering-enhanced stock price prediction using deep learning. World Wide Web, 26(1), 207-232. 10.1007/s11280-021-01003-0
Kanwal, A., Lau, M. F., Ng, S. P., Sim, K. Y., & Chandrasekaran, S. (2022). BiCuDNNLSTM-1dCNN—A hybrid deep learning-based predictive model for stock price prediction. Expert Systems with Applications, 202, 117123. 10.1016/j.eswa.2022.117123
Muhammad, T., Aftab, A. B., Ibrahim, M., Ahsan, M. M., Muhu, M. M., Khan, S. I., & Alam, M. S. (2023). Transformer-based deep learning model for stock price prediction: A case study on Bangladesh stock market. International Journal of Computational Intelligence and Applications, 22(03), 2350013. https://doi.org/10.1142/S146902682350013X
Yang, S., Ding, Y., Xie, B., Guo, Y., Bai, X., Qian, J., ... & Ren, J. (2023). Advancing Financial Forecasts: A Deep Dive into Memory Attention and Long-Distance Loss in Stock Price Predictions. Applied Sciences, 13(22), 12160. https://doi.org/10.3390/app132212160
Chen, J., Wen, Y., Nanehkaran, Y. A., Suzauddola, M. D., Chen, W., & Zhang, D. (2023). Machine learning techniques for stock price prediction and graphic signal recognition. Engineering Applications of Artificial Intelligence, 121, 106038. https://doi.org/10.1016/j.engappai.2023.106038
Wang, J., Liu, J., & Jiang, W. (2024). An enhanced interval-valued decomposition integration model for stock price prediction based on comprehensive feature extraction and optimized deep learning. Expert Systems with Applications, 243, 122891. https://doi.org/10.1016/j.eswa.2023.122891
Doğan, F., & Türkoğlu, İ. (2018). Derin öğrenme algoritmalarının yaprak sınıflandırma başarımlarının karşılaştırılması. Sakarya University Journal of Computer and Information Sciences, 1(1), 10-21. http://saucis.sakarya.edu.tr/en/pub/issue/37127/427798
Şeker, A., Diri, B., & Balık, H. H. (2017). Derin öğrenme yöntemleri ve uygulamaları hakkında bir inceleme. Gazi Mühendislik Bilimleri Dergisi, 3(3), 47-64. https://dergipark.org.tr/en/pub/gmbd/issue/31064/372661
Doğan, F., & Türkoğlu, İ. (2019). Derin öğrenme modelleri ve uygulama alanlarına ilişkin bir derleme. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 10(2), 409-445. https://doi.org/10.24012/dumf.411130
Keskenler, M. F., & Keskenler, E. F. (2017). Geçmişten günümüze yapay sinir ağları ve tarihçesi. Takvim-i Vekayi, 5(2), 8-18. https://dergipark.org.tr/en/download/article-file/396994
Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of pharmaceutical and biomedical analysis, 22(5), 717-727. https://doi.org/10.1016/S0731-7085(99)00272-1
McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5, 115-133. https://link.springer.com/article/10.1007/BF02478259
Chauhan, R., Ghanshala, K. K., & Joshi, R. C. (2018, December). Convolutional neural network (CNN) for image detection and recognition. In 2018 first international conference on secure cyber computing and communication (ICSCCC) (pp. 278-282). IEEE. 10.1109/ICSCCC.2018.8703316
Hossain, M. A., & Sajib, M. S. A. (2019). Classification of image using convolutional neural network (CNN). Global Journal of Computer Science and Technology, 19(2), 13-14. 10.34257/GJCSTDVOL19IS2PG13
Wu, J. M. T., Li, Z., Herencsar, N., Vo, B., & Lin, J. C. W. (2023). A graph-based CNN-LSTM stock price prediction algorithm with leading indicators. Multimedia Systems, 29(3), 1751-1770. https://link.springer.com/article/10.1007/s00530-021-00758-w
Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2016). LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems, 28(10), 2222-2232.10.1109/TNNLS.2016.2582924
Chen, S., & Ge, L. (2019). Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction. Quantitative Finance, 19(9), 1507-1515. https://doi.org/10.1080/14697688.2019.1622287
Wöllmer, M., Eyben, F., Schuller, B., & Rigoll, G. (2011, May). A multi-stream ASR framework for BLSTM modeling of conversational speech. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4860-4863). IEEE. 10.1109/ICASSP.2011.5947444
Althelaya, K. A., El-Alfy, E. S. M., & Mohammed, S. (2018, April). Evaluation of bidirectional LSTM for short-and long-term stock market prediction. In 2018 9th international conference on information and communication systems (ICICS) (pp. 151-156). IEEE. 10.1109/IACS.2018.8355458
Kumar, P., Batra, S., & Raman, B. (2021). Deep neural network hyper-parameter tuning through twofold genetic approach. Soft Computing, 25(13), 8747-8771. https://doi.org/10.1007/s00500-021-05770-w
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