Uzun-Kısa Süreli Bellek Ağı Kullanarak Hisse Senedi Fiyatı Tahmini

Year 2022, Volume: 6 Issue: 2, 309 - 322, 29.09.2022

Mahmut Tokmak

https://doi.org/10.31200/makuubd.1164099

Cited By: 1

Abstract

Finans dünyasında hisse senedi ticareti en önemli faaliyetlerden biridir. Hisse senedi verileri finansal bir zaman serisi olarak ele alınmaktadır. Zaman serisi olarak hisse senedi tahmini, bir finansal borsada işlem gören hisse senedinin gelecekteki değerini belirlemeye çalışma eylemidir. Finansal varlıkların fiyatını tahmin etmek, doğru tahminlerle yatırımcıların alacağı kararlarda risk faktörünü azaltabileceğinden önemlidir. Ancak hisse senedi piyasası çok çeşitli faktörlere bağlı olarak değişkenlik gösterdiğinden tahminlemesi zor bir sektör olarak bilinmektedir. Makine öğrenme yöntemleri özellikle de derin öğrenme algoritmaları birçok alanda olduğu gibi finans alanında da tahminlemede sıkça kullanılmaktadır. Bu çalışmada, derin öğrenme yöntemlerinden olan Uzun-Kısa Süreli Bellek ağları kullanarak hisse senedi tahmini yapılmıştır. Borsa İstanbul, Teknoloji Endeksi kapsamındaki dört hisse belirlenerek 2012-2022 yılları arasında 2578 günlük bir veri seti oluşturulmuş ve kurulan model ile eğitim ve test işlemi gerçekleştirilmiştir. Test işlemi sonucunda tutarlı ve gerçeğe yakın tahminler elde edilmiştir.

Keywords

Derin Öğrenme, Uzun-Kısa Süreli Bellek Ağı, Hisse Senedi Fiyatı Tahmini

Stock Price Prediction Using Long-Short-Term Memory Network

Abstract

One of the most important transactions of the financial system is stock trading. Stock price data is handle as a financial time series. Stock price predictions using time series analysis are the activity of determining the future value of stocks listed on the stock market. Predicting the price of the stock correctly reduces the risk factor in the decisions to be taken by the investors. Therefore, it is an important issue for the investor. However, because there are many variables that affect the stock price, it is a very complex process to predict. Machine learning methods, especially deep learning algorithms, are frequently used in prediction in the field of finance, as in many other fields. In this study, stock price prediction was made using Long-Short-Term Memory networks, which is one of the deep learning methods. Four stocks within the scope of Borsa İstanbul Technology Index were determined and a 2578-day data set was created between 2012 and 2022, and training and testing was carried out with the established model. As a result of the test process, consistent and realistic predictions were obtained.

Keywords

Deep Learning, Long-Short-Term Memory Network, Stock Price Prediction

References

• Aggarwal, D. (2018). Random walk model and asymmetric effect in Korean composite stock price index. Afro-Asian Journal of Finance and Accounting, 8(1), 85-104.
• Ahmar, A. S. (2019). Sutte Indicator: an approach to predict the direction of stock market movements. Songklanakarin J. Sci. Technol., 40(5), 1229-1231.
• Almasarweh, M., & Alwadi, S. (2018). ARIMA model in predicting banking stock market data. Modern Applied Science, 12(11), 309.
• Althelaya, K. A., El-Alfy, E.-S. M., & Mohammed, S. (2018). Evaluation of bidirectional LSTM for short-and long-term stock market prediction. 2018 9th international conference on information and communication systems (ICICS), 151-156. IEEE.
• Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science, 7, e623. doi: 10.7717/peerj-cs.623
• Chopra, N. (2019). Time and Frequency Analysis Using the ARMA Model: Evidence from the Indian Stock Market. İçinde Advances in Management Research (ss. 101-114). CRC Press.
• Daver, G., Karacaer, M., & Ünlü, H. (2013). Testing of BIST and TURKDEX: Random walk and market efficiency. International Journal of Economics and Finance Studies, 5(2), 10-22.
• Delavar, M., Gholami, A., Shiran, G., Rashidi, Y., Nakhaeizadeh, G., Fedra, K., & Hatefi Afshar, S. (2019). A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran. ISPRS International Journal of Geo-Information, 8(2), 99. doi: 10.3390/ijgi8020099
• Elagamy, M. N., Stanier, C., & Sharp, B. (2018). Stock market random forest-text mining system mining critical indicators of stock market movements. 2018 2nd international conference on natural language and speech processing (ICNLSP), 1-8. IEEE.
• Fan, J., Li, Q., Hou, J., Feng, X., Karimian, H., & Lin, S. (2017). A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-4/W2, 15-22. doi: 10.5194/isprs-annals-IV-4-W2-15-2017
• Google Colaboratory. (2022). Colaboratory. Erişim Tarihi: 10 Ağustos 2022, https://colab.research. google.com/
• Gururaj, V., Shriya, V. R., & Ashwini, K. (2019). Stock market prediction using linear regression and support vector machines. Int J Appl Eng Res, 14(8), 1931-1934.
• Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi: 10.1162/neco.1997.9.8.1735
• Hossain, M. A., Karim, R., Thulasiram, R., Bruce, N. D., & Wang, Y. (2018). Hybrid deep learning model for stock price prediction. 2018 ieee symposium series on computational intelligence (ssci), 1837-1844. IEEE.
• Hu, H., Tang, L., Zhang, S., & Wang, H. (2018). Predicting the direction of stock markets using optimized neural networks with Google Trends. Neurocomputing, 285, 188-195.
• Hu, Z., Zhao, Y., & Khushi, M. (2021). A survey of forex and stock price prediction using deep learning. Applied System Innovation, 4(1), 9.
• İleri, S., Karabina, A., & Kılıç, E. (2020). Comparison of Different Normalization Techniques on Speakers’ Gender Detection. Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi, 2(2), 1-12.
• Kanat, E. (2018). Türkiye’nin Teknoloji Sektörü Ve Döviz Kurları İle İlişkisi: Borsa İstanbul Teknoloji Endeksi. Finans Politik & Ekonomik Yorumlar, 55(645), 61-74.
• Liu, D., Lee, S., Huang, Y., & Chiu, C. (2020). Air pollution forecasting based on attention‐based LSTM neural network and ensemble learning. Expert Systems, 37(3). doi: 10.1111/exsy.12511
• Lu, W., Li, J., Li, Y., Sun, A., & Wang, J. (2020). A CNN-LSTM-based model to forecast stock prices. Complexity, 2020.
• Madhuri, C. R., Chinta, M., & Kumar, V. P. (2020). Stock market prediction for time-series forecasting using prophet upon arima. 2020 7th International Conference on Smart Structures and Systems (ICSSS), 1-5. IEEE.
• Mehtab, S., & Sen, J. (2019). A robust predictive model for stock price prediction using deep learning and natural language processing. arXiv preprint arXiv:1912.07700.
• Mehtab, S., & Sen, J. (2020). Stock price prediction using convolutional neural networks on a multivariate timeseries. arXiv preprint arXiv:2001.09769.
• Mehtab, S., Sen, J., & Dutta, A. (2020). Stock price prediction using machine learning and LSTM-based deep learning models. Symposium on Machine Learning and Metaheuristics Algorithms, and Applications, 88-106. Springer.
• Metin, İ. A., & Karasulu, B. (2019). İnsan aktivitelerinin sınıflandırılmasında tekrarlayan sinir ağı kullanan derin öğrenme tabanlı yaklaşım. Veri Bilimi, 2(2), 1-10.
• M’ng, J. C. P. (2018). Dynamically Adjustable Moving Average (AMA’) technical analysis indicator to forecast Asian Tigers’ futures markets. Physica A: Statistical Mechanics and its Applications, 509, 336-345.
• Moghaddam, A. H., Moghaddam, M. H., & Esfandyari, M. (2016). Stock market index prediction using artificial neural network. Journal of Economics, Finance and Administrative Science, 21(41), 89-93.
• Morris, K. J., Egan, S. D., Linsangan, J. L., Leung, C. K., Cuzzocrea, A., & Hoi, C. S. (2018). Token-based adaptive time-series prediction by ensembling linear and non-linear estimators: a machine learning approach for predictive analytics on big stock data. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 1486-1491. IEEE.
• Naik, N., & Mohan, B. R. (2019). Study of stock return predictions using recurrent neural networks with LSTM. International conference on engineering applications of neural networks, 453-459. Springer.
• Nti, K. O., Adekoya, A., & Weyori, B. (2019). Random forest based feature selection of macroeconomic variables for stock market prediction. American Journal of Applied Sciences, 16(7), 200-212.
• Panwar, B., Dhuriya, G., Johri, P., Yadav, S. S., & Gaur, N. (2021). Stock Market Prediction Using Linear Regression and SVM. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 629-631. IEEE.
• Polamuri, S. R., Srinivas, K., & Mohan, A. K. (2019). Stock market prices prediction using random forest and extra tree regression. Int. J. Recent Technol. Eng, 8(1), 1224-1228.
• Reddy, G. T., Reddy, M. P. K., Lakshmanna, K., Kaluri, R., Rajput, D. S., Srivastava, G., & Baker, T. (2020). Analysis of Dimensionality Reduction Techniques on Big Data. IEEE Access, 8, 54776-54788. doi: 10.1109/ACCESS.2020.2980942
• Rezaei, H., Faaljou, H., & Mansourfar, G. (2021). Stock price prediction using deep learning and frequency decomposition. Expert Systems with Applications, 169, 114332.
• Saxena, H., Anurag, A. V., Chirayath, N., Bendale, R., & Kaul, S. (2018). Stock prediction using ARMA. International Journal of Engineering and Management Research (IJEMR), 8(2), 1-4.
• Sevinç, A., & Buket, K. (2021). Derin Öğrenme ve İstatistiksel Modelleme Yöntemiyle Sıcaklık Tahmini ve Karşılaştırılması. Avrupa Bilim ve Teknoloji Dergisi, (28), 1222-1228.
• Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied soft computing, 90, 106181.
• Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018). A comparison of ARIMA and LSTM in forecasting time series. 2018 17th IEEE international conference on machine learning and applications (ICMLA), 1394-1401. IEEE.
• Singh, S., Parmar, K. S., & Kumar, J. (2021). Soft computing model coupled with statistical models to estimate future of stock market. Neural Computing and Applications, 33(13), 7629-7647.
• Sunny, M. A. I., Maswood, M. M. S., & Alharbi, A. G. (2020). Deep learning-based stock price prediction using LSTM and bi-directional LSTM model. 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES), 87-92. IEEE.
• Tsai, Y.-T., Zeng, Y.-R., & Chang, Y.-S. (2018). Air Pollution Forecasting Using RNN with LSTM. 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), 1074-1079. IEEE. doi: 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00178
• Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia computer science, 167, 599-606.
• Wang, H. Z., Wang, G. B., Li, G. Q., Peng, J. C., & Liu, Y. T. (2016). Deep belief network based deterministic and probabilistic wind speed forecasting approach. Applied Energy, 182, 80-93.
• Yahoo Finance. (2022). Yahoo finance. Erişim Tarihi: 06 Ağustos 2022, https://finance.yahoo.com/
• Yang, F., Chen, J., & Liu, Y. (2021). Improved and optimized recurrent neural network based on PSO and its application in stock price prediction. Soft Computing, 1-16.
• Yu, P., & Yan, X. (2020). Stock price prediction based on deep neural networks. Neural Computing and Applications, 32(6), 1609-1628.