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Prediction Turkish Airlines BIST Stock Price Through Deep Artificial Neural Network Considering Transaction Volume and Seasonal Values

Year 2023, Volume: 16 Issue: 1, 43 - 53, 31.01.2023
https://doi.org/10.17671/gazibtd.1180350

Abstract

The collection of data in the information age has led to its analysis and use in different fields. Data can be used for different purposes, such as historical information, reporting, analysis, artificial intelligence, and machine learning. Artificial intelligence is used for different purposes in different disciplines such as engineering, health, industry, production, transportation, the stock market, education, and the social sciences. In this study, Turkish Airlines’ stock price prediction was made using machine learning. Different artificial neural network methods were used, such as an FNN, LSTM, and GRU. The data set consists of daily stock market index information for Turkish Airlines in BIST between the dates of January 4, 2010, and January 31, 2022. During the training of the system, it was assessed together with the transaction volume data to reduce the effect of possible speculative behavior. Since the income of airlines carrying passengers is seasonally affected, seasonal data are also considered. The system has been trained and tested with different short-long term memory-based artificial neural network models. The performance indicators of the models were used as R-Square MSE, RMSE, and MAE. According to the R-Square, performance score of the test, the success rate of system was 97% in FNN, and 99% in LSTM and GRU. It performed well despite extreme price fluctuations due to the pandemic and economic crisis. According to these results, machine learning can be used as a decision support system for sequential data set prediction. In this study, it can be concluded that FNN, LSTM, and its derivative machine learning methods can be successfully used in air transport sector index prediction.

References

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  • J. F. Torres, D. Hadjout, A. Sebaa, F. Martínez-Álvarez, and A. Troncoso, “Deep Learning for Time Series Forecasting: A Survey”, Big Data, 9(1), 2011.
  • O. Kaynar and S. Taştan, “Comparasion of MLP Artifical Neural Network and Arima Method in Time Series Analysis”, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 33, 161-172, 2009.
  • B. K. Bayraktar, and B. Badur B. “Yapay Sinir Ağları ile Borsa Endeksi Tahmini”, Yönetim, 20(63), 2009.
  • H. Aygören, H. Sarıtaş, T. Moralı, “İMKB 100 Endeksinin Yapay Sinir Ağları ve Newton Nümerik Arama Modelleri ile Tahmini”, International Journal of Alanya Faculty of Business, 4(1), 73-88, 2012.
  • S. Siami-Namini, N. Tavakoli, A. S. Namin, “A Comparison of ARIMA and LSTM in Forecasting Time Series”, 17th IEEE International Conference on Machine Learning and Applications, IEEE, 1394-1401, 2018.
  • M. Yücesan, “Sales Forecast with YSA, ARIMA and ARIMAX Methods: An Application in the White Goods Sector”, Journal of Business Research-Türk, DOI: 10.20491/isarder.2018.414, 2018.
  • S. Kalyoncu, A. Jamil, E. Karatas, J. Rasheed, C. Djeddi, “Stock Market Value Prediction using Deep Learning”, 3rd International Conference on Data Science and Applications (ICONDATA’20), 2020.
  • E. Çınaroğlu, T. Avcı, “THY Hisse Senedi Değerinin Yapay Sinir Ağları İle Kestirimi”, Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 34(1), 1-20, 2020.
  • D. Güleryüz, E. Özden, Ü. Gülhan, “Predicting BIST 30 Index with ARIMA and RNN-LSTM Models”, 24. Finans Sempozyumu, 2021.
  • S. Ranjan, P. Kayal and M. Saraf, “Bitcoin Price Prediction: A Machine Learning Sample Dimension Approach”, Computational Economics, DOI: 10.1007/s10614-022-10262-6, 2022.
  • R. Solgi, H. A. Lo´ aiciga and M. Kram, “Long short-term memory neural network (LSTM-NN) for aquifer level time series forecasting using in-situ piezometric observations”, Journal of Hydrology, 601, 2021.
  • B. Lindemann, T. Müller, H. Vietz, N. Jazdi, M. Weyrich, “A survey on long short-term memory networks for time series prediction”, Pocedia CIRP, (99), 650-655, 2021.
  • U. Demirel, H. Cam, R. Unlu, “Predicting Stock Prices Using Machine Learning Methods and Deep Learning Algorithms: The Sample of the Istanbul Stock Exchange”, Gazi University Journal of Science, 34 (1): 63-82, 2021.
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  • K. Zarzycki, and M. Lawrynczuk, “LSTM and GRU Neural Networks as Models of Dynamical Processes Used in Predictive Control: A Comparison of Models Developed for Two Chemical Reactors”, Sensors, 21(16), 2021.
  • M. B. Er, İ. Işık, “LSTM Tabanlı Derin Ağlar Kullanılarak Diyabet Hastalığı Tahmini”, Türk Doğa ve Fen Dergisi, 10(1), 68-74, 2021.
  • S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory”, Neural Computation, 9 (8): 1735–1780, 1997.
  • X. H. Le, H. V. Ho, G. Lee, S. Jung, “Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting”, Water, 11, 1387, 2019.
  • S. Tanışman, A. A. Karcıoğlu, A. Uğur, H. Bulut, “LSTM Sinir Ağı ve ARIMA Zaman Serisi Modelleri Kullanılarak Bitcoin Fiyatının Tahminlenmesi ve Yöntemlerin Karşılaştırılması”, European Journal of Science and Technology, 32, 514-520, 2021.
  • K. Cho, B. Merrienboer, D. Bahdanau, Y. Bengio, On the Properties of Neural Machine Translation: Encoder-Decoder Approaches, arXiv:1409.1259, 2014.
  • K. Cho, B. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, “Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation”, arXiv:1406.107, 8 (3) , 2014.

İşlem Hacmi ve Mevsimsel Değerler Dikkate Alınarak Derin Yapay Sinir Ağı ile Türk Hava Yolları BIST Hisse Fiyatı Tahmini

Year 2023, Volume: 16 Issue: 1, 43 - 53, 31.01.2023
https://doi.org/10.17671/gazibtd.1180350

Abstract

Bilişim çağının getirdiği veri birikimi, bunların analiz edilerek farklı alanlarda kullanılmasını da beraberinde getirmiştir Veriler, geçmişe dönük bilgi edinme, raporlama, analiz, yapay zekâ ve makine öğrenimi gibi farklı amaçlar için kullanılabilmektedir. Yapay zekâ mühendislik, sağlık, sanayi, üretim, ulaşım, borsa, eğitim, sosyal bilimler gibi farklı disiplinlerde farklı amaçlarla kullanılmaktadır. Bu çalışmada, makine öğrenmesi ile Türk Hava Yolları hisse senedi fiyat tahmini yapılmıştır. Makine öğrenmesi olarak FNN, LSTM ve GRU gibi farklı yapay sinir ağı derin öğrenme yöntemleri kullanılmıştır. Veri seti, Türk Hava Yolları'nın 4 Ocak 2010 ile 31 Ocak 2022 tarihleri arasında BİST' teki günlük borsa endeks bilgilerinden oluşmaktadır. Sistemin eğitimi sırasında olası spekülatif davranışların etkisini azaltmak için işlem hacmi verileri ile birlikte değerlendirilmiştir. Yolcu taşıyan havayollarının gelirleri mevsimsel olarak etkileneceğinden mevsimsel veriler de dikkate alınmıştır. Sistem, farklı kısa-uzun süreli bellek tabanlı yapay sinir ağı modelleri ile eğitilmiş ve test edilmiştir. Modellerin performans göstergeleri olarak R-kare, MSE, RMSE ve MAE kullanılmıştır. Test R-kare performans değerlerine göre sistem FNN' de %97, LSTM ve GRU' da ise %99 başarı göstermiştir. Pandemi nedeniyle aşırı fiyat dalgalanmalarına ve ekonomik krize rağmen yüksek bir performans sergilediği söylenebilir. Bu sonuçlara göre, makine öğrenmesi, sıralı veri seti tahmini için bir karar destek sistemi olarak kullanılabilir. Çalışma ile FNN, LSTM ve türevleri makine öğrenme metotlarının hava yolu taşımacılık sektörü endeks tahmininde başarılı bir şekilde kullanılabileceği sonucuna varılabilir.

References

  • Internet: M. Johnston, Biggest Companies in the World by Market Cap, https://www.investopedia.com/ biggest-companies-in-the-world-by-market-cap-5212784, 31.01.2022.
  • P.N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Pearson Education, Inc, 2006, ISBN 0-321-42052-2, 2006.
  • Ö. Duru, Zaman Serileri Analizinde Arıma Modelleri ve Bir Uygulama, Yüksek Lisans Tezi, İstanbul Üniversitesi, Sosyal Bilimler Enstitüsü, 2007.
  • S. Hansun, “A new approach of moving average method in time series analysis”, Conference on New Media Studies (CoNMedia), Tangerang, Indonesia, 2014.
  • R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice. OTexts Publisher, 2018.
  • J. F. Torres, D. Hadjout, A. Sebaa, F. Martínez-Álvarez, and A. Troncoso, “Deep Learning for Time Series Forecasting: A Survey”, Big Data, 9(1), 2011.
  • O. Kaynar and S. Taştan, “Comparasion of MLP Artifical Neural Network and Arima Method in Time Series Analysis”, Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 33, 161-172, 2009.
  • B. K. Bayraktar, and B. Badur B. “Yapay Sinir Ağları ile Borsa Endeksi Tahmini”, Yönetim, 20(63), 2009.
  • H. Aygören, H. Sarıtaş, T. Moralı, “İMKB 100 Endeksinin Yapay Sinir Ağları ve Newton Nümerik Arama Modelleri ile Tahmini”, International Journal of Alanya Faculty of Business, 4(1), 73-88, 2012.
  • S. Siami-Namini, N. Tavakoli, A. S. Namin, “A Comparison of ARIMA and LSTM in Forecasting Time Series”, 17th IEEE International Conference on Machine Learning and Applications, IEEE, 1394-1401, 2018.
  • M. Yücesan, “Sales Forecast with YSA, ARIMA and ARIMAX Methods: An Application in the White Goods Sector”, Journal of Business Research-Türk, DOI: 10.20491/isarder.2018.414, 2018.
  • S. Kalyoncu, A. Jamil, E. Karatas, J. Rasheed, C. Djeddi, “Stock Market Value Prediction using Deep Learning”, 3rd International Conference on Data Science and Applications (ICONDATA’20), 2020.
  • E. Çınaroğlu, T. Avcı, “THY Hisse Senedi Değerinin Yapay Sinir Ağları İle Kestirimi”, Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 34(1), 1-20, 2020.
  • D. Güleryüz, E. Özden, Ü. Gülhan, “Predicting BIST 30 Index with ARIMA and RNN-LSTM Models”, 24. Finans Sempozyumu, 2021.
  • S. Ranjan, P. Kayal and M. Saraf, “Bitcoin Price Prediction: A Machine Learning Sample Dimension Approach”, Computational Economics, DOI: 10.1007/s10614-022-10262-6, 2022.
  • R. Solgi, H. A. Lo´ aiciga and M. Kram, “Long short-term memory neural network (LSTM-NN) for aquifer level time series forecasting using in-situ piezometric observations”, Journal of Hydrology, 601, 2021.
  • B. Lindemann, T. Müller, H. Vietz, N. Jazdi, M. Weyrich, “A survey on long short-term memory networks for time series prediction”, Pocedia CIRP, (99), 650-655, 2021.
  • U. Demirel, H. Cam, R. Unlu, “Predicting Stock Prices Using Machine Learning Methods and Deep Learning Algorithms: The Sample of the Istanbul Stock Exchange”, Gazi University Journal of Science, 34 (1): 63-82, 2021.
  • Internet: THYAO, Türk Hava Yolları Anonim Ortaklığı, https://tr.investing.com/equities/turk-hava-yollari-historical-data, 02.05.2022.
  • J. Zou, Y. Han, S. S. So, Overview of Artificial Neural Networks. In: Livingstone D.J. (Eds.) Artificial Neural Networks. Methods in Molecular Biology™, 458, 14-22, Humana Press, 2008.
  • Z. R. Yang, and Z. Yang, Artificial Neural Networks. Comprehensive Biomedical Physics, Elsevier. Editor(s): Anders Brahme, 1-17, 2014.
  • K. Zarzycki, and M. Lawrynczuk, “LSTM and GRU Neural Networks as Models of Dynamical Processes Used in Predictive Control: A Comparison of Models Developed for Two Chemical Reactors”, Sensors, 21(16), 2021.
  • M. B. Er, İ. Işık, “LSTM Tabanlı Derin Ağlar Kullanılarak Diyabet Hastalığı Tahmini”, Türk Doğa ve Fen Dergisi, 10(1), 68-74, 2021.
  • S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory”, Neural Computation, 9 (8): 1735–1780, 1997.
  • X. H. Le, H. V. Ho, G. Lee, S. Jung, “Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting”, Water, 11, 1387, 2019.
  • S. Tanışman, A. A. Karcıoğlu, A. Uğur, H. Bulut, “LSTM Sinir Ağı ve ARIMA Zaman Serisi Modelleri Kullanılarak Bitcoin Fiyatının Tahminlenmesi ve Yöntemlerin Karşılaştırılması”, European Journal of Science and Technology, 32, 514-520, 2021.
  • K. Cho, B. Merrienboer, D. Bahdanau, Y. Bengio, On the Properties of Neural Machine Translation: Encoder-Decoder Approaches, arXiv:1409.1259, 2014.
  • K. Cho, B. Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, “Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation”, arXiv:1406.107, 8 (3) , 2014.
There are 28 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Articles
Authors

Muhammer İlkuçar 0000-0001-9908-7633

Publication Date January 31, 2023
Submission Date September 26, 2022
Published in Issue Year 2023 Volume: 16 Issue: 1

Cite

APA İlkuçar, M. (2023). Prediction Turkish Airlines BIST Stock Price Through Deep Artificial Neural Network Considering Transaction Volume and Seasonal Values. Bilişim Teknolojileri Dergisi, 16(1), 43-53. https://doi.org/10.17671/gazibtd.1180350