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Derin Öğrenme Yöntemleri ile Borsada Fiyat Tahmini

Year 2020, , 434 - 445, 13.03.2020
https://doi.org/10.17798/bitlisfen.571386

Abstract

Son yıllarda, bilgisayarların donanımındaki teknolojik gelişmeler ve makine
öğrenme tekniklerindeki gelişmeler nedeniyle, "Büyük Veri" ve
"Paralel İşleme" kullanımı olmak üzere problem çözmek için iki artan
yaklaşım vardır. Özellikle GPU'lar gibi çok çekirdekli bilgi işlem aygıtlarında
paralel olarak gerçekleştirilebilen Derin Öğrenme algoritmalarının ortaya
çıkmasıyla, bu yaklaşımlarla birçok gerçek dünya problemleri çözülebilmektedir.
Derin öğrenme modelleri eğitildikleri veri ile sınıflandırma, regresyon analizi
ve zaman serilerinde tahmin gibi uygulamalarda büyük başarılar göstermektedir.
Bu modellerin finansal piyasadaki en aktif uygulama alanlarından biri özellikle
borsada işlem gören hisse senetlerinin tahmini işlemleridir. Bu alanda amaç,
pazardaki değişim süreci hakkındaki hisse senedinin önceki günlük verilerine
bakarak kısa veya uzun vadeli gelecekteki değerini tahmin etmeye çalışmaktır. Bu
çalışmada, LSTM, GRU ve BLSTM isimli 3 farklı derin öğrenme modeli kullanılarak
bir hisse senedi tahmin sistemi geliştirilip, kullanılan modeller arasında
karşılaştırmalı bir analiz yapıldı. Spekülatif hareketlerden uzak olması için veri
seti olarak 1968'den 2018'e kadar olan New York Borsası'ndan hisse senedinin
zaman serisi değerlerini kullanıldı. Spesifik olarakta IBM hisse senedi ile
test çalışmaları yapıldı. Deneysel sonuçlar BLSTM modelinin 5 günlük girdi
verileriyle eğitilmesi ile %63,54 lük bir yönsel doğruluk değerine ulaşıldığını
göstermektedir. 

References

  • [1] Cavalcante R. C., Brasileiro R. C., Souza V. L., Nobrega J. P. and Oliveira A. L., “Computational intelligence and financial markets: A survey and future directions,” Expert Systems with Applications, vol. 55, pp. 194–211, 2016.[2] Namdari A. and Li Z. S., "Integrating Fundamental and Technical Analysis of Stock Market through Multi-layer Perceptron," 2018 IEEE Technology and Engineering Management Conference (TEMSCON), Evanston, IL, 2018, pp. 1-6. doi: 10.1109/TEMSCON.2018.8488440[3] Beyaz E., Tekiner F., Zeng X. and Keane J., "Comparing Technical and Fundamental Indicators in Stock Price Forecasting," 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, United Kingdom, 2018, pp. 1607-1613. doi: 10.1109/HPCC/SmartCity/DSS.2018.00262[4] Chou J. and Nguyen T., "Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression," in IEEE Transactions on Industrial Informatics, vol. 14, no. 7, pp. 3132-3142, July 2018. doi: 10.1109/TII.2018.2794389[5] Lien Minh D., A. Sadeghi-Niaraki, H. D. Huy, K. Min and H. Moon, "Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network," in IEEE Access, vol. 6, pp. 55392-55404, 2018. doi: 10.1109/ACCESS.2018.2868970[6] Trelewicz J. Q., "Big Data and Big Money: The Role of Data in the Financial Sector," in IT Professional, vol. 19, no. 3, pp. 8-10, 2017. doi: 10.1109/MITP.2017.45[7] Mohammadi M., Al-Fuqaha A., Sorour S. and Guizani M., "Deep Learning for IoT Big Data and Streaming Analytics: A Survey," in IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2923-2960, Fourthquarter 2018. doi: 10.1109/COMST.2018.2844341[8] Korczak, J., & Hernes, M. (2017). Deep Learning for Financial Time Series Forecasting in A-Trader System. Proceedings of the 2017 Federated Conference on Computer Science and Information Systems.[9] Arévalo, A., Niño, J., Hernández, G., & Sandoval, J. (2016). High-Frequency Trading Strategy Based on Deep Neural Networks. Intelligent Computing Methodologies Lecture Notes in Computer Science,424-436.[10] Zhou, X., Pan, Z., Hu, G., Tang, S., & Zhao, C. (2018). Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets. Mathematical Problems in Engineering, Vol. 2018,1-11.[11] Ganesh, P., & Rakheja, P. (2018). Deep Neural Networks in High Frequency Trading.[12] Gudelek, M. U. , Boluk S. A. and Ozbayoglu A. M., "A deep learning based stock trading model with 2-D CNN trend detection," 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, 2017, pp. 1-8. doi: 10.1109/SSCI.2017.8285188[13] Karatas, G., Demir, O., and Sahingoz, O. K. (2018). Deep Learning in Intrusion Detection Systems. 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT).[14] Sahingoz, O. K., Baykal, S. I., and Bulut, D. (2018). Phishing Detection From Urls By Using Neural Networks. Computer Science & Information Technology (CS & IT)
Year 2020, , 434 - 445, 13.03.2020
https://doi.org/10.17798/bitlisfen.571386

Abstract

References

  • [1] Cavalcante R. C., Brasileiro R. C., Souza V. L., Nobrega J. P. and Oliveira A. L., “Computational intelligence and financial markets: A survey and future directions,” Expert Systems with Applications, vol. 55, pp. 194–211, 2016.[2] Namdari A. and Li Z. S., "Integrating Fundamental and Technical Analysis of Stock Market through Multi-layer Perceptron," 2018 IEEE Technology and Engineering Management Conference (TEMSCON), Evanston, IL, 2018, pp. 1-6. doi: 10.1109/TEMSCON.2018.8488440[3] Beyaz E., Tekiner F., Zeng X. and Keane J., "Comparing Technical and Fundamental Indicators in Stock Price Forecasting," 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, United Kingdom, 2018, pp. 1607-1613. doi: 10.1109/HPCC/SmartCity/DSS.2018.00262[4] Chou J. and Nguyen T., "Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression," in IEEE Transactions on Industrial Informatics, vol. 14, no. 7, pp. 3132-3142, July 2018. doi: 10.1109/TII.2018.2794389[5] Lien Minh D., A. Sadeghi-Niaraki, H. D. Huy, K. Min and H. Moon, "Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network," in IEEE Access, vol. 6, pp. 55392-55404, 2018. doi: 10.1109/ACCESS.2018.2868970[6] Trelewicz J. Q., "Big Data and Big Money: The Role of Data in the Financial Sector," in IT Professional, vol. 19, no. 3, pp. 8-10, 2017. doi: 10.1109/MITP.2017.45[7] Mohammadi M., Al-Fuqaha A., Sorour S. and Guizani M., "Deep Learning for IoT Big Data and Streaming Analytics: A Survey," in IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2923-2960, Fourthquarter 2018. doi: 10.1109/COMST.2018.2844341[8] Korczak, J., & Hernes, M. (2017). Deep Learning for Financial Time Series Forecasting in A-Trader System. Proceedings of the 2017 Federated Conference on Computer Science and Information Systems.[9] Arévalo, A., Niño, J., Hernández, G., & Sandoval, J. (2016). High-Frequency Trading Strategy Based on Deep Neural Networks. Intelligent Computing Methodologies Lecture Notes in Computer Science,424-436.[10] Zhou, X., Pan, Z., Hu, G., Tang, S., & Zhao, C. (2018). Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets. Mathematical Problems in Engineering, Vol. 2018,1-11.[11] Ganesh, P., & Rakheja, P. (2018). Deep Neural Networks in High Frequency Trading.[12] Gudelek, M. U. , Boluk S. A. and Ozbayoglu A. M., "A deep learning based stock trading model with 2-D CNN trend detection," 2017 IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, 2017, pp. 1-8. doi: 10.1109/SSCI.2017.8285188[13] Karatas, G., Demir, O., and Sahingoz, O. K. (2018). Deep Learning in Intrusion Detection Systems. 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT).[14] Sahingoz, O. K., Baykal, S. I., and Bulut, D. (2018). Phishing Detection From Urls By Using Neural Networks. Computer Science & Information Technology (CS & IT)
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Details

Primary Language Turkish
Journal Section Araştırma Makalesi
Authors

Gözde Şişmanoğlu This is me 0000-0003-0284-4752

Furkan Koçer This is me 0000-0002-0053-2459

Mehmet Ali Önde This is me 0000-0001-9269-8554

Ozgur Koray Sahingoz 0000-0002-1588-8220

Publication Date March 13, 2020
Submission Date May 29, 2019
Acceptance Date October 11, 2019
Published in Issue Year 2020

Cite

IEEE G. Şişmanoğlu, F. Koçer, M. A. Önde, and O. K. Sahingoz, “Derin Öğrenme Yöntemleri ile Borsada Fiyat Tahmini”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 9, no. 1, pp. 434–445, 2020, doi: 10.17798/bitlisfen.571386.



Bitlis Eren Üniversitesi
Fen Bilimleri Dergisi Editörlüğü

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Beş Minare Mah. Ahmet Eren Bulvarı, Merkez Kampüs, 13000 BİTLİS        
E-posta: fbe@beu.edu.tr