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Boyut İndirgeme Teknikleri ve LSTM Derin Öğrenme Ağı İle BIST100 Endeksi Fiyat Tahmini

Year 2022, Issue: 34, 519 - 524, 31.03.2022
https://doi.org/10.31590/ejosat.1083255

Abstract

Son dönemde teknolojide gözlenen gelişim ile birlikte derin öğrenme yöntemlerinin çok farklı alanlarda kullanımı da hız kazanmıştır. Bu alanların en popülerlerinden biri de finansal piyasalardır. Birçok girdinin etken olduğu finansal veriler üzerinde gerçekleştirilen tahmin ve analizlerin, yatırımcıların ve kurumsal yapıların karar alma mekanizmalarına yardımcı etkisi büyük öneme sahiptir. Çalışmada bir derin öğrenme ağı ile Borsa İstanbul 100 (BIST100) endeksi tahmin edilmeye çalışılmaktadır. Ayrıca veri ön işleme aşamasında Faktör Analizi (FA), Temel Bileşen Analizi (PCA), Bağımsız Bileşen Analizi (ICA) gibi istatistiksel boyut indirgeme yöntemlerin kullanımının, Uzun Kısa Süreli Bellek (LSTM) derin öğrenme ağı performansına olan etkisi araştırılmaktadır. Deneyler esnasında kullanılan veri seti; BIST100 endeksine ait günlük geçmiş verilere ve teknik analiz bilgilerine dayalı olarak hazırlanmaktadır. Veri ön işleme aşamasında, derin öğrenme ağına eklenen istatistiksel boyut indirgeme yöntemlerinden oluşturulan modeller, 5 gün sonraki fiyatı tahmin etmeye çalışırken, R2 ve RMSE ölçütleri üzerinden karşılaştırılmıştır. Bu işlemler sırasında derin öğrenme hiper-parametreleri dışında kalan, teknik göstergelerin ve tahmin modelinin performansını etkiyeceği düşünülen parametreler iyileştirilmeye çalışılmıştır. Buna göre PCA+LSTM hibrit modeli, diğer boyut indirgeme yöntemleri ile oluşturulan hibrit modelleri geride bırakarak daha rekabetçi sonuçlar elde etmiştir. Aynı zamanda PCA+LSTM hibrit modelinin, LSTM modelinin tek başına elde ettiği sonuçları, R2 ve RMSE için sırası ile %4.60 ve %13.35 oranlarında iyileştirdiği görülmüştür.

References

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  • Gao, T., Chai, Y., & Liu, Y. (2017, November). Applying long short term momory neural networks for predicting stock closing price. In 2017 8th IEEE international conference on software engineering and service science (ICSESS) (pp. 575-578). IEEE.
  • Roondiwala, M., Patel, H., & Varma, S. (2017). Predicting stock prices using LSTM. International Journal of Science and Research (IJSR), 6(4), 1754-1756.
  • Pang, X. W., Zhou, Y., Wang, P., Lin, W., & Chang, V. (2018, March). Stock Market Prediction based on Deep Long Short Term Memory Neural Network. In COMPLEXIS (pp. 102-108).
  • Wen, Y., Lin, P., & Nie, X. (2020, March). Research of stock price prediction based on PCA-LSTM model. In IOP Conference Series: Materials Science and Engineering (Vol. 790, No. 1, p. 012109). IOP Publishing.
  • Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26.
  • Zhuge, Q., Xu, L., & Zhang, G. (2017). LSTM Neural Network with Emotional Analysis for prediction of stock price. Engineering letters, 25(2).
  • Faurina, R., Winduratna, B., & Nugroho, P. (2018). Predicting stock movement using unidirectional LSTM and feature reduction: the case of an Indonesia stock. In 2018 International Conference on Electrical Engineering and Computer Science (ICEECS). Bali, Indonesia (pp. 180-5).
  • Unal, B., & Aladag, C. H. (2019). Stock Exchange Prediction via Long Short-Term Memory Networks. Proceedings Book, 246.
  • Santur, Y. Deep Learning Based Regression Approach for Algorithmic Stock Trading: A Case Study of the Bist30. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 10(4), 1195-1211.
  • Demirel, U., Handan, Ç. A. M., & Ramazan, Ü. N. L. Ü. (2021). 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.
  • Kilimci, H., Kilimci, Z. H., & Yıldırım, M. (2021, November). Deep Learning-based Decision Integration Strategy for the Price Prediction of Istanbul Stock Exchange (BIST100). In 2021 13th International Conference on Electrical and Electronics Engineering (ELECO) (pp. 411-414). IEEE.
  • Selçuk, A. L. P., Yiğit, Ö. E., & Ersoy, Ö. Z. (2020). Prediction of bist price indices: a comparative study between traditional and deep learning methods. Sigma Journal of Engineering and Natural Sciences, 38(4), 1693-1704.
  • YahooFinance. https://finance.yahoo.com/quote/XU100.IS/history?p=XU100.IS. Erişim: 23 Şubat 2022.
  • Yaşlıoğlu, M. M. (2017). Sosyal bilimlerde faktör analizi ve geçerlilik: Keşfedici ve doğrulayıcı faktör analizlerinin kullanılması. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 46, 74-85.
  • Johnson, R. A., & Wichern, D. W. (2014). Applied multivariate statistical analysis (Vol. 6). London, UK:: Pearson.
  • Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin philosophical magazine and journal of science, 2(11), 559-572.
  • Anowar, F., Sadaoui, S., & Selim, B. (2021). Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE). Computer Science Review, 40, 100378.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
  • Thakkar, A., & Chaudhari, K. (2021). A Comprehensive Survey on Deep Neural Networks for Stock Market: The Need, Challenges, and Future Directions. Expert Systems with Applications, 114800.

BIST100 Index Price Prediction with Dimension Reduction Techniques and LSTM Deep Learning Network

Year 2022, Issue: 34, 519 - 524, 31.03.2022
https://doi.org/10.31590/ejosat.1083255

Abstract

With the recent development in technology, the use of deep learning methods in many fields has gained momentum. One of the most popular of these areas is financial markets. The estimations and analyzes performed on financial data, where many inputs are effective, have great importance on the decision-making mechanisms of investors and institutional structures. In the study, Borsa Istanbul 100 (BIST100) index is tried to be estimated with a deep learning network. In addition, the effect of the use of statistical dimension reduction methods such as Factor Analysis (FA), Principal Component Analysis (PCA), Independent Component Analysis (ICA) in the data preprocessing stage on Long Short Term Memory (LSTM) deep learning network performance is investigated. The data set used during the experiments; is prepared based on daily historical data and technical analysis information of the BIST100 index. In the data preprocessing stage, the models created from the statistical dimension reduction methods added to the deep learning network were compared over the R2 and RMSE criteria while trying to predict the price at the end of 5-days. During these processes, parameters other than deep learning hyper-parameters, which are thought to affect the performance of technical indicators and forecasting models, were tried to be improved. Accordingly, the PCA+LSTM hybrid model outperformed the hybrid models created by the other, dimensional reduction methods and achieved more competitive results. At the same time, it was observed that the PCA+LSTM hybrid model improved the results of the LSTM model alone by 4.60% and 13.35% for R2 and RMSE, respectively.

References

  • Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211.
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 61, 85-117.
  • Ozbayoglu, Ahmet Murat, Mehmet Ugur Gudelek, and Omer Berat Sezer. "Deep learning for financial applications: A survey." Applied Soft Computing 93 (2020): 106384.
  • Gao, T., Chai, Y., & Liu, Y. (2017, November). Applying long short term momory neural networks for predicting stock closing price. In 2017 8th IEEE international conference on software engineering and service science (ICSESS) (pp. 575-578). IEEE.
  • Roondiwala, M., Patel, H., & Varma, S. (2017). Predicting stock prices using LSTM. International Journal of Science and Research (IJSR), 6(4), 1754-1756.
  • Pang, X. W., Zhou, Y., Wang, P., Lin, W., & Chang, V. (2018, March). Stock Market Prediction based on Deep Long Short Term Memory Neural Network. In COMPLEXIS (pp. 102-108).
  • Wen, Y., Lin, P., & Nie, X. (2020, March). Research of stock price prediction based on PCA-LSTM model. In IOP Conference Series: Materials Science and Engineering (Vol. 790, No. 1, p. 012109). IOP Publishing.
  • Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017). A survey of deep neural network architectures and their applications. Neurocomputing, 234, 11-26.
  • Zhuge, Q., Xu, L., & Zhang, G. (2017). LSTM Neural Network with Emotional Analysis for prediction of stock price. Engineering letters, 25(2).
  • Faurina, R., Winduratna, B., & Nugroho, P. (2018). Predicting stock movement using unidirectional LSTM and feature reduction: the case of an Indonesia stock. In 2018 International Conference on Electrical Engineering and Computer Science (ICEECS). Bali, Indonesia (pp. 180-5).
  • Unal, B., & Aladag, C. H. (2019). Stock Exchange Prediction via Long Short-Term Memory Networks. Proceedings Book, 246.
  • Santur, Y. Deep Learning Based Regression Approach for Algorithmic Stock Trading: A Case Study of the Bist30. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 10(4), 1195-1211.
  • Demirel, U., Handan, Ç. A. M., & Ramazan, Ü. N. L. Ü. (2021). 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.
  • Kilimci, H., Kilimci, Z. H., & Yıldırım, M. (2021, November). Deep Learning-based Decision Integration Strategy for the Price Prediction of Istanbul Stock Exchange (BIST100). In 2021 13th International Conference on Electrical and Electronics Engineering (ELECO) (pp. 411-414). IEEE.
  • Selçuk, A. L. P., Yiğit, Ö. E., & Ersoy, Ö. Z. (2020). Prediction of bist price indices: a comparative study between traditional and deep learning methods. Sigma Journal of Engineering and Natural Sciences, 38(4), 1693-1704.
  • YahooFinance. https://finance.yahoo.com/quote/XU100.IS/history?p=XU100.IS. Erişim: 23 Şubat 2022.
  • Yaşlıoğlu, M. M. (2017). Sosyal bilimlerde faktör analizi ve geçerlilik: Keşfedici ve doğrulayıcı faktör analizlerinin kullanılması. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 46, 74-85.
  • Johnson, R. A., & Wichern, D. W. (2014). Applied multivariate statistical analysis (Vol. 6). London, UK:: Pearson.
  • Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin philosophical magazine and journal of science, 2(11), 559-572.
  • Anowar, F., Sadaoui, S., & Selim, B. (2021). Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE). Computer Science Review, 40, 100378.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
  • Thakkar, A., & Chaudhari, K. (2021). A Comprehensive Survey on Deep Neural Networks for Stock Market: The Need, Challenges, and Future Directions. Expert Systems with Applications, 114800.
There are 22 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Mehmet Sarıkoç 0000-0002-3081-1686

Mete Çelik 0000-0002-1488-1502

Early Pub Date January 30, 2022
Publication Date March 31, 2022
Published in Issue Year 2022 Issue: 34

Cite

APA Sarıkoç, M., & Çelik, M. (2022). Boyut İndirgeme Teknikleri ve LSTM Derin Öğrenme Ağı İle BIST100 Endeksi Fiyat Tahmini. Avrupa Bilim Ve Teknoloji Dergisi(34), 519-524. https://doi.org/10.31590/ejosat.1083255