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Makine Öğrenmesi Teknikleri İle Hisse Senedi Fiyat Tahmini

Year 2021, Volume: 16 Issue: 1, 1 - 16, 01.04.2021
https://doi.org/10.17153/oguiibf.636017

Abstract

Bu çalışma, Borsa İstanbul Anonim Şirketi (BİST) 30 Endeksi’nde işlem gören firmaların hisse senetlerinin gelecek fiyatlarını tahmin etmeyi amaçlamaktadır. Bu amaçla öncelikle BİST 30 Endeksi firmalarının 2010-2019 yılları arasındaki üçer aylık finansal tabloları temin edilmiş daha sonra bu tablolar vasıtasıyla firmalara ait finansal oranlar hesaplanmıştır. Ayrıca firma hisse senetlerinin aylık kapanış fiyatlarına ulaşılmış ve firmalara ait finansal oranlarla denk olacak şekilde üçer aylık ortalamaları alınmıştır. Bu şekilde veriler temin edildikten sonra Yapay Sinir Ağları (YSA), Rastgele Orman (RO) algoritması ve XGBoost algoritması kullanılarak her bir firmaya ait hisse senedinin gelecek fiyatı tahmin edilmiştir. Daha sonra her bir yönteme göre elde edilen tahmin sonuçları karşılaştırılmıştır. XGBoost ve Rastgele Orman algoritmaları birbirlerine yakın sonuçlar vermelerine rağmen XGBoost algoritması en iyi sonucu vermektedir. Ayrıca her iki modelin de YSA’ya göre daha yüksek performans gösterdiği tespit edilmiştir.

References

  • Adebiyi, Ayodele A.; Ayo, Charles K.; Adebiyi, Marion O.; Otokiti, Sunday O. (2012), “Stock Price Prediction Using Neural Network with Hybridized Market Indicators”, Journal of Emerging Trends in Computing and Information Sciences, C. 3, S. 1: 1-9.
  • Akcan, Ahmet; Kartal, Cem (2011), “İMKB Sigorta Endeksini Oluşturan Şirketlerin Hisse Senedi Fiyatlarının Yapay Sinir Ağları ile Tahmini”, Muhasebe ve Finansman Dergisi, S. 51: 27-40.
  • Breıman, Leo (2001), “Random Forests”, Machine Learning, C. 45, S.1: 5-32.
  • Chen, Tai L.; Cheng, Ching H.; Teoh, Hia J. (2007), “Fuzzy Time-Series Based on Fibonacci Sequence for Stock Price Forecasting”, Physica A: Statistical Mechanics and Its Applications, S. 380: 377-390.
  • Chen, Tianqi; Guestrin, Carlos (2016), “XGBoost: A Scalable Tree Boosting System”, in Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, ACM, 785-794.
  • Elmas, Çetin (2003), Yapay Sinir Ağları (Kuram, Mimari, Eğitim, Uygulama), Ankara: Seçkin Yayıncılık.
  • Elmas, Çetin (2007), Yapay Zeka Uygulamaları, Ankara: Seçkin Yayıncılık.
  • Guresen, Erkam; Kayakutlu, Gulgun; Daim, Tugrul U. (2011), “Using Artificial Neural Network Models in Stock Market Index Prediction”, Expert Systems with Applications, C. 38, S. 8: 10389-10397.
  • Hadavandi, Esmaeil; Shavandi, Hassan; Ghanbari, Arash (2010), “Integration of Genetic Fuzzy Systems and Artificial Neural Networks for Stock Price Forecasting”, Knowledge-Based Systems, C. 23, S. 8: 800-808.
  • https://www.kap.org.tr/tr/Endeksler, (Erişim: 03.11.2018).
  • İlarslan, Kenan (2014), “Hisse Senedi Fiyat Hareketlerinin Tahmin Edilmesinde Markov Zincirlerinin Kullanılması: İMKB 10 Bankacılık Endeksi İşletmeleri Üzerine Ampirik Bir Çalışma/The Use Of Markov Chains for Tthe Prediction of Stock Price Movements: An Empirical Study on the İMKB 10 Banking Index Firms”, Journal of Yaşar University, C. 9, S. 35: 6158-6198.
  • Liu, Chih F.; Yeh, Chi Y.; Lee, Shie J. (2012), “Application of Type-2 Neuro-Fuzzy Modeling in Stock Price Prediction”, Applied Soft Computing, C. 12, S. 4; 1348-1358.
  • Mitchell, Rory; Frank, Eibe (2017), “Accelerating the XGBoost Algorithm Using GPU Computing”, PeerJ Computer Science, S.3; 127-164.
  • Okka, Osman (2009), Finansal Yönetim Teori ve Çözümlü Problemler, Ankara: Nobel Yayıncılık.
  • Okumuş, Hatice; Aydemir, Önder (2017), “Random Forest Classification for Brain Computer Interface Applications”, In Signal Processing and Communications Applications Conference (SIU), S.25: 1-4.
  • Öztemel, Ercan (2006), Yapay Sinir Ağları, İstanbul: Papatya Yayıncılık.
  • Pal, Mahesh (2005), “Random Forest Classifier for Remote Sensing Classification”, International Journal of Remote Sensing, C. 26, S. 1: 217-222.
  • Pai, Ping F.; Lin, Chih S. (2005), “A Hybrid Arıma and Support Vector Machines Model in Stock Price Forecasting”. Omega, C. 33, S. 6: 497-505.
  • Tektaş, Arzu; Karataş, Abdülmecit (2004), “Yapay Sinir Ağları ve Finans Alanına Uygulanması: Hisse Senedi Fiyat Tahminlemesi”, Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, C. 18, S. 3-4: 337-349.
  • Toraman, Cengiz (2008), “Demir-Çelik Sektöründe Yapay Sinir Ağları ile Hisse Senedi Fiyat Tahmini: Erdemir A.Ş. ve Kardemir A.Ş. Üzerine Bir Tahmin Uygulaması”, Muhasebe ve Finansman Dergisi, S. 39: 44-57.
  • Tsai, Chih F.; Wang, Sammy P. (2009), “Stock Price Forecasting by Hybrid Machine Learning Techniques”, In Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol. 1, No. 755: 60-66.
  • Watts, Jennifer D.; Powell, Scott L.; Lawrence, Rick L.; Hilker,Thomas (2011), “Improved Classification of Conservation Tillage Adoption Using High Temporal and Synthetic Satellite Imagery”, Remote Sensing of Environment, C. 115, S. 1: 66-75.
  • Yang, Joey W.; Parwada, Jerry (2012), “Predicting Stock Price Movements: An Ordered Probit Analysis on the Australian Securities Exchange”, Quantitative Finance, C. 12, S. 5: 791-804.
  • Zahedi, Javad; Rounaghi, Mohammad. 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, S. 438: 178-187.
  • Zheng, Huiting; Yuan, Jiabin; Chen, Long (2017), “Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation”, Energies, C. 10, S. 8; 1168-1188.
Year 2021, Volume: 16 Issue: 1, 1 - 16, 01.04.2021
https://doi.org/10.17153/oguiibf.636017

Abstract

This study aims to estimate the future prices of stocks of firms listed in Borsa Istanbul Joint Stock Company (BIST) 30 Index. For this purpose, firstly, quarterly financial statements of BIST 30 Index companies between 2010-2019 have been provided and then financial ratios of firms have been calculated through these tables. In addition, monthly closing prices of company stocks were reached, and quarterly averages were taken in line with the financial ratios of firms. After obtaining the data, the future price of each company's stock was estimated by using Artificial Neural Networks (ANN), Random Forest (RF) algorithm and XGBoost algorithm. Then, the estimation results obtained according to each method were compared. It was determined that although XGBoost and Random Forest algorithms gave similar results, XGBoost has slightly better forecast results. Also, both models performed better than ANN.

References

  • Adebiyi, Ayodele A.; Ayo, Charles K.; Adebiyi, Marion O.; Otokiti, Sunday O. (2012), “Stock Price Prediction Using Neural Network with Hybridized Market Indicators”, Journal of Emerging Trends in Computing and Information Sciences, C. 3, S. 1: 1-9.
  • Akcan, Ahmet; Kartal, Cem (2011), “İMKB Sigorta Endeksini Oluşturan Şirketlerin Hisse Senedi Fiyatlarının Yapay Sinir Ağları ile Tahmini”, Muhasebe ve Finansman Dergisi, S. 51: 27-40.
  • Breıman, Leo (2001), “Random Forests”, Machine Learning, C. 45, S.1: 5-32.
  • Chen, Tai L.; Cheng, Ching H.; Teoh, Hia J. (2007), “Fuzzy Time-Series Based on Fibonacci Sequence for Stock Price Forecasting”, Physica A: Statistical Mechanics and Its Applications, S. 380: 377-390.
  • Chen, Tianqi; Guestrin, Carlos (2016), “XGBoost: A Scalable Tree Boosting System”, in Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, ACM, 785-794.
  • Elmas, Çetin (2003), Yapay Sinir Ağları (Kuram, Mimari, Eğitim, Uygulama), Ankara: Seçkin Yayıncılık.
  • Elmas, Çetin (2007), Yapay Zeka Uygulamaları, Ankara: Seçkin Yayıncılık.
  • Guresen, Erkam; Kayakutlu, Gulgun; Daim, Tugrul U. (2011), “Using Artificial Neural Network Models in Stock Market Index Prediction”, Expert Systems with Applications, C. 38, S. 8: 10389-10397.
  • Hadavandi, Esmaeil; Shavandi, Hassan; Ghanbari, Arash (2010), “Integration of Genetic Fuzzy Systems and Artificial Neural Networks for Stock Price Forecasting”, Knowledge-Based Systems, C. 23, S. 8: 800-808.
  • https://www.kap.org.tr/tr/Endeksler, (Erişim: 03.11.2018).
  • İlarslan, Kenan (2014), “Hisse Senedi Fiyat Hareketlerinin Tahmin Edilmesinde Markov Zincirlerinin Kullanılması: İMKB 10 Bankacılık Endeksi İşletmeleri Üzerine Ampirik Bir Çalışma/The Use Of Markov Chains for Tthe Prediction of Stock Price Movements: An Empirical Study on the İMKB 10 Banking Index Firms”, Journal of Yaşar University, C. 9, S. 35: 6158-6198.
  • Liu, Chih F.; Yeh, Chi Y.; Lee, Shie J. (2012), “Application of Type-2 Neuro-Fuzzy Modeling in Stock Price Prediction”, Applied Soft Computing, C. 12, S. 4; 1348-1358.
  • Mitchell, Rory; Frank, Eibe (2017), “Accelerating the XGBoost Algorithm Using GPU Computing”, PeerJ Computer Science, S.3; 127-164.
  • Okka, Osman (2009), Finansal Yönetim Teori ve Çözümlü Problemler, Ankara: Nobel Yayıncılık.
  • Okumuş, Hatice; Aydemir, Önder (2017), “Random Forest Classification for Brain Computer Interface Applications”, In Signal Processing and Communications Applications Conference (SIU), S.25: 1-4.
  • Öztemel, Ercan (2006), Yapay Sinir Ağları, İstanbul: Papatya Yayıncılık.
  • Pal, Mahesh (2005), “Random Forest Classifier for Remote Sensing Classification”, International Journal of Remote Sensing, C. 26, S. 1: 217-222.
  • Pai, Ping F.; Lin, Chih S. (2005), “A Hybrid Arıma and Support Vector Machines Model in Stock Price Forecasting”. Omega, C. 33, S. 6: 497-505.
  • Tektaş, Arzu; Karataş, Abdülmecit (2004), “Yapay Sinir Ağları ve Finans Alanına Uygulanması: Hisse Senedi Fiyat Tahminlemesi”, Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, C. 18, S. 3-4: 337-349.
  • Toraman, Cengiz (2008), “Demir-Çelik Sektöründe Yapay Sinir Ağları ile Hisse Senedi Fiyat Tahmini: Erdemir A.Ş. ve Kardemir A.Ş. Üzerine Bir Tahmin Uygulaması”, Muhasebe ve Finansman Dergisi, S. 39: 44-57.
  • Tsai, Chih F.; Wang, Sammy P. (2009), “Stock Price Forecasting by Hybrid Machine Learning Techniques”, In Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol. 1, No. 755: 60-66.
  • Watts, Jennifer D.; Powell, Scott L.; Lawrence, Rick L.; Hilker,Thomas (2011), “Improved Classification of Conservation Tillage Adoption Using High Temporal and Synthetic Satellite Imagery”, Remote Sensing of Environment, C. 115, S. 1: 66-75.
  • Yang, Joey W.; Parwada, Jerry (2012), “Predicting Stock Price Movements: An Ordered Probit Analysis on the Australian Securities Exchange”, Quantitative Finance, C. 12, S. 5: 791-804.
  • Zahedi, Javad; Rounaghi, Mohammad. 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, S. 438: 178-187.
  • Zheng, Huiting; Yuan, Jiabin; Chen, Long (2017), “Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation”, Energies, C. 10, S. 8; 1168-1188.
There are 25 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Nesrin Koç Ustalı 0000-0003-4217-4212

Nedret Tosun This is me 0000-0003-4566-6693

Ömür Tosun 0000-0003-1571-7373

Publication Date April 1, 2021
Submission Date October 22, 2019
Published in Issue Year 2021 Volume: 16 Issue: 1

Cite

APA Koç Ustalı, N., Tosun, N., & Tosun, Ö. (2021). Makine Öğrenmesi Teknikleri İle Hisse Senedi Fiyat Tahmini. Eskişehir Osmangazi Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 16(1), 1-16. https://doi.org/10.17153/oguiibf.636017