Araştırma Makalesi
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Stock Market Index Prediction Using Machine Learning Techniques: Application of BIST Indices

Yıl 2024, Cilt: 9 Sayı: 2, 96 - 106

Sevgi Sumerli Sarıgül , Ramazan Aaldeimir , Hayrettin Uzunoğlu

Öz

The stock market is one of the important indicators of national economies and the relationships between the components of this market have been investigated in many studies. Forecasting in the stock market is very important for both firm owners and investors. Therefore, many models have been developed to predict the future price of stocks. Especially in today's world where artificial intelligence is gaining importance, machine learning models have become popular in future forecasting models. In this context, in our study, the 2019-2022 data of the Industrial Index (XUSIN), Services Index (XUHIZ) and Financial Index (XUMAL) companies, which are among the Borsa Istanbul sector indices, were analysed using various machine learning algorithms.

Anahtar Kelimeler

BIST Artificial Intelligence Machine Learning

Kaynakça

  • Altay, E., Satman, M.H. (2005), Stock Market Forecasting: Artifical Neural Network And Linear Regression Comparison In An Emerging Market, Journal Of Financial Management And Analysis, 18(2), 18-33.
  • Akpınar, H. (2017). Data, Veri Madenciliği Veri Analizi. 2.Baskı. Papatya YayıncılıkEğitim, İstanbul
  • Akyol Özcan K. (2023), Borsa endeksi yönünün makine öğrenmesi yöntemleri ile tahmini: bist 100 örneği. Gümüşhane Üniversitesi Sosyal Bilimler Dergisi, 14(3), 1001-1018.
  • Ayyıldız, N., İskenderoğlu, Ö. (2023), Prediction of Stock İndex Movement Using Machine Learning Methods: an Application on BIST 100 İndex, II. Eurasian Conference on Economics, Finance and Entrepreneurship, 20-21 May, İstanbul, 101-113.
  • Bengoechea, A.G., Ureta, C.O., Saavedra, M.M., Medina, N.O. (1996), Stock Market Indexes In Santiago De Chile: Forecasting Using Neural Networks, In Proceedings of International Conference on Neural Networks (ICNN'96) 4, 2172-2175.
  • Bilik, M., Aydın, Ü. (2018), Finansal Hizmetlerde Dijital Dönüşüm ve Etkileri. In book of Proceedings, 3rd. International Congress on Economics, Finance, and Energy, ISBN: 978-601-7805-32-6.
  • Cao, Q., Parry, M.E., Leggio, K.B. (2011), The Three-factor Model and Artificial Neural Networks: Predicting Stock Price Movement in China, Annals of Operations Research, 185(1), 25-44.
  • Choudhry, R., Garg, K. (2008), A hybrid machine learning system for stock market forecasting, World Academy of Science, Engineering and Technology, 39(3), 315-318.
  • Çalışkan, M.M.T., Deniz, D. (2015), Yapay Sinir Ağlarıyla Hisse Senedi Fiyatları ve Yönlerinin Tahmini, Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 31, 177-194.
  • Fernandez, A., Gomez, S. (2007), Portfolio Selection Using Neural Networks, Computers & Operations Research, 34(4), 1177-1191.
  • Freund, Y., Schapire, R.E. (1997), A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, 55, 119-139.
  • Harrell, F. (2015), Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis, New York, NY: Springer.
  • Hastie, T., Tibshirani, R., Friedman, J. (2008), The Elements of Statistical Learning, New York: Springer.
  • Haykın, S. (2009), Neural Networks and Learning Machines. Boston: Pearson International Edition.
  • Jandaghı, G., Tehranı, R., Hossınpour, D., Gholıpour, R., Shadkam, S.A.S. (2010), Application Of Fuzzyneural Networks In Multi-Ahead Forecast Of Stock Price, African Journal Of Business Management, 4(6), 903-914.
  • Kara, Y., Boyacıoğlu, M.A., Baykan, Ö.K. (2011), Predicting Direction Of Stock Price Index Movement Using Artificial Neural Networks And Support Vector Machines: The Sample Of The Istanbul Stock Exchange, Expert Systems With Applications, 38, 5311–5319.
  • Karaatlı, M., Güngör, İ., Demir, Y., Kalaycı Ş. (2005), Hisse Senedi Fiyat Hareketlerinin Yapay Sinir Ağları Yöntemi İle Tahmin Edilmesi, Yönetim Ve Ekonomi Araştırmaları Dergisi, 3, 22-48.
  • Khansa, L., Liginlal, D. (2011), Predicting Stock Market Returns From Malicious Attacks: A Comparative Analysis of Vector Autoregression And TimeDelayed Neural Networks, Decision Support Systems, 51(4). 745-759.
  • Kim, S., Kang, M. (2019), Financial series prediction using attention LSTM, arXiv preprint arXiv:1902.10877.
  • Kimoto, T., Asakawa, K., Yoda, M., Takeoka, M. (1990), Stock Market Prediction System With Modular Neural Network, Proceedings Of The International Joint Conference On Neural Networks, 1-6.
  • Li, X., Li, Y., Liu, X.Y., Wang, C.D. (2019), Risk management via anomaly circumvent: mnemonic deep learning for midterm stock prediction, in Proc. 2nd KDD Workshop on Anomaly Detection in Finance, Anchorage, AK, USA.
  • Malakooti, M.V., AghaSharif, A. (2015), Prediction of Stock Market Index based on Neural Networks, Genetic Algorithms and Data Mining Using SVD, The Prooceedings of the International Conference on Digital Information Processing, Data Mining, and Wireless Communications, Dubai, UAE.
  • Manurung, A.H., Budiharto, W., Prabowo, H. (2018), Algorithm and modeling of stock prices forecasting based on long short-term memory (LSTM), International Journal of Innovative Computing Information and Control, 12, 1277-1283.
  • Mızuno, H., Kosaka, M., Yajıma, H., Komoda N. (1998), Application Of Neural Network To Technical Analysis Of Stock Market Prediction, Studies In Informatic And Control, 7(3), 111-120.
  • Opitz, D., Maclin, R. (1999), Popüler Topluluk Yöntemleri: Ampirik Bir Çalışma,  Yapay Zeka Araştırmaları Dergisi, 11, 169-198.
  • Örnek, A. (2023). Yapay Zeka ve Makine Öğrenmesinin Hisse Senedi Fiyat Tahmini Üzerindeki Etkileri. Finans Araştırmaları Dergisi, 10(2), 45-58.
  • Özçalıcı, M. (2016), Yapay Sinir Ağları ile Çok Aşamalı Fiyat Tahmini: Bist 30 Senetleri Üzerine Bir Araştırma, Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 31(2), 209-227.
  • Özer, A., Sarı, S.S., Başakın, E.E. (2017), Bulanık Mantık ve Yapay Sinir Ağları ile Borsa Endeks Tahmini: Gelişmiş ve Gelişmekte Olan Ülkeler Örneği, Hitit Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 11(1), 99-123.
  • C. Özgür, V. Sarıkovanlık (2022), Forecasting Bist100 and Nasdaq Indices with Single and Hybrid Machine Learning Algorithms, Economic Computation and Economic Cybernetics Studies and Research, 56(3), 235-250.
  • Pabuçcu, H. (2019), Borsa Endeksi Hareketlerinin Tahmini: Trend Belirleyici Veri, Selçuk Üniversitesi Sosyal Bilimler Yüksekokulu Dergisi, 22(1), 246-256.
  • Patel, J., Shah, S., Thakkar, P., Kotecha, K. (2015), Predicting stock market index using fusion of machine learning techniques, Expert Systems with Applications, 42(4), 2162-2172.
  • Phua, P.K.H., Mıng, D., Lın, W. (2001), Neural Network With Genetic Algorithms For Stocks Prediction, Asia Pacific Journal of Operational Research, 18(1), 103-107.
  • Phua, P.K.H., Zhu, X., Koh, C.H. (2003), Forecasting Stock Index Increments Using Neural Networks With Trust Region Methods, Proceedings Of The International Joint Conference, 1, 260-265.
  • Polikar, R. (2006). Karar vermede topluluk tabanlı sistemler, IEEE Devreler ve Sistemler Dergisi, 6(3), 21–45.
  • Rasmussen, C.E., Williams, C.K.I. (2006), Gaussian Processes for Machine Learning, Massachusetts: MIT Press. Cambridge.
  • Rast, M. (1999), Forecasting With Fuzzy Neural Networks: A Case Study in Stock Market Crash Situations. Fuzzy Information Processing Society, Nafıps, 18th International Conference Of The North American, 418-420.
  • Rokach, L. (2010), Ensemble tabanlı sınıflandırıcılar, Yapay Zeka İncelemesi, 33(1), 1-39.
  • Saka Ilgın, K., & Sercan Sarı, S. (2022). BIST-100 Endeks Hareketlerinin BRICS Endeksleri Aracılığıyla Tahmin Edilmesi: Yapay Sinir Ağları Uygulaması. Abant Sosyal Bilimler Dergisi, 22(1), 350-366.
  • Shen, S., Jiang, H., Zhang, T. (2012), Stock Market Forecasting Using Machine Learning Algorithms, Department of Electrical Engineering, Stanford University, Stanford, CA, 1–5.
  • Şerbetçi, A. (2022). Brıcs, Mıst, Kırılgan Beşli Kapsamında Yer Alan Ülkelerin Menkul Kıymetler Borsaları Arasındaki Kısa Ve Uzun Dönemli Etkileşime İlişkin Amprik Bir Araştırma. Premium E-Journal Of Social Sciences (Pejoss), 6(19), 76–97.
  • Taş, A.İ., Gülüm, P., Tulum, G. (2021), Finansal Piyasalarda Hisse Fiyatlarının Derin Öğrenme ve Yapay Sinir Ağı Yöntemleri ile Tahmin Edilmesi; S&P 500 Endeksi Örneği, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, Additional Issue, 446-460.
  • Ticknor J.L. (2013), A Bayesian Regularized Artificial Neural Network for Stock Market Forecasting, Expert Systems with Applications, 40(14), 5501-5506.
  • Usul, H., Küçüksille, E., Karaoğlan, D. S. (2017), Güven Endekslerindeki Değişimlerin Hisse Senedi Piyasalarına Etkileri: Borsa İstanbul Örneği, Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22(3), 685-695.
  • Vaisla, K.S., Bhatt, A.K. (2010) An Analysis of The Performance of Artificial Neural Network Technique For Stock Market Forecasting, International Journal On Computer Science And Engineering, 2(6), 2104-2109.
  • Vapnik, V. (1995), The Nature of Statistical Learning Theory. New York: Springer.
  • Vassakis, K., Petrakis, E., Kopanakis, I. (2018), Big Data Analytics: Applications, Prospects and Challenges, Mobil Big Data, Lecture Notes on Data Engineering and Communications Technologies, 10, 3-20.
  • Yao, J., Li, Y., Tan, C.L. (2000), Option price forecasting using neural networks, Omega, 28(4), 455-466, ISSN 0305-0483, https://doi.org/10.1016/S0305-0483(99)00066-3.
  • Yoo, P.D., Kim, M.H., Jan, T. (2005), Machine learning techniques and use of event information for stock market prediction: A survey and evaluation, in: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, CIMCA-IAWTIC’06, 835–841.
  • Zorın, A., Borısov, A. (2002), Modelling Riga Stock Exchange Index using neural networks, Proceedings of the International Conference Traditions and Innovations in Sustainable Development of Society. Rezekne, Latvia.

Makine Öğrenme Tekniklerini Kullanarak Borsa Endeksi Tahmini: BIST Endeksleri Uygulaması

Yıl 2024, Cilt: 9 Sayı: 2, 96 - 106

Sevgi Sumerli Sarıgül , Ramazan Aaldeimir , Hayrettin Uzunoğlu

Öz

Hisse senedi piyasası, ülke ekonomilerinin önemli göstergelerinden biri olup, bu piyasanın bileşenleri arasındaki ilişkiler birçok çalışmada araştırılmıştır. Hisse senedi piyasasında geleceğe yönelik tahmin çalışmaları hem firma sahipleri hem de yatırımcılar için oldukça önemlidir. Bu yüzden hisse senetlerinin gelecek fiyatını tahmin etmeye yönelik çok sayıda model geliştirilmiştir. Özellikle yapay zekânın önem kazanmaya başladığı günümüzde geleceğe yönelik tahmin modellerinde artık makine öğrenmesi modelleri popüler hale gelmiştir. Bu kapsamda çalışmamızda, çeşitli makine öğrenme algoritmaları kullanılarak Borsa İstanbul sektör endeksleri arasında yer alan; Sınai Endeksi (XUSIN), Hizmetler Endeksi (XUHIZ) ve Mali Endeks (XUMAL) firmalarının 2019-2024 yılı verileri analiz edilmiştir.

Anahtar Kelimeler

BIST Yapay Zekâ Makine Öğrenme

Kaynakça

  • Altay, E., Satman, M.H. (2005), Stock Market Forecasting: Artifical Neural Network And Linear Regression Comparison In An Emerging Market, Journal Of Financial Management And Analysis, 18(2), 18-33.
  • Akpınar, H. (2017). Data, Veri Madenciliği Veri Analizi. 2.Baskı. Papatya YayıncılıkEğitim, İstanbul
  • Akyol Özcan K. (2023), Borsa endeksi yönünün makine öğrenmesi yöntemleri ile tahmini: bist 100 örneği. Gümüşhane Üniversitesi Sosyal Bilimler Dergisi, 14(3), 1001-1018.
  • Ayyıldız, N., İskenderoğlu, Ö. (2023), Prediction of Stock İndex Movement Using Machine Learning Methods: an Application on BIST 100 İndex, II. Eurasian Conference on Economics, Finance and Entrepreneurship, 20-21 May, İstanbul, 101-113.
  • Bengoechea, A.G., Ureta, C.O., Saavedra, M.M., Medina, N.O. (1996), Stock Market Indexes In Santiago De Chile: Forecasting Using Neural Networks, In Proceedings of International Conference on Neural Networks (ICNN'96) 4, 2172-2175.
  • Bilik, M., Aydın, Ü. (2018), Finansal Hizmetlerde Dijital Dönüşüm ve Etkileri. In book of Proceedings, 3rd. International Congress on Economics, Finance, and Energy, ISBN: 978-601-7805-32-6.
  • Cao, Q., Parry, M.E., Leggio, K.B. (2011), The Three-factor Model and Artificial Neural Networks: Predicting Stock Price Movement in China, Annals of Operations Research, 185(1), 25-44.
  • Choudhry, R., Garg, K. (2008), A hybrid machine learning system for stock market forecasting, World Academy of Science, Engineering and Technology, 39(3), 315-318.
  • Çalışkan, M.M.T., Deniz, D. (2015), Yapay Sinir Ağlarıyla Hisse Senedi Fiyatları ve Yönlerinin Tahmini, Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 31, 177-194.
  • Fernandez, A., Gomez, S. (2007), Portfolio Selection Using Neural Networks, Computers & Operations Research, 34(4), 1177-1191.
  • Freund, Y., Schapire, R.E. (1997), A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, 55, 119-139.
  • Harrell, F. (2015), Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis, New York, NY: Springer.
  • Hastie, T., Tibshirani, R., Friedman, J. (2008), The Elements of Statistical Learning, New York: Springer.
  • Haykın, S. (2009), Neural Networks and Learning Machines. Boston: Pearson International Edition.
  • Jandaghı, G., Tehranı, R., Hossınpour, D., Gholıpour, R., Shadkam, S.A.S. (2010), Application Of Fuzzyneural Networks In Multi-Ahead Forecast Of Stock Price, African Journal Of Business Management, 4(6), 903-914.
  • Kara, Y., Boyacıoğlu, M.A., Baykan, Ö.K. (2011), Predicting Direction Of Stock Price Index Movement Using Artificial Neural Networks And Support Vector Machines: The Sample Of The Istanbul Stock Exchange, Expert Systems With Applications, 38, 5311–5319.
  • Karaatlı, M., Güngör, İ., Demir, Y., Kalaycı Ş. (2005), Hisse Senedi Fiyat Hareketlerinin Yapay Sinir Ağları Yöntemi İle Tahmin Edilmesi, Yönetim Ve Ekonomi Araştırmaları Dergisi, 3, 22-48.
  • Khansa, L., Liginlal, D. (2011), Predicting Stock Market Returns From Malicious Attacks: A Comparative Analysis of Vector Autoregression And TimeDelayed Neural Networks, Decision Support Systems, 51(4). 745-759.
  • Kim, S., Kang, M. (2019), Financial series prediction using attention LSTM, arXiv preprint arXiv:1902.10877.
  • Kimoto, T., Asakawa, K., Yoda, M., Takeoka, M. (1990), Stock Market Prediction System With Modular Neural Network, Proceedings Of The International Joint Conference On Neural Networks, 1-6.
  • Li, X., Li, Y., Liu, X.Y., Wang, C.D. (2019), Risk management via anomaly circumvent: mnemonic deep learning for midterm stock prediction, in Proc. 2nd KDD Workshop on Anomaly Detection in Finance, Anchorage, AK, USA.
  • Malakooti, M.V., AghaSharif, A. (2015), Prediction of Stock Market Index based on Neural Networks, Genetic Algorithms and Data Mining Using SVD, The Prooceedings of the International Conference on Digital Information Processing, Data Mining, and Wireless Communications, Dubai, UAE.
  • Manurung, A.H., Budiharto, W., Prabowo, H. (2018), Algorithm and modeling of stock prices forecasting based on long short-term memory (LSTM), International Journal of Innovative Computing Information and Control, 12, 1277-1283.
  • Mızuno, H., Kosaka, M., Yajıma, H., Komoda N. (1998), Application Of Neural Network To Technical Analysis Of Stock Market Prediction, Studies In Informatic And Control, 7(3), 111-120.
  • Opitz, D., Maclin, R. (1999), Popüler Topluluk Yöntemleri: Ampirik Bir Çalışma,  Yapay Zeka Araştırmaları Dergisi, 11, 169-198.
  • Örnek, A. (2023). Yapay Zeka ve Makine Öğrenmesinin Hisse Senedi Fiyat Tahmini Üzerindeki Etkileri. Finans Araştırmaları Dergisi, 10(2), 45-58.
  • Özçalıcı, M. (2016), Yapay Sinir Ağları ile Çok Aşamalı Fiyat Tahmini: Bist 30 Senetleri Üzerine Bir Araştırma, Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 31(2), 209-227.
  • Özer, A., Sarı, S.S., Başakın, E.E. (2017), Bulanık Mantık ve Yapay Sinir Ağları ile Borsa Endeks Tahmini: Gelişmiş ve Gelişmekte Olan Ülkeler Örneği, Hitit Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 11(1), 99-123.
  • C. Özgür, V. Sarıkovanlık (2022), Forecasting Bist100 and Nasdaq Indices with Single and Hybrid Machine Learning Algorithms, Economic Computation and Economic Cybernetics Studies and Research, 56(3), 235-250.
  • Pabuçcu, H. (2019), Borsa Endeksi Hareketlerinin Tahmini: Trend Belirleyici Veri, Selçuk Üniversitesi Sosyal Bilimler Yüksekokulu Dergisi, 22(1), 246-256.
  • Patel, J., Shah, S., Thakkar, P., Kotecha, K. (2015), Predicting stock market index using fusion of machine learning techniques, Expert Systems with Applications, 42(4), 2162-2172.
  • Phua, P.K.H., Mıng, D., Lın, W. (2001), Neural Network With Genetic Algorithms For Stocks Prediction, Asia Pacific Journal of Operational Research, 18(1), 103-107.
  • Phua, P.K.H., Zhu, X., Koh, C.H. (2003), Forecasting Stock Index Increments Using Neural Networks With Trust Region Methods, Proceedings Of The International Joint Conference, 1, 260-265.
  • Polikar, R. (2006). Karar vermede topluluk tabanlı sistemler, IEEE Devreler ve Sistemler Dergisi, 6(3), 21–45.
  • Rasmussen, C.E., Williams, C.K.I. (2006), Gaussian Processes for Machine Learning, Massachusetts: MIT Press. Cambridge.
  • Rast, M. (1999), Forecasting With Fuzzy Neural Networks: A Case Study in Stock Market Crash Situations. Fuzzy Information Processing Society, Nafıps, 18th International Conference Of The North American, 418-420.
  • Rokach, L. (2010), Ensemble tabanlı sınıflandırıcılar, Yapay Zeka İncelemesi, 33(1), 1-39.
  • Saka Ilgın, K., & Sercan Sarı, S. (2022). BIST-100 Endeks Hareketlerinin BRICS Endeksleri Aracılığıyla Tahmin Edilmesi: Yapay Sinir Ağları Uygulaması. Abant Sosyal Bilimler Dergisi, 22(1), 350-366.
  • Shen, S., Jiang, H., Zhang, T. (2012), Stock Market Forecasting Using Machine Learning Algorithms, Department of Electrical Engineering, Stanford University, Stanford, CA, 1–5.
  • Şerbetçi, A. (2022). Brıcs, Mıst, Kırılgan Beşli Kapsamında Yer Alan Ülkelerin Menkul Kıymetler Borsaları Arasındaki Kısa Ve Uzun Dönemli Etkileşime İlişkin Amprik Bir Araştırma. Premium E-Journal Of Social Sciences (Pejoss), 6(19), 76–97.
  • Taş, A.İ., Gülüm, P., Tulum, G. (2021), Finansal Piyasalarda Hisse Fiyatlarının Derin Öğrenme ve Yapay Sinir Ağı Yöntemleri ile Tahmin Edilmesi; S&P 500 Endeksi Örneği, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, Additional Issue, 446-460.
  • Ticknor J.L. (2013), A Bayesian Regularized Artificial Neural Network for Stock Market Forecasting, Expert Systems with Applications, 40(14), 5501-5506.
  • Usul, H., Küçüksille, E., Karaoğlan, D. S. (2017), Güven Endekslerindeki Değişimlerin Hisse Senedi Piyasalarına Etkileri: Borsa İstanbul Örneği, Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22(3), 685-695.
  • Vaisla, K.S., Bhatt, A.K. (2010) An Analysis of The Performance of Artificial Neural Network Technique For Stock Market Forecasting, International Journal On Computer Science And Engineering, 2(6), 2104-2109.
  • Vapnik, V. (1995), The Nature of Statistical Learning Theory. New York: Springer.
  • Vassakis, K., Petrakis, E., Kopanakis, I. (2018), Big Data Analytics: Applications, Prospects and Challenges, Mobil Big Data, Lecture Notes on Data Engineering and Communications Technologies, 10, 3-20.
  • Yao, J., Li, Y., Tan, C.L. (2000), Option price forecasting using neural networks, Omega, 28(4), 455-466, ISSN 0305-0483, https://doi.org/10.1016/S0305-0483(99)00066-3.
  • Yoo, P.D., Kim, M.H., Jan, T. (2005), Machine learning techniques and use of event information for stock market prediction: A survey and evaluation, in: International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, CIMCA-IAWTIC’06, 835–841.
  • Zorın, A., Borısov, A. (2002), Modelling Riga Stock Exchange Index using neural networks, Proceedings of the International Conference Traditions and Innovations in Sustainable Development of Society. Rezekne, Latvia.
Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Finansal Öngörü ve Modelleme
Bölüm Araştırma Makalesi
Yazarlar

Sevgi Sumerli Sarıgül 0000-0002-3820-6288

Ramazan Aaldeimir 0000-0002-4279-3649

Hayrettin Uzunoğlu 0000-0003-1550-5549

Erken Görünüm Tarihi 22 Ekim 2024
Yayımlanma Tarihi
Gönderilme Tarihi 2 Nisan 2024
Kabul Tarihi 22 Ekim 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 9 Sayı: 2

Kaynak Göster

APA Sumerli Sarıgül, S., Aaldeimir, R., & Uzunoğlu, H. (2024). Stock Market Index Prediction Using Machine Learning Techniques: Application of BIST Indices. JOEEP: Journal of Emerging Economies and Policy, 9(2), 96-106.
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