PREDICTION OF STOCK MARKET INDEX DIRECTION WITH TREE-BASED ENSEMBLE MACHINE LEARNING METHODS: THE CASE OF BIST-100

Yasin Büyükkör; Seyyide Doğan

Makale detayları

PREDICTION OF STOCK MARKET INDEX DIRECTION WITH TREE-BASED ENSEMBLE MACHINE LEARNING METHODS: THE CASE OF BIST-100

Yazarlar:

Yasin Büyükkör | Seyyide Doğan

Yayıncı:

Bingol University

Ana Başlıklar:

Ekonometri Teorisi | Ekonometrik ve İstatistiksel Yöntemler | Zaman Serileri Analizi

Dergi:

Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi Issue 27, pp.324-335

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001316484

pISSN:

1309-6672

Anahtar kelimeler:

Özet:

 The establishment of an effective prediction model for the direction of stock market indices is quite challenging due to the complex and non-stationary nature of financial data. Predicting the upward or downward movements of the stock market index, especially in emerging market exchanges where the impacts of external factors and shocks are observed more deeply, is of significant importance to stakeholders such as investors, governments, financial institutions, and creditors, as well as researchers. The aim of this study is to predict the direction of the stock market index with tree-based ensemble Machine Learning ML methods. Technical Indicators calculated after applying Exponential Smoothing to daily Opening, Closing, Highest, Lowest and Volume data of three years were considered as input variables of the model. In addition, the input variable space was expanded by increasing the window lengths of the Technical Indicators. In the study, Random Forest, XGBoost and CatBoost methods,which based on Decision Trees, are used as ensemble ML methods. Bayesian Search was employed to determine the optimal parameters. According to the findings of the study, all selected methods demonstrated accuracy rates ranging from 89.7 to 90.4, and considering other performance evaluation criterias, XGBoost was identified as the best prediction method.

 The establishment of an effective prediction model for the direction of stock market indices is quite challenging due to the complex and non-stationary nature of financial data. Predicting the upward or downward movements of the stock market index, especially in emerging market exchanges where the impacts of external factors and shocks are observed more deeply, is of significant importance to stakeholders such as investors, governments, financial institutions, and creditors, as well as researchers. The aim of this study is to predict the direction of the stock market index with tree-based ensemble Machine Learning ML methods. Technical Indicators calculated after applying Exponential Smoothing to daily Opening, Closing, Highest, Lowest and Volume data of three years were considered as input variables of the model. In addition, the input variable space was expanded by increasing the window lengths of the Technical Indicators. In the study, Random Forest, XGBoost and CatBoost methods,which based on Decision Trees, are used as ensemble ML methods. Bayesian Search was employed to determine the optimal parameters. According to the findings of the study, all selected methods demonstrated accuracy rates ranging from 89.7 to 90.4, and considering other performance evaluation criterias, XGBoost was identified as the best prediction method.

 The establishment of an effective prediction model for the direction of stock market indices is quite challenging due to the complex and non-stationary nature of financial data. Predicting the upward or downward movements of the stock market index, especially in emerging market exchanges where the impacts of external factors and shocks are observed more deeply, is of significant importance to stakeholders such as investors, governments, financial institutions, and creditors, as well as researchers. The aim of this study is to predict the direction of the stock market index with tree-based ensemble Machine Learning ML methods. Technical Indicators calculated after applying Exponential Smoothing to daily Opening, Closing, Highest, Lowest and Volume data of three years were considered as input variables of the model. In addition, the input variable space was expanded by increasing the window lengths of the Technical Indicators. In the study, Random Forest, XGBoost and CatBoost methods,which based on Decision Trees, are used as ensemble ML methods. Bayesian Search was employed to determine the optimal parameters. According to the findings of the study, all selected methods demonstrated accuracy rates ranging from 89.7 to 90.4, and considering other performance evaluation criterias, XGBoost was identified as the best prediction method.

 Borsa endeks yönünün tahmininde finansal verilerin karmaşık ve durağan olmayan yapısı nedeniyle etkin bir tahmin modelinin kurulması oldukça zordur. Bazı dışsal faktörlerin ve şokların etkilerinin daha derin gözlendiği gelişmekte olan ülke borsalarında, borsa endeksinin aşağı veya yukarı yönlü hareketini tahmin etmek gerek yatırımcılar, hükümetler, finansal kurumlar ve kreditörler gibi paydaşlar gerekse de araştırmacılar için önemli bir konudur. Bu çalışmanın amacı, Borsa İstanbul 100 BİST-100 endeksinin borsa endeksinin yönünü ağaç tabanlı topluluk Makine Öğrenmesi ML yöntemleriyle tahmin etmektir. Üç yılın günlük Açılış, Kapanış, En Yüksek, En Düşük ve Hacim verilerine Üstel Düzgünleştirme uygulandıktan sonra hesaplanan Teknik Göstergeler modelin girdi değişkenleri olarak ele alınmıştır. Ayrıca Teknik Göstergelerin pencere uzunlukları artırılarak girdi değişkeni uzayı genişletilmiştir. Çalışmada Karar Ağaçlarına dayanan topluluk makine öğrenmesi yöntemlerinden Random Forest, XGBoost ve CatBoost kullanılmıştır. Modelin parametreleri Bayesyan Arama Bayesian Search yöntemi ile optimize edilmiştir. Çalışmanın bulgularına göre, tercih edilen bütün yöntemler 89,7 ile 90,4 aralığında doğruluk oranına sahipken ve diğer performans değerlendirme kriterleri de dikkate alındığında en iyi performansa sahip yöntemin XGBoost olduğu görülmektedir.

Makale detayları

PREDICTION OF STOCK MARKET INDEX DIRECTION WITH TREE-BASED ENSEMBLE MACHINE LEARNING METHODS: THE CASE OF BIST-100

Yazarlar:

Yasin Büyükkör | Seyyide Doğan

Yayıncı:

Bingol University

Ana Başlıklar:

Ekonometri Teorisi | Ekonometrik ve İstatistiksel Yöntemler | Zaman Serileri Analizi

Dergi:

Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi Issue 27, pp.324-335

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001316484

pISSN:

1309-6672

Ensemble Machine Learning

Tecnical Indicators

Stock Market Way Prediction

Random Forest

XGBoost

CatBoostTopluluk Makine Öğrenmesi

Teknik Göstergeler

Borsa Yönü Tahmini

Rassal Orman

XGBoost

CatBoost

Sayı

Makale detayları

Yazarlar: Yasin Büyükkör | Seyyide Doğan

Yayıncı: Bingol University

Ana Başlıklar: Ekonometri Teorisi | Ekonometrik ve İstatistiksel Yöntemler | Zaman Serileri Analizi

Dergi: Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi Issue 27, pp.324-335

Belge Tipi: Makale

Yayın Tarihi: 4/2024

Belge Dili: Türkçe

Document ID (Org No.): 2001316484

pISSN: 1309-6672

Sayı

Yazarlar:

Yasin Büyükkör | Seyyide Doğan

Yayıncı:

Bingol University

Ana Başlıklar:

Ekonometri Teorisi | Ekonometrik ve İstatistiksel Yöntemler | Zaman Serileri Analizi

Dergi:

Bingöl Üniversitesi Sosyal Bilimler Enstitüsü Dergisi Issue 27, pp.324-335

Belge Tipi:

Makale

Yayın Tarihi:

4/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001316484

pISSN:

1309-6672