Borsa endeks hareket yönlerinin makine öğrenmesi yöntemleriyle tahmini: Gelişmiş ve gelişmekte olan ülke borsaları üzerine bir uygulama
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Niğde Ömer Halisdemir Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Menkul kıymet borsaları; makroekonomik faktörler, küresel gelişmeler ve insan davranışları gibi birçok faktörden etkilenen karmaşık bir yapıya sahiptir. Borsa endeksleri ise, borsaların genel durumunu ve yönünü yansıtan önemli göstergelerdir. Borsa endeks hareket yönlerinin tahmin edilmesi, yatırım kararlarının alınması ve risklerin yönetilmesi açısından önemlidir. Geçmiş verilerdeki ilişkileri ve eğilimleri tespit edebilen makine öğrenmesi yöntemleri sayesinde borsa endekslerinin hareket yönleri tahmin edilebilmektedir. Bu çalışmanın amacı, gelişmiş ve gelişmekte olan ülkelerin borsa endekslerinin hareket yönlerinin makine öğrenmesi yöntemleriyle tahmin edilerek yöntemlerin performanslarının karşılaştırılması ve en iyi tahmin yönteminin belirlenmesidir. Gelişmiş ülkelerin borsa endeksleri olarak NYSE 100, NIKKEI 225, FTSE 100, CAC 40, DAX 30, FTSE MIB, TSX; gelişmekte olan ülkelerin borsa endeksleri olarak ise SSE, BOVESPA, RTS, NIFTY 50, IDX, IPC ve BIST 100 endeksleri seçilmiştir. Borsa endekslerinin hareket yönleri ise karar ağaçları, rastgele orman, k-en yakın komşuluk, naive bayes, lojistik regresyon, destek vektör makineleri ve yapay sinir ağları yöntemleri ile tahmin edilmiştir. Analizlerde 01.01.2012-31.12.2021 dönemine ait günlük veri seti ve teknik göstergeler girdi verisi olarak kullanılmıştır. Ulaşılan sonuçlara göre, incelenen dönemde en iyi yöntemin yapay sinir ağları olduğu tespit edilmiştir. Yapay sinir ağlarıyla birlikte lojistik regresyon ve destek vektör makineleri yöntemlerinin de tüm endekslerin hareket yönünü %70'in üzerinde doğrulukla tahmin ettiği belirlenmiştir. Ayrıca, en iyi yöntem olarak belirlenen yapay sinir ağlarının tüm endeksler için geçerli olmadığı da tespit edilmiştir. Bu bağlamda, incelenen yöntemlerin performansının ülkeler ve endeksler arasında farklılık gösterdiği ancak ülkelerin gelişmişlik seviyelerine göre farklılık göstermediği belirlenmiştir. Sonuç olarak, borsa endeks hareket yönlerinin tahmininde yapay sinir ağları, lojistik regresyon ve destek vektör makineleri yöntemleri en avantajlı yöntemler olarak önerilmektedir.
Stock markets possess a complex structure influenced by numerous factors such as macroeconomic indicators, global developments, and human behaviors. Stock market indices, in particular, stand as crucial indicators reflecting the overall health and direction of these markets. Predicting the movement directions of these indices holds significant importance in terms of making investment decisions and managing risks. Thanks to machine learning methods capable of identifying relationships and trends within historical data, it becomes possible to predict the movement directions of stock market indices. The objective of this study is to compare the performance of machine learning methods in predicting the movement directions of stock market indices in both developed and developing countries, and to determine the best prediction method. In line with this aim, the stock market indices of developed countries were selected as NYSE 100, NIKKEI 225, FTSE 100, CAC 40, DAX 30, FTSE MIB, and TSX; while the stock market indices of developing countries included SSE, BOVESPA, RTS, NIFTY 50, IDX, IPC, and BIST 100. The movement directions of the stock market indices were predicted using decision trees, random forests, k-nearest neighbors, naive Bayes, logistic regression, support vector machines, and artificial neural networks. The analyses employed a daily dataset and input data consisting of technical indicators from January 1, 2012, to December 31, 2021. Based on the results obtained, artificial neural networks are identified as the most effective method during the examined period. Alongside artificial neural networks, logistic regression and support vector machines are recognized as methods accurately predicting the movement directions of all indices with an accuracy rate exceeding 70%. Furthermore, it is observed that the identified best method, artificial neural networks, is not universally applicable across all indices. In this context, it is concluded that the performance of the explored methods varies across countries and indices but does not significantly differ based on countries levels of development. In conclusion, artificial neural networks, logistic regression, and support vector machines are recommended as the most advantageous methods for predicting the movement directions of stock market indices.
Stock markets possess a complex structure influenced by numerous factors such as macroeconomic indicators, global developments, and human behaviors. Stock market indices, in particular, stand as crucial indicators reflecting the overall health and direction of these markets. Predicting the movement directions of these indices holds significant importance in terms of making investment decisions and managing risks. Thanks to machine learning methods capable of identifying relationships and trends within historical data, it becomes possible to predict the movement directions of stock market indices. The objective of this study is to compare the performance of machine learning methods in predicting the movement directions of stock market indices in both developed and developing countries, and to determine the best prediction method. In line with this aim, the stock market indices of developed countries were selected as NYSE 100, NIKKEI 225, FTSE 100, CAC 40, DAX 30, FTSE MIB, and TSX; while the stock market indices of developing countries included SSE, BOVESPA, RTS, NIFTY 50, IDX, IPC, and BIST 100. The movement directions of the stock market indices were predicted using decision trees, random forests, k-nearest neighbors, naive Bayes, logistic regression, support vector machines, and artificial neural networks. The analyses employed a daily dataset and input data consisting of technical indicators from January 1, 2012, to December 31, 2021. Based on the results obtained, artificial neural networks are identified as the most effective method during the examined period. Alongside artificial neural networks, logistic regression and support vector machines are recognized as methods accurately predicting the movement directions of all indices with an accuracy rate exceeding 70%. Furthermore, it is observed that the identified best method, artificial neural networks, is not universally applicable across all indices. In this context, it is concluded that the performance of the explored methods varies across countries and indices but does not significantly differ based on countries levels of development. In conclusion, artificial neural networks, logistic regression, and support vector machines are recommended as the most advantageous methods for predicting the movement directions of stock market indices.
Açıklama
Sosyal Bilimler Enstitüsü, İşletme Ana Bilim Dalı, Muhasebe Finansman Bilim Dalı
Anahtar Kelimeler
İşletme, Business Administration
