Hibrit makine öğrenme algoritmalarının finansal zaman serileri verileri üzerindeki performansı

Estimating stock indices that reflect the market has been an essential issue for a long time. Although various models have been studied in this direction, historically, statistical methods and then various machine learning methods have to introduced artificial intelligence into our lives. Related literature shows that neural networks and treebased models are mostly used. In this direction, in this thesis, four different models are examined. The first one is the most preferred neural network method for financial data called LSTM, and the second one is one of the most preferred tree-based models called XGBoost, and the third and the fourth models are the hybridizations of LSTM and XGBoost. Besides, these models have been applied to the total of nine stock market indexes, three from European markets, three from Asian and three from American markets, and the model that gives the best results is determined according to the Mean Absolute Scaled Error (MASE) evaluation criteria.