Makine Öğrenimi ile MSCI Endekslerinin Zaman Serisi Tahmini

Year 2025, Volume: 13 Issue: 4, 1732 - 1744, 30.10.2025

Diler Türkoğlu , Mehmet Ali Cengiz

https://doi.org/10.29130/dubited.1567866

Abstract

Bu çalışmada, MSCI Türkiye ve MSCI Almanya Endekslerinin 29.03.2009 - 28.03.2024 tarihleri arasındaki 15 yıllık günlük verileri kullanılarak ARIMA, XGBoost, LSTM ve Prophet yöntemleriyle yapılan tahminler aracılığıyla en başarılı modelin belirlenmesi amaçlanmaktadır. En başarılı modeli belirlemede ölçüt olarak elde edilen Root Mean Square Error (RMSE) değerleri alınmıştır. Python JupyterNotebook programı kullanılarak yapılan analizlerde, diğer tüm değişkenlerin sabit kaldığı varsayıldığında, XGBoost yön-temi MSCI Türkiye Endeksi için en başarılı model olarak belirlenirken, LSTM Modeli MSCI Almanya Endeksi için en iyi sonuçları vermiştir. Bu nedenle, sonuçlar göz önüne alındığında, makine öğrenmesi yöntemlerinin klasik yöntemlere göre daha iyi performans gösterdiği söylenebilmektedir.

Keywords

Makine Öğrenimi , ARIMA , XGBoost , Prophet , LSTM.

Time Series Forecasting of MSCI Indices With Machine Learning

Abstract

Machine learning has become an increasingly important tool for understanding the dynamic nature of financial markets and predicting future price movements. The aim of this study is to determine the most successful forecasting model by comparing the forecasts made by ARIMA, XGBoost, LSTM and Prophet methods using the 15-year daily data of MSCI Turkey and MSCI Germany Indices between 29.03.2009 and 28.03.2024. Root Mean Square Error (RMSE) value is taken as a benchmark to evaluate the model's success. The analyses were conducted using the Python JupyterNotebook program and assumed that all other variables are constant. According to the results, the XGBoost method was found to be the most successful model for the MSCI Turkey Index, while the LSTM model gave the best results for the MSCI Germany Index. These findings suggest that machine learning methods outperform classical forecasting techniques. This study reveals that machine learning is a powerful tool for making more accurate forecasts in financial markets, and that these methods provide more efficient results compared to classical models.

Keywords

Machine Learning , ARIMA , XGBoost , Prophet , LSTM

Ethical Statement

This study does not involve human or animal participants. All procedures followed scientific and ethical principles, and all referenced studies are appropriately cited.

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