Treffer: Time Series Forecasting of MSCI Indices With Machine Learning.

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. [ABSTRACT FROM AUTHOR]

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