Time Series Forecasting of MSCI Indices With Machine Learning

Diler Türkoğlu; Mehmet Ali Cengiz

doi:10.29130/dubited.1567866

TR EN

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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Details

Primary Language

English

Subjects

Machine Learning (Other)

Journal Section

Review Article

Authors

Diler Türkoğlu ^*
0000-0001-5247-1590
Türkiye

Mehmet Ali Cengiz
0000-0002-1271-2588
Türkiye

Publication Date

October 30, 2025

Submission Date

October 15, 2024

Acceptance Date

September 25, 2025

Published in Issue

Year 2025 Volume: 13 Number: 4

DOI

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

IZ

https://izlik.org/JA96XW36YX

APA

Türkoğlu, D., & Cengiz, M. A. (2025). Time Series Forecasting of MSCI Indices With Machine Learning. Duzce University Journal of Science and Technology, 13(4), 1732-1744. https://doi.org/10.29130/dubited.1567866

AMA

1.Türkoğlu D, Cengiz MA. Time Series Forecasting of MSCI Indices With Machine Learning. DUBİTED. 2025;13(4):1732-1744. doi:10.29130/dubited.1567866

Chicago

Türkoğlu, Diler, and Mehmet Ali Cengiz. 2025. “Time Series Forecasting of MSCI Indices With Machine Learning”. Duzce University Journal of Science and Technology 13 (4): 1732-44. https://doi.org/10.29130/dubited.1567866.

EndNote

Türkoğlu D, Cengiz MA (October 1, 2025) Time Series Forecasting of MSCI Indices With Machine Learning. Duzce University Journal of Science and Technology 13 4 1732–1744.

IEEE

[1]D. Türkoğlu and M. A. Cengiz, “Time Series Forecasting of MSCI Indices With Machine Learning”, DUBİTED, vol. 13, no. 4, pp. 1732–1744, Oct. 2025, doi: 10.29130/dubited.1567866.

ISNAD

Türkoğlu, Diler - Cengiz, Mehmet Ali. “Time Series Forecasting of MSCI Indices With Machine Learning”. Duzce University Journal of Science and Technology 13/4 (October 1, 2025): 1732-1744. https://doi.org/10.29130/dubited.1567866.

JAMA

1.Türkoğlu D, Cengiz MA. Time Series Forecasting of MSCI Indices With Machine Learning. DUBİTED. 2025;13:1732–1744.

MLA

Türkoğlu, Diler, and Mehmet Ali Cengiz. “Time Series Forecasting of MSCI Indices With Machine Learning”. Duzce University Journal of Science and Technology, vol. 13, no. 4, Oct. 2025, pp. 1732-44, doi:10.29130/dubited.1567866.

Vancouver

1.Diler Türkoğlu, Mehmet Ali Cengiz. Time Series Forecasting of MSCI Indices With Machine Learning. DUBİTED. 2025 Oct. 1;13(4):1732-44. doi:10.29130/dubited.1567866

Time Series Forecasting of MSCI Indices With Machine Learning

Makine Öğrenimi ile MSCI Endekslerinin Zaman Serisi Tahmini

Abstract

Keywords

Time Series Forecasting of MSCI Indices With Machine Learning

Abstract

Keywords

Ethical Statement

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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