Forecasting the XBANK Index in Türkiye Using Macroeconomic Indicators: A Model Comparison with Ensemble Learning Methods

Abstract

The objective of this study is to predict the monthly closing prices of the BIST Bank Index (XBANK) utilising macroeconomic and financial indicators. The explanatory variables encompass the real exchange rate, inflation, the consumer confidence index, the policy rate of the Central Bank of the Republic of Türkiye (CBRT), the growth rate of M2 money supply, CBRT reserves, deposits, the industrial production index, the Türkiye CDS spread, and the VIX fear index. In the initial evaluation, three machine learning models – GradientBoosting, XGBoost, and RandomForest Regressor – with the highest predictive power were identified using the LazyRegressor method, and hyperparameter optimization was performed on these models. The performance of the models was evaluated using the R² and RMSE criteria. The most successful result was obtained with the GradientBoosting model, which had an R² score of 0.99i Pursuant to feature importance analysis, it was determined that inflation (37%), policy interest rate (29%), and Central Bank of the Republic of Türkiye (CBRT) reserves (13%) were the variables exerting the most influence on the movements of the banking index. The findings of this study suggest that monetary policy and macroeconomic stability exert a significant influence on the stock performance of the Turkish banking sector.

Keywords

• Stock Market Prediction
• Machine Learning
• XBANK
• Gradient Boosting.

References

1. Akbulut, S. and K. Adem (2023). Derin öğrenme ve makine öğrenmesi yöntemleri kullanılarak gelişmekte olan ülkelerin finansal enstrümanlarının etkileşimi ile Bist 100 tahmini. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 12(1), 52-63.
2. Bansal, M., A. Goyal and A. Choudhary (2022). Stock market prediction with high accuracy using machine learning techniques. Procedia Computer Science, 215, 247-265.
3. Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32.
4. Brown, S.J., W.N. Goetzmann and A. Kumar (1998). The Dow theory: William Peter Hamilton's track record reconsidered. The Journal of Finance, 53(4), 1311-1333.
5. Burton, N. (2018). An analysis of Burton G. Malkiel's A random walk down Wall Street. Macat Library.
6. Chen, T. and C. Guestrin (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). ACM.
7. Dalkıran, İ. and M. Ozan (2022). Derin Öğrenme Teknikleri Kullanılarak Borsadaki Hisse Değerlerinin Tahmin Edilmesi. Avrupa Bilim ve Teknoloji Dergisi, (39), 143-148.
8. Doğan, S. and Y. Büyükkör (2022). Makine öğrenmesi ile finansal zaman serisi tahminleme. Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 24(3), 1205-1230.

9. Egüz, B., F. E. Çorbacı, F. and T. Kaya (2022). Stock price prediction of Turkish banks using machine learning methods. In Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation: Proceedings of the INFUS 2021 Conference, held August 24-26, 2021. Volume 2 (pp. 222-229). Springer International Publishing.
10. Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383–417.
11. Fischer, T. And C. Krauss (2018). Deep learning with long short-term memory networks for financial market predictions. European journal of operational research, 270(2), 654-669.
12. Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29(5), 1189–1232.
13. Gu, S., B Kelly and D. Xiu (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
14. Hull, J. (2012). Risk management and financial institutions,+ Web Site (Vol. 733). John Wiley & Sons.
15. Karaca, H. M. and U. Dökmen (2024). Comparative Analysis of Machine Learning Algorithms in Stock Price Prediction. Bilgisayar Bilimleri ve Teknolojileri Dergisi, 5(2), 36-46.
16. Koç Ustalı, N., N. Tosun and Ö. Tosun (2021). Makine Öğrenmesi Teknikleri ile Hisse Senedi Fiyat Tahmini. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 16(1), 1-16.
17. Krauss, C., X. A. Do and N. Huck (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689-702.
18. Levine, R. (1997). Financial development and economic growth: views and agenda. Journal of economic literature, 35(2), 688-726.
19. Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 17(1), 59-82.
20. Osama, A., H. Saeid, S. Mohsen and S.S. Eldin (2024). Comparative Analysis of Stock Price Prediction Using Machine Learning. In 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) (pp. 69-75). IEEE.
21. Pamukçu, D. E., Y. Aygül and O. Uğurlu (2023). Prediction of Financial Time Series with Deep Learning Algorithms. Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 28(3), 935-946.
22. Phuoc, T., P.T.K. Anh, P.H. Tam and C.V.Nguyen (2024). Applying machine learning algorithms to predict the stock price trend in the stock market–The case of Vietnam. Humanities and Social Sciences Communications, 11(1), 1-18.
23. Shiller, R. J. (2002). Irrational exuberance in the media. The Right to Tell, 83. Teixeira, D. M. and R. S. Barbosa (2025). Stock Price Prediction in the Financial Market Using Machine Learning Models. Computation, 13(1), 3.
24. Timmermann, A. and C. W. Granger (2004). Efficient market hypothesis and forecasting. International Journal of forecasting, 20(1), 15-27.
25. Toprak, Ş., G. Çağıl and A. H. Kökçam, (2023). Stock Closing Price Prediction with Machine Learning Algorithms: PETKM Stock Example in BIST. Duzce University Journal of Science and Technology, 11(2), 958-976.
26. Tsay, R.S. (2005). Analysis of financial time series. John wiley & sons.