The Role of Financial Markets in Predicting BIST Sustainability Index Performance: New Evidence from Hybrid Machine Learning Models

Year 2025, Volume: 10 Issue: Özel Sayı, 383 - 402, 31.10.2025

Zeynep Çolak

https://doi.org/10.30784/epfad.1813752

Abstract

The increasing importance of sustainable finance makes it critical to understand and accurately model the performance dynamics of investment instruments in this area. This study aims to forecast the return of the BIST Sustainability Index using financial market indicators and to explain the underlying dynamics of this forecasting process, thereby understanding the complex structures of financial markets, investor behavior, and information flow. In this study, eleven different machine learning models were compared with a validation strategy suitable for the time series structure, and the most successful candidates were subjected to hyperparameter optimization. In order to overcome the limitations of single models, a sequential hybrid model based on the Residual Fitting approach was developed. According to the results of the study, the two-stage hybrid model, which uses the Voting Regressor as the main predictor and Random Forest as the error corrector, provided the lowest error (RMSE) and the highest R² value. The findings indicate that the BIST_100 index is the most critical determinant, while risk aversion indicators such as Gold, USD, and VIX have a negative effect. This evidence has far-reaching implications for understanding the dynamic relationships between the Sustainability Index and macroeconomic variables.

Keywords

Sustainable Finance , BIST Sustainability Index , Machine Learning , SHAP , Explainable Artificial Intelligence

BİST Sürdürülebilirlik Endeksi Performansının Tahmininde Finans Piyasalarının Rolü: Hibrid Makine Öğrenmesi Modellerinden Yeni Kanıtlar

Abstract

Sürdürülebilir finansmanın artan önemi, bu alandaki yatırım araçlarının performans dinamiklerini anlamayı ve doğru bir şekilde modellemeyi kritik hale getirmektedir. Bu çalışma, finansal piyasa göstergelerini kullanarak BIST Sürdürülebilirlik Endeksi'nin getirisini tahmin etmeyi ve bu tahmin sürecinin altında yatan dinamikleri açıklamayı, böylece finansal piyasaların karmaşık yapılarını, yatırımcı davranışlarını ve bilgi akışını anlamayı amaçlamaktadır. Bu çalışmada, zaman serisi yapısına uygun bir doğrulama stratejisi ile on bir farklı makine öğrenimi modeli karşılaştırılmış ve en başarılı adaylar hiperparametre optimizasyonuna tabi tutulmuştur. Tekil modellerin sınırlamalarını aşmak için, Residual Fitting yaklaşımına dayalı sıralı bir hibrit model geliştirilmiştir. Çalışmanın sonuçlarına göre, ana tahminci olarak Voting Reressor ve hata düzeltici olarak Rastgele Orman kullanan iki aşamalı hibrit model, en düşük hata (RMSE) ve en yüksek R² değerini sağlamıştır. Bulgular, BIST_100 endeksinin en kritik belirleyici olduğunu, Altın, USD ve VIX gibi riskten kaçınma göstergelerinin ise olumsuz bir etkiye sahip olduğunu göstermektedir.

Keywords

Sürdürülebilir Finans , BIST Sürdürülebilir Endeksi , Makine Öğrenimi , SHAP , Açıklanabilir YapayZeka

References

• AlGhazali, A., Mensi, W., Morley, B. and Kang, S.H. (2025). Connectedness and hedging strategies between European sustainability and conventional stock markets. Journal of Sustainable Finance & Investment, Advance online publication. 1-30. https://doi.org/10.1080/20430795.2025.2520523
• Aslanargun, A., Mammadov, M., Yazici, B. and Yolacan, S. (2007). Comparison of ARIMA, neural networks and hybrid models in time series: Tourist arrival forecasting. Journal of Statistical Computation and Simulation, 77(1), 29-53. https://doi.org/10.1080/10629360600564874
• Başkaya, H. (2025). BİST sürdürülebilirlik endeksi ile diğer finansal endeksler arasındaki ilişkinin ve nedenselliğin analizi. Fiscaoeconomia, 9(2), 1003-1021. Retrieved from https://www.ceeol.com/
• Bergmeir, C. and Benítez, J.M. (2012). On the use of cross-validation for time series prediction. Information Sciences, 191, 192-213. https://doi.org/10.1016/j.ins.2011.12.028
• Bhattacharya, A. (2022). Applied machine learning explainability techniques. Birmingham: Packt Publishing.
• Bhutta, U.S., Tariq, A., Farrukh, M., Raza, A. and Iqbal, M.K. (2022). Green bonds for sustainable development: Review of literature on development and impact of green bonds. Technological Forecasting and Social Change, 175, 121378. https://doi.org/10.1016/j.techfore.2021.121378
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
• Broadstock, D.C. and Cheng, L.T. (2019). Time-varying relation between black and green bond price benchmarks: Macroeconomic determinants for the first decade. Finance Research Letters, 29, 17-22. https://doi.org/10.1016/j.frl.2019.02.006
• Chen, S., Song, Y. and Gao, P. (2023). Environmental, social, and governance (ESG) performance and financial outcomes: Analyzing the impact of ESG on financial performance. Journal of Environmental Management, 345, 118829. https://doi.org/10.1016/j.jenvman.2023.118829
• Çankal, A. and Ever, D. (2025). The effects of renewable energy consumption on financial performance: An explainable artificial ıntelligence (XAI)-based research on the BIST sustainability index. International Journal of Energy Economics and Policy, 15(4), 204-213. https://doi.org /10.32479/ijeep.19602
• Dietterich, T.G. (2000). Ensemble methods in machine learning. In J. Kittler and F. Roli (Eds.), Multiple classifier systems, first ınternational workshop, MCS 2000 (pp. 1–15). https://doi.org/10.1007/3-540-45014-9_1
• Drimbetas, E., Sariannidis, N., Giannarakis, G. and Litinas, N. (2010). The effects of macroeconomic factor on the sustainability, large-cap and mid-cap Dow Jones indexes. International Journal of Business Policy and Economics, 3, 21-36. Retrieved from https://serialsjournals.com/
• Ehlers, T. and Packer, F. (2017). Green bond finance and certification. BIS Quarterly Review, September, 89-104. Retrieved from https://www.bis.org/
• Friede, G., Busch, T. and Bassen, A. (2015). ESG and financial performance: Aggregated evidence from more than 2000 empirical studies. Journal of Sustainable Finance & Investment, 5(4), 210-233. https://doi.org/10.1080/20430795.2015.1118917
• Friedman, J.H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. Retrieved from https://www.jstor.org/
• Hastie, T., Tibshirani, R. and Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Berlin: Springer.
• Haykin, S. (2009). Neural networks and learning machines, 3/E. Bangalore: Pearson Education
• Heaton, J.B., Polson, N.G. and Witte, J.H. (2016). Deep learning in finance. arXiv preprint arXiv:1602.06561. https://doi.org/10.48550/arXiv.1602.06561
• Kaur, J. and Chaudhary, R. (2022). Relationship between macroeconomic variables and sustainable stock market index: An empirical analysis. Journal of Sustainable Finance & Investment, 1-18. https://doi.org/10.1080/20430795.2022.2073957
• Kavas, Ü.Y.B. (2025). Finansal enstrümanların BİST sürdürülebilirlik endeksi üzerindeki dinamik etkilerinin TVP-VAR modeliyle araştırılması. Mali Cözüm Dergisi, 35, 1201-1225. Retrieved from https://ismmmo.org.tr/Yayinlar/Mali-Cozum-Dergisi--1
• Kaya, M. (2023). BİST sürdürülebilirlik endeksi ile fosil yakıt fiyatları arasındaki ilişkinin analizi. Abant Sosyal Bilimler Dergisi, 23(3), 1475-1495. https://doi.org/10.11616/asbi.1327883
• Kocamiş, T.U. and Yildirim, G. (2016). Sustainability reporting in Turkey: Analysis of companies in the BIST sustainability index. European Journal of Economics and Business Studies, 2(3), 41-51. Retrieved from https://revistia.com/ejes
• LeCun, Y., Bengio, Y. and Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
• Lundberg, S.M. and Lee, S.I. (2017). A unified approach to interpreting model predictions. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan and R. Garnett (Eds.), Advances in neural information processing systems (pp. 1-10). California: Curran Associates.
• Morales, L., Soler-Domínguez, A. and Hanly, J. (2019). The power of ethical investment in the context of political uncertainty. Journal of Applied Economics, 22(1), 554-580. https://doi.org/10.1080/15140326.2019.1683264
• Özçim, H. (2022). Bist sürdürülebilirlik endeksi ve makroekonomik veriler arasindaki ilişkinin GARCH modelleri çerçevesinde incelenmesi. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 50, 115-126. https://doi.org/10.30794/pausbed.1015216
• Quinn, B. (2023). Explaining AI in finance: Past, present, prospects. New York: Cornell University.
• Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386. https://doi.org/10.1037/h0042519
• Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. Retrieved from https://www.nature.com/
• Seow, R.Y.C. (2025). Transforming ESG analytics with machine learning: A systematic literature review using TCCM framework. Corporate Social Responsibility and Environmental Management, Advance online publication. https://doi.org/10.1002/csr.70089
• Shaikh, I. (2022). On the relationship between policy uncertainty and sustainable investing. Journal of Modelling in Management, 17(4), 1504-1523. https://doi.org/10.1108/JM2-12-2020-0320
• Shapley, L.S. (1953). A value for n-person games. In H.W. Kuhn and A.W. Tucker (Eds.), Contributions to the theory of games II (307–317). New Jersey: Princeton University Press.
• Sharma, P., Shrivastava, A.K., Rohatgi, S. and Mishra, B.B. (2023). Impact of macroeconomic variables on sustainability indices using ARDL model. Journal of Sustainable Finance & Investment, 13(1), 572-588. https://doi.org/10.1080/20430795.2021.1972679
• Siddique, M.A. and Karim, S. (2025). Can ESG disclosure predict carbon risk? Evidence from machine and deep learning models. Finance Research Letters, 83, 107672. https://doi.org/10.1016/j.frl.2025.107672
• Şahin, S. (2024). Finans Sektöründe yapay zeka, makine öğrenmesi ve büyük veri kullanımı: Fırsatlar, zorluklar ve politika yapıcılar için çıkarımlar. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 9(4), 364-381. https://doi.org/10.29106/fesa.1542860
• Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
• Tsay, R.S. (2010). Analysis of financial time series (3rd ed.). USA: Wiley.
• Vardari, L., Gashi, R. and Aahmeti, H.G. (2020). The impact of corporate sustainability index on BIST sustainability index. European Journal of Sustainable Development, 9(2), 375-375. https://doi.org/10.14207/ejsd.2020.v9n2p375
• Wolpert, D.H. (1992). Stacked generalization. Neural Networks, 5(2), 241-259. https://doi.org/10.1016/S0893-6080(05)80023-1
• Zhang, J. and Zhao, Z. (2026). Corporate ESG rating prediction based on XGBoost-SHAP interpretable machine learning model. Expert Systems with Applications, 295, 128809. https://doi.org/10.1016/j.eswa.2025.128809