[Alessi, L., & Battiston, S. (2022). Two sides of the same coin: Green Taxonomy alignment versus transition risk in financial portfolios. International Review of Financial Analysis, 84, 102319.]Search in Google Scholar
[Bua, G., Kapp, D., Ramella, F., & Rognone, L. (2024). Transition versus physical climate risk pricing in European financial markets: A text-based approach. The European Journal of Finance, 30(17), 2076–2110]Search in Google Scholar
[Carattini, S., Heutel, G., & Melkadze, G. (2023). Climate policy, financial frictions, and transition risk. Review of Economic Dynamics, 51, 778–794.]Search in Google Scholar
[Carbone, S., Giuzio, M., Kapadia, S., Krämer, J. S., Nyholm, K., & Vozian, K. (2022). The low-carbon transition, climate commitments, and firm credit risk. Sveriges Riksbank Working Paper Series, No. 409. Sveriges Riksbank.]Search in Google Scholar
[Chen, T. and Guestrin, C. (2016) ‘XGBoost: A scalable tree boosting system’, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16), 13–17 August, San Francisco, CA, USA, ACM, pp. 785–794.]Search in Google Scholar
[Esmailizade, S., Ebrahimi, A., Soltani, H., Sam, A. and Rahimi, M. (2024) ‘Machine Learning Approaches for Retail Forecasting: A Study on XGBoost and Time-Series Models’, TechRxiv.]Search in Google Scholar
[Martini, F., Sautner, Z., Steffen, S., & Theunisz, C. (2024). Climate transition risks of banks. SSRN Electronic Journal.]Search in Google Scholar
[Meinerding, C., Schüler, Y. S., & Zhang, P. (2024). Shocks to transition risk. SSRN Electronic Journal.]Search in Google Scholar
[Reboredo, J. C., & Ugolini, A. (2022). Climate transition risk, profitability, and stock prices. International Review of Financial Analysis, 83, 102271.]Search in Google Scholar
[Semieniuk, G., Campiglio, E., Mercure, J.-F., Volz, U., & Edwards, N. R. (2020). Low-carbon transition risks for finance. WIREs Climate Change, 12(1), e678.]Search in Google Scholar
[Tan, B., Gan, Z. and Wu, Y. (2023) ‘The measurement and early warning of daily financial stability index based on XGBoost and SHAP: Evidence from China’, Expert Systems with Applications, 227, p. 120375.]Search in Google Scholar
[Xu, J., He, J., Gu, J., Wu, H., Wang, L., Zhu, Y., Wang, T., He, X. and Zhou, Z. (2022) ‘Financial Time Series Prediction Based on XGBoost and Generative Adversarial Networks’, International Journal of Circuits, Systems and Signal Processing, 16, pp. 637–647.]Search in Google Scholar