seymagnn / irsysc2023_bank_failure_prediction

Bank Failure Prediction Models Addressing Imbalanced Data and Out-of-Time Performance

This repository consists of the supplemental materials of a conference paper.

If you use the paper or codes, please cite it:

@INPROCEEDINGS{,
  author={Gunonu, Seyma and Altun, Gizem and Cavus, Mustafa},
  booktitle={Proceedings Book of the 7th International Researchers, Statisticians and Young Statisticians Congress}, 
  title={Bank failure prediction models addressing imbalanced data and out-of-time performance}, 
  year={2023},
  pages={185-199},
  ISBN={978-625-8368-61-1}}

Code

You can see how the data was with {fdicdata} package or how data manipulations were made in the data set,.

References

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Carmona, P., Climent, F., & Momparler, A. (2019). Predicting failure in the US banking sector: An extreme gradient boosting approach. Int. Rev. Econ. Finance, 61, 304-323.

Dar, U., & Pillmore, B. (2023). fdicdata: Accessing FDIC Bank Data. R package version 0.1.0. Link

Du Jardin, P., & Séverin, E. (2011). Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model. Decis Support Syst, 51(3), 701-711.

Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine learning, 63, 3-42.

Gogas, P., Papadimitriou, T., & Agrapetidou, A. (2018). Forecasting bank failures and stress testing: A machine learning approach. Int. J. Forecast., 34(3), 440-455.

Grinsztajn, L., Oyallon, E., & Varoquaux, G. (2022). Why do tree-based models still outperform deep learning on typical tabular data?. Adv. Neural Inf. Process, 35, 507-520.

Manthoulis, G., Doumpos, M., Zopounidis, C., & Galariotis, E. (2020). An ordinal classification framework for bank failure prediction: Methodology and empirical evidence for US banks. Eur. J. Oper. Res., 282(2), 786-801.

Petropoulos, A., Siakoulis, V., Stavroulakis, E., & Vlachogiannakis, N. E. (2020). Predicting bank insolvencies using machine learning techniques. Int. J. Forecast., 36(3), 1092-1113.