What Matters in Bank Bankruptcy: An Empirical Study Based on Variable Selection
Journal: Modern Economics & Management Forum DOI: 10.32629/memf.v5i4.2576
Abstract
This paper explores the prediction of bank bankruptcy in the context of global economic instability and recurrent financial crises. The study concludes that Lasso and elastic-net models are recommended for predicting bank bankruptcies, as they efficiently balance variable selection with model sparsity, maintaining high prediction accuracy. These models are particularly valuable for banking managers and regulators in enhancing risk management and ensuring financial market stability. This research provides significant insights and tools for banking risk management, contributing to the stability and sustainable development of the financial sector.
Keywords
Bankruptcy Prediction; Variable Selection; Financial Risk Management
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[2] Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy.
[3] Hillegeist, S. A., Keating, E. K., Cram, D. P., & Lundstedt, K. G. (2004). Assessing the Probability of Bankruptcy.
[4] Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction.
[5] Matthew Baron, Moritz Schularick, and Kaspar Zimmermann (2023). Survival of the Biggest: Large Banks and Financial Crises.
[6] Pierre Durand, Ga¨etan Le Quang (2020). What do bankruptcy prediction models tell us about banking regulation?
[7] Cortes, C., & Vapnik, V. (1995). Support-vector networks.
[8] Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors.
[9] Breiman, L. (2001). Random forests.
[10] Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine.
[11] Tibshirani, R. (1996). Regression shrinkage and selection via the lasso.
[12] Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems.
[13] Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net.
[14] Jing-Shiang Hwang, Tsuey-Hwa Hu (2015). A stepwise regression algorithm for high-dimensional variable selection.
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