Machine Learning for Corporate Default Risk: Multi-Period Prediction, Frailty Correlation, Loan Portfolios, and Tail Probabilities

2021 ◽  
Author(s):  
Fabio Sigrist ◽  
Nicola Leuenberger
Keyword(s):  
Machine Learning ◽  
Default Risk ◽  
Tail Probabilities ◽  
Corporate Default ◽  
Loan Portfolios

The Bloomberg Corporate Default Risk Model (DRSK) for Public Firms

2021 ◽  
Author(s):  
Mario Bondioli ◽  
Martin Goldberg ◽  
Nan Hu ◽  
Chengrui Li ◽  
Olfa Maalaoui Chun ◽  
...  
Keyword(s):  
Default Risk ◽  
Risk Model ◽  
Public Firms ◽  
Corporate Default

scholarly journals Corporate Default Predictions and Machine Learning

2019 ◽  
Vol 18 (3) ◽  
pp. 131-152
Author(s):  
김형준 ◽  
조훈 ◽  
류두진
Keyword(s):  
Machine Learning ◽  
Corporate Default

The Bloomberg Corporate Default Risk Model (DRSK) for Private Firms

2021 ◽  
Author(s):  
Mario Bondioli ◽  
Martin Goldberg ◽  
Nan Hu ◽  
Chengrui Li ◽  
Olfa Maalaoui Chun ◽  
...  
Keyword(s):  
Default Risk ◽  
Risk Model ◽  
Private Firms ◽  
Corporate Default

scholarly journals Corporate Default Predictions Using Machine Learning: Literature Review

2020 ◽  
Vol 12 (16) ◽  
pp. 6325 ◽  
Author(s):  
Hyeongjun Kim ◽  
Hoon Cho ◽  
Doojin Ryu
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Statistical Models ◽  
Stock Price ◽  
Global Financial Crisis ◽  
Support Vector ◽  
Corporate Default ◽  
Default Prediction ◽  
Future Outcomes ◽  
The Global Financial Crisis

Corporate default predictions play an essential role in each sector of the economy, as highlighted by the global financial crisis and the increase in credit risk. This study reviews the corporate default prediction literature from the perspectives of financial engineering and machine learning. We define three generations of statistical models: discriminant analyses, binary response models, and hazard models. In addition, we introduce three representative machine learning methodologies: support vector machines, decision trees, and artificial neural network algorithms. For both the statistical models and machine learning methodologies, we identify the key studies used in corporate default prediction. By comparing these methods with findings from the interdisciplinary literature, our review suggests some new tasks in the field of machine learning for predicting corporate defaults. First, a corporate default prediction model should be a multi-period model in which future outcomes are affected by past decisions. Second, the stock price and the corporate value determined by the stock market are important factors to use in default predictions. Finally, a corporate default prediction model should be able to suggest the cause of default.

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