scholarly journals Multi-Period Corporate Default Prediction with Stochastic Covariates

2005 ◽  
Author(s):  
Darrell Duffie ◽  
Leandro Saita ◽  
Ke Wang
Keyword(s):  
Corporate Default ◽  
Default Prediction ◽  
Stochastic Covariates

scholarly journals Multi-period corporate default prediction with stochastic covariates

2007 ◽  
Vol 83 (3) ◽  
pp. 635-665 ◽  
Author(s):  
Darrell Duffie ◽  
Leandro Saita ◽  
Ke Wang
Keyword(s):  
Corporate Default ◽  
Default Prediction ◽  
Stochastic Covariates

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.

Debt Market Liquidity and Corporate Default Prediction

2014 ◽  
Vol 04 (04) ◽  
pp. 1550003
Author(s):  
Deming Wu ◽  
Suning Zhang
Keyword(s):  
Market Liquidity ◽  
Subprime Crisis ◽  
Short Term ◽  
Time Dynamic ◽  
Corporate Default ◽  
Rollover Risk ◽  
Default Prediction ◽  
Commercial Paper ◽  
Debt Market ◽  
Nonfinancial Firms

Recent research on the subprime crisis and rollover risk suggests that debt market liquidity is a major factor affecting the risk of default. This implies that firms that rely heavily on short-term debt, such as commercial paper (CP), are at greater risk of default. Debt market illiquidity could reduce the value of the firm and thus impact the firm's leverage, which is a major factor in predicting default. We estimate the effect of debt market conditions on the probability of default with a discrete-time dynamic hazard model that takes into account measurement error in firm leverage. Our results indicate that rollover risk is a significant factor in causing default, but the risk was higher for nonfinancial firms around 2000–2001 and considerably less entering the subprime crisis.

scholarly journals Efficacy of industry factors for corporate default prediction

2019 ◽  
Vol 31 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Khushbu Agrawal ◽  
Yogesh Maheshwari
Keyword(s):  
Corporate Default ◽  
Default Prediction

scholarly journals Adaboost Ensemble Classifiers for Corporate Default Prediction

2015 ◽  
Vol 9 (3) ◽  
pp. 224-230
Author(s):  
Suresh Ramakrishnan ◽  
Maryam Mirzaei ◽  
Mahmoud Bekri
Keyword(s):  
Ensemble Classifiers ◽  
Corporate Default ◽  
Default Prediction

Firm Default Prediction: A Bayesian Model-Averaging Approach

2017 ◽  
Vol 52 (3) ◽  
pp. 1211-1245 ◽  
Author(s):  
Jeffrey Traczynski
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Market Returns ◽  
Corporate Default ◽  
Specific Effects ◽  
Aggregate Information ◽  
Default Prediction ◽  
Individual Industry ◽  
Firm Default

I develop a new predictive approach using Bayesian model averaging to account for incomplete knowledge of the true model behind corporate default and bankruptcy filing. I find that uncertainty over the correct model is empirically large, with far fewer variables being significant predictors of default compared with conventional approaches. Only the ratio of total liabilities to total assets and the volatility of market returns are robust default predictors in the overall sample and individual industry groups. Model-averaged forecasts that aggregate information across models or allow for industry-specific effects substantially outperform individual models.

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