Corporate Default Predictions Using Machine Learning: Literature Review

Abstract

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.

Classifying a Lending Portfolio of Loans with Dynamic Updates via a Machine Learning Technique

“…Single-label and multi-label classifiers are tested in [20], demonstrating that multilabel classifiers perform better. In addition, the ML methods furnish a better fit for the nonlinear relations between the default risk and explanatory variables [21]. See also [22] for further discussions.…”

Corporate governance and financial distress: lessons learned from an unconventional approach

“…One of the most famous machine learning techniques is the artificial neural network, which is very useful when it comes to solving complex and non-linear relationships "by mimicking the structure of the brain and connecting artificial neurons using simple structures" (Kim et al, 2020). Machine learning techniques have been used in default predictions before the 1990s, and several authors (Yang et al, 1999;Zhao et al, 2014;Geng et al, 2015;Jones et al, 2017) have demonstrated that they have both better prediction performances than Logit/probit and, on average, good levels of efficiency.…”

A Hybrid Algorithm-Level Ensemble Model for Imbalanced Credit Default Prediction in the Energy Industry

“…A significant strand of literature has found that intelligent models in credit default prediction models are efficient in predicting corporate defaulting [20,[26][27][28]. Without the strict assumptions of the traditional statistical models (e.g., independence and normality among predictor variables), intelligent techniques can automatically derive knowledge from training data [28][29][30].…”