Using Cutting-Edge Tree-Based Stochastic Models to Predict Credit Risk

Abstract

Credit risk is a critical issue that affects banks and companies on a global scale. Possessing the ability to accurately predict the level of credit risk has the potential to help the lender and borrower. This is achieved by alleviating the number of loans provided to borrowers with poor financial health, thereby reducing the number of failed businesses, and, in effect, preventing economies from collapsing. This paper uses state-of-the-art stochastic models, namely: Decision trees, random forests, and stochastic gradient boosting to add to the current literature on credit-risk modelling. The Australian mining industry has been selected to test our methodology. Mining in Australia generates around $138 billion annually, making up more than half of the total goods and services. This paper uses publicly-available financial data from 750 risky and not risky Australian mining companies as variables in our models. Our results indicate that stochastic gradient boosting was the superior model at correctly classifying the good and bad credit-rated companies within the mining sector. Our model showed that 'Property, Plant, & Equipment (PPE) turnover', 'Invested Capital Turnover', and 'Price over Earnings Ratio (PER)' were the variables with the best explanatory power pertaining to predicting credit risk in the Australian mining sector.

Risk of Insolvency and Return of Shares: Empirical Analysis of Altman's Z-Score in the Peruvian Mining Sector Between 2008-2018

“…This leads to increased "deadweight" losses, such as penalties for non-payment, accelerated debt repayment, and budget inflexibility. Third, the company would have to "let go" profitable investment projects, given the cost of external financing (Halteh et al, 2018).…”

دراسة تحليلية لنماذج التنبؤ بالمخاطر المالية لمنشآت الأعمال

“…‫فقط‬ ‫المالية‬ ‫المؤشرات‬ ‫على‬ ‫اعتمد‬ ) Al Janabi, 2016; Bu, et al, 2018;Tavana, et al, Bushman, et al, ( Hull, 2018;El Bouchti, et al, 2018;Hopkin, 2018 FitzPatrick, 1932;Beaver, 1966;1968;Altman, 1968;Deakin, 1972;Altman, et al, 1977;Kida, 1980;Ohlson, 1980;Zavgren, 1985;Gentry, et al, 1985;Laitinen, 1993;Shirata, Gupta, et al, 2016;Su, and Zhang, ;1998Bu, et al, 2017;Balina, 2018;Du Jardin 2018;Halteh, et al, 2018;Khemakhem and Boujelbene, Tang, et 2018;Li, and Quan, 2019;Hussain, et Ansari, et al,2020;Abidin, et al, 2020;0;Wang, and Xie, al., 2020;Teles, et al, 202 2020;Zhao, 2020;Balci, and Ogul, 2021 Du Jardin, 2021;Hanafi, et al, 2021;Jencova, et al, 2021;Uddin, 2021;Wu, et al, 2021;Zhao, 2021 2000FitzPatrick, 1932Beaver, 1966;1968;…”

The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans

“…Logistic regression looks at borrower's loan performance over a specific time period and characteristics it in relation to their regular repayments. When it comes to the methodological framework to predict defaults and credit risk, the study by Halteh et al (2018) developed cutting-edge tree-based stochastic models to model credit risk. Addo et al (2018) built binary classifiers based on machine and deep learning models on real data to predict loan default probability.…”