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.

Highlights

  • It is vital to have an understanding of a company’s credit risks, as they can provide an invaluable insight into its financial state

  • There are a number of factors that can lead to a high credit risk, including, but not limited to: Financial, economic, disaster, neglect, and fraud/clandestine activities (Anderson 2006)

  • Credit Risk Modelling is interchangeably used in the literature by many other names, including: Financial Distress Prediction, Bankruptcy

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Summary

Introduction

It is vital to have an understanding of a company’s credit risks, as they can provide an invaluable insight into its financial state. Credit Risk Modelling is interchangeably used in the literature by many other names, including: Financial Distress Prediction, Bankruptcy. From this point forward, this paper will use them term Credit Risk. CRM involves developing statistical models that can predict the level of financial risk of companies based on information, such as publicly available financial ratios from financial statements (Gepp and Kumar 2012). The predictive statistical models have wide applications, including for the company itself, creditors, and other stakeholders. These models can assist financial institutions in determining whether to provide credit. They can be used to develop proactive and preventive financial and managerial decisions in order to avoid business failure (Jaikengit 2004). Due to the models’ wide applicability and important implications, the literature is quickly becoming filled with

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