PREDICTING THE FINANCIAL STABILITY OF A COMPANY

Predicting the financial stability is a critical area of research in finance and accounting. The goal of this prediction is to develop models that can accurately identify financially distressed companies before they file for bankruptcy. Various financial ratios and accounting metrics are used to evaluate a company’s financial health and assess its likelihood of bankruptcy. In recent years, the application of machine learning algorithms and statistical techniques has significantly improved the accuracy of bankruptcy prediction models. These models have practical applications in various domains, including corporate finance, investment analysis, and risk management. Accurate bankruptcy prediction can help stakeholders take preemptive measures to mitigate potential financial losses, thereby contributing to the stability of financial markets. We find that tree-based ensemble methods, especially R-tree, can achieve a high degree of accuracy in out-of-sample bankruptcy prediction By analyzing financial data, bankruptcy prediction models can provide early warning signals to stakeholders, enabling them to take preventive measures to mitigate potential financial losses. This abstract provides a brief overview of the importance of bankruptcy prediction and the role of predictive models in identifying financially distressed companies. While the prediction accuracy is similar to several previous models in the literature, our model is very simple to implement and represents an accurate and user-friendly tool to discriminate between bankrupt and non-bankrupt firms.