article · Journal of risk and financial management
Evaluating corporate financial distress is vital for economic stability. This research compares several statistical, machine learning, and deep learning algorithms to predict corporate bankruptcy using financial data from Tunisia. The evaluated techniques include Linear Discriminant Analysis, Logistic Regression, Decision Trees, Support Vector Machines, Random Forest, and a Deep Neural Network. The empirical investigation assesses 25 financial ratios drawn from a sample of 732 Tunisian enterprises observed between 2011 and 2017. Performance is measured using classification accuracy, the F1 score, and the area under the curve. The findings demonstrate that the Deep Neural Network achieves the highest overall accuracy in forecasting corporate bankruptcy. Among the remaining machine learning and statistical approaches, the Random Forest model demonstrates superior predictive capability over the other conventional techniques.
Early warning systems for business failure help financial institutions, investors, and regulators mitigate credit risk. By identifying the most effective machine learning and deep learning tools using real corporate records, this research provides empirical evidence on how advanced computational algorithms can assist in detecting insolvency risks in emerging markets.
The research represents an applied and tested study using historical financial records. The models could be adapted into automated credit scoring systems and insolvency risk dashboards used by banks, institutional lenders, and financial analysts evaluating Tunisian companies. Operational deployment would require embedding the models into live credit evaluation workflows and validating their performance on more recent corporate reporting.
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The present paper aims to compare the predictive performance of five models namely the Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Trees (DT), Support Vector Machine (SVM) and Random Forest (RF) to forecast the bankruptcy of Tunisian companies. A Deep Neural Network (DNN) model is also applied to conduct a prediction performance comparison with other statistical and machine learning algorithms. The data used for this empirical investigation covers 25 financial ratios for a large sample of 732 Tunisian companies from 2011–2017. To interpret the prediction results, three performance measures have been employed; the accuracy percentage, the F1 score, and the Area Under Curve (AUC). In conclusion, DNN shows higher accuracy in predicting bankruptcy compared to other conventional models, whereas the random forest performs better than other machine learning and statistical methods.
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DOI: 10.3390/jrfm17040132
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