Bankruptcy Prediction Using Machine Learning And Deep Learning Models - Mestiri, Sami; Hamdi, Manel (Asosiasi Fakultas Ekonomi dan Bisnis Indonesia, 2023)
In this study, we have compared the predictive power 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 more advanced deep learning model, the 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 covering 26 financial ratios for a large sample of 528 Tunisian firms. To interpret the prediction results, three performance measures have been employed; the accuracy rate, the F1 score and the Area Under Curve (AUC). By conclusion, DNN shows higher accuracy in predicting bankruptcy compared to other conventional models. Whereas, RF model performs better than other machine learning and statistical methods.