Corporate Bankruptcy Prediction in the Republic of Serbia - Stanišić, Nemanja M; Mizdraković, Vule M; Knežević, Goranka R (Industrija, 2013)
The aim of this paper is to present corporate default prediction models constructed in the specific market conditions that prevail in the Republic of Serbia, and to compare their prediction accuracy with the most frequently used model – Altman’s Z-score. Many authors have constructed models for the purpose of bankruptcy prediction, but predominantly in stable market conditions or in times of economic growth. We have presented three models that use standard ratios and some specific variables in order to predict corporate bankruptcy in emerging and distressed markets. For that purpose, we have used the following statistical and machine learning methods on a training sample (130 companies): Logistic Regression, Decision Trees and Artificial Neural Networks. Finally, we have compared accuracies of predictions of our models to those of the Altman’s Z-score models using an independent hold-out sample (102 companies). Results show that, out of the aforementioned three models, only the one relying on the artificial neural network algorithm performs better when applied on the hold-out sample, compared to Altman’s Z-score models.