Default Prediction Model for Emerging Capital Market Service Companies

Abstract

The author tested the hypothesis that default prediction based on financial data may be inapplicable to Russian service sector organizations by analyzing the differences in the accuracy of models based solely on financial data for service providers from Russia and developed European countries.

Logistic regression, Random Forest and K-nearest neighbors machine learning methods were used as modeling tools on a sample of 404 Russian firms and 304 firms from developed European countries.
The results suggest that the prediction error is significantly higher in the case of Russian firms than in the case of firms from the control group (European service firms). Thus, the use of financial ratios for default prediction for service firms in Russia seems insufficient.

These findings can be used by organizations that provide credit scoring, and by any other market participants interested in the financial stability assessment of their counterparties.