Early Insolvency Prediction as a Key for Sustainable Business Growth

Abstract

This research aimed to determine whether and how financial analysis combined with machine learning can support decision-making for sustainable business growth. This study was conducted using a sample of 100 Serbian companies whose bankruptcies were initiated between 2019 and 2021 to identify key factors that distinguish solvent from insolvent companies. Two neural networks (NNs) were trained and tested to predict these discriminating factors one year (Y-1) and two years (Y-2) before bankruptcy initiation. Initially, a total of 37 predictor variables were included, but prior to modeling, variable reduction was performed through VIF analysis and t-tests. The training dataset comprised 70% of the sample, while the remaining 30% was used for testing. Both NNs utilized a softmax activation function for the output layer and a hyperbolic tangent for the hidden layers. Two hidden layers were included, and training was conducted over 2000 epochs using the gradient descent algorithm for optimization. The research results indicate that poor cash management is the first sign of possible insolvency one year in advance. Additionally, the findings reveal that retained earnings management can serve as a reliable bankruptcy predictor two years in advance. The overall predictive accuracy of the NN models is 80.0% (Y-1) and 73.3% (Y-2) for the testing dataset. These findings demonstrate how selected ratios can support bankruptcy prediction, providing valuable insights for company proprietors, management, and external stakeholders.