Financial predictors of sme failure: variable selection with lasso versus random forest
DOI:
https://doi.org/10.56294/dm20261289Keywords:
Prediction SME failure, Variable selection, Financial ratios, LASSO regression, Random ForestAbstract
Introduction: The study aimed to identify the financial variables that best predicted the failure of small and medium-sized enterprises (SMEs). It addressed the need for reliable financial indicators capable of signaling early distress and supporting risk-management practices.
Methods: A quantitative methodology was adopted within a hypothetico-deductive framework. Two complementary variable-selection techniques were applied. First, the LASSO regression method introduced a regularization constraint to eliminate variables with weak explanatory power. Second, the Random Forest algorithm assessed the relative importance of financial variables in overall model performance. The two approaches were compared to determine their effectiveness in identifying the most relevant predictors of SME failure.
Results: The LASSO model produced a negative coefficient of determination (R² = –1.2179), demonstrating performance inferior to a simple mean-based prediction and indicating that LASSO was not suitable in this context. In contrast, the Random Forest model achieved a very high R² value (0.9571), reflecting strong predictive accuracy and robustness. Based on the Random Forest results, six key financial predictors of SME failure were identified: financial structure, return on assets, return on sales, return on equity, liquidity, and solvency.
Conclusions: The study demonstrated that Random Forest outperformed LASSO in selecting meaningful financial predictors of SME failure. The six identified variables offered a reliable analytical framework for understanding and anticipating financial distress. These findings provided valuable insights for academic research and practical applications in risk assessment and early warning systems for SMEs.
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