Financial Distress Prediction Model in Indonesian State-Owned Enterprises (SOEs)

Abstract

This paper aims to test the capacity of financial distress model using financial variables (liquidity, cash flow operation, leverage, gross profit margin, return on asset), and non-financial variables (going concern opinion, audit report lag, opinion shopping, additional paid-in capital, subsidies). This study uses 7 years of panel data with a total of 350 observations. The model developed, namely Model 1 (financial variables), Model 2 (non-financial variables), and Model 3 (financial and non-financial variables). Finally, Model 4 (financial and non-financial variables in listed SOEs; and Model 5 (financial and non-financial variables in non-listed SOEs). Data analysis used ordinal logistic regression, where the dependent variable used Altman Z-score. The model’s results prove that the predictive ability of Model 1 is better than Model 2. Likewise, the predictive ability of Model 3 is better than Model 1, and Model 2 predictive ability of Model 4 is better than Model 5. Empirical evidence shows that the variables that are consistently significant across Models (1,2,3,4,5) are Leverage, Gross Profit Margin, and ROA. Meanwhile, the model with the highest prediction accuracy value is Model 4. Financial distress prediction could indeed supervise which businesses have the strongest capital adequacy and which are giving up their ability to compete and are consequently in danger.