A Comparative Analysis of Financial Distress Prediction Model Accuracy in the Textile and Garment Subsector Listed on the Indonesia Stock Exchange - Nadia Safa Shabira; Euphrasia Susy Suhendra (Heca Sentra Analitika, 2026)
This study aims to identify the potential for financial distress and compare the predictive accuracy of the Springate, Ohlson, Zmijewski, and Grover models in textile and garment subsector companies listed on the Indonesia Stock Exchange (IDX) during the 2019–2024 period. Financial distress prediction is important as an early warning tool that enables companies, investors, and other stakeholders to anticipate financial difficulties and make appropriate strategic decisions. This study applies a descriptive quantitative approach using secondary data obtained from annual financial statements. The sample was selected through purposive sampling, resulting in 7 companies and 42 observations. The analysis includes model score calculations, descriptive statistics, normality testing, the Kruskal–Wallis test, and accuracy and error testing. The results indicate that the four models produce different financial distress classifications. The Kruskal–Wallis test generated a significance value of less than 0.001, confirming significant differences among the prediction results of the models. The Springate model achieved the highest accuracy rate of 83%, followed by the Zmijewski and Grover models at 69%, while the Ohlson model recorded the lowest accuracy rate of 45%. These findings suggest that the predictive performance of financial distress models varies depending on the characteristics of the sample and industry. Therefore, the Springate model can be considered the most appropriate and reliable model for predicting financial distress in textile and garment companies listed on IDX.