Comparative Accuracy of Altman Z-Score and Springate S-Score in Predicting Financial Distress: Evidence from Indonesia's Textile Subindustry Under Multi-Shock Conditions, 2021–2025

Abstract

Indonesia’s textile and garment industry faced mounting pressure during the 2021–2025 period as the sector confronted several overlapping challenges, including Rupiah depreciation, the implementation of Permendag 8/2024, and stricter ESG-related requirements in international apparel markets. This study examines the ability of the Altman Z-Score and Springate S-Score models to predict financial distress among eight textile issuers listed on the Indonesia Stock Exchange (IDX), using 40 firm-year observations. Data were collected from audited annual reports available on the IDX website. Actual distress conditions were identified based on negative equity, trading suspensions related to financial problems, and PKPU or bankruptcy status. The predictive performance of both models was then evaluated using a confusion matrix approach. The results show that the Altman Z-Score achieved a higher level of accuracy (47.50 percent) compared with the Springate S-Score (37.50 percent). Although both models successfully identified all distressed firms, their ability to classify healthy firms differed considerably. Additional findings also indicate that several issuers experienced worsening financial conditions after the implementation of Permendag 8/2024, reflected in declining sales performance, weaker liquidity, shrinking margins, and lower Z-Scores. Overall, the findings suggest that market-based indicators remain relatively useful during periods of broad industry disruption, whereas models relying heavily on accounting ratios tend to classify too many firms as financially distressed.