Which Model Is the Most Accurate? A Comparative Study of Bankruptcy Prediction Models in Indonesia’s Automotive Sector - Latifah Putranti; Rizka Dwi Afriyanti; Ahsan Sumantika (UPY Business and Management Journal (UMBJ), 2026)
Purpose: Financial distress has become a critical issue in corporate finance as it reflects a company’s ability to maintain business continuity before experiencing bankruptcy. The purpose of this study is to analyze and compare the accuracy levels of the Altman Z-Score, Springate, and Zmijewski models in predicting financial distress among automotive sub-sector companies listed on the Indonesia Stock Exchange during the 2019–2022 period. Methodology: This study employs a quantitative research approach using secondary data obtained from the annual financial statements of automotive sub-sector companies listed on the Indonesia Stock Exchange for the 2019–2022 period. The sample consists of 44 companies selected based on predetermined criteria. The analysis method involves descriptive statistical analysis and the application of the Altman Z-Score, Springate, and Zmijewski models to measure prediction accuracy in identifying financial distress. Findings: The findings indicate that the Altman Z-Score and Springate models can be used to predict financial distress; however, their accuracy levels are relatively low, at 22% and 61% respectively, with higher type error rates. Conversely, the Zmijewski model demonstrates superior predictive performance with an accuracy rate of 93% and a type error of 7%, suggesting it is the most effective model for predicting financial distress potential among automotive sub-sector companies in Indonesia. These results highlight that the Zmijewski model provides the most reliable identification of financial distress compared to the other models. Originality: The originality of this study lies in its comparative analysis of three well-known financial distress prediction models, Altman Z-Score, Springate, and Zmijewski, explicitly applied to Indonesia’s automotive sub-sector during the post-pandemic period (2019–2022). This focus provides new empirical evidence on model accuracy within an industry significantly affected by economic fluctuations and supply chain disruptions. Practical implications: The findings provide valuable insights for investors, managers, and policymakers in assessing financial distress risk and improving financial decision-making within the automotive industry.