Comparison of the Accuracy of the Altman, Springate, Zmijewski, Grover, Ohlson, Fulmer, and Taffler Models in Predicting Bankruptcy Potential in Companies Delisted from the Indonesia Stock Exchange in 2025

Abstract

The increasing number of companies delisted from the Indonesia Stock Exchange (IDX) in 2025 highlights the importance of early detection of potential corporate bankruptcy. This study aims to analyze the accuracy levels of the Altman, Springate, Zmijewski, Grover, Ohlson, Fulmer, and Taffler models in predicting corporate bankruptcy prior to delisting from the IDX in 2025. This research employs a quantitative approach using secondary data in the form of corporate financial statements obtained from the official IDX website and other supporting sources. The sampling technique used is purposive sampling, with the criterion that companies must have complete financial statements for the 2021–2024 period. Based on these criteria, four sample companies were obtained, namely PT Smartfren Telecom Tbk (FREN), PT Panasia Indo Resources Tbk (HDTX), PT Jakarta Kyoei Steel Works Tbk (JKSW), and PT Multistrada Arah Sarana Tbk (MASA). The data were analyzed using Microsoft Excel to calculate the financial ratios for each bankruptcy prediction model. The results show differences in the accuracy levels of each bankruptcy prediction model. The Altman and Springate models achieved the highest accuracy rate of 75 percent. Furthermore, the Zmijewski, Grover, and Fulmer models each achieved an accuracy rate of 50 percent, while the Ohlson and Taffler models had the lowest accuracy rate of 37.5 percent. These findings indicate that the Altman and Springate models are more capable of providing early predictions of potential corporate bankruptcy prior to delisting compared to the other models. This study is expected to serve as a consideration for investors, companies, and researchers in assessing corporate financial distress conditions.