EVALUATION OF THE ACCURACY OF THE ALTMAN, SPRINGATE, ZMIJEWSKI MODEL IN THE CASE OF DELISTING ON THE INDONESIAN STOCK EXCHANGE

Abstract

The forced delisting incident on the Indonesia Stock Exchange (IDX) in 2025 illustrates that several issuers had already shown signs of financial distress before their removal from trading. This condition emphasizes the relevance of reliable prediction models that function as early warning instruments for investors, regulators, and corporate decision makers. This study investigates the predictive accuracy of three financial distress models  Altman Z-Score, Springate S-Score, and Zmijewski X-Score  in identifying distress among companies that were forcibly delisted from the IDX in 2025. The research adopts a quantitative descriptive-comparative approach using a purposive sampling method and consists of eight delisted companies. The analysis relies on the firms’ most recent financial statements before delisting and classifies their conditions according to the criteria of each model. The results show that the Altman Z-Score and Springate S-Score models both achieved an identical accuracy rate of 87.5%, while the Zmijewski X-Score model recorded only 12.5% accuracy. The finding suggests that the Altman and Springate models are more responsive to short-term distress signals related to liquidity, profitability, and operational performance, whereas the Zmijewski model is less sensitive to short-term deterioration. Overall, the analysis confirms that Altman and Springate serve as more effective early warning tools for detecting potential financial distress in the Indonesian capital market. The study provides an empirical reference for developing improved early detection mechanisms to support risk mitigation and strengthen financial decision-making for stakeholders.