Financial Distress Analysis Using The Springate and Altman Z-Score Methods in Manufacturing Companies Listed on The Indonesia Stock Exchange - Stephanie Dwi Wahyuning Tyas; Shelly Brilliant; Titin Suhartini; Muhammad Fajar Raharjo; Muhammad Zacky Syauqy Ibnu Shodiq (Journal of Governance, Taxation and Auditing, 2026)
This study analyzes the ability of the Springate and Altman Z-Score methods in predicting financial distress in manufacturing companies listed on the Indonesia Stock Exchange (IDX) for the period 2018- 2022. Although the Indonesian capital market shows positive growth, external challenges such as global economic fluctuations and geopolitical pressures (The Perfect Storm) emphasize the importance of early detection of financial distress to prevent the risk of bankruptcy. This study uses an associative quantitative approach. The research sample consists of 75 financial reports from 15 manufacturing companies, selected through a purposive sampling technique over five years of observation. Data analysis involves descriptive statistics, normality tests, and hypothesis testing (F-Test and t-Test) using SPSS 23.0. The results of the hypothesis testing indicate that both the Springate and Altman Z-Score methods simultaneously and partially have a positive and significant effect in predicting financial distress in manufacturing companies on the IDX. However, based on the accuracy analysis (R-Square), the Springate model (S-Score) is proven to be superior with an accuracy level of 73% (or 73.7%), which is categorized as having very strong closeness. Meanwhile, the Altman Z-Score model had an accuracy rate of 42.3%, categorized as having a strong correlation. The superior accuracy of the Springate model is supported by the use of the Earnings Before Taxes to Current Liabilities (EBTCL) ratio, which is considered more representative. This study concluded that the Springate method was the most accurate model in predicting financial distress in manufacturing companies on the Indonesian Stock Exchange (IDX).