Analysis of the Calculation of the Altman Z-Score Method (Modified) and the Springate Method in Predicting Potential Financial Distress in Retail Companies Listed on the Indonesia Stock Exchange (IDX) in 2020 – 2022
DOI:
https://doi.org/10.59613/v8mcpw63Keywords:
Financial Distress, Altman Z-Score, SpringateAbstract
This study evaluates the potential for financial distress in retail companies listed on the Indonesia Stock Exchange (IDX) for the 2020–2022 period using the Altman Z-Score Modified and Springate methods. Financial distress is a company's unhealthy financial condition and can lead to bankruptcy. This research aims to identify the financial condition of retail companies whether they are in the safe, vulnerable, or at risk of bankruptcy category, as well as provide strategic recommendations to overcome them. Data is obtained from the company's annual financial statements which are analyzed using both methods. The Altman Z-Score method assesses a company's ability to meet short-term obligations, maintain profits, and manage assets well. The Springate method evaluates working capital efficiency, income, and the ability to pay short-term debt. Data analysis techniques include descriptive statistical tests, normality tests, and calculation of accuracy levels and prediction errors. The results show that several companies, such as PT Global Teleshop (GLOB) and PT Trikomsel Oke (TRIO), consistently experience financial distress, with the average Z-Score and Springate below the threshold. Meanwhile, companies such as PT Mitra Komunikasi Nusantara (MKNT) and PT Song Topas Tourism Industry (SONA) showed financial stability with positive results in both methods. The main recommendations include evaluating the company's operations, reducing the debt burden, and implementing lean management for efficiency. Regulators are advised to strengthen supervision of high-risk companies to protect investors. This research provides important guidance for companies, investors, and policymakers in facing financial challenges.
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Copyright (c) 2025 Chikita Audyna Satoto, Krisnawuri Handayani (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.