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All Journal International Journal of Digital Entrepreneurship and Business (IDEB)
Adi Rizfal Efriadi
Accounting Program, ITB Ahmad Dahlan, Indonesia
Economics, Econometrics & Finance Education Environmental Science Social Sciences Other

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Comparative Analysis of Financial Distress Models in Indonesian Multi-Industrial Manufacturing During COVID-19 Siska Fifi Andriani; Liana Rahardja; Adi Rizfal Efriadi; Ramitha Janira Cindi
International Journal of Digital Entrepreneurship and Business Vol 4 No 2 (2023): International Journal of Digital Entrepreneurship and Business (IDEB)
Publisher : STIE-JIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52238/ideb.v4i2.130

Abstract

Financial distress is a critical financial condition characterized by a company’s financial performance decline, resulting in reduced net income and challenges in meeting short- and long-term financial obligations. Left unaddressed, financial distress can ultimately lead to bankruptcy. Various predictive models, including the Altman Z-Score, Springate, Grover, Zmijweski, and Zavgren methods, are employed to forecast such distress. This study aims to assess the predictive accuracy of these models in analyzing financial distress and predicting bankruptcy across a diverse spectrum of manufacturing companies. Employing a quantitative and descriptive methodology, the research focuses on manufacturing firms listed on the IDX for 2016-2020, encompassing the impact of the COVID-19 pandemic. Data collection employs a purposive sampling method, with statistical analysis involving the computation of financial ratios from each bankruptcy prediction model. The study assesses the accuracy levels and error types of these models. Results indicate that Altman Z-Score, Springate, Grover, and Zmijweski demonstrated accuracy rates of 46.15%, 35.90%, 82.05%, and 69.23%, respectively. These models exhibit various error types and rates. In contrast, the Zavgren method displayed a remarkable accuracy rate of 100%, with no identified errors, establishing it as the most reliable predictor of bankruptcy, particularly within the Multi-Industrial Manufacturing Sector.
Co-Authors Liana Rahardja Ramitha Janira Cindi Siska Fifi Andriani