An Analysis of Financial Distress Accuracy Models in Indonesia Coal Mining Industry: An Altman, Springate, Zmijewski, Ohlson and Grover Approaches

Abstract

The purpose of this research is to determine companies financial distress base on Altman, Springate, Zmijewski, Ohlson and Grover Models and to assess the accuracy of those five prediction models in coal mining sector firms listed in Indonesia Stock Exchange (IDX) for the period 2015 – 2019. This research has 22 samples of 23 coal mining firms listed in IDX base on the purposive sampling technique. This study is a descriptive design using quantitative and panel data. The research data is analyzed using the Kruskal Wallis test because there are more than two prediction models to compare and the data are not normally distributed. The result indicates that the Modified Altman and Ohlson Models are the most accurate predictive models because these models have the highest accuracy rate of 90.91%, followed by Zmijewski Model, which has an accuracy rate of 86.36%, then Grover Model has 81.82% accuracy rate, and the lowest prediction rate is Springate Model with the value of 63.64%.