FINANCIAL DISTRESS PREDICTION MODELS: CASE STUDY OF TEXTILE INDUSTRY IN INDONESIA

Abstract

Financial distress is company’s inability in completing financial obligation. Preventive action should be applied to maintain financial performance and to avoid any financial issues. This study aims to find the statistically significant difference and to compare the accuracy level of accounting-based financial distress prediction models by focusing on the research objects of 13 textile firms listed on the Indonesia Stock Exchange (IDX) during 2014-2018. The analyzed four prediction models are: Altman (Z-Score), Springate (S-Score), Grover (G-Score), and Zmijewski (X- Score). By employing a non-parametric approach, this study adopts Kruskal-Wallis and Mann Whitney Post Hoc as the difference tests, along with accuracy rate formulation. Type I Error and Type II Error are used to examine the accuracy level of each model. The Kruskal-Wallis test reveals that these models are statistically significantly different with the p-value of .000. Meanwhile, in pairs, Mann Whitney Post Hoc results prove that there is no significant difference between Springate’s and Grover’s models where the result is greater than 5%. Additionally, the result also designates that the most accurate prediction model to predict financial distress of textile firms is Zmijewski’s which has the accuracy level of 66.15%, while the accuracy rate of Grover’s and Altman’s models are 63.08% and 53.85%, respectively. Therefore, Springate’s model becomes the lowest accuracy level at 52.31%.