PREDICTING FINANCIAL DISTRESS USING DEA AND ALTMAN'S MODEL ON STEEL AND IRON INDUSTRY INDONESIA

Abstract

Research purpose - Financial distress is a company’s inability to meet their financial obligation, which finally leads to going bankruptcy. Financial distress is then used as an early warning signal before going bankrupt. Therefore, financial distress should be predicted as preventive actions. This study main objective is to compare the traditional predictions tools, Altman’s Z-score model, with the new propose Data Envelopment Analysis (DEA) approach method. Methods – Focusing on Indonesia steel and iron industry, this study examines using 7 steels and iron companies which listed in IDX from the period of 2013- 2018. Starting from constructing the model of DEA in predicting distress, the accuracy test of both models is compared. Result - The results reveal that DEA’s approach prediction has a higher accuracy rate compared to the Altman’s model. DEA with a total of 39 correct predictions out of 42 samples generate an accuracy rate of 92.86%. This rate is higher than the Altman’s model with the accuracy rate of 85.71% which resulting from a total of 36 correct predictions out of 42 samples. Originality / value – The method, especially DEA to predict financial distress for Steel and Iron Companies in Indonesia is the significant contribution to science.