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The Comparative Analysis of Altman Z-Score, Springate, Zmijewski, And Internal Growth Rate Model in Predicting the Financial Distress (Empirical Study on Mining Companies Listed on Indonesia Stock Exchange 2014-2017)
Author(s) -
Sri Mulyati,
Syahrul Ilyasa
Publication year - 2020
Publication title -
kinerja
Language(s) - English
Resource type - Journals
eISSN - 2549-1709
pISSN - 0853-6627
DOI - 10.24002/kinerja.v24i1.3231
Subject(s) - financial distress , stock exchange , econometrics , issuer , statistics , type i and type ii errors , exchange rate , nonprobability sampling , population , finance , business , mathematics , medicine , financial system , environmental health
This study aims to: (1) determine whether there are differences in scores between the Altman model, Springate, Zmijewski and Internal Growth Rate in predicting financial distress, (2) find out the most accurate prediction model in predicting financial distress of mining companies in Indonesia. The data used in this study is the company's financial statements published on Indonesia Stock Exchange. The population in this study is the mining companies listed on the Indonesia Stock Exchange during 2014-2017 which are 41 issuers. The sampling technique used purposive sampling so that 36 issuers were obtained as the research samples. This study compares the scores of four financial distress prediction models using statistical techniques and the accuracy of the prediction model by considering the level of accuracy and type I error. The conclusions from this study indicate the differences between the four prediction models. The Springate model is the best with an accuracy rate of 88.89% and an 8% type I error, the second is the Zmijewski model with an accuracy rate of 88.89% and a type I error rate of 42.86%, the third is the Altman model with 75% accuracy and error type I 46.67%, and the last is an internal growth rate model with an accuracy rate of 66.69% and type I error rate of 11.11%.Keywords: financial distress, financial statements, mining, prediction models

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