Title: Corporate failure prediction model in Indonesia: revisiting the Z-scores, discriminant analysis, logistic regression and artificial neural network

Authors: Aurelius Aaron; Yunieta Anny Nainggolan; Irwan Trinugroho

Addresses: School of Business and Management, Institut Teknologi Bandung, Jl. Ganesha No. 10, Bandung, 40132, Indonesia ' School of Business and Management, Institut Teknologi Bandung, Jl. Ganesha No. 10, Bandung, 40132, Indonesia ' Faculty of Economics and Business, Universitas Sebelas Maret, Jl. Ir. Sutami 36 Surakarta, 57126, Indonesia

Abstract: We investigate the accuracy of several corporate failure prediction models, namely the original Altman's Z-score and Z˝-score, discriminant analysis, logistic regression and artificial neural network (ANN) by studying Indonesian firms. Using hand-collected data of forced delisting and healthy listed firms in Indonesia stock exchange, our results show that our ANN model has the highest accuracy among other models, respectively, followed by Z˝-score, logistic regression model, discriminant analysis model and the original Z-score. In addition, among these models, we also find that the original Z-score has the smallest type I error, or it is the most sensitive model, whereas Z˝-score has the smallest type II error, or it is the most specific model. Thus, in the view of efficiency, even though those models are very simple and were developed more than 30 years ago, the predictive ability of their combination is still pertinent to predict corporate failure in Indonesia.

Keywords: corporate failure prediction; Z-scores; discriminant analysis; logistic regression; ANNs; artificial neural networks; Indonesia.

DOI: 10.1504/JGBA.2017.083415

Journal for Global Business Advancement, 2017 Vol.10 No.2, pp.187 - 209

Received: 19 Dec 2016
Accepted: 09 Jan 2017

Published online: 22 Mar 2017 *

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