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Corporate failure prediction model in Indonesia: Revisiting the Z-scores, discriminant analysis, logistic regression and artificial neural network
Aaron A.a, Nainggolan Y.A.a, Trinugroho I.b
a School of Business and Management, Institut Teknologi Bandung, Bandung, 40132, Indonesia
b Faculty of Economics and Business, Universitas Sebelas Maret, Surakarta, 57126, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]Copyright © 2017 Inderscience Enterprises Ltd.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.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]ANN,Artificial neural network,Corporate failure prediction,Discriminant analysis,Logistic regression,Z-scores[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1504/JGBA.2017.083415[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]