Atlantis Press Paper Details: Applying Grey Relation Analysis to Establish the Financial Distress Prediction Model for Electronic Companies in Taiwan

Most researches have focused on the use of document feedback or factor analysis as metrics for financial distress prediction. The theoretical basis for the former is relatively weak, while the latter is severely limited by data requirements. As such, this paper will instead use grey relation analysis to determine several indices with high levels of relation, and from these select several representative indicators. This method will provide the indicators a more sound theoretical basis. Additionally, unlike previous financial distress prediction models which have frequently overlooked the differences between industries, this paper will use logistic regression analysis in the use of 26 electronics companies as research subjects, and after removing from the sample those with inadequate data, a total of nine companies will be analyzed, building a financial distress prediction model for the electronics industry and then comparing the rate of error in both this and the traditional document-based model. Results show that 7 financial and 2 corporate governance indicators are applicable to financial distress prediction in the electronics industry—. In terms of accuracy ratio, there are minimally different over one year ahead, but going back over two years, the GRA model is less likely to be incorrect.

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title:		Applying Grey Relation Analysis to Establish the Financial Distress Prediction Model for Electronic Companies in Taiwan
publication:		JCIS-2006 Proceedings
part of series:		Advances in Intelligent Systems Research
ISBN:		978-90-78677-01-7
ISSN:		1951-6851
DOI:		doi:10.2991/jcis.2006.148 (how to use a DOI)
author(s):		Meng-Fen Hsieh, Rong-Tsu Wang, I-Chuan Lu
corresponding author:		Meng-Fen Hsieh
publication date:		October 2006
keywords:		Grey Relation Analysis, Financial Distress Prediction, Logistic Regression Model.
abstract:		Most researches have focused on the use of document feedback or factor analysis as metrics for financial distress prediction. The theoretical basis for the former is relatively weak, while the latter is severely limited by data requirements. As such, this paper will instead use grey relation analysis to determine several indices with high levels of relation, and from these select several representative indicators. This method will provide the indicators a more sound theoretical basis. Additionally, unlike previous financial distress prediction models which have frequently overlooked the differences between industries, this paper will use logistic regression analysis in the use of 26 electronics companies as research subjects, and after removing from the sample those with inadequate data, a total of nine companies will be analyzed, building a financial distress prediction model for the electronics industry and then comparing the rate of error in both this and the traditional document-based model. Results show that 7 financial and 2 corporate governance indicators are applicable to financial distress prediction in the electronics industry—. In terms of accuracy ratio, there are minimally different over one year ahead, but going back over two years, the GRA model is less likely to be incorrect.
copyright:		© Atlantis Press. This article is distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.
full text:		JCIS06-CIEF-186.pdf (29 K)