Applying Grey Relation Analysis to Establish the Financial Distress Prediction Model for Electronic Companies in Taiwan
- 10.2991/jcis.2006.148How to use a DOI?
- Grey Relation Analysis, Financial Distress Prediction, Logistic Regression Model.
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.
- © 2006, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Meng-Fen Hsieh AU - Rong-Tsu Wang AU - I-Chuan Lu PY - 2006/10 DA - 2006/10 TI - Applying Grey Relation Analysis to Establish the Financial Distress Prediction Model for Electronic Companies in Taiwan BT - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06) PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/jcis.2006.148 DO - 10.2991/jcis.2006.148 ID - Hsieh2006/10 ER -