Predicting default probability in construction industry basing on Over-Sampling Technique for Grey SystemTheory
Date Issued
2014
Date
2014
Author(s)
Chien, Truong Khac
Abstract
Bankruptcy Prediction has been a hotly-debated topic among many people in business area. The fact is that once the firm goes bankrupt, it will be disastrous to not only firm itself but also other stakeholders. Many available methods have been applied to predict the possibility of business collapse; almost all of them were based on financial ratio analysis. Grey System Theory, used in the previous thesis for predicting default probability of construction firms, has brought some feasible results, by relying on the 19 initial financial ratios.
This study, with the aim of enhancing the Grey Theory application, employs Over-sampling technique before applying Grey System Theory. The results of this study are then compared with those of the prior research. Furthermore, replication and Synthetic Minority Over-sampling Technique (SMOTE), two over-sampling techniques are proposed to resolve the imbalance problem in data set.
The results reveal that over-sampling techniques could improve the predicting performance of Grey System theory. Additionally, between these two kinds of over-sampling techniques, SMOTE surpasses Replication in terms of prediction capability.
Subjects
灰色理論
破產幾率預測
強化樣本技術
Type
thesis
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