重覆取樣BPN模型應用於營建公司財務危機預測之研究

A Back Propagation Neural Network using Over-sampling techniques in bankruptcy prediction in construction industry

陳明(Minh Tran)

指導教授 ： 曾惠斌

國立臺灣大學/工學院/土木工程學研究所/碩士(2011年)

https://doi.org/10.6342/NTU.2011.00628

摘要

無資料

關鍵字

違約概率預測 ； 建築業 ； 人工神經網絡 ； 反向傳播 算法 ； 類間的不平衡 ； 強迫訓練 ； 合成少數股東採樣技術

並列摘要

Construction industry plays a major part in any nation economy. However, the construction industry tends to face high risk due to the particular characteristic of the environment and high competition. Therefore, many researches have been conducted to find an appropriate model to forecast bankruptcy in construction sector. Artificial Neural Network (ANN) using Back Propagation Algorithm has been applied in this area since the early 1990s, and has been showed the promising outcome. Accordingly, in this study Back Propagation Network (BPN) was selected to construct a model in bankruptcy prediction for construction industry. In the previous study employing ANN methods, the sample-matching technique was usually used, which lead to sample selection biases, likely due to ANN’s inability to tackle between-class imbalance problem. In this research Back Propagation Network (BPN) using over-sampling techniques with all available firm-year data was proposed so as to tackle between-class imbalance challenge. The two over-sampling techniques used were: Enforce training and Synthetic Minority Over-Sampling TEchnique (SMOTE). The empirical result of this study showed that the BPN using SMOTE was out performed the BPN original and EBPN. Accordingly, BPN using SMOTE are suggested as an alternative to the existing model

並列關鍵字

default probability prediction ； construction industry ； Artificial Neural Network ； Back Propagation Algorithm ； between-class imbalance ； Enforced training ； Synthetic Minority Over-Sampling TEchnique

參考文獻

1. Altman, E (1968). “Financial Ratios, Discriminant analysis and the prediction of corporate bankruptcy”, Journal of Finance. pp. 589-609.

2. Abidali, A.,& Harris, F (1995). “A methodology for predicting company failure in the construction industry”. Construction Management and Economics. pp. 189-196.

3. Smith, Kate (2002). “Neural Networks for Business: An Introduction. Neural Networks in Business” , Idea Group Publishing.

4. Beaver,W (1966).“ Financial ratio as predictors of failure”. Journal of Accounting Research. pp 71-111.

5. Chava,S., & Jarrow, R (2004). “Bankruptcy prediction with industry effects”. Review of Finance. pp. 537-569.