使用集成模型進行營建公司財務違約預測之研究─以美國營建領域公司之資料為例

林奇鴻

doi:10.6342/NTU201900354

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使用集成模型進行營建公司財務違約預測之研究─以美國營建領域公司之資料為例

Default Prediction of Construction Firms Using Ensemble Model - Lesson Learned from US Construction Industry

林奇鴻 , Masters　　Advisor：曾惠斌　　

英文 DOI： 10.6342/NTU201900354

公司破產預測； AdaBoost ；混合模型； SMOTE ； default prediction ； AdaBoost ； hybrid model ； SMOTE

Abstract │ Reference (20) │ Altmetrics

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Since financial crisis in 2008, public started to evaluate a firm’s financial condition with strict demand. Reviewing past studies, most of which are based on overall industries, only few of them focus on particular industry. However, Huang (2011) pointed out that construction industry had its own financial characteristics. Hence, we do state that specific research on construction industry is necessary. In addition, as the development of artificial intelligence technology and data mining techniques, people are able to build up prediction model with efficiency. In this research, our purpose is to find an appropriate algorithm to create the prediction model which will then be proved by empirical experiment.

This research inherited from Chen (2015), analyzing on US construction industry data set, which collected firm data from 1970 to 2006. Totally 1560 observations were collected, in which 29 observations are default and the remaining 1531 are normal. In this thesis, the prediction model is based on Adaptive Boosting algorithm (Fruend and Schapire, 1996), which is then compared to Back-Propagation Neural Network (Rumelhart et al., 1986) model. The result shows that Adaptive Boosting model can be well performed on default prediction. In addition, followed the concept of hybrid model and the affection of sample unbalance problem proposed by Chen (2015), this research conduct further experiment on both issue. The result reveals that hybrid model can only make slight improvement on our model, while market variable does really hold a high weight to the model. Also, sample unbalance problem can significantly affect the prediction accuracy, which we then use Synthetic Minority Over-sampling Technique (SMOTE) to deal with, and obtain the results responded to Chen (2015).

Reference ( 20 ) 〈TOP〉

1. Abidali, A. & Harris, F. (1995). “A methodology for predicting company failure in the construction industry.” Construction Management and Economics, 13(3), 189-196.
2. Altman, E. (1968). “Financial Ratios, Discriminant analysis and the prediction of corporate bankruptcy.” Journal of Finance, 23(4), 589-609.
3. Beaver, W. (1966). “Financial ratio as predictors of failure.” Journal of Accounting Research, 4, 71-111.
4. Black, F. & Scholes, M. (1973). “The Pricing of Options and Corporate Liabilities.” Journal of Political Economy, 81(3), 637-654.
5. Chava, S. & Jarrow, R. A. (2004). “Bankruptcy prediction with industry effects.” Review of Finance, 8(4), 537-569.

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