ntegration of Accounting-Based & Option-Based Models with Sampling Techniques to Predict Construction Contractor Default

Due to the special financial characteristic of construction industry, past researches on bankruptcy prediction models mostly excluded the construction industry from their sample. However, the financial health of construction contractors is critical in successfully completing a project. The financial default probability of the construction industry is always an important issue for governmental organizations, construction owners, lending institutions, surety underwriters, and contractors. Thus, this research aims to measure and predict the construction contractor default risk. The financial default predicting models developed in past literatures are in large built by historical accounting information. They were called as “accounting-based models”. These researches supposed that there may be different patterns between defaulters and non-defaulters in historical accounting information, and tried to find out these patterns by some regression or data mining analysis. Thus, scholars usually need numerous of samples to build accounting-based models. Most of the previous studies on prediction construction contractor default used sample-match method to build their sample set, which produces sample selection biases. In order to avoid the sample selection biases, this research used all available firm-years samples during the sample period. Yet this brings a new challenge: the number of non-defaulted samples greatly exceeds the defaulted samples, which is referred to as between-class imbalance. Accounting-based models only demonstrate the distribution of the major parts of input points, ignoring the small parts of input points. Thus using the accounting-based models on default prediction with imbalance data set is not satisfactory. The primary objective of this research is to improve this shortcoming by 2 kinds of over-sampling technique: “replication” and “Synthetic Minority Over-sampling Technique (SMOTE)”. The purpose of these over-sampling techniques is to increase the number of default samples, and reduce the between-class imbalance. Besides the accounting-based models, the option-based model is another way to predict company default. The option-based model doesn’t catch the information by data mining, but depicts the physical mechanism of company’s default by using option-pricing equations with the main input: company stock price. In an efficient market, the company’s stock price could be a good source of information because it not only reflects accounting and economic information but also reflects qualitative factors such as management and technique. The second objective of this research is to build hybrid models which combine accounting and stock market information. The empirical results of this research show that the hybrid models outperform the accounting-based models and the option-based model. With the over-sampling techniques, the predicting performance of models could be even better. Thus, this research recommends the proposed hybrid models with over-sampling techniques as an alternative to the traditionally used models.