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

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 mode