A Grey System Theory‐Based Default Prediction Model for Construction Firms

Abstract

As the prediction of construction firm failure is of great importance for owners, contractors, investors, banks, insurance firms, and creditors, previous studies have developed several models for predicting the probability of construction firm default based on financial ratio analysis. However, to be applied, these models require a considerable quantity of data, including normally distributed data, and the models cannot tolerate too many changing factors. Furthermore, most of the approaches produce sample selection biases. To avoid these disadvantages, this study is the first to integrate the grey system theory with all available firm‐year samples during the sample period to provide a new method for predicting the probability of construction firm default. This method not only offers an improved rate of prediction accuracy, but it also offers simpler and clearer procedures as a reference for examining firm default probability and ranks all financial ratios in terms of their level of importance. The research collects and analyzes the financial reports of 92 construction firms in the United States. The proposed model includes only eight ranked variables (financial ratios), and it achieves an 84.8% level of accuracy for predicting construction firm default probability. As a result, practitioners may directly use the model as a means of quickly and conveniently examining their firm default probability with the simple procedures.

Comparison of Single Classifier Models for Predicting Long-term Business Failure of Construction Companies Using Finance-based Definition of the Failure

“…Therefore, the models for predicting business failure using data mining is actively proposed [1,2,[6][7][8][9]. However, previous studies have focused on short-term (within one year) prediction and have defined business failure based on legal events.…”

“…The results showed that the proposed AdaBoost model outperforms other models. Cheng et al [8] and Tserng et al [9] proposed a model for predicting the default of construction companies within one year. They defined default as delisting due to bankruptcy, liquidation, or poor performance, as in Tserng et al [2].…”

“…As a result, the numbers of failed companies in the three years' datasets were 28, 41, and 49, as shown in Table 1. th International Symposium on Automation and Robotics in Construction (ISARC 2017) Twenty-one financial ratios were selected as input variables based on a review of the previous studies [1,2,[6][7][8][9]. Following Horta and Camanho [1] and Tserng et al [2], financial ratios were classified into four categories: activity, leverage, liquidity, and profitability.…”

“…These models are based on statistical techniques, such as multivariate discriminant analysis (MDA) [4] and logistic regression (LR) [5], and, more recently, data mining techniques [2,[6][7][8][9]. Most of the previous studies have focused on short-term (within one year) predictions, even though construction projects typically have a longer duration.…”

Hybrid Multiple Criteria Decision Making Methods: A Review of Applications in Engineering

“…The mentioned combination of ZOGP, ANP, and DEMATEL is applied for evaluating entrepreneurship policy of a company [170]. The latest applications analyze a variety of problems: measuring enterprises readiness for institutionalization [164], decision support for corporate social responsibility [165], examining organizational value cocreation [166], default prediction [181], and prioritizing risks [182]. In terms of methodology, Hashemkhani Zolfani et al [168] apply a hybrid of two rather novel methods, i.e.…”