Dynamic Prediction Model for Financial Distress in Construction Industry Using Data Mining

Abstract

The early awareness of a potential financial distress is crucial to firm's managers for understanding their clients, suppliers and their own firms, and crucial to fund suppliers for assessing the construction firm's credit worthiness. The purpose of this paper is to develop a dynamic prediction model for financial distress in construction industry using Data Mining. This research expects to provide construction firm managers and creditors an effective index for evaluating the credit risk a construction firm. Results show that the proposed model has higher accuracy and stability for distress prediction and can provide a more effective quantitative framework for evaluating the financial standing of a construction firm.