Business Failure Prediction with LSTM RNN in the Construction Industry

Abstract

Due to the characteristics of the construction projects, construction contractors are often vulnerable to business failure compared to those in other industries. Thus, predicting the potential business failure of construction contractors has been a crucial issue. This study proposes a model that predicts business failure using long short term memory recurrent neural network (LSTM RNN), which is one of the deep-learning algorithms. The proposed model uses not only a set of accounting data but also proxies for the construction market condition and the macroeconomic environment as input variables. The prediction performance of the proposed model is examined by varying the combination of input variable groups. The results showed that adding construction market and macroeconomic variables to accounting variables could increase the performance of business failure prediction. It was also found that macroeconomic variables had a slightly higher impact on the business failure prediction than construction market variables. The results of this study are expected to be useful references for both researchers and practitioners to develop business failure prediction models of construction contractors. © 2019 American Society of Civil Engineers.