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Basheer, M, Elnour, Z, Siddig, K and Grethe, H (2025) Economic impacts of large dams on downstream brickmaking in developing countries. Construction Management and Economics, 43(03), 213–25.
Brager, G, Zhang, H and Arens, E (2015) Evolving opportunities for providing thermal comfort. Building Research & Information, 43(03), 274-87.
Castelblanco, G, Fenoaltea, E M, De Marco, A and Chiaia, B (2025) Influence of macroeconomic factors on construction costs: an analysis of project cases. Construction Management and Economics, 43(03), 196–212.
de Dear, R, Kim, J, Candido, C and Deuble, M (2015) Adaptive thermal comfort in Australian school classrooms. Building Research & Information, 43(03), 383-98.
Farnham, C, Emura, K and Mizuno, T (2015) Evaluation of cooling effects: outdoor water mist fan. Building Research & Information, 43(03), 334-45.
Gao, Y, Gan, Y, Chen, Y and Chen, Y (2025) Application of large language models to intelligently analyze long construction contract texts. Construction Management and Economics, 43(03), 226–42.
Gauthier, S and Shipworth, D (2015) Behavioural responses to cold thermal discomfort. Building Research & Information, 43(03), 355-70.
Hellwig, R T (2015) Perceived control in indoor environments: a conceptual approach. Building Research & Information, 43(03), 302-15.
Mavrogianni, A, Taylor, J, Davies, M, Thoua, C and Kolm-Murray, J (2015) Urban social housing resilience to excess summer heat. Building Research & Information, 43(03), 316-33.
Parkinson, T and de Dear, R (2015) Thermal pleasure in built environments: physiology of alliesthesia. Building Research & Information, 43(03), 288-301.
Teli, D, James, P A B and Jentsch, M F (2015) Investigating the principal adaptive comfort relationships for young children. Building Research & Information, 43(03), 371-82.
Verhaart, J, Veselý, M and Zeiler, W (2015) Personal heating: effectiveness and energy use. Building Research & Information, 43(03), 346-54.
Wang, J, Li, M, Moorhead, M and Skitmore, M (2025) Forecasting financial distress in listed Chinese construction firms: leveraging ensemble learning and non-financial variables. Construction Management and Economics, 43(03), 175–95.
- Type: Journal Article
- Keywords: Financial distress prediction; Chinese construction industry; ensemble learning; non-financial variables; early warning systems;
- ISBN/ISSN: 0144-6193
- URL: https://doi.org/10.1080/01446193.2024.2403553
- Abstract:
The construction industry, characterized by high business failure rates due to complex, capital-intensive projects, is crucial for China’s economy. Predicting financial distress and early warnings is crucial to prevent crises. While financial ratios are commonly used, non-financial information remains underexplored, with limited empirical evidence supporting its effectiveness in enhancing predictive accuracy. Additionally, most studies on financial distress prediction focus on constructing single classifiers without utilizing ensemble models that integrate multiple algorithms, resulting in suboptimal prediction accuracy. To address these problems, this study presents a model that uses accounting variables and firm characteristics, construction market dynamics, and macroeconomic indicators to predict the probability of failure one to two years in advance. The model uses a soft voting-based ensemble algorithm, financial ratios refined through the recursive feature elimination with cross-validation (RFECV) algorithm, and dataset balancing via Synthetic Minority Over-Sampling Technique + Tomek Link (SMOTETomek). The comparison results indicate that the predictive performance of the soft voting ensemble model outperforms all single classifiers across all combinations of input variables and prediction years. Additionally, incorporating non-financial variables, such as firm characteristics, construction market dynamics, and macroeconomic indicators, further enhances the model’s accuracy. The proposed model can be effectively employed to help stakeholders mitigate the risks associated with the financial distress of construction companies before the project implementation phase.