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PREDICTION OF DEFAULT PROBABILITY FOR CONSTRUCTION FIRMS USING THE LOGIT MODEL

H. Ping Tserng · Po‐Cheng Chen · Wen-Haw Huang · Man Cheng Lei · Quang Hung Tran

51 citations0/0 files storedJournal of Civil Engineering and Management

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Financial Distress and Bankruptcy Prediction Financial Analysis and Corporate Governance Corporate Insolvency and Governance

Abstract

Recently, the high incidence of construction firm bankruptcies has underlined the importance of forecasting defaults in the construction industry. Early warning systems need to be developed to prevent or avert contractor default; additionally, this evaluation result could facilitate the selection of firms as collaboration or investment partners. Financial statements are considered one of the key basic evaluation tools for demonstrating firm strength. This investigation provides a framework for assessing the probability of construction contractor default based on financial ratios by using the Logit model. A total of 21 ratios, gathered into five financial groups, are utilized to perform univariate logit analysis and multivariate logit analysis for assessing contractor default probability. The empirical results indicate that using multivariate analysis by adding market factor to the liquidity, leverage, activity and profitability factors can increase the accuracy of default prediction more than using only four financial factors. While considering the market factor in the multivariate Logit model, clear incremental prediction performance appears in 1-year evaluation. This study thus suggests that the market factor comprises important information to increase the prediction performance of the model when applied to construction contractors, particularly in short-term evaluation.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1966Financial Ratios As Predictors of Failure 2010Risk assessment of construction projects 2008Support vector machines for credit scoring and discovery of significant features 1988Essentials of corporate finance 1991Optimal Allocation of Project Management Resources for Achieving Success 1995A methodology for predicting company failure in the construction industry

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2019Business Failure Prediction of Construction Contractors Using a LSTM RNN with Accounting, Construction Market, and Macroeconomic Variables 2017Predicting the Bankruptcy of Construction Companies: A CART-Based Model 2016Critical factors for insolvency prediction: towards a theoretical model for the construction industry 2021A survey on deep learning for financial risk prediction 2019Predicting Business Failure of Construction Contractors Using Long Short-Term Memory Recurrent Neural Network

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