Company failure prediction in the construction industry

•The paper proposes an innovative model to predict construction company failure.•The model is based on the SVM technique and its performance is compared with Logistic regression.•The model includes financial and strategic variables as predictors.•Two different sampling techniques to handle imbalance...

Full description

Saved in:
Bibliographic Details
Published in:Expert systems with applications 2013-11, Vol.40 (16), p.6253-6257
Main Authors: Horta, I.M., Camanho, A.S.
Format: Article
Language:English
Subjects:
Applied sciences
Company failure
Computer science
control theory
systems
Computer systems performance. Reliability
Construction industry
Data processing. List processing. Character string processing
Exact sciences and technology
Expert systems
Failure
Firm modelling
Logistic regression
Logistics
Mathematical models
Mathematics
Memory organisation. Data processing
Operational research and scientific management
Operational research. Management science
Oversampling
Probability and statistics
Regression
Sampling theory, sample surveys
Sciences and techniques of general use
Software
Statistics
Support vector machine
Support vector machines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•The paper proposes an innovative model to predict construction company failure.•The model is based on the SVM technique and its performance is compared with Logistic regression.•The model includes financial and strategic variables as predictors.•Two different sampling techniques to handle imbalanced datasets are tested.•The performance of the SVM model, evaluated using the AUC measure, is very good (over 90%). This paper proposes a new model to predict company failure in the construction industry. The model includes three major innovative aspects. The use of strategic variables reflecting the key specificities of construction companies, which are critical to explain company failure. The use of data mining techniques, i.e. support vector machine to predict company failure. The use of two different sampling methods (random undersampling and random oversampling with replacement) to balance class distributions. The model proposed was empirically tested using all Portuguese contractors that operated in 2009. It is concluded that support vector machine, with random oversampling and including strategic variables, is a very robust tool to predict company failure in the context of the construction industry. In particular, this model outperforms the results obtained with logistic regression.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2013.05.045