Hindawi Publishing Corporation Advances in Decision Sciences Volume 2013, Article ID 459751, 14 pages http://dx.doi.org/10.1155/2013/459751 Research Article Entropy for Business Failure Prediction: An Improved Prediction Model for the Construction Industry Jay Bal, 1 Yen Cheung, 2 and Hsu-Che Wu 3 1 International Digital Laboratory, University of Warwick, Coventry CV4 7AL, UK 2 Clayton School of IT, Monash University, Melbourne, Vic 3800, Australia 3 Department of Accounting and Information Technology, National Chung Cheng University, 168 University Road, Min-Hsiung, Chia-Yi County 621, Taiwan Correspondence should be addressed to Jay Bal; jay.bal@warwick.ac.uk Received 9 April 2013; Accepted 31 October 2013 Academic Editor: David Bulger Copyright © 2013 Jay Bal et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. his paper examines empirically the efectiveness of entropy measures derived from information theory combined with discriminant analysis in the prediction of construction business failure. Such failure in modern complex supply chains is an extremely disruptive force, and its likelihood is a key factor in the prequaliication appraisal of contractors. he work described, using inancial data from the Taiwanese construction industry, extends the classical methods by applying Shannon’s information theory to improve their prediction ability and provides an alternative to newer artiicial-intelligence-based approaches. 1. Introduction Over the last 35 years, business failure prediction has become a major research domain especially with increased global business competition [1]. Business failure is an extremely disruptive force in the construction industry [2]. Kangari et al. [3] indicated that the construction industry in the USA has several unique characteristics that sharply distinguish it from other sectors of the economy. he bankruptcy rate within the American construction industry has been increasing in recent years and the USA has the highest percentage of construction company failures each year [4, 5]. he construc- tion industry is also a major industry in the UK and has the highest percentage of company failures each year [6, 7]. Similarly, in Asian countries like Taiwan where there has been phenomenal growth in the last few decades, the construction sector also plays a major economic role. Beaver [8] was one of the irst researchers to study business failure prediction. He analysed inancial ratios one by one to evaluate their predictive ability. He then developed their predictive abilities using cutof scores to classify each company as either failed or nonfailed company. However, this classiication technique uses one ratio at a time and conlicts arise when one ratio classiies the company as healthy whilst another detects distress. His work was followed by Altman’s [9] model based on discriminant analysis and Ohlson’s work [10] based on the use of logistic regression. Like many other problems in science and engineering, popular machine learning techniques from the 1990s such as neural networks and genetic algorithms have also been applied to business problems such as bankruptcy or busi- ness distress detection [11, 12] with some successes. When qualitative data and uncertainties abound, these techniques are very useful indeed. However, techniques such as arti- icial neural networks require large datasets for training purposes and large models are oten less easy to interpret [13]. Recent research trends in this area have also employed hybrid methodologies combining both machine learning techniques with the traditional statistical approach with some successes [13]. In this paper, the research methodology employs quantitative inancial data as applied in previous research, but augmented using Shannon’s information theory to better predict business distress. As shown in previous works, particularly in the construction industry (i.e., the sector addressed in this paper), the inancial ratios are very important characteristics when modelling and detecting