Yang, Z R (1997) UK construction company failure prediction: Robust heteroscedastic parzen window classifier. PhD thesis, University of Portsmouth, UK.
Abstract
The construction industry is one of the major industries in the UK and it has the highest percentage of company failures each year. Systematic investigation of the whole process of company failure prediction and development of a robust and consistent methodology for identifying likely failures of UK construction companies is therefore critical and is the main objective of this study. Data analysis, a novel classifier, a non-failed company selection strategy and a consistent prediction methodology form the four main parts of this thesis. The data analysis was done to investigate the characteristics of the data space spanned by the financial ratios derived from the annual accounts of UK private construction companies and is mainly composed of linearity analysis and overlap measurement between the failed and non-failed companies. The linearity analysis proposed in this study aimed to find the basic relationship between the linearity of the data space and distribution characteristics of the financial ratio. A method for measuring the degree of overlap between the failed and non-failed companies is proposed for the purpose of revealing the relationship between the overlap degree and the misclassification rate. A homoscedastic model is unable to deal with heteroscedastic data spaces and heteroscedastic classifiers often experience numerical difficulty; the jack-knife, a robust statistic, was therefore introduced to build a novel robust heteroscedastic Parzen window classifier for company failure prediction. Multiple-output models and classification trees were studied so as to avoid inconsistent predictions encountered by multiple discriminant analysis models. It has been found that the methodology of randomly selecting the non-failed companies can have the same, even better, performance compared with the methodology of selecting those non-failed companies whose turnover size, accounting year match with the failed companies. Several case studies are conducted to illustrate the reliability of the novel classifier developed in this study.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | UK; case studies; discriminant analysis; failure; financial ratio; measurement; reliability |
| Index terms: | investigation, construction industry, case study, discriminant analysis, methodology, window, accounting, financial ratio, company failure, data analysis, turnover, construction company, strategy |
| Subjects: | organization, business management, economic analysis, architectural elements, research methods, data collection methods, risk assessment, data analysis and analytics, management, industry analysis, statistical analysis |
| Topics: | Research Practice, Business Strategy, Design Practice, Risk Management |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here