Suarez, J J (2004) A neural network model to predict business failure in construction companies, in the United States of America. PhD thesis, University of Florida, USA.
Abstract
The construction industry has one of the highest rates of bankruptcy in the United States of America. Although there are many generic prediction models developed to help company managers to predict whether their companies are still healthy or will fail, there was not a specific model trained and tested just using data from construction companies (heavy, utility and commercial construction). The purpose of this dissertation was to create a model using neural networks that was able to predict business failure in construction companies one, two and up to three years before it happened. Data from sixty-seven healthy and bankrupt companies were collected. Although twenty-six financial ratios were first calculated, seven ratios were found to be the most significant indicators and were used to train and test the neural networks. Three neural networks (one, two and three years prior to business failure) were trained and tested. In order to understand the importance of the results, data from randomly chosen construction companies were entered into Altman's model, which is a generic predictor of business health. The results obtained using the neural network models were more accurate than those obtained using Altman's model. Afterwards, a numerical analysis was performed to identify which of the financial ratios were the most important. The results showed that the debt-to-equity ratio, debt-to-assets ratio and the gross profit margin ratio could generate higher changes to the financial condition of a construction company. It was hoped that the results obtained in this dissertation showed that future development of this models could become an important tool for construction companies.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Glagola, C |
| Uncontrolled Keywords: | failure; business failure; United States; numerical analysis; financial ratio; neural network |
| Index terms: | dissertation, construction industry, bankruptcy, United States of America, commercial construction, financial ratio, business failure, United States, manager, prediction model, profit, construction company, numerical analysis, neural network |
| Subjects: | industry analysis, Geography, economic analysis, prediction and forecasting, artificial intelligence, data analysis and analytics, practitioner, organization, research dissemination and communication, construction type |
| Topics: | Geographical Context, Digital Applications, Business Strategy, Research Practice, Roles and Professions, Construction Technology |
| Descriptive scope: | 4 PCTA |
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