Financial and Cost Management in Data Warehousing and Data Mining：Building a Decision Support System in Construction Industry (Ⅰ) distress in construction industry.
Date Issued
2003
Date
2003
Author(s)
Ho, S.Ping
DOI
912211E002090
Abstract
The purpose of this study is to develop a dynamic prediction model for financial distress in
construction industry. Three main issues are discussed in this study. First, we study the basic
process of financial distress in construction and identify the dependencies used for sequence
pattern technique. Second, this study compares two methodologies in financial distress
prediction for their accuracy and stability. Lastly, the performances of various distress prediction
approaches will be evaluated and compared. The impacts of economic, industrial, and business
characteristics on prediction performance will be assessed. Real world financial distress data and
statistics from the construction companies in Taiwan are used to formulate and validate the
distress prediction models derived in this study. This research expects to provide a quantitative
framework in evaluating the financial standing of firms in construction industry. The financial
standing evaluation information is crucial to both firm’s managers for understanding the clients
and suppliers, and fund suppliers for assessing the credit worthiness of the firm. The early
awareness of a potential financial distress can also help to reduce the overall numbers of actual distress in construction industry.
Subjects
Financial Distress
Cost Management
Construction Industry
Data Mining
Decision Support System
Publisher
臺北市：國立臺灣大學土木工程學系暨研究所
Type
report
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