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  4. Financial and Cost Management in Data Warehousing and Data Mining:Building a Decision Support System in Construction Industry (Ⅰ) distress in construction industry.
 
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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
URI
http://ntur.lib.ntu.edu.tw//handle/246246/2823
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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912211E002090.pdf

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(MD5):40086590d6c59470c1d5c146f4370c08

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