Data Warehousing及Data Mining在營建財務及成本管理之決策支援之研究(II)
Date Issued
2004
Date
2004
Author(s)
Ho, S.Ping
DOI
922211E002098
Abstract
S. Ping Ho
Nation Taiwan University
spingho@ntu.edu.tw
Abstract
The early awareness of a potential financial distress is crucial to firm’s
managers for understanding their clients, suppliers and their own firms, and
crucial to fund suppliers for assessing the construction firm’s credit
worthiness. The purpose of this paper is to develop a dynamic prediction model
for financial distress in construction industry using Data Mining. This research
expects to provide construction firm managers and creditors an effective index
for evaluating the credit risk a construction firm. Results show that the
proposed model has higher accuracy and stability for distress prediction and
can provide a more effective quantitative framework for evaluating the financial
standing of a construction firm.
Nation Taiwan University
spingho@ntu.edu.tw
Abstract
The early awareness of a potential financial distress is crucial to firm’s
managers for understanding their clients, suppliers and their own firms, and
crucial to fund suppliers for assessing the construction firm’s credit
worthiness. The purpose of this paper is to develop a dynamic prediction model
for financial distress in construction industry using Data Mining. This research
expects to provide construction firm managers and creditors an effective index
for evaluating the credit risk a construction firm. Results show that the
proposed model has higher accuracy and stability for distress prediction and
can provide a more effective quantitative framework for evaluating the financial
standing of a construction firm.
Subjects
Financial Distress
Distress Prediction
Data Mining
CART(Classification and Regression Tree)
Construction Industry.
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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