Predicting Construction Contractor Financial Distress in Taiwan –Integration of Accounting-based & Market-based Models

碩士 === 國立臺灣大學 === 土木工程學研究所 === 99 === Building default-predicting models is an important issue in all areas of business, yet past researches mostly excluded the construction industry from their sample due to its distinct characteristics and accounting principles. This study aims at predicting constr...

Full description

Bibliographic Details
Main Authors: Wen-Pei Wang, 王文珮
Other Authors: 曾惠斌
Format: Others
Language:en_US
Published: 2010
Online Access:http://ndltd.ncl.edu.tw/handle/84432953808990098547
id ndltd-TW-099NTU05015020
record_format oai_dc
spelling ndltd-TW-099NTU050150202015-10-28T04:11:42Z http://ndltd.ncl.edu.tw/handle/84432953808990098547 Predicting Construction Contractor Financial Distress in Taiwan –Integration of Accounting-based & Market-based Models 台灣營建公司財務危機預測-會計基礎與市場基礎模型之整合 Wen-Pei Wang 王文珮 碩士 國立臺灣大學 土木工程學研究所 99 Building default-predicting models is an important issue in all areas of business, yet past researches mostly excluded the construction industry from their sample due to its distinct characteristics and accounting principles. This study aims at predicting construction contractor default in Taiwan using an accounting-based model, market-based models, and a hybrid approach. Furthermore we compare the results of these different approaches using the Area Under Curve (AUC) to identify the most suitable model for predicting default in the construction industry in Taiwan. Default-predicting models are in large built by accounting information, yet accounting sheets are subject to manipulation and unable to show immediate symptoms. Market-based models use market information to predict default and are based on the presumption that all information is reflected in the stock price, which is only consistent in an efficient market. Both accounting-based and market-based models face some kind of limitation, thus in this study we try putting both accounting and market information into account to predict company default. We propose two hybrid default-predicting models that combine accounting and market information by inputting the default probability calculated from the market-based models as a variable into the accounting-based model. The input data of listed and unlisted construction firms in the Taiwan market was collected from the Taiwan Economic Journal. Results show that the hybrid models (Hybrid 1 AUC: 0.7232 and Hybrid 2 AUC: 0.7364) outperform both the accounting-based model (AUC: 0.6912) and the market-based models (Merton Model AUC: 0.7038 and Barrier Model AUC: 0.7079) in predicting construction contractor default. 曾惠斌 2010 學位論文 ; thesis 88 en_US
collection NDLTD
language en_US
format Others
sources NDLTD
description 碩士 === 國立臺灣大學 === 土木工程學研究所 === 99 === Building default-predicting models is an important issue in all areas of business, yet past researches mostly excluded the construction industry from their sample due to its distinct characteristics and accounting principles. This study aims at predicting construction contractor default in Taiwan using an accounting-based model, market-based models, and a hybrid approach. Furthermore we compare the results of these different approaches using the Area Under Curve (AUC) to identify the most suitable model for predicting default in the construction industry in Taiwan. Default-predicting models are in large built by accounting information, yet accounting sheets are subject to manipulation and unable to show immediate symptoms. Market-based models use market information to predict default and are based on the presumption that all information is reflected in the stock price, which is only consistent in an efficient market. Both accounting-based and market-based models face some kind of limitation, thus in this study we try putting both accounting and market information into account to predict company default. We propose two hybrid default-predicting models that combine accounting and market information by inputting the default probability calculated from the market-based models as a variable into the accounting-based model. The input data of listed and unlisted construction firms in the Taiwan market was collected from the Taiwan Economic Journal. Results show that the hybrid models (Hybrid 1 AUC: 0.7232 and Hybrid 2 AUC: 0.7364) outperform both the accounting-based model (AUC: 0.6912) and the market-based models (Merton Model AUC: 0.7038 and Barrier Model AUC: 0.7079) in predicting construction contractor default.
author2 曾惠斌
author_facet 曾惠斌
Wen-Pei Wang
王文珮
author Wen-Pei Wang
王文珮
spellingShingle Wen-Pei Wang
王文珮
Predicting Construction Contractor Financial Distress in Taiwan –Integration of Accounting-based & Market-based Models
author_sort Wen-Pei Wang
title Predicting Construction Contractor Financial Distress in Taiwan –Integration of Accounting-based & Market-based Models
title_short Predicting Construction Contractor Financial Distress in Taiwan –Integration of Accounting-based & Market-based Models
title_full Predicting Construction Contractor Financial Distress in Taiwan –Integration of Accounting-based & Market-based Models
title_fullStr Predicting Construction Contractor Financial Distress in Taiwan –Integration of Accounting-based & Market-based Models
title_full_unstemmed Predicting Construction Contractor Financial Distress in Taiwan –Integration of Accounting-based & Market-based Models
title_sort predicting construction contractor financial distress in taiwan –integration of accounting-based & market-based models
publishDate 2010
url http://ndltd.ncl.edu.tw/handle/84432953808990098547
work_keys_str_mv AT wenpeiwang predictingconstructioncontractorfinancialdistressintaiwanintegrationofaccountingbasedmarketbasedmodels
AT wángwénpèi predictingconstructioncontractorfinancialdistressintaiwanintegrationofaccountingbasedmarketbasedmodels
AT wenpeiwang táiwānyíngjiàngōngsīcáiwùwēijīyùcèhuìjìjīchǔyǔshìchǎngjīchǔmóxíngzhīzhěnghé
AT wángwénpèi táiwānyíngjiàngōngsīcáiwùwēijīyùcèhuìjìjīchǔyǔshìchǎngjīchǔmóxíngzhīzhěnghé
_version_ 1718113966797881344