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Applying Z-score model to distinguish insolvent construction companies in China · Study

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ArticleApplying Z-score model to distinguish insolvent construction companies in China

S. Thomas Ng2011

Article2011

Applying Z-score model to distinguish insolvent construction companies in China

S. Thomas Ng · James M.W. Wong · Jiajie Zhang

75 citations·0/0 files stored·Habitat International

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Financial Distress and Bankruptcy Prediction Financial Analysis and Corporate Governance Corporate Insolvency and Governance

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1966Financial Ratios As Predictors of Failure 1977ZETATM analysis A new model to identify bankruptcy risk of corporations 1972A Discriminant Analysis of Predictors of Business Failure 2007Introduction to Data Mining 200535 years of studies on business failure: an overview of the classic statistical methodologies and their related problems 1972An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction

Cited by

2020Enhancing the Knowledge of Construction Business Failure: A Social Network Analysis Approach 2019Financial Distress Prediction Through Cash Flow Ratios Analysis 2021Distinctive determinants of financial indebtedness: evidence from Slovak and Czech enterprises 2018A Big Data Analytics Approach for Construction Firms Failure Prediction Models 2017Predicting the Bankruptcy of Construction Companies: A CART-Based Model

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Year2011

Typearticle

Published2011-04-18

VenueHabitat International

ISSN-L0197-3975

Languageen

Citations75

Files0 public / 0

ShelfFinancial Distress and Bankruptcy Prediction

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doi10.1016/j.habitatint.2011.03.008

openalex_workW2086428095

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2016Critical factors for insolvency prediction: towards a theoretical model for the construction industry

2024Predicting financial distress using machine learning approaches: Evidence China

2020Cash Flows Indicators in the Prediction of Financial Distress