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Predicting Financial Distress in the Hong Kong Growth Enterprises Market from the Perspective of Financial Sustainability

Author

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  • Hui Hu

    (Economics and Management School, Wuhan University, Wuhan 430072, China)

  • Milind Sathye

    (Faculty of Business, Government and Law, University of Canberra, Bruce ACT 2601, Australia)

Abstract

The present study, according to our knowledge, is the first attempt to establish a financial distress prediction model for a unique set of enterprises, which are the enterprises listed on the specialized Hong Kong Growth Enterprise Market. It also makes an analysis of corporate financial sustainability and its relationship to financial distress prediction. The logistic regression and jackknife method are used to test the predictability of various models with data drawn from the Growth Enterprise Market for the years 2000–2010. The study finds that a model that includes firm-specific financial variables, firm-specific non-financial variables and a macro-economic variable is a better predictor of financial distress than is a model that includes only the first set of variables or a model that includes the latter two sets of variables. It also finds that a model that includes the latter two sets of variables is a better predictor of financial distress than is a model that includes only the first set of variables. These findings are vital for financial sustainability, as investors, policymakers, auditors and stakeholders of this market would find the conclusions emanating from the study extremely useful.

Suggested Citation

  • Hui Hu & Milind Sathye, 2015. "Predicting Financial Distress in the Hong Kong Growth Enterprises Market from the Perspective of Financial Sustainability," Sustainability, MDPI, vol. 7(2), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:2:p:1186-1200:d:45039
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    References listed on IDEAS

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    5. Ahsan Akbar & Minhas Akbar & Wenjin Tang & Muhammad Azeem Qureshi, 2019. "Is Bankruptcy Risk Tied to Corporate Life-Cycle? Evidence from Pakistan," Sustainability, MDPI, vol. 11(3), pages 1-22, January.

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