predicting financial distress in the hong kong growth enterprises market from the perspective of financial sustainability
Clicks: 242
ID: 185296
2015
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Popular Article
30.0
/100
242 views
25 readers
AI Quality Assessment
Not analyzed
Readership in this journal
PopularRanked #319 of 935 articles by views in journal of physics: conference series
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 935 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
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.
| Reference Key |
hu2015sustainabilitypredicting
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Hui Hu;Milind Sathye |
| Journal | journal of physics: conference series |
| Year | 2015 |
| DOI |
10.3390/su7021186
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.