A Comparative Study of Australian and Korean Accounting Data in Business Failure Prediction Models
The purpose of this paper is to evaluate comparability of Australian and Korean accounting data in their predictive power and usefulness in business failure prediction models. To this end, the predictive ability of total 52 individual financial ratios is assessed for the sample from each country, along with three different significance tests (univariate approach). In addition, four alternative multivariate models, i.e., linear discriminant model, quadratic discriminant model, logit model, and probit model, are also constructed (multivariate approach). The classification results from each multivariate model are then compared to assess the robustness of our findings. Main findings are: (i) in most cases, Australian financial ratios have more information content than their Korean counterparts in failure prediction; (ii) ROE (return on equity), NI/TTA (Total Liabilities/Total Tangible Assets), CF/INT (Cash Flow/Interests) are the most efficient and inter temporally stable univariate discriminators for the Australian case and EBIT/INT (Earnings before Interest and Taxes/Interests), RE/TTA (Retained Earnings/Total Tangible Assets) for the Korean case; (iii) compared with the results from an univariate approach, the multivariate approach in the Australian case made only marginal improvement of prediction accuracy, while the Korean multivariate models increased their prediction accuracy significantly.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
When requesting a correction, please mention this item's handle: RePEc:ltr:wpaper:1999.07. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Stephen Scoglio)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.