Dynamic forecasts of financial distress of Australian firms
Maria H. Kim and
Graham Partington
Additional contact information
Maria H. Kim: Faculty of Business, University of Wollongong, Australia
Graham Partington: Business School, The University of Sydney, Australia
Australian Journal of Management, 2015, vol. 40, issue 1, 135-160
Abstract:
Dynamic forecasts of financial distress have received far less attention than static forecasts, particularly in Australia. This study, therefore, investigates dynamic probability forecasts for Australian firms. Novel features of the modelling are the use of time-varying variables in forecasts from a Cox model. Not only is this one of relatively few studies to apply dynamic variables in forecasting financial distress, but to the authors’ knowledge it is the first to provide forecasts of survival probabilities using the Cox model with time-varying variables. Forecast accuracy is evaluated using receiver operating characteristics curves and the Brier Score. It was found that the dynamic model had superior predictive power, in out-of-sample forecasts, to the traditional Cox model and to the logit model.
Keywords: Baseline hazard; dynamic forecasts; financial distress prediction; proportional hazard; survival analysis; time-varying Cox regression model (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0312896213514237 (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:sae:ausman:v:40:y:2015:i:1:p:135-160
DOI: 10.1177/0312896213514237
Access Statistics for this article
More articles in Australian Journal of Management from Australian School of Business
Bibliographic data for series maintained by SAGE Publications ().