EconPapers    
Economics at your fingertips  
 

Estimating corporate bankruptcy forecasting models by maximizing discriminatory power

Chris Charalambous (), Spiros H. Martzoukos () and Zenon Taoushianis ()
Additional contact information
Chris Charalambous: University of Cyprus
Spiros H. Martzoukos: University of Cyprus
Zenon Taoushianis: University of Southampton

Review of Quantitative Finance and Accounting, 2022, vol. 58, issue 1, No 9, 297-328

Abstract: Abstract In this paper, we estimate coefficients of bankruptcy forecasting models, such as logistic and neural network models, by maximizing their discriminatory power as measured by the Area Under Receiver Operating Characteristics (AUROC) curve. A method is introduced and compared with traditional logistic and neural network models, using out-of-sample analysis, in terms of discriminatory power, information content and economic impact while we forecast bankruptcy one year ahead, two years ahead but also financial distress, which is a situation that precedes firm bankruptcy. Using US public firms over the period 1990–2015, in all, we find that training models to maximize AUROC, provides more accurate out-of-sample forecasts relative to training them with traditional methods, such as maximizing the log-likelihood function, highlighting the benefits arising by using models with maximized AUROC. Among all models, however, a neural network trained with our method is the best performing one, even when we compare it with other methods proposed in the literature to maximize AUROC. Finally, our results are more pronounced when we increase the forecasting difficulty, such as forecasting financial distress. The implementation of our method to train bankruptcy models is robust in various settings and therefore well-justified.

Keywords: Bankruptcy Forecasting; Discriminatory Power; AUROC; Optimization; Economic Benefits (search for similar items in EconPapers)
JEL-codes: C18 C45 C53 C61 G33 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s11156-021-00995-0 Abstract (text/html)
Access to full text is restricted to subscribers.

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:kap:rqfnac:v:58:y:2022:i:1:d:10.1007_s11156-021-00995-0

Ordering information: This journal article can be ordered from
http://www.springer.com/finance/journal/11156/PS2

DOI: 10.1007/s11156-021-00995-0

Access Statistics for this article

Review of Quantitative Finance and Accounting is currently edited by Cheng-Few Lee

More articles in Review of Quantitative Finance and Accounting from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2022-05-12
Handle: RePEc:kap:rqfnac:v:58:y:2022:i:1:d:10.1007_s11156-021-00995-0