Estimating Corporate Bankruptcy Forecasting Models by Maximizing Discriminatory Power

Review of Quantitative Finance and Accounting (forthcoming)

44 Pages Posted: 19 Aug 2020 Last revised: 2 Jun 2021

See all articles by Christakis Charalambous

Christakis Charalambous

University of Cyprus - Department of Public and Business Administration

Spiros Martzoukos

University of Cyprus - Department of Public and Business Administration; George Washington University - School of Business

Zenon Taoushianis

University of Southampton - Department of Banking and Finance

Date Written: April 10, 2020

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

JEL Classification: C18, C61, C45, C53, G33

Suggested Citation

Charalambous, Christakis and Martzoukos, Spiros Harilaos Spiridon and Taoushianis, Zenon, Estimating Corporate Bankruptcy Forecasting Models by Maximizing Discriminatory Power (April 10, 2020). Review of Quantitative Finance and Accounting (forthcoming), Available at SSRN: https://ssrn.com/abstract=3653065 or http://dx.doi.org/10.2139/ssrn.3653065

Christakis Charalambous

University of Cyprus - Department of Public and Business Administration ( email )

75 Kallipoleos Street
P.O. Box 20537
Nicosia CY-1678
CYPRUS
00357-2-892258 (Phone)
00357-2-339063 (Fax)

Spiros Harilaos Spiridon Martzoukos

University of Cyprus - Department of Public and Business Administration ( email )

75 Kallipoleos Street
P.O. Box 20537
Nicosia CY-1678
CYPRUS
357-2-892474 (Phone)
357-2-892460 (Fax)

George Washington University - School of Business ( email )

Washington, DC 20052
United States
202-994-5996 (Phone)
202-994-5014 (Fax)

Zenon Taoushianis (Contact Author)

University of Southampton - Department of Banking and Finance ( email )

Southampton
United Kingdom

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