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Estimating corporate bankruptcy forecasting models by maximizing discriminatory power

Estimating corporate bankruptcy forecasting models by maximizing discriminatory power
Estimating corporate bankruptcy forecasting models by maximizing discriminatory power

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.

AUROC, Bankruptcy Forecasting, Discriminatory Power, Economic Benefits, Optimization
0924-865X
297-328
Taoushianis, Zenon
5c536511-1155-4a5b-8249-0a944572b7fc
Charalambous, Chris
fa22f27b-3050-4b7e-b3b8-91ac0680de3f
Martzoukos, Spiros
a54285f3-83a0-4010-bfe1-6960fb1d6b44
Taoushianis, Zenon
5c536511-1155-4a5b-8249-0a944572b7fc
Charalambous, Chris
fa22f27b-3050-4b7e-b3b8-91ac0680de3f
Martzoukos, Spiros
a54285f3-83a0-4010-bfe1-6960fb1d6b44

Taoushianis, Zenon, Charalambous, Chris and Martzoukos, Spiros (2021) Estimating corporate bankruptcy forecasting models by maximizing discriminatory power. Review of Quantitative Finance and Accounting, 58 (1), 297-328.

Record type: Article

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.

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Accepted/In Press date: 2 June 2021
e-pub ahead of print date: 19 June 2021
Keywords: AUROC, Bankruptcy Forecasting, Discriminatory Power, Economic Benefits, Optimization

Identifiers

Local EPrints ID: 450359
URI: http://eprints.soton.ac.uk/id/eprint/450359
ISSN: 0924-865X
PURE UUID: b67fe9ae-4685-4e1e-9238-03b851f44f79
ORCID for Zenon Taoushianis: ORCID iD orcid.org/0000-0003-2002-6040

Catalogue record

Date deposited: 26 Jul 2021 16:30
Last modified: 28 Apr 2022 02:28

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Contributors

Author: Chris Charalambous
Author: Spiros Martzoukos

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