Hernandez Tinoco, M and Wilson, N orcid.org/0000-0001-5250-9894 (2013) Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30. pp. 394-419. ISSN 1057-5219
Abstract
Using a sample of 23,218 company-year observations of listed companies during the period 1980–2011, the paper investigates empirically the utility of combining accounting, market-based and macro-economic data to explain corporate credit risk. The paper develops risk models for listed companies that predict financial distress and bankruptcy. The estimated models use a combination of accounting data, stock market information and proxies for changes in the macro-economic environment. The purpose is to produce models with predictive accuracy, practical value and macro dependent dynamics that have relevance for stress testing. The results show the utility of combining accounting, market and macro-economic data in financial distress prediction models for listed companies. The performance of the estimated models is benchmarked against models built using a neural network (MLP) and against Altman's (1968) original Z-score specification.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Bankruptcy; Listed companies; Financial distress; Logit regression; Neural networks |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Accounting & Finance Division (LUBS) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Jul 2016 14:15 |
Last Modified: | 03 Nov 2016 06:30 |
Published Version: | http://doi.org/10.1016/j.irfa.2013.02.013 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.irfa.2013.02.013 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:101445 |