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Polytomous response financial distress models: The role of accounting, market and macroeconomic variables

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  • Hernandez Tinoco, Mario
  • Holmes, Phil
  • Wilson, Nick

Abstract

We apply polytomous response logit models to investigate financial distress and bankruptcy across three states for UK listed companies over a period exceeding 30 years and utilising around 20,000 company year observations. Results suggest combining accounting, market and macroeconomic variables enhances the performance, accuracy and timeliness of models of corporate credit risk. Models produced contribute to the prediction and early warning systems literature by investigating the distress/failure process with enhanced granularity. We employ marginal effects to assess individual covariates' impact on the probability of falling into each state. The new insights on individual risk factors are confirmed by analysis of vectors of changes in predicted probabilities of falling into a state of financial distress and corporate failure following changes in the level of individual covariates. Resulting models provide a better understanding of different risk factors and can help practitioners detect financial distress and failure in a timely fashion.

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  • Hernandez Tinoco, Mario & Holmes, Phil & Wilson, Nick, 2018. "Polytomous response financial distress models: The role of accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 276-289.
    • Handle: RePEc:eee:finana:v:59:y:2018:i:c:p:276-289
    DOI: 10.1016/j.irfa.2018.03.017
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    4. Jiaming Liu & Chengzhang Li & Peng Ouyang & Jiajia Liu & Chong Wu, 2023. "Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1112-1137, August.
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    7. Magali Costa & Inês Lisboa & Ana Gameiro, 2022. "Is the Financial Report Quality Important in the Default Prediction? SME Portuguese Construction Sector Evidence," Risks, MDPI, vol. 10(5), pages 1-24, May.

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