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Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?

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Abstract

A huge diversity exists within the broad category of small and medium size enterprises (SMEs). They differ widely in their capital structure, firm size, access to external finance, management style, numbers of employees etc. We contribute to the literature by acknowledging this diversity while modeling credit risk for them, using a relatively large UK database, covering the analysis period between 2000 and 2009. Our analysis partially employs the definition provided by the European Union to distinguish between ‘micro’, ‘small’, and ‘medium’ sized firms. We use both financial and non-financial information to predict firm’s failure hazard. We estimate separate hazard models for each sub-category of SMEs, and compare their performance with a SMEs hazard model including all the three sub-categories. We test our hypotheses using discrete-time duration-dependent hazard rate modelling techniques, which controls for both macro-economic conditions and survival time. Our test results strongly highlight the differences in the credit risk attributes of ‘micro’ firms and SMEs, while it does not support the need to consider ‘small’ and ‘medium’ firms’ category separately while modelling credit risk for them, as almost the same sets of explanatory variables affect the failure hazard of SMEs, ‘small’ and ‘medium’ firms.

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Notes

  1. It includes all micro, small and medium firms.

  2. Which we further classify into micro, small and medium firms as listed in Table 1.

  3. AUROC calculated using hold-out sample.

  4. UK companies are required to file accounts at ‘Companies House’ (www.companieshouse.gov.uk) which defines a small company as one for which at least two of the following conditions are met: (1) annual turnover is £6.5 million or less; (2) the balance sheet total is £3.26 million or less; (3) the average number of employees is 50 or fewer. It defines medium company as one for which at least two of the following conditions are met: (1) annual turnover must be no more than £25.9 million; (2) the balance sheet total must be no more than £12.9 million; (3) the average number of employees must be no more than 250. .

  5. Once a firm has become insolvent, the UK Act provides to choose one from the five courses of action: administration, company voluntary arrangement (CVA), receivership, liquidation and dissolution. In this study to represent the failed sample group we take under consideration only those SMEs whose failure followed any of the three common routes, i.e. administration, receivership or liquidation.

  6. We take ‘log’ to capture any non-linear relationship.

  7. Source: https://www.gov.uk/audit-exemptions-for-private-limited-companies

  8. We apply size control only for SMEs sample.

  9. The two restrictive assumptions of MDA analysis are: (1) the independent variables included in the model are multivariate normally distributed; (2) the group dispersion matrices (or variance–covariance matrices) are equal across the failing and the non-failing group. See Barnes (1982) and Karels and Prakash (1987) for further discussions about this topic.

  10. We also use natural logarithm of firm’s age [Ln(Age)].

  11. A firm actually defaults and the model has classified it as expected default.

  12. A firm actually defaults and the model has classified it as expected non-default.

  13. If a firm has reported EBITDA as 35,000 GBP and no interest expense, then the ratio EBITDAIE is 35,000.

  14. Age is computed in years.

  15. We re-organize our database to incorporating the effect of time-varying covariates in our logistic model as stated in Eq. (4).

  16. We re-organize our database to incorporating the effect of time-varying covariates in our logistic model as stated in Eq. 4.

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Acknowledgments

The authors are highly grateful to the Credit Management Research Centre (http://www.cmrc.co.uk/) of the University of Leeds for providing the data.

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Authors and Affiliations

  1. Department of Accounting and Finance, Hull University Business School, Hull, HU6 7RX, UK

    Jairaj Gupta, Andros Gregoriou & Jerome Healy

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  1. Jairaj Gupta
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  2. Andros Gregoriou
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Correspondence to Jerome Healy.

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Gupta, J., Gregoriou, A. & Healy, J. Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?. Rev Quant Finan Acc 45, 845–869 (2015). https://doi.org/10.1007/s11156-014-0458-0

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