The Role of Financial, Macroeconomic, and Non-financial Information in Bank Loan Default Timing Prediction

Abstract

We assess the use of bank loan information in predicting the timing to default. We use unique data on defaults in small and medium enterprises maintained by the Central Bank of Portugal which includes financial accounting and macroeconomic indicators, as well as non-financial information. The findings are indicative of the incremental predictive ability of non-financial information over and above macroeconomic and financial accounting information in the baseline, industry, and in- and out-of-sample models. Specifically, total credit secured by firms is, as expected, negatively and significantly related to default. Gross domestic product is negatively and significantly related to default, and benchmark market rate is positively and significantly associated with default. The findings also reveal that firms which are operated by partners, which have stronger financial support from partners, and which possess operational assets exhibit lower hazards of default. The study indicates that non-financial information and macroeconomic indicators assessed alongside financial accounting data can significantly improve the forecasting performance of default models.

Acknowledgements

We benefitted from helpful discussions and suggestions made by Andrew Clare, Christine Wiedman, Duane Kennedy, Peter Wysocki, Phil Molyneux, Sarkis Khoury, and Steve Fortin. We also thank the anonymous reviewers and especially the editor for their incisive comments on earlier drafts of the paper. The views expressed in the paper are those of the authors and not of the institutions of their affiliation. We acknowledge financial support from ‘Fundação para a Ciência e Tecnologia’ (PTDC/EGE-ECO/114977/2009).

Notes

^‡ are available in an online appendix: http://dx.doi.org/10.1080/09638180.2013.770967

¹ Information on financial support from partners is based on the indication of effective guarantees provided by partners.

² Hazard rates can be estimated implicitly by using debt prices or explicitly by using actual defaults (Jarrow et al., 2000). These hazard rates are extensively used in pricing credit derivatives (see, among many others, Jarrow and Turnbull, 1995).

³ Default models are particularly useful in assessing the solidity of banks and of the overall financial system through the capital buffers held by banks in relation to the hazards of default in their loan portfolio. The hazards of default (and the composition of the loan portfolio and recovery rates) are key inputs for calculating the riskiness of the loan portfolios and the required capital buffers. The greater the hazards of default, the greater the capital buffer required by banks.

⁴ Data on defaults for non-listed firms are not easily accessible. Many commercial firms are expending significant resources to develop benchmarks for non-listed firms (see, e.g. Moody's Investors Service, 2000a,b).

⁵ The influence of reporting information on the financial crisis in the specific case of banks is studied by Barth and Landsman (2010).

⁶ Our sample comprises non-listed firms; hence, we do not use stock market returns and volatility.

⁷ Detailed discussions of proportional hazard models are also provided in Ansell and Phillips (1994), Collett, (1994), Kalbfleisch and Prentice (1980), Kaplan and Meier (1958), and Lawless (1981) among others. Chava and Jarrow (2004) and Chava et al. (2011) in particular provide a detailed discussion on the applicability of these models in distress prediction.

⁸ Credit scores and ratings can be determined from the hazard rates estimated in our study but the reverse is more tedious and less precise. Thomas (2000) reviews the literature on credit and behavioral scoring in detail.