Abstract
Developing ‘Internal Rating Systems’ (IRB) for corporate risk management requires building risk (PD) models geared to the specific characteristics of corporate sub-populations (eg small and medium-sized enterprises (SMEs), private companies, listed companies, sector specific models), tuned to changes in the macro environment, and, of course, tailored to the available data. Tracking the risk of ‘newly incorporated companies’ provides a particular challenge since there is very limited publically available data in the time period from incorporation date until the submission of the first accounts. Yet a large number of these companies fail (via bankruptcy). We employ a substantial database to estimate discrete time hazard models (DHM) over the period 2000–2008 (4 427 896 firm-year observations and 34 903 incidences of insolvency), inclusive of macro and regional economic conditions, that capture early indicators of financial stress and measure aspects of the characteristics of board of directors in order to assess the utility of this type of non-financial information in failure prediction models.
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Notes
Small and medium-sized enterprises (SMEs) provide around 75 million jobs and represent 99% of all enterprises in the European Union and are defined by the European Commission (2005, p 5) as those with fewer than 250 employees and annual turnovers not exceeding 50 million euro and/or an annual balance sheet total not exceeding 43 million euro.
Basel Committee on Banking Supervision (2001), in Consultative Document-The New Basel Capital Accord, states that banks also need to consider qualitative in addition to quantitative factors. In ‘Minimum Requirements for Corporate Exposures’ sub-section, the document states that as one of the criteria on risk assessment of a borrower, ‘as a minimum, a bank should look at each of the following factors for each borrower: … depth and skill of management to effectively respond to changing conditions and deploy resources …’ (p 51).
The researchers had access to data compiled by a UK Credit Reference Agency (CRA) associated with a University spin-out company. The CRA compiles data on all filings to Companies House including incorporations, directors and director histories, the processing of annual returns and accounts, the tracking of insolvency events from Companies House filings and Insolvency Service data. County Court judgments provided by Registry Trust are matched to individual companies. The CRA undertake a considerable amount of checking and standardizing of the database.
‘Companies Act 2006’, which came into force in April 2008, reduced the delivery time of accounts from 10 months to 9 months for private companies.
NUTS is a hierarchical classification of spatial units that provides a breakdown of the European Union's territory for producing regional statistics which are comparable across the European Union. NUTS1 units (12 in total) consist of government office regions in England and Wales, Scotland, and Northern Ireland.
The National Statistics Postcode Directory (NSPD) relates both current and terminated postcodes in the United Kingdom to a range of current statutory administrative, electoral, health and other area geographies. It helps support the production of area-based statistics from postcoded data. The NSPD is produced by ONS Geography, which provides geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The NSPD is issued quarterly. ‘© Crown Copyright 2006 Source: National Statistics/Ordnance Survey Extracts are Crown Copyright and may only be reproduced by permission.’
Increasing gender diversity on boards has been cited as a priority for several European governments (eg Davies Report in the UK, 2011) and has resulted in legislation to establish female quotas in Norway. For example, see ‘Women on Boards’, February 2011, Lord Davies, Department for Business Innovation and Skills, UK Government (http://www.bis.gov.uk/assets/biscore/business-law/docs/w/11-745-women-on-boards), accessed 18 October 2012.
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We wish to thank the editor and three anonymous referees for their valuable comments and suggestions that greatly improved this paper.
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Wilson, N., Altanlar, A. Company failure prediction with limited information: newly incorporated companies. J Oper Res Soc 65, 252–264 (2014). https://doi.org/10.1057/jors.2013.31
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DOI: https://doi.org/10.1057/jors.2013.31