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Company failure prediction with limited information: newly incorporated companies

Author

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  • N Wilson

    (University of Leeds, Leeds, UK)

  • A Altanlar

    (University of Leeds, Leeds, UK)

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.

Suggested Citation

  • N Wilson & A Altanlar, 2014. "Company failure prediction with limited information: newly incorporated companies," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(2), pages 252-264, February.
    • Handle: RePEc:pal:jorsoc:v:65:y:2014:i:2:p:252-264
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    Cited by:

    1. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    2. Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.
    3. Meng-Meng Tan & Dong-Ling Xu & Jian-Bo Yang, 2022. "Corporate Failure Risk Assessment for Knowledge-Intensive Services Using the Evidential Reasoning Approach," JRFM, MDPI, vol. 15(3), pages 1-29, March.
    4. Oliver Lukason & Art Andresson, 2019. "Tax Arrears Versus Financial Ratios in Bankruptcy Prediction," JRFM, MDPI, vol. 12(4), pages 1-13, December.
    5. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..

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