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A Note on Selecting a Response Measure for Financial Distress

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  • Terry J. Ward
  • Benjamin P. Foster

Abstract

Since 1966, researchers have examined financial distress prediction models to determine the usefulness of accounting information to lenders. These researchers primarily used legal bankruptcy as the response variable for economic financial distress, or included legal bankruptcy with other events in dichotomous prediction models. However, theoretical models of financial distress normally define financial distress as an economic event, the inability to pay debts when due (insolvency). This study uses a loan default/accommodation response variable as a proxy for the inability to pay debts when due. The purpose of this note is to empirically test whether or not using the inability of a firm to pay debts when due, loan default/accommodation, as a response measure produces different results than using legal bankruptcy as the response measure. The study's empirical results show that legal bankruptcy and loan default/accommodation financial distress prediction models produce different statistical results, thus suggesting that the responses measure different constructs. A loan default/accommodation model also fits the data better than a bankrupt model. Our results suggest that a loan default/accommodation response may be a more appropriate measure to determine which accounting information is most useful to lenders in evaluating a firm's credit risk.

Suggested Citation

  • Terry J. Ward & Benjamin P. Foster, 1997. "A Note on Selecting a Response Measure for Financial Distress," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 24(6), pages 869-879, July.
    • Handle: RePEc:bla:jbfnac:v:24:y:1997:i:6:p:869-879
    DOI: 10.1111/1468-5957.00138
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    Citations

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    Cited by:

    1. Nadine Levratto & Luc Tessier & Messaoud Zouikri, 2011. "Small, alone and poor: a merciless portrait of insolvent French firms, 2007-2010," Working Papers hal-04140945, HAL.
    2. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    3. Nadine Levratto & Luc Tessier & Messaoud Zouikri, 2011. "Small, alone and poor: a merciless portrait of insolvent French firms, 2007-2010," EconomiX Working Papers 2011-36, University of Paris Nanterre, EconomiX.
    4. Ijaz, Muhammad Shahzad & Hunjra, Ahmed Imran & Hameed, Zahid & Maqbool, Adnan & Azam, Rauf i, 2013. "Assessing the Financial Failure Using Z-Score and Current Ratio: A Case of Sugar Sector Listed Companies of Karachi Stock Exchange," MPRA Paper 60787, University Library of Munich, Germany.
    5. Aaro Hazak & Kadri Männasoo, 2007. "Indicators of corporate default : an EU based empirical study," Bank of Estonia Working Papers 2007-10, Bank of Estonia, revised 04 Sep 2007.
    6. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    7. Welc Jacek, 2017. "EBITDA vs. Cash Flows in Bankruptcy Prediction on the Polish Capital Market," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2017(2), pages 91-103.
    8. Paulo V. Carvalho & José D. Curto & Rodrigo Primor, 2022. "Macroeconomic determinants of credit risk: Evidence from the Eurozone," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2054-2072, April.
    9. Chyan-long Jan, 2018. "An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan," Sustainability, MDPI, vol. 10(2), pages 1-14, February.

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