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A Note on Selecting a Response Measure for Financial Distress

Author

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  • Terry J. Ward

    (Associate Professor of Accounting at Middle Tennessee State University,)

  • 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. Copyright Blackwell Publishers Ltd 1997.

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.
  • Handle: RePEc:bla:jbfnac:v:24:y:1997-07:i:6:p:869-879
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. repec:prg:jnlefa:v:2017:y:2017:i:2:id:183:p:91-103 is not listed on IDEAS
    4. Julio Pindado & Luis Rodrigues, 2005. "Determinants of Financial Distress Costs," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 19(4), pages 343-359, December.
    5. 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.
    6. Shih, Kuang Hsun & Cheng, Ching Chan & Wang, Yi Hsien, 2011. "Financial Information Fraud Risk Warning for Manufacturing Industry - Using Logistic Regression and Neural Network," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 54-71, March.
    7. repec:gam:jsusta:v:10:y:2018:i:2:p:513-:d:131948 is not listed on IDEAS
    8. Fougère, D. & Golfier, C. & Horny, G. & Kremp, E., 2013. "What has been the impact of the 2008 crisis on firms’ default? (in French)," Working papers 463, Banque de France.
    9. 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.

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