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A Multinomial Logit Analysis of Problem Loan Resolution Choices in Banking

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  • Lawrence, Edward C
  • Arshadi, Nasser

Abstract

This paper presents a conceptual framework of problem loan resolution choices that is a function of the combined borrower and lender decisions. A bank will choose a workout option if its expected value is greater than the outcome under a no-workout plan. For the borrower, if the reputational penalty due to a default is less than the opportunity cost of the best new alternative, the borrower will have an incentive to default. If the reverse holds then the borrower will be better-off with a loan workout. Using a unique data set composed of borrower, lender and economic factors we empirically examine the problem loan resolution choices. The, results provide support for our conceptual framework that problem loan choices are based on combined borrower/lender variables. Copyright 1995 by Ohio State University Press.

Suggested Citation

  • Lawrence, Edward C & Arshadi, Nasser, 1995. "A Multinomial Logit Analysis of Problem Loan Resolution Choices in Banking," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 27(1), pages 202-216, February.
    • Handle: RePEc:mcb:jmoncb:v:27:y:1995:i:1:p:202-16
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    Cited by:

    1. Anthony Pennington-Cross, 2010. "The Duration of Foreclosures in the Subprime Mortgage Market: A Competing Risks Model with Mixing," The Journal of Real Estate Finance and Economics, Springer, vol. 40(2), pages 109-129, February.
    2. Bramma, Keith M., 2000. "Pricing farm loans for credit risk," 2000 Conference (44th), January 23-25, 2000, Sydney, Australia 123607, Australian Agricultural and Resource Economics Society.
    3. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.
    4. Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(6), pages 80-98, November.
    5. Hernandez Tinoco, Mario & Holmes, Phil & Wilson, Nick, 2018. "Polytomous response financial distress models: The role of accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 276-289.
    6. Ha Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," Working Papers hal-04133309, HAL.
    7. Alexander N. Bogin & William M. Doerner & William D. Larson, 2016. "Missing the Mark: House Price Index Accuracy and Mortgage Credit Modeling," Working Papers 2016-010, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    8. Fabián Enrique Salazar Villano, 2013. "Cuantificación del riesgo de incumplimiento en créditos de libre inversión: un ejercicio econométrico para una entidad bancaria del municipio de Popayán, Colombia," Estudios Gerenciales, Universidad Icesi, December.
    9. Sorokina, Nonna & Thornton, John H., 2016. "Reactions of equity markets to recent financial reforms," Journal of Economics and Business, Elsevier, vol. 87(C), pages 50-69.
    10. Rais Ahmad Itoo & Selvarasu Appasamy Mutharasu & José António Filipe, 2013. "Effect of Loan Value and Collateral on Value of Mortgage Default," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 3(4), pages 635-635.
    11. Michelle A. Danis & Anthony Pennington-Cross, 2005. "A dynamic look at subprime loan performance," Working Papers 2005-029, Federal Reserve Bank of St. Louis.
    12. Ha-Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," EconomiX Working Papers 2014-26, University of Paris Nanterre, EconomiX.
    13. Ulrich Kaiser & Andrea Szczesny, 2003. "Ökonometrische Verfahren zur Modellierung von Kreditausfallwahrscheinlichkeiten: Logit- und Probit-Modelle," Schmalenbach Journal of Business Research, Springer, vol. 55(8), pages 790-822, December.
    14. Ha Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," Working Papers hal-04141336, HAL.
    15. Danis, Michelle A. & Pennington-Cross, Anthony, 2008. "The delinquency of subprime mortgages," Journal of Economics and Business, Elsevier, vol. 60(1-2), pages 67-90.
    16. Timotej Jagric & Vita Jagric & Davorin Kracun, 2011. "Does Non-linearity Matter in Retail Credit Risk Modeling?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(4), pages 384-402, August.
    17. Jennifer C. Ireland, 2003. "An Empirical Investigation of Determinants of Audit Reports in the UK," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 30(7‐8), pages 975-1016, September.
    18. Peng XU, 2019. "Exit of Small Businesses: Differentiating between Insolvency, Voluntary Closures and M&A," Discussion papers 19051, Research Institute of Economy, Trade and Industry (RIETI).

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