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Predicting Default More Accurately: To Proxy Or Not To Proxy For Default

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Listed:
  • Koresh Galil

    (BGU)

  • Neta Gilat

    (BGU)

Abstract

Previous studies targeting accuracy improvement of default models mainly focused on the choice of the explanatory variables and the statistical approach. We alter the focus to the choice of the dependent variable. We particularly explore whether the common practice (in the literature) of using proxies for default events (bankruptcy or delisting) to increase sample size indeed improves accuracy. We examine four definitions of financial distress and show that each definition carries considerably different characteristics. We discover that rating agencies effort to measure correctly the timing of default is valuable. Our main conclusion is that one cannot improve default prediction by making use of other distress events.
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Suggested Citation

  • Koresh Galil & Neta Gilat, 2018. "Predicting Default More Accurately: To Proxy Or Not To Proxy For Default," Working Papers 1801, Ben-Gurion University of the Negev, Department of Economics.
    • Handle: RePEc:bgu:wpaper:1801
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    References listed on IDEAS

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    Cited by:

    1. Koresh Galil & Margalit Samuel & Offer Moshe Shapir & Wolf Wagner, 2023. "Bailouts and the modeling of bank distress," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 46(1), pages 7-30, February.

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    More about this item

    Keywords

    Default; Bankruptcy; Financial Distress; Delisting; Bankruptcy Prediction; Default Prediction.;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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