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Searching For Landmines In Equity Markets

Author

Listed:
  • BI-JUAN CHANG

    (Graduate Institute of International Business, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei City 106, Taiwan (ROC))

  • JOW-RAN CHANG

    (Department and Graduate Institute of Quantitative Finance, National Tsing Hua University, 101, Section 2, Kuang Fu Road, Hsinchu, 300, Taiwan (ROC))

  • MAO-WEI HUNG

    (Graduate Institute of International Business, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei City 106, Taiwan (ROC))

Abstract

Distressed firms in equity markets are like landmines in the battlefields due to their undetectability and devastating effects. This paper is concerned with distressed firms forecasting by the distance-to-default (DTD) and rare event logit (REL) models via public available data. Comparing these two models by cumulative accuracy profiles (CAP) and receiver operating characteristic (ROC) curves, we conclude that the REL model performs better than the DTD model. The data contains US-listed firms on the S&P 500 for the period January 1986 to December 2012, including 2138 companies and 271,912 firm months, with 444 distressed firms. We set the dynamic thresholds as the last 6% of firms based on the historical cross-section distress rates. Upon Bayesian posterior probability examination, the REL model shows about 40–60% affinity with S&P Domestic Long Term Issuer Credit Rating records on average, and the rate increases to 70% in some situations. We conclude that the REL model can be a good warning indicator of distress in firms at least three years ahead.

Suggested Citation

  • Bi-Juan Chang & Jow-Ran Chang & Mao-Wei Hung, 2014. "Searching For Landmines In Equity Markets," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(02), pages 1-24.
    • Handle: RePEc:wsi:afexxx:v:09:y:2014:i:02:n:s2010495214400041
    DOI: 10.1142/S2010495214400041
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    References listed on IDEAS

    as
    1. Cole, Rebel A. & Wu, Qiongbing, 2009. "Is hazard or probit more accurate in predicting financial distress? Evidence from U.S. bank failures," MPRA Paper 24688, University Library of Munich, Germany, revised 01 Aug 2010.
    2. Jens Hilscher & Mungo Wilson, 2011. "Credit ratings and credit risk," Working Papers 31, Brandeis University, Department of Economics and International Business School.
    3. van Damme, E.E.C., 2008. "Verdwaald tussen 'ja, tenzij' en 'nee, mits'," Other publications TiSEM 29967477-8e71-4257-abb6-5, Tilburg University, School of Economics and Management.
    4. Chou, Hsin-I & Li, Hui & Yin, Xiangkang, 2010. "The effects of financial distress and capital structure on the work effort of outside directors," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 300-312, June.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Distance-to-default (DTD) model; rare event logit (REL) model; cumulative accuracy profiles (CAP) curve; receiver operating characteristic (ROC) curve; G17; G33;
    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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