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Can statistics-based early warning systems detect problem banks before markets?

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  • Kimmel, Randall K.
  • Thornton, John H.
  • Bennett, Sara E.

Abstract

Statistical early warning systems (EWS) to identify problematic banks have grown in sophistication, complexity, and accuracy, but can they inform markets? We utilize five “archetypical” EWS using a unique dataset which accumulates data from 1986 through 2009. An arbitrage portfolio is formed by shorting problematic banks and going long the remaining banks. We find accumulating data allows the models to function during long periods with few or no bank failures and that the factors used are stable. While all models studied do a good job predicting bank failure, we find that EWS are unable to inform markets.

Suggested Citation

  • Kimmel, Randall K. & Thornton, John H. & Bennett, Sara E., 2016. "Can statistics-based early warning systems detect problem banks before markets?," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 190-216.
    • Handle: RePEc:eee:ecofin:v:37:y:2016:i:c:p:190-216
    DOI: 10.1016/j.najef.2016.04.004
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    2. Mpho Bosupeng, 2018. "Leading Indicators and Financial Crisis: A Multi-Sectoral Approach Using Signal Extraction," Journal of Empirical Studies, Conscientia Beam, vol. 5(1), pages 20-44.
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    4. Irfan Nurfalah & Aam Slamet Rusydiana & Nisful Laila & Eko Fajar Cahyono, 2018. "Early Warning to Banking Crises in the Dual Financial System in Indonesia: The Markov Switching Approach التحذير المبكر من الأزمات المصرفية في النظام المالي المزدوج في إندونيسيا: مقاربة ماركوف للتحويل," Journal of King Abdulaziz University: Islamic Economics, King Abdulaziz University, Islamic Economics Institute., vol. 31(2), pages 133-156, July.
    5. Fu, Junhui & Zhou, Qingling & Liu, Yufang & Wu, Xiang, 2020. "Predicting stock market crises using daily stock market valuation and investor sentiment indicators," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).

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