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Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model

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  • Katsuyuki Tanaka

    (Graduate School of Economics, Kobe University)

  • Takuji Kinkyo

    (Graduate School of Economics, Kobe University)

  • Shigeyuki Hamori

    (Graduate School of Economics, Kobe University)

Abstract

This study develops a systematic framework for assessing a country’s financial vulnerability using a predictive classification model of random forests. We introduce a new indicator that quantifies the potential loss in bank assets and measures a country’s overall vulnerability by aggregating these indicators across the banking sector. We also visualize the degree of vulnerability by creating a Financial Hazard Map that highlights countries and regions with underlying risks in their banking sectors.
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Suggested Citation

  • Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2017. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Discussion Papers 1720, Graduate School of Economics, Kobe University.
    • Handle: RePEc:koe:wpaper:1720
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    as
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    Cited by:

    1. Tai-Hock Kuek & Chin-Hong Puah & M. Affendy Arip, 2020. "Financial Vulnerability and Economic Dynamics in Malaysia," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 9(special i), pages 55-73.
    2. Hitoshi Hamori & Shigeyuki Hamori, 2020. "Does Ensemble Learning Always Lead to Better Forecasts?," Applied Economics and Finance, Redfame publishing, vol. 7(2), pages 51-56, March.
    3. Takuo Higashide & Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2021. "New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?," JRFM, MDPI, vol. 14(5), pages 1-18, May.
    4. Beutel, Johannes & List, Sophia & von Schweinitz, Gregor, 2019. "Does machine learning help us predict banking crises?," Journal of Financial Stability, Elsevier, vol. 45(C).
    5. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
    6. Lei Xu & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Predicting Currency Crises: A Novel Approach Combining Random Forests and Wavelet Transform," JRFM, MDPI, vol. 11(4), pages 1-11, December.
    7. Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.

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    More about this item

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
    • Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

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