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

Author

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  • Katsuyuki Tanaka

    (Graduate School of Economics, Kobe University, 2-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan)

  • Takuji Kinkyo

    (Graduate School of Economics, Kobe University, 2-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan)

  • Shigeyuki Hamori

    (Graduate School of Economics, Kobe University, 2-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan)

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.

Suggested Citation

  • Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Sustainability, MDPI, vol. 10(5), pages 1-18, May.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1530-:d:145799
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    1. Berger, Allen N. & Bouwman, Christa H.S., 2013. "How does capital affect bank performance during financial crises?," Journal of Financial Economics, Elsevier, vol. 109(1), pages 146-176.
    2. Graciela Kaminsky & Saul Lizondo & Carmen M. Reinhart, 1998. "Leading Indicators of Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 1-48, March.
    3. Liran Einav & Jonathan Levin, 2014. "The Data Revolution and Economic Analysis," Innovation Policy and the Economy, University of Chicago Press, vol. 14(1), pages 1-24.
    4. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency Crashes in Emerging Markets: Empirical Indicators," Center for International and Development Economics Research (CIDER) Working Papers 233424, University of California-Berkeley, Department of Economics.
    5. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency crashes in emerging markets: An empirical treatment," Journal of International Economics, Elsevier, vol. 41(3-4), pages 351-366, November.
    6. Alessi, Lucia & Detken, Carsten, 2018. "Identifying excessive credit growth and leverage," Journal of Financial Stability, Elsevier, vol. 35(C), pages 215-225.
    7. Rose, Andrew K. & Spiegel, Mark M., 2011. "Cross-country causes and consequences of the crisis: An update," European Economic Review, Elsevier, vol. 55(3), pages 309-324, April.
    8. Frankel, Jeffrey & Saravelos, George, 2012. "Can leading indicators assess country vulnerability? Evidence from the 2008–09 global financial crisis," Journal of International Economics, Elsevier, vol. 87(2), pages 216-231.
    9. Katsuyuki Tanaka & Takuo Higashide & Takuji Kinkyo & Shigeyuki Hamori, 2017. "Forecasting the Vulnerability of Industrial Economic Activities: Predicting the Bankruptcy of Companies," Discussion Papers 1721, Graduate School of Economics, Kobe University.
    10. Bank for International Settlements, 2012. "Operationalising the selection and application of macroprudential instruments," CGFS Papers, Bank for International Settlements, number 48, december.
    11. Swati R. Ghosh & Atish R. Ghosh, 2003. "Structural Vulnerabilities and Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 50(3), pages 1-7.
    12. Charles Engel & Kristin Forbes & Jeffrey Frankel, 2012. "Global Financial Crisis," NBER Books, National Bureau of Economic Research, Inc, number enge11-2.
    13. Vazquez, Francisco & Federico, Pablo, 2015. "Bank funding structures and risk: Evidence from the global financial crisis," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 1-14.
    14. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    15. Tanaka, Katsuyuki & Kinkyo, Takuji & Hamori, Shigeyuki, 2016. "Random forests-based early warning system for bank failures," Economics Letters, Elsevier, vol. 148(C), pages 118-121.
    16. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    17. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
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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

    Keywords

    financial hazard map; random forests; early warning system; bank failure;
    All these keywords.

    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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