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An alternative approach to predicting bank credit risk in Europe with Google data

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  • González-Fernández, Marcos
  • González-Velasco, Carmen

Abstract

The aim of this paper is to construct an alternative approach based on a sentiment index to measure bank credit risk in European countries using an alternative approach instead of traditional measures. Specifically, we use Google data for a set of keywords related to bank credit risk to capture investor sentiment. The resulting index shows a great similarity to traditional indexes based on bank CDS. The out-of-sample analysis demonstrates that our sentiment index is helpful for predicting bank credit risk during periods of financial distress, since it enhances the accuracy of the estimations.

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  • González-Fernández, Marcos & González-Velasco, Carmen, 2020. "An alternative approach to predicting bank credit risk in Europe with Google data," Finance Research Letters, Elsevier, vol. 35(C).
    • Handle: RePEc:eee:finlet:v:35:y:2020:i:c:s1544612319305318
    DOI: 10.1016/j.frl.2019.08.029
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    References listed on IDEAS

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    1. Alemany, Aida & Ballester, Laura & González-Urteaga, Ana, 2015. "Volatility spillovers in the European bank CDS market," Finance Research Letters, Elsevier, vol. 13(C), pages 137-147.
    2. Theologos Dergiades & Costas Milas & Theodore Panagiotidis, 2015. "Tweets, Google trends, and sovereign spreads in the GIIPS," Oxford Economic Papers, Oxford University Press, vol. 67(2), pages 406-432.
    3. Avino, Davide & Cotter, John, 2014. "Sovereign and bank CDS spreads: Two sides of the same coin?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 32(C), pages 72-85.
    4. Drago, Danilo & Tommaso, Caterina Di & Thornton, John, 2017. "What determines bank CDS spreads? Evidence from European and US banks," Finance Research Letters, Elsevier, vol. 22(C), pages 140-145.
    5. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    6. Smith, Geoffrey Peter, 2012. "Google Internet search activity and volatility prediction in the market for foreign currency," Finance Research Letters, Elsevier, vol. 9(2), pages 103-110.
    7. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    8. Liyan Han & Ziying Li & Libo Yin, 2017. "The effects of investor attention on commodity futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(10), pages 1031-1049, October.
    9. Peri, Massimo & Vandone, Daniela & Baldi, Lucia, 2014. "Internet, noise trading and commodity futures prices," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 82-89.
    10. Azi Ben-Rephael & Zhi Da & Ryan D. Israelsen, 2017. "It Depends on Where You Search: Institutional Investor Attention and Underreaction to News," The Review of Financial Studies, Society for Financial Studies, vol. 30(9), pages 3009-3047.
    11. Thornton, John & Tommaso, Caterina di, 2018. "Credit default swaps and regulatory capital relief: Evidence from European banks," Finance Research Letters, Elsevier, vol. 26(C), pages 255-260.
    12. Guesmi, Khaled & Dhaoui, Abderrazak & Goutte, Stéphane & Abid, Ilyes, 2018. "On the determinants of industry-CDS index spreads: Evidence from a nonlinear setting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 56(C), pages 233-254.
    13. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    14. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    15. Black, Lamont & Correa, Ricardo & Huang, Xin & Zhou, Hao, 2016. "The systemic risk of European banks during the financial and sovereign debt crises," Journal of Banking & Finance, Elsevier, vol. 63(C), pages 107-125.
    16. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    17. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    18. Beetsma, Roel & Giuliodori, Massimo & de Jong, Frank & Widijanto, Daniel, 2013. "Spread the news: The impact of news on the European sovereign bond markets during the crisis," Journal of International Money and Finance, Elsevier, vol. 34(C), pages 83-101.
    19. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    20. Han, Liyan & Lv, Qiuna & Yin, Libo, 2017. "Can investor attention predict oil prices?," Energy Economics, Elsevier, vol. 66(C), pages 547-558.
    21. Aït-Sahalia, Yacine & Andritzky, Jochen & Jobst, Andreas & Nowak, Sylwia & Tamirisa, Natalia, 2012. "Market response to policy initiatives during the global financial crisis," Journal of International Economics, Elsevier, vol. 87(1), pages 162-177.
    22. Apergis, Nicholas, 2015. "Forecasting Credit Default Swaps (CDSs) spreads with newswire messages: Evidence from European countries under financial distress," Economics Letters, Elsevier, vol. 136(C), pages 92-94.
    23. Vozlyublennaia, Nadia, 2014. "Investor attention, index performance, and return predictability," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 17-35.
    24. Smales, Lee A., 2016. "News sentiment and bank credit risk," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 37-61.
    25. Cucinelli, Doriana & Battista, Maria Luisa Di & Marchese, Malvina & Nieri, Laura, 2018. "Credit risk in European banks: The bright side of the internal ratings based approach," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 213-229.
    26. Dzielinski, Michal, 2012. "Measuring economic uncertainty and its impact on the stock market," Finance Research Letters, Elsevier, vol. 9(3), pages 167-175.
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    Cited by:

    1. Fang, Yi & Wang, Qi & Wang, Yanru & Yuan, Yan, 2024. "Media sentiment, deposit stability and bank systemic risk: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 1150-1172.

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    More about this item

    Keywords

    Sentiment index; Google data; Credit risk; Credit default swaps;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G40 - Financial Economics - - Behavioral Finance - - - General

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