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What drives bank performance?

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  • Guerrieri, Luca
  • Harkrader, James Collin

Abstract

Changes in macroeconomic conditions explain the preponderance of the fluctuations in loan charge-offs. Idiosyncratic factors account for a sizable share of the variation in bank revenues, which points to the importance of bank-specific business models as drivers of performance.

Suggested Citation

  • Guerrieri, Luca & Harkrader, James Collin, 2021. "What drives bank performance?," Economics Letters, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:ecolet:v:204:y:2021:i:c:s0165176521001610
    DOI: 10.1016/j.econlet.2021.109884
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    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    3. Galina Hale & John Krainer & Erin McCarthy, 2020. "Aggregation Level in Stress-Testing Models," International Journal of Central Banking, International Journal of Central Banking, vol. 16(4), pages 1-46, September.
    4. Anna, Petrenko, 2016. "Мaркування готової продукції як складова частина інформаційного забезпечення маркетингової діяльності підприємств овочепродуктового підкомплексу," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 2(1), March.
    5. James R. Barth & Sunghoon Joo & Hyeongwoo Kim & Kang Bok Lee & Stevan Maglic & Xuan Shen, 2020. "Forecasting Net Charge-Off Rates of Banks: A PLS Approach," World Scientific Book Chapters, in: Cheng Few Lee & John C Lee (ed.), HANDBOOK OF FINANCIAL ECONOMETRICS, MATHEMATICS, STATISTICS, AND MACHINE LEARNING, chapter 63, pages 2265-2301, World Scientific Publishing Co. Pte. Ltd..
    6. Hirtle, Beverly & Kovner, Anna & Vickery, James & Bhanot, Meru, 2016. "Assessing financial stability: The Capital and Loss Assessment under Stress Scenarios (CLASS) model," Journal of Banking & Finance, Elsevier, vol. 69(S1), pages 35-55.
    7. Alexander J. McNeil & Rüdiger Frey & Paul Embrechts, 2015. "Quantitative Risk Management: Concepts, Techniques and Tools Revised edition," Economics Books, Princeton University Press, edition 2, number 10496.
    8. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    9. Jon Frye & Eduard A. Pelz, 2008. "BankCaR (Bank Capital-at-Risk): a credit risk model for U.S. commercial bank charge-offs," Working Paper Series WP-08-03, Federal Reserve Bank of Chicago.
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    Cited by:

    1. Joaqui-Barandica, Orlando & Manotas-Duque, Diego F. & Uribe, Jorge M., 2022. "Commonality, macroeconomic factors and banking profitability," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).

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    More about this item

    Keywords

    Pre-provision net revenues; Charge-off rates; Macroeconomic factors; Banking factors; Principal components; Backcasting;
    All these keywords.

    JEL classification:

    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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