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Forecasting Net Charge-Off Rates of Banks: A PLS Approach

In: HANDBOOK OF FINANCIAL ECONOMETRICS, MATHEMATICS, STATISTICS, AND MACHINE LEARNING

Author

Listed:
  • James R. Barth
  • Sunghoon Joo
  • Hyeongwoo Kim
  • Kang Bok Lee
  • Stevan Maglic
  • Xuan Shen

Abstract

This chapter relies on a factor-based forecasting model for net charge-off rates of banks in a data-rich environment. More specifically, we employ a partial least squares (PLS) method to extract target-specific factors and find that it outperforms the principal component approach in-sample by construction. Further, we apply PLS to out-of-sample forecasting exercises for aggregate bank net charge-off rates on various loans as well as for similar individual bank rates using over 250 quarterly macroeconomic data from 1987Q1 to 2016Q4. Our empirical results demonstrate superior performance of PLS over benchmark models, including both a stationary autoregressive type model and a nonstationary random walk model. Our approach can help banks identify important variables that contribute to bank losses so that they are better able to contain losses to manageable levels.

Suggested Citation

  • 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..
    • Handle: RePEc:wsi:wschap:9789811202391_0063
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    Cited by:

    1. Kim, Hyeongwoo & Son, Jisoo, 2024. "What charge-off rates are predictable by macroeconomic latent factors?," Journal of Financial Stability, Elsevier, vol. 74(C).
    2. Guerrieri, Luca & Harkrader, James Collin, 2021. "What drives bank performance?," Economics Letters, Elsevier, vol. 204(C).
    3. Cañizares Martínez, Carlos, 2025. "Dating housing booms fueled by credit: A Markov switching approach," Journal of Financial Stability, Elsevier, vol. 78(C).
    4. Mohamed M. Khalifa Tailab, 2020. "Using Importance-Performance Matrix Analysis to Evaluate the Financial Performance of American Banks During the Financial Crisis," SAGE Open, , vol. 10(1), pages 21582440209, January.

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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