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A penalized U-MIDAS multinomial logit model with applications to corporate credit ratings

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  • Jiang, Cuixia
  • Sun, Junwei
  • Xu, Qifa

Abstract

We develop a penalized U-MIDAS-Mlogit model by introducing the group LASSO penalty into the unrestricted MIDAS multinomial logit model. This penalized U-MIDAS-Mlogit model can implement multinomial classification in a high-dimensional mixed-frequency data environment. We apply it to credit ratings for listed companies in China over the period 2008–2023. The penalized U-MIDAS-Mlogit model can extract pivotal information from high-frequency financial variables and low-frequency internal and external governance indicators. It outperforms several competing models in predicting credit ratings.

Suggested Citation

  • Jiang, Cuixia & Sun, Junwei & Xu, Qifa, 2025. "A penalized U-MIDAS multinomial logit model with applications to corporate credit ratings," The North American Journal of Economics and Finance, Elsevier, vol. 76(C).
    • Handle: RePEc:eee:ecofin:v:76:y:2025:i:c:s106294082500021x
    DOI: 10.1016/j.najef.2025.102381
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    More about this item

    Keywords

    Corporate credit ratings; U-MIDAS regression; Multinomial logit model; Group LASSO;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • 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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