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Predicting default of listed companies in mainland China via U-MIDAS Logit model with group lasso penalty

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  • Jiang, Cuixia
  • Xiong, Wei
  • Xu, Qifa
  • Liu, Yezheng

Abstract

We introduce the group LASSO penalty into the U-MIDAS logistic regression context to develop a U-MIDAS-Logit-GL model. The U-MIDAS-Logit-GL model enables us to identify important variables at group level in high dimensional mixed frequency data analysis. We then apply it to a real-world application on studying the default of listed companies in mainland China. The U-MIDAS-Logit-GL model is able to effectively identify important determinants from high-frequency financial factors and low-frequency corporate governance profiles simultaneously. It also successfully predicts the default and outperforms the other competitive models for both in-sample and out-of-sample tests.

Suggested Citation

  • Jiang, Cuixia & Xiong, Wei & Xu, Qifa & Liu, Yezheng, 2021. "Predicting default of listed companies in mainland China via U-MIDAS Logit model with group lasso penalty," Finance Research Letters, Elsevier, vol. 38(C).
    • Handle: RePEc:eee:finlet:v:38:y:2021:i:c:s1544612319309183
    DOI: 10.1016/j.frl.2020.101487
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    Cited by:

    1. Li, Zhe & Liang, Shuguang & Pan, Xianyou & Pang, Meng, 2024. "Credit risk prediction based on loan profit: Evidence from Chinese SMEs," Research in International Business and Finance, Elsevier, vol. 67(PA).
    2. Sha, Yezhou, 2022. "Rating manipulation and creditworthiness for platform economy: Evidence from peer-to-peer lending," International Review of Financial Analysis, Elsevier, vol. 84(C).
    3. Cuixia Jiang & Tingting Zhao & Qifa Xu & Dan Hu, 2024. "An unrestricted MIDAS ordered logit model with applications to credit ratings," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 2722-2739, July.
    4. Goldmann, Leonie & Crook, Jonathan & Calabrese, Raffaella, 2024. "A new ordinal mixed-data sampling model with an application to corporate credit rating levels," European Journal of Operational Research, Elsevier, vol. 314(3), pages 1111-1126.

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