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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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    1. 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).

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