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

Cuixia Jiang, Wei Xiong, Qifa Xu and Yezheng Liu

Finance Research Letters, 2021, vol. 38, issue C

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.

Keywords: Default prediction; Logit model; U-MIDAS Regressions; Group LASSO; U-MIDAS-Logit-GL (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:38:y:2021:i:c:s1544612319309183

DOI: 10.1016/j.frl.2020.101487

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