Credit Risk Assessment of Heavy-Polluting Enterprises: A Wide- ℓ p Penalty and Deep Learning Approach

Wanying Song, Jian Min () and Jianbo Yang
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Wanying Song: School of Management, Wuhan University of Technology, Wuhan 430070, China
Jian Min: School of Management, Wuhan University of Technology, Wuhan 430070, China
Jianbo Yang: Alliance Manchester Business School, The University of Manchester, Manchester M15 6PB, UK

Mathematics, 2023, vol. 11, issue 16, 1-19

Abstract: Effective credit risk assessment of heavy-polluting enterprises can achieve a balance between environmental and economic benefits. It requires the consideration of risk indicators for both the carbon information dimension and the compliance dimension. However, as the feature dimensions of the model continue to increase, so does the irrelevant feature or noise. Therefore, we investigate the use of non-integers for regularization from high-dimensional data under the conditions of a large number of irrelevant features. In this paper, a novel Wide- ℓ p Penalty and Deep Learning (WPDL) method for credit risk assessment is proposed, which could provide a sparse solution. The Wide- ℓ p Penalty component allows feature selection using a linear model with an ℓ p Penalty regularization mechanism, where 0 < p ≤ 2. The deep component is a DNN that can generalize indicator features from the credit risk data. The experimental results show that the minimum prediction error occurs at a non-integer ℓ p Penalty . Furthermore, the WPDL outperforms other models such as KNN, DT, RF, SVM, MLP, DNN, Gradient Boosting, and Bagging.

Keywords: wide and deep learning; ? p Penalty; feature selection; non-integer regularization; credit risk assessment (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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