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Machine learning approaches for explaining determinants of the debt financing in heavy-polluting enterprises

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  • Lin, Boqiang
  • Bai, Rui

Abstract

Under the background of green credit policy, more and more attention has been paid to the debt financing of high-polluting enterprises. This paper collects 224 financial and non-financial indicators in 40 listed enterprises in the mining, steel, and power industries to investigate their relationship with those measurement indicators. This paper selects the XGBoost method for feature selection to sort out the top six indicators of the combination of subsets under the condition of high dimension. The screened indicators have a good effect in predicting long-term debt. Further explanation is given based on the Shapley additive explanation value.

Suggested Citation

  • Lin, Boqiang & Bai, Rui, 2022. "Machine learning approaches for explaining determinants of the debt financing in heavy-polluting enterprises," Finance Research Letters, Elsevier, vol. 44(C).
    • Handle: RePEc:eee:finlet:v:44:y:2022:i:c:s1544612321001756
    DOI: 10.1016/j.frl.2021.102094
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    References listed on IDEAS

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    1. Zhang, Dayong & Li, Jun & Ji, Qiang, 2020. "Does better access to credit help reduce energy intensity in China? Evidence from manufacturing firms," Energy Policy, Elsevier, vol. 145(C).
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    Cited by:

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    2. Berger, Theo, 2023. "Explainable artificial intelligence and economic panel data: A study on volatility spillover along the supply chains," Finance Research Letters, Elsevier, vol. 54(C).
    3. Kovvuri, Veera Raghava Reddy & Fu, Hsuan & Fan, Xiuyi & Seisenberger, Monika, 2023. "Fund performance evaluation with explainable artificial intelligence," Finance Research Letters, Elsevier, vol. 58(PB).

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