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Tax Avoidande and Corporate Risk Evidences from Germany Machine Learning Method

Author

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  • Mongi Gharsellaoui

    (University of Manouba, Tunisia)

  • Riadh Ben Ali

    (University of Manouba, Tunisia)

Abstract

The purpose of this paper is to focus on the effect of tax avoidance on corporate risk. This research contributes to the previous literatures by examining the impact of the coronavirus pandemic on corporate risk and also by making predictions though machine learning. This thesis also contributes to the litteratures by the choice of germany as a country of studies. We took a sample of 35 companies listed on the Frankfurt stock exchange over the period 2012-2021. The results of our regression show that tax avoidance does not have a significant effect on the corporate risk.

Suggested Citation

  • Mongi Gharsellaoui & Riadh Ben Ali, 2025. "Tax Avoidande and Corporate Risk Evidences from Germany Machine Learning Method," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(2), pages 2681-2692, February.
    • Handle: RePEc:bcp:journl:v:9:y:2025:issue-2:p:2681-2692
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    References listed on IDEAS

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    1. Yuqiang Cao & Zhuoan Feng & Meiting Lu & Yaowen Shan, 2021. "Tax avoidance and firm risk: evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4967-5000, September.
    2. Trung Kien Tran & Minh Tuan Truong & Kim Tu Bui & Phung Duc Duong & Minh Vuong Huynh & Tran Thai Ha Nguyen, 2023. "Firm Risk and Tax Avoidance in Vietnam: Do Good Board Characteristics Interfere Effectively?," Risks, MDPI, vol. 11(2), pages 1-17, February.
    3. Mouna Guedrib & Ghazi Marouani, 2023. "The interactive impact of tax avoidance and tax risk on the firm value: new evidence in the Tunisian context," Asian Review of Accounting, Emerald Group Publishing Limited, vol. 31(2), pages 203-226, January.
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