IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v65y2024ics1544612324005762.html
   My bibliography  Save this article

ESG performance and financial distress prediction of energy enterprises

Author

Listed:
  • Song, Yang
  • Li, Runfei
  • Zhang, Zhipeng
  • Sahut, Jean-Michel

Abstract

In the current drive to cut global carbon emissions, energy companies are facing intensifying policy pressures. This study investigates the impact of ESG (Environmental, Social, Governance) performance on the risk of corporate financial distress in the energy sector. Using a tripartite methodology of sentiment, topic, and word frequency analysis, we measure the characteristics of texts of ESG reports. These ESG-related textual variables, combined with company carbon performance and other variables, are integrated into the CatBoost algorithm to predict financial distress. The empirical findings indicate that text words, topics and sentiments derived from ESG reports prove to be effective in forecasting financial distress in energy companies. Additionally, the CatBoost used in this study surpasses other models such as logistic regression and decision trees in predictive capability. This study demonstrates how incorporating textual analysis of ESG reports enhances the predictive accuracy for financial distress in energy companies, highlighting the important role of comprehensive ESG evaluation in financial risk assessment.

Suggested Citation

  • Song, Yang & Li, Runfei & Zhang, Zhipeng & Sahut, Jean-Michel, 2024. "ESG performance and financial distress prediction of energy enterprises," Finance Research Letters, Elsevier, vol. 65(C).
    • Handle: RePEc:eee:finlet:v:65:y:2024:i:c:s1544612324005762
    DOI: 10.1016/j.frl.2024.105546
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612324005762
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2024.105546?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Juxian & Ma, Mengdi & Dong, Tianyi & Zhang, Zheyuan, 2023. "Do ESG ratings promote corporate green innovation? A quasi-natural experiment based on SynTao Green Finance's ESG ratings," International Review of Financial Analysis, Elsevier, vol. 87(C).
    2. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
    3. Mbanyele, William & Huang, Hongyun & Li, Yafei & Muchenje, Linda T. & Wang, Fengrong, 2022. "Corporate social responsibility and green innovation: Evidence from mandatory CSR disclosure laws," Economics Letters, Elsevier, vol. 212(C).
    4. Achakzai, Muhammad Atif Khan & Juan, Peng, 2022. "Using machine learning Meta-Classifiers to detect financial frauds," Finance Research Letters, Elsevier, vol. 48(C).
    5. Citterio, Alberto & King, Timothy, 2023. "The role of Environmental, Social, and Governance (ESG) in predicting bank financial distress," Finance Research Letters, Elsevier, vol. 51(C).
    6. Florian Berg & Julian F Kölbel & Roberto Rigobon, 2022. "Aggregate Confusion: The Divergence of ESG Ratings [Corporate social responsibility and firm risk: theory and empirical evidence]," Review of Finance, European Finance Association, vol. 26(6), pages 1315-1344.
    7. Robert G. Eccles & Ioannis Ioannou & George Serafeim, 2014. "The Impact of Corporate Sustainability on Organizational Processes and Performance," Management Science, INFORMS, vol. 60(11), pages 2835-2857, November.
    8. Jiang, Cuiqing & Zhou, Yiru & Chen, Bo, 2023. "Mining semantic features in patent text for financial distress prediction," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    9. Gillan, Stuart L. & Koch, Andrew & Starks, Laura T., 2021. "Firms and social responsibility: A review of ESG and CSR research in corporate finance," Journal of Corporate Finance, Elsevier, vol. 66(C).
    10. Mingzhe Yu & Qiang Zhou & Mui Yee Cheok & Jakub Kubiczek & Nadeem Iqbal, 2022. "Does green finance improve energy efficiency? New evidence from developing and developed economies," Economic Change and Restructuring, Springer, vol. 55(1), pages 485-509, February.
    11. Becchetti, Leonardo & Ciciretti, Rocco & Hasan, Iftekhar, 2015. "Corporate social responsibility, stakeholder risk, and idiosyncratic volatility," Journal of Corporate Finance, Elsevier, vol. 35(C), pages 297-309.
    12. Zabihollah Rezaee, 2016. "Business sustainability research: A theoretical and integrated perspective," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 36(1), pages 48-64, June.
    13. Ma, Yuanyuan & Zhang, Pingping & Duan, Shaodong & Zhang, Tianjie, 2023. "Credit default prediction of Chinese real estate listed companies based on explainable machine learning," Finance Research Letters, Elsevier, vol. 58(PA).
    14. Jabeur, Sami Ben & Gharib, Cheima & Mefteh-Wali, Salma & Arfi, Wissal Ben, 2021. "CatBoost model and artificial intelligence techniques for corporate failure prediction," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    15. Mare, Davide Salvatore, 2015. "Contribution of macroeconomic factors to the prediction of small bank failures," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 39(C), pages 25-39.
    16. Erdiaw-Kwasie, Michael Odei & Abunyewah, Matthew & Baah, Charles, 2023. "Corporate social responsibility (CSR) and cognitive bias: A systematic review and research direction," Resources Policy, Elsevier, vol. 86(PA).
    17. Francesco Grimaldi & Alessandra Caragnano & Marianna Zito & Massimo Mariani, 2020. "Sustainability Engagement and Earnings Management: The Italian Context," Sustainability, MDPI, vol. 12(12), pages 1-16, June.
    18. Bevilacqua, Mattia & Tunaru, Radu & Vioto, Davide, 2023. "Options-based systemic risk, financial distress, and macroeconomic downturns," Journal of Financial Markets, Elsevier, vol. 65(C).
    19. Bevilacqua, Mattia & Tunaru, Radu & Vioto, Davide, 2023. "Options-based systemic risk, financial distress, and macroeconomic downturns," LSE Research Online Documents on Economics 119289, London School of Economics and Political Science, LSE Library.
    20. He, Feng & Qin, Shuqi & Liu, Yuanyuan & Wu, Ji (George), 2022. "CSR and idiosyncratic risk: Evidence from ESG information disclosure," Finance Research Letters, Elsevier, vol. 49(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu, Haixu & Liang, Chuanyu & Liu, Zhaohua & Wang, He, 2023. "News-based ESG sentiment and stock price crash risk," International Review of Financial Analysis, Elsevier, vol. 88(C).
    2. Ricci, Ornella & Santilli, Gianluca & Scardozzi, Giulia & Stentella Lopes, Francesco Saverio, 2024. "ESG resilience in conflictual times," Research in International Business and Finance, Elsevier, vol. 71(C).
    3. Bagh, Tanveer & Zhou, Bingjun & Alawi, Suha Mahmoud & Azam, Rauf I, 2024. "ESG resilience: Exploring the non-linear effects of ESG performance on firms sustainable growth," Research in International Business and Finance, Elsevier, vol. 70(PA).
    4. Liu, Xiaoqian & Cifuentes-Faura, Javier & Zhao, Shikuan & Wang, Long, 2024. "The impact of government environmental attention on firms’ ESG performance: Evidence from China," Research in International Business and Finance, Elsevier, vol. 67(PA).
    5. Zou, Jin & Yan, Jingzhou & Deng, Guoying, 2023. "ESG rating confusion and bond spreads," Economic Modelling, Elsevier, vol. 129(C).
    6. Bruno, Elena & Iacoviello, Giuseppina & Giannetti, Caterina, 2024. "Bank credit loss and ESG performance," Finance Research Letters, Elsevier, vol. 59(C).
    7. Fiordelisi, Franco & Ricci, Ornella & Santilli, Gianluca, 2023. "Environmental engagement and stock price crash risk: Evidence from the European banking industry," International Review of Financial Analysis, Elsevier, vol. 88(C).
    8. Tiantian Meng & Dan Lu & Danni Yu & M. H. Yahya & Mohd Ashhari Zariyawati, 2024. "Is executive compensation aligned with the company’s ESG objectives? Evidence from Chinese listed companies based on the PSM-DID approach," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    9. Liu, Xufeng & Wan, Die, 2023. "Retail investor trading and ESG pricing in China," Research in International Business and Finance, Elsevier, vol. 65(C).
    10. Ahmed Mohamed Habib, 2024. "Does real earnings management affect a firm's environmental, social, and governance (ESG), financial performance, and total value? A moderated mediation analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(11), pages 28239-28268, November.
    11. Ginglinger, Edith & Raskopf, Caroline, 2023. "Women directors and E&S performance: Evidence from board gender quotas," Journal of Corporate Finance, Elsevier, vol. 83(C).
    12. D'Souza, Reagan & Ho, Choy Yeing (Chloe) & Yang, Joey W., 2024. "The cost of corporate social irresponsibility for acquirers," Journal of Banking & Finance, Elsevier, vol. 162(C).
    13. Zhou, Mengling & Huang, Zizhen & Jiang, Kangqi, 2024. "Environmental, social, and governance performance and corporate debt maturity in China," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    14. Liu, Xiangqiang & Yang, Qingqing & Wei, Kai & Dai, Peng-Fei, 2024. "ESG rating disagreement and idiosyncratic return volatility: Evidence from China," Research in International Business and Finance, Elsevier, vol. 70(PB).
    15. Lewis Liu, 2024. "Green innovation, firm performance, and risk mitigation: evidence from the USA," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(9), pages 24009-24030, September.
    16. Petridis, Konstantinos & Tampakoudis, Ioannis & Drogalas, George & Kiosses, Nikolaos, 2022. "A Support Vector Machine model for classification of efficiency: An application to M&A," Research in International Business and Finance, Elsevier, vol. 61(C).
    17. Liang, Jinma & Zhang, Yicheng & Li, Yuanheng, 2024. "The role of ESG scores in ESG fund performance and institutional investor selection," Finance Research Letters, Elsevier, vol. 65(C).
    18. Deng, Xiang & Li, Weihao & Ren, Xiaohang, 2023. "More sustainable, more productive: Evidence from ESG ratings and total factor productivity among listed Chinese firms," Finance Research Letters, Elsevier, vol. 51(C).
    19. Cincinelli, Peter & Pellini, Elisabetta & Urga, Giovanni, 2024. "Is there an optimal level of leverage? The case of banks and non-bank institutions in Europe," International Review of Financial Analysis, Elsevier, vol. 94(C).
    20. Andrieș, Alin Marius & Sprincean, Nicu, 2023. "ESG performance and banks’ funding costs," Finance Research Letters, Elsevier, vol. 54(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:65:y:2024:i:c:s1544612324005762. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.