IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i7p2390-d778908.html
   My bibliography  Save this article

The Bias Analysis of Oil and Gas Companies’ Credit Ratings Based on Textual Risk Disclosures

Author

Listed:
  • Lu Wei

    (School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, China)

  • Chen Han

    (School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, China)

  • Yinhong Yao

    (School of Management and Engineering, Capital University of Economics and Business, Beijing 100070, China)

Abstract

Credit rating bias would affect the capital funding of oil and gas companies, and thus influence the development of the whole economy. Credit rating bias has been mostly analyzed based on different quantitative data sources, and inconsistent results have been obtained. This study first analyzes credit rating bias from the perspective of qualitative textual risk disclosures. By comparing the external credit rating with the internal risk perception expressed in the textual risk disclosures of Form 10-K filings, we can study the consistency of risk assessment of the company by the company’s management and the third-party rating agency. To be specific, four internal textual risk measures and one external risk measure are constructed to quantify the internal risk perception and external risk assessment on oil and gas companies. Then, Spearman’s rho is applied to measure the direction and magnitude of credit rating bias. In the experiment, based on the 357 samples of 174 U.S. oil and gas companies, ranging from 2009 to 2018, we find that the credit ratings mostly overestimate the internal risks perceived by the company managers, and the bias is becoming larger with the credit ratings downgraded from AAA to D.

Suggested Citation

  • Lu Wei & Chen Han & Yinhong Yao, 2022. "The Bias Analysis of Oil and Gas Companies’ Credit Ratings Based on Textual Risk Disclosures," Energies, MDPI, vol. 15(7), pages 1-12, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2390-:d:778908
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/7/2390/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/7/2390/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wei, Lu & Li, Guowen & Zhu, Xiaoqian & Sun, Xiaolei & Li, Jianping, 2019. "Developing a hierarchical system for energy corporate risk factors based on textual risk disclosures," Energy Economics, Elsevier, vol. 80(C), pages 452-460.
    2. Xueqing Kang & Farman Ullah Khan & Raza Ullah & Muhammad Arif & Shams Ur Rehman & Farid Ullah, 2021. "Does Foreign Direct Investment Influence Renewable Energy Consumption? Empirical Evidence from South Asian Countries," Energies, MDPI, vol. 14(12), pages 1-15, June.
    3. Duff, Angus & Einig, Sandra, 2009. "Understanding credit ratings quality: Evidence from UK debt market participants," The British Accounting Review, Elsevier, vol. 41(2), pages 107-119.
    4. Hasan, Iftekhar & Huang, He & To, Thomas Y., 2021. "Are credit rating disagreements priced in the M&A market?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).
    5. Lu Wei & Guowen Li & Xiaoqian Zhu & Jianping Li, 2019. "Discovering bank risk factors from financial statements based on a new semi‐supervised text mining algorithm," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(3), pages 1519-1552, September.
    6. Jens Hilscher & Mungo Wilson, 2017. "Credit Ratings and Credit Risk: Is One Measure Enough?," Management Science, INFORMS, vol. 63(10), pages 3414-3437, October.
    7. Majd Olleik & Hassan Hamie & Hans Auer, 2022. "Using Natural Gas Resources to De-Risk Renewable Energy Investments in Lower-Income Countries," Energies, MDPI, vol. 15(5), pages 1-22, February.
    8. Yang Bao & Anindya Datta, 2014. "Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures," Management Science, INFORMS, vol. 60(6), pages 1371-1391, June.
    9. Tao Shu & Zhiyi Wang & Ling Lin & Huading Jia & Jixian Zhou, 2022. "Customer Perceived Risk Measurement with NLP Method in Electric Vehicles Consumption Market: Empirical Study from China," Energies, MDPI, vol. 15(5), pages 1-23, February.
    10. Friberg, Richard & Seiler, Thomas, 2017. "Risk and ambiguity in 10-Ks: An examination of cash holding and derivatives use," Journal of Corporate Finance, Elsevier, vol. 45(C), pages 608-631.
    11. Blöchlinger, Andreas & Leippold, Markus, 2018. "Are Ratings the Worst Form of Credit Assessment Except for All the Others?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(1), pages 299-334, February.
    12. Ole-Kristian Hope & Danqi Hu & Hai Lu, 2016. "The benefits of specific risk-factor disclosures," Review of Accounting Studies, Springer, vol. 21(4), pages 1005-1045, December.
    13. Ryan, Patricia A. & Villupuram, Sriram V. & Zygo, Jeffrey G., 2017. "The value of credit rating changes across economic cycles," Journal of Economics and Business, Elsevier, vol. 92(C), pages 1-9.
    14. Abad, Pilar & Robles, M. Dolores, 2014. "Credit rating agencies and idiosyncratic risk: Is there a linkage? Evidence from the Spanish Market," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 152-171.
    15. Bennett, Benjamin & Milbourn, Todd & Wang, Zexi, 2018. "Corporate Investment Under the Cloud of Litigation," Working Paper Series 2018-18, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    16. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    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. Wei, Lu & Jing, Haozhe & Huang, Jie & Deng, Yuqi & Jing, Zhongbo, 2023. "Do textual risk disclosures reveal corporate risk? Evidence from U.S. fintech corporations," Economic Modelling, Elsevier, vol. 127(C).
    2. Wei, Lu & Miao, Xiyuan & Jing, Haozhe & Liu, Zhidong & Xie, Zezhong, 2023. "Bank risk aggregation based on the triple perspectives of bank managers, credit raters, and financial analysts," Finance Research Letters, Elsevier, vol. 57(C).
    3. Li, Jingyu & Li, Jianping & Zhu, Xiaoqian, 2020. "Risk dependence between energy corporations: A text-based measurement approach," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 33-46.
    4. Li, Jianping & Feng, Yuyao & Li, Guowen & Sun, Xiaolei, 2020. "Tourism companies' risk exposures on text disclosure," Annals of Tourism Research, Elsevier, vol. 84(C).
    5. Wei, Lu & Li, Guowen & Li, Jianping & Zhu, Xiaoqian, 2019. "Bank risk aggregation with forward-looking textual risk disclosures," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    6. Düsterhöft, Maximilian & Schiemann, Frank & Walther, Thomas, 2023. "Let’s talk about risk! Stock market effects of risk disclosure for European energy utilities," Energy Economics, Elsevier, vol. 125(C).
    7. Kim, Hyonok & Yasuda, Yukihiro, 2018. "Business risk disclosure and firm risk: Evidence from Japan," Research in International Business and Finance, Elsevier, vol. 45(C), pages 413-426.
    8. Mehrzad Azmi Shabestari & Kevin Moffitt & Bharat Sarath, 2020. "Did the banking sector foresee the financial crisis? Evidence from risk factor disclosures," Review of Quantitative Finance and Accounting, Springer, vol. 55(2), pages 647-669, August.
    9. Senave, Elseline & Jans, Mieke J. & Srivastava, Rajendra P., 2023. "The application of text mining in accounting," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).
    10. Blum, Avinoam & Raviv, Alon, 2023. "The effects of the financial crisis and Basel III on banks’ risk disclosure: A textual analysis," Finance Research Letters, Elsevier, vol. 53(C).
    11. Li, Jianping & Li, Guowen & Liu, Mingxi & Zhu, Xiaoqian & Wei, Lu, 2022. "A novel text-based framework for forecasting agricultural futures using massive online news headlines," International Journal of Forecasting, Elsevier, vol. 38(1), pages 35-50.
    12. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2022. "Media-expressed tone, option characteristics, and stock return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    13. Pastwa, Anna M. & Shrestha, Prabal & Thewissen, James & Torsin, Wouter, 2021. "Unpacking the black box of ICO white papers: a topic modeling approach," LIDAM Discussion Papers LFIN 2021018, Université catholique de Louvain, Louvain Finance (LFIN).
    14. Elsayed, Mohamed & Elshandidy, Tamer, 2021. "Internal control effectiveness, textual risk disclosure, and their usefulness: U.S. evidence," Advances in accounting, Elsevier, vol. 53(C).
    15. M. J. Histen, 2022. "Taking Information Seriously: A Firm-side Interpretation of Risk Factor Disclosure," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 28(3), pages 119-131, November.
    16. Huijue Kelly Duan & Hanxin Hu & Yangin (Ben) Yoon & Miklos Vasarhelyi, 2022. "Increasing the utility of performance audit reports: Using textual analytics tools to improve government reporting," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 201-218, October.
    17. Roman Frydman & Nicholas Mangee, 2021. "Expectations Concordance and Stock Market Volatility: Knightian Uncertainty in the Year of the Pandemic," JRFM, MDPI, vol. 14(11), pages 1-13, November.
    18. Li, He & No, Won Gyun & Wang, Tawei, 2018. "SEC's cybersecurity disclosure guidance and disclosed cybersecurity risk factors," International Journal of Accounting Information Systems, Elsevier, vol. 30(C), pages 40-55.
    19. Goel, Tirupam & Lewrick, Ulf & Mathur, Aakriti, 2021. "Does regulation only bite the less profitable? Evidence from the too-big-to-fail reforms," Bank of England working papers 946, Bank of England.
    20. Jaeschke, Reemda & Lopatta, Kerstin & Yi, Cheong, 2018. "Managers’ use of language in corrupt firms’ financial disclosures: Evidence from FCPA violators," Scandinavian Journal of Management, Elsevier, vol. 34(2), pages 170-192.

    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:gam:jeners:v:15:y:2022:i:7:p:2390-:d:778908. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.