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Developing a hierarchical system for energy corporate risk factors based on textual risk disclosures

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Listed:
  • Wei, Lu
  • Li, Guowen
  • Zhu, Xiaoqian
  • Sun, Xiaolei
  • Li, Jianping

Abstract

Selecting risk factors is essential for measuring energy corporate risk. However, the comprehensive identification of energy corporate risk factors is still a difficult issue. This paper innovatively uses the text mining approach to comprehensively identify energy corporate risk factors from textual risk disclosures reported in financial statements. Based on 131,755 risk factor headings from 3707 Form 10-K filings from 840 U.S. energy corporations over the period 2010–2016, 66 types of risk factors that affect energy corporate risks are identified. Furthermore, we develop a hierarchical system for 66 energy corporate risk factors by dividing energy corporations into nine subsectors. Thus, the hierarchical energy corporate risk factor system provides fundamental support for further energy corporate risk measurement. Researchers can comprehensively and effectively select risk factors in measuring risks of the entire energy industry or each of nine energy subsectors.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:eneeco:v:80:y:2019:i:c:p:452-460
    DOI: 10.1016/j.eneco.2019.01.020
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    References listed on IDEAS

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    Cited by:

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    3. Li, Jianping & Li, Jingyu & Zhu, Xiaoqian & Yao, Yinhong & Casu, Barbara, 2020. "Risk spillovers between FinTech and traditional financial institutions: Evidence from the U.S," International Review of Financial Analysis, Elsevier, vol. 71(C).
    4. Ji, Qiang & Li, Jianping & Sun, Xiaolei, 2019. "Measuring the interdependence between investor sentiment and crude oil returns: New evidence from the CFTC's disaggregated reports," Finance Research Letters, Elsevier, vol. 30(C), pages 420-425.
    5. 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).
    6. 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).
    7. 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).
    8. 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.
    9. Wang, Jun & Sun, Xiaolei & Li, Jianping, 2020. "How do sovereign credit default swap spreads behave under extreme oil price movements? Evidence from G7 and BRICS countries," Finance Research Letters, Elsevier, vol. 34(C).
    10. 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.
    11. Zhu, Xiaoqian & Wei, Lu & Li, Jianping, 2021. "A two-stage general approach to aggregate multiple bank risks," Finance Research Letters, Elsevier, vol. 40(C).
    12. Li, Guowen & Jing, Zhongbo & Li, Jingyu & Feng, Yuyao, 2023. "Drivers of risk correlation among financial institutions: A study based on a textual risk disclosure perspective," Economic Modelling, Elsevier, vol. 128(C).
    13. Liu, Mingxi & Li, Guowen & Li, Jianping & Zhu, Xiaoqian & Yao, Yinhong, 2021. "Forecasting the price of Bitcoin using deep learning," Finance Research Letters, Elsevier, vol. 40(C).
    14. 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.
    15. Li, Jianping & Feng, Yuyao & Li, Guowen & Sun, Xiaolei, 2020. "Tourism companies' risk exposures on text disclosure," Annals of Tourism Research, Elsevier, vol. 84(C).
    16. 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).

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    More about this item

    Keywords

    Risk management; Energy industry; Risk factor; Text mining; Form 10-K;
    All these keywords.

    JEL classification:

    • P28 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Natural Resources; Environment
    • Q20 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - General
    • Q30 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - General
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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