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Bank risk aggregation with forward-looking textual risk disclosures

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  • Wei, Lu
  • Li, Guowen
  • Li, Jianping
  • Zhu, Xiaoqian

Abstract

Approaches based on financial statements are important to the field of bank risk aggregation. However, previous studies only used numerical data recorded in financial statements to aggregate bank risk. Time lags in numerical data can bias risk aggregation results. Thus, this paper first incorporates forward-looking textual risk disclosures reported in financial statements into bank risk aggregation. In doing so, we overcome the drawback of risk aggregation resulting from using only historical numerical data, and in turn, we obtain more reasonable aggregate risk results. In our experiment, based on 812 pieces of numerical risk data for each type of credit, market and operational risk and based on 36,178 summary headings of textual risk disclosures drawn from 1224 Form 10-K filings of 153 U.S. commercial banks for 2010–2017, we aggregate credit, market and operational risks for U.S. commercial banks. In comparing total risks with and without forward-looking textual risk disclosures, our empirical results show that disregarding forward-looking textual risk disclosures overestimates the total risk of 2010–2013 while underestimating the total risk of 2014–2017.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:ecofin:v:50:y:2019:i:c:s1062940818306168
    DOI: 10.1016/j.najef.2019.101016
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    2. Zhu, Xiaoqian & Wei, Lu & Li, Jianping, 2021. "A two-stage general approach to aggregate multiple bank risks," Finance Research Letters, Elsevier, vol. 40(C).
    3. 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).
    4. Dimitris Anastasiou & Apostolos Katsafados, 2023. "Bank deposits and textual sentiment: When an European Central Bank president's speech is not just a speech," Manchester School, University of Manchester, vol. 91(1), pages 55-87, January.
    5. Li, Jianping & Feng, Yuyao & Li, Guowen & Sun, Xiaolei, 2020. "Tourism companies' risk exposures on text disclosure," Annals of Tourism Research, Elsevier, vol. 84(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. Ming-Fu Hsu & Chingho Chang & Jhih‐Hong Zeng, 2022. "Automated text mining process for corporate risk analysis and management," Risk Management, Palgrave Macmillan, vol. 24(4), pages 386-419, December.
    8. Han, Chen & Wu, Chengliang & Wei, Lu, 2023. "The impact of the disclosure characteristics of the application material on the successful listing of companies on China’s Science and Technology Innovation Board," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    9. Acheampong, Albert & Elshandidy, Tamer, 2021. "Does soft information determine credit risk? Text-based evidence from European banks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    10. Ming-Fu Hsu & Ying-Shao Hsin & Fu-Jiing Shiue, 2022. "Business analytics for corporate risk management and performance improvement," Annals of Operations Research, Springer, vol. 315(2), pages 629-669, August.
    11. 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.

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    More about this item

    Keywords

    Bank risk; Risk aggregation; Form 10-K; Financial statements; Text mining;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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