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Using Annual Report Sentiment as a Proxy for Financial Distress in U.S. Banks

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  • Priyank Gandhi
  • Tim Loughran
  • Bill McDonald

Abstract

Current measures of bank distress find marginal value in predictive variables beyond a capital adequacy ratio and tend to miss extreme events impacting the entire sector. The authors advocate a new proxy for bank distress: sentiment measures from banks’ annual reports. After controlling for popular forecasting variables used in the literature, they find that more negative sentiment in the annual report is associated with larger delisting probabilities, lower odds of paying subsequent dividends, higher subsequent loan loss provisions, and lower future return on assets. The findings suggest that regulators could augment current early warning systems for banks and the banking sector—where the measures are based exclusively on financial statement data—by using the frequency of negative words in banks’ annual reports.

Suggested Citation

  • Priyank Gandhi & Tim Loughran & Bill McDonald, 2019. "Using Annual Report Sentiment as a Proxy for Financial Distress in U.S. Banks," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 20(4), pages 424-436, October.
    • Handle: RePEc:taf:hbhfxx:v:20:y:2019:i:4:p:424-436
    DOI: 10.1080/15427560.2019.1553176
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    Cited by:

    1. Alzamil, Zamil & Appelbaum, Deniz & Nehmer, Robert, 2020. "An ontological artifact for classifying social media: Text mining analysis for financial data," International Journal of Accounting Information Systems, Elsevier, vol. 38(C).
    2. Katsafados, Apostolos G. & Androutsopoulos, Ion & Chalkidis, Ilias & Fergadiotis, Emmanouel & Leledakis, George N. & Pyrgiotakis, Emmanouil G., 2021. "Using textual analysis to identify merger participants: Evidence from the U.S. banking industry," Finance Research Letters, Elsevier, vol. 42(C).
    3. Nicolas S. Magner & Nicolás Hardy & Tiago Ferreira & Jaime F. Lavin, 2023. "“Agree to Disagree”: Forecasting Stock Market Implied Volatility Using Financial Report Tone Disagreement Analysis," Mathematics, MDPI, vol. 11(7), pages 1-16, March.
    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. Bilal Hafeez & M. Humayun Kabir & Udomsak Wongchoti, 2022. "Are retail investors really passive? Shareholder activism in the digital age," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 49(3-4), pages 423-460, March.
    6. Javid Iqbal & Khalid Riaz, 2022. "Predicting future financial performance of banks from management’s tone in the textual disclosures," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2691-2721, August.
    7. John Garcia, 2021. "Analyst herding and firm-level investor sentiment," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(4), pages 461-494, December.
    8. Stefan Claus & Massimo Stella, 2022. "Natural Language Processing and Cognitive Networks Identify UK Insurers’ Trends in Investor Day Transcripts," Future Internet, MDPI, vol. 14(10), pages 1-18, October.
    9. Mushtaq, Rizwan & Gull, Ammar Ali & Shahab, Yasir & Derouiche, Imen, 2022. "Do financial performance indicators predict 10-K text sentiments? An application of artificial intelligence," Research in International Business and Finance, Elsevier, vol. 61(C).
    10. Königstorfer, Florian & Thalmann, Stefan, 2020. "Applications of Artificial Intelligence in commercial banks – A research agenda for behavioral finance," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    11. Katsafados, Apostolos & Anastasiou, Dimitris, 2022. "Short-term Prediction of Bank Deposit Flows: Do Textual Features matter?," MPRA Paper 111418, University Library of Munich, Germany.
    12. Rob Bauer & Dirk Broeders & Annick van Ool, 2023. "Walk the green talk? A textual analysis of pension funds’ disclosures of sustainable investing," Working Papers 770, DNB.
    13. Katsafados, Apostolos G. & Leledakis, George N. & Pyrgiotakis, Emmanouil G. & Androutsopoulos, Ion & Fergadiotis, Manos, 2024. "Machine learning in bank merger prediction: A text-based approach," European Journal of Operational Research, Elsevier, vol. 312(2), pages 783-797.
    14. Meng‐Feng Yen & Yu‐Pei Huang & Liang‐Chih Yu & Yueh‐Ling Chen, 2022. "A Two-Dimensional Sentiment Analysis of Online Public Opinion and Future Financial Performance of Publicly Listed Companies," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1677-1698, April.
    15. Brückbauer, Frank & Cezanne, Thibault, 2022. "Bank manager sentiment, loan growth and bank risk," ZEW Discussion Papers 22-066, ZEW - Leibniz Centre for European Economic Research.
    16. Javid Iqbal & Muhammad Khalid Sohail & Aymen Irshad & Rao Aamir Khan, 2024. "Risk management disclosures and banks financial performance: evidence from emerging markets," Risk Management, Palgrave Macmillan, vol. 26(1), pages 1-21, February.
    17. Chiaramonte, Laura & Dreassi, Alberto & Piserà, Stefano & Khan, Ashraf, 2023. "Mergers and acquisitions in the financial industry: A bibliometric review and future research directions," Research in International Business and Finance, Elsevier, vol. 64(C).
    18. Iqbal, Javid & Saeed, Abubakr, 2023. "Managerial sentiments, non-performing loans, and banks financial performance: A causal mediation approach," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    19. Tzomakas, Christos & Anastasiou, Dimitrios & Katsafados, Apostolos & Krokida, Styliani Iris, 2023. "Crisis sentiment and banks’ stock price crash risk: A missing piece of the puzzle?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 87(C).

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