IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2405.01881.html
   My bibliography  Save this paper

Explainable Risk Classification in Financial Reports

Author

Listed:
  • Xue Wen Tan
  • Stanley Kok

Abstract

Every publicly traded company in the US is required to file an annual 10-K financial report, which contains a wealth of information about the company. In this paper, we propose an explainable deep-learning model, called FinBERT-XRC, that takes a 10-K report as input, and automatically assesses the post-event return volatility risk of its associated company. In contrast to previous systems, our proposed model simultaneously offers explanations of its classification decision at three different levels: the word, sentence, and corpus levels. By doing so, our model provides a comprehensive interpretation of its prediction to end users. This is particularly important in financial domains, where the transparency and accountability of algorithmic predictions play a vital role in their application to decision-making processes. Aside from its novel interpretability, our model surpasses the state of the art in predictive accuracy in experiments on a large real-world dataset of 10-K reports spanning six years.

Suggested Citation

  • Xue Wen Tan & Stanley Kok, 2024. "Explainable Risk Classification in Financial Reports," Papers 2405.01881, arXiv.org, revised May 2024.
  • Handle: RePEc:arx:papers:2405.01881
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2405.01881
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    2. Xin Wang & Kai Zong & Cuicui Luo, 2022. "Credit risk detection based on machine learning algorithms," International Journal of Financial Services Management, Inderscience Enterprises Ltd, vol. 11(3), pages 183-189.
    3. Gustaf Bellstam & Sanjai Bhagat & J. Anthony Cookson, 2021. "A Text-Based Analysis of Corporate Innovation," Management Science, INFORMS, vol. 67(7), pages 4004-4031, July.
    4. Yi Yang & Kunpeng Zhang & Yangyang Fan, 2022. "Analyzing Firm Reports for Volatility Prediction: A Knowledge-Driven Text-Embedding Approach," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 522-540, January.
    5. Runshan Fu & Manmohan Aseri & Param Vir Singh & Kannan Srinivasan, 2022. "“Un”Fair Machine Learning Algorithms," Management Science, INFORMS, vol. 68(6), pages 4173-4195, June.
    6. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    7. Dyer, Travis & Lang, Mark & Stice-Lawrence, Lorien, 2017. "The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation," Journal of Accounting and Economics, Elsevier, vol. 64(2), pages 221-245.
    8. Gah-Yi Ban & Noureddine El Karoui & Andrew E. B. Lim, 2018. "Machine Learning and Portfolio Optimization," Management Science, INFORMS, vol. 64(3), pages 1136-1154, March.
    9. Angela K. Davis & Isho Tama†Sweet, 2012. "Managers’ Use of Language Across Alternative Disclosure Outlets: Earnings Press Releases versus MD&A," Contemporary Accounting Research, John Wiley & Sons, vol. 29(3), pages 804-837, September.
    10. Feng Li, 2010. "The Information Content of Forward‐Looking Statements in Corporate Filings—A Naïve Bayesian Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 48(5), pages 1049-1102, December.
    11. Balakrishnan, Ramji & Qiu, Xin Ying & Srinivasan, Padmini, 2010. "On the predictive ability of narrative disclosures in annual reports," European Journal of Operational Research, Elsevier, vol. 202(3), pages 789-801, May.
    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. Berkin, Anil & Aerts, Walter & Van Caneghem, Tom, 2023. "Feasibility analysis of machine learning for performance-related attributional statements," International Journal of Accounting Information Systems, Elsevier, vol. 48(C).
    2. David F. Larcker & Anastasia A. Zakolyukina, 2012. "Detecting Deceptive Discussions in Conference Calls," Journal of Accounting Research, Wiley Blackwell, vol. 50(2), pages 495-540, May.
    3. Ingrid E. Fisher & Margaret R. Garnsey & Mark E. Hughes, 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 157-214, July.
    4. Durnev, Art & Mangen, Claudine, 2020. "The spillover effects of MD&A disclosures for real investment: The role of industry competition," Journal of Accounting and Economics, Elsevier, vol. 70(1).
    5. Liu, Xiaoming & Shen, Xieyang & Wang, Changyun & Zeng, Jianyu, 2023. "Do fund managers' tones predict future performance? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    6. Li, Ken, 2022. "Textual fundamentals in earnings press releases," Advances in accounting, Elsevier, vol. 57(C).
    7. Guo, Chunying & Yang, Baochen & Fan, Ying, 2022. "Does mandatory CSR disclosure improve stock price informativeness? Evidence from China," Research in International Business and Finance, Elsevier, vol. 62(C).
    8. Yekini, Liafisu Sina & Wisniewski, Tomasz Piotr & Millo, Yuval, 2016. "Market reaction to the positiveness of annual report narratives," The British Accounting Review, Elsevier, vol. 48(4), pages 415-430.
    9. Peng Liang & Nan Hu & Ling Liu & Ting Zhang, 2023. "Managerial tone and investors' hedging activities: Evidence from credit default swaps," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(4), pages 3971-3998, December.
    10. Fengler, Matthias & Phan, Minh Tri, 2023. "A Topic Model for 10-K Management Disclosures," Economics Working Paper Series 2307, University of St. Gallen, School of Economics and Political Science.
    11. John L. Campbell & Hye Seung “Grace” Lee & Hsin‐Min Lu & Logan B. Steele, 2020. "Express Yourself: Why Managers' Disclosure Tone Varies Across Time and What Investors Learn From It," Contemporary Accounting Research, John Wiley & Sons, vol. 37(2), pages 1140-1171, June.
    12. Gehan A. Mousa & Elsayed A. H. Elamir & Khaled Hussainey, 2022. "Using machine learning methods to predict financial performance: Does disclosure tone matter?," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 19(1), pages 93-112, March.
    13. John Donovan & Jared Jennings & Kevin Koharki & Joshua Lee, 2021. "Measuring credit risk using qualitative disclosure," Review of Accounting Studies, Springer, vol. 26(2), pages 815-863, June.
    14. Yan Luo & Linying Zhou, 2020. "Textual tone in corporate financial disclosures: a survey of the literature," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 17(2), pages 101-110, September.
    15. Lauren Stagnol, 2015. "Designing a corporate bond index on solvency criteria," EconomiX Working Papers 2015-39, University of Paris Nanterre, EconomiX.
    16. Andres, Christian & Cumming, Douglas & Karabiber, Timur & Schweizer, Denis, 2014. "Do markets anticipate capital structure decisions? — Feedback effects in equity liquidity," Journal of Corporate Finance, Elsevier, vol. 27(C), pages 133-156.
    17. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    18. Yi Yang & Kunpeng Zhang & Yangyang Fan, 2023. "sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics," Information Systems Research, INFORMS, vol. 34(1), pages 137-156, March.
    19. Mselmi, Nada & Hamza, Taher & Lahiani, Amine & Shahbaz, Muhammad, 2019. "Pricing corporate financial distress: Empirical evidence from the French stock market," Journal of International Money and Finance, Elsevier, vol. 96(C), pages 13-27.
    20. Sara Kelly Anzinger & Chinmoy Ghosh & Milena Petrova, 2017. "The Other Side of Value: The Effect of Quality on Price and Return in Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 54(3), pages 429-457, April.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:arx:papers:2405.01881. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.