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Form 10-K Itemization

Author

Listed:
  • Yanci Zhang
  • Mengjia Xia
  • Mingyang Li
  • Haitao Mao
  • Yutong Lu
  • Yupeng Lan
  • Jinlin Ye
  • Rui Dai

Abstract

Form 10-K report is a financial report disclosing the annual financial state of a public company. It is an important evidence to conduct financial analysis, i.e., asset pricing, corporate finance. Practitioners and researchers are constantly designing algorithms to better conduct analysis on information in the Form 10-K report. The vast majority of previous works focus on quantitative data. With recent advancement on natural language processing (NLP), textual data in financial filing attracts more attention. However, to incorporate textual data for analyzing, Form 10-K Itemization is a necessary pre-process step. It aims to segment the whole document into several Item sections, where each Item section focuses on a specific financial aspect of the company. With the segmented Item sections, NLP techniques can directly apply on those Item sections related to downstream tasks. In this paper, we develop a Form 10-K Itemization system which can automatically segment all the Item sections in 10-K documents. The system is both effective and efficient. It reaches a retrieval rate of 93%.

Suggested Citation

  • Yanci Zhang & Mengjia Xia & Mingyang Li & Haitao Mao & Yutong Lu & Yupeng Lan & Jinlin Ye & Rui Dai, 2023. "Form 10-K Itemization," Papers 2303.04688, arXiv.org.
    • Handle: RePEc:arx:papers:2303.04688
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    References listed on IDEAS

    as
    1. Yanci Zhang & Tianming Du & Yujie Sun & Lawrence Donohue & Rui Dai, 2021. "Form 10-Q Itemization," Papers 2104.11783, arXiv.org, revised Oct 2021.
    2. 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.
    3. Feng Li & Russell Lundholm & Michael Minnis, 2013. "A Measure of Competition Based on 10‐K Filings," Journal of Accounting Research, Wiley Blackwell, vol. 51(2), pages 399-436, May.
    4. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    5. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    6. Rong Liu & Jujun Huang & Zhongju Zhang, 2023. "Tracking disclosure change trajectories for financial fraud detection," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 584-602, February.
    7. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    8. 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).
    9. Rong Yang & Yang Yu & Manlu Liu & Kean Wu, 2018. "Corporate Risk Disclosure and Audit Fee: A Text Mining Approach," European Accounting Review, Taylor & Francis Journals, vol. 27(3), pages 583-594, May.
    10. 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.
    11. 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.
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