Auditing Strategic-Renewal Disclosure in SEC 10-K Reports: A Traceable Text-Analytic Framework

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Zixuan Yin

Keywords

content analysis, corporate annual reports, SEC 10-K, strategic renewal disclosure, text analysis

Abstract

An SEC 10-K often places discussion of new markets, capabilities, and business scope beside routine growth and accounting language. We constructed a traceable audit for the strategic-renewal signal in those disclosures. It combines a theory-guided vocabulary, a sentence-level context screen, and exact-passage repetition adjustment, while retaining the source passages used in every filing score. The corpus comprises 1,663 filings from 1,530 companies in the 2018–2020 source test partitions of EDGAR-CORPUS. We drew 320 passages for a company-disjoint audit, including 120 length-matched renewal–growth pairs, and had a blinded language model code them. Context screening increased precision from 0.2333 to 0.2714 but reduced recall from 0.5600 to 0.3800. The resulting F1 was 0.3167, below the dictionary trigger’s 0.3294. Renewal-cue passages exceeded growth-only passages by 0.0750 in concrete-initiative proportion, yet the 95% bootstrap interval, [-0.0250, 0.1833], included zero. The evidence points to a rule-design trade-off, not classifier superiority or a verified renewal measure. Component and parameter checks identify the filing scores most affected by processing choices. Pending independent expert coding and verified organizational events, the scores are exploratory disclosure signals.

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References

  • [1]Agarwal, Rajshree, and Constance E. Helfat. 2009. “Strategic Renewal of Organizations.” Organization Science 20 (2): 281–93. https://doi.org/10.1287/orsc.1090.0423.
  • [2]Schmitt, Achim, Sebastian Raisch, and Henk W. Volberda. 2018. “Strategic Renewal: Past Research, Theoretical Tensions and Future Challenges.” International Journal of Management Reviews 20 (1): 81–98. https://doi.org/10.1111/ijmr.12117.
  • [3]Short, Jeremy C., J. Christian Broberg, Claudia C. Cogliser, and Keith H. Brigham. 2010. “Construct Validation Using Computer-Aided Text Analysis (CATA).” Organizational Research Methods 13 (2): 320–47. https://doi.org/10.1177/1094428109335949.
  • [4]Grimmer, Justin, and Brandon M. Stewart. 2013. “Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts.” Political Analysis 21 (3): 267–97. https://doi.org/10.1093/pan/mps028.
  • [5]Loughran, Tim, and Bill McDonald. 2011. “When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks.” The Journal of Finance 66 (1): 35–65. https://doi.org/10.1111/j.1540-6261.2010.01625.x.
  • [6]Bowman, Edward H. 1984. “Content Analysis of Annual Reports for Corporate Strategy and Performance.” Academy of Management Journal 27 (1): 61–73. https://doi.org/10.2307/255958.
  • [7]Barr, Pamela S., J. L. Stimpert, and Anne Sigismund Huff. 1992. “Cognitive Change, Strategic Action, and Organizational Renewal.” Strategic Management Journal 13 (S1): 15–36. https://doi.org/10.1002/smj.4250131004.
  • [8]Maharani, Ida Ayu Kartika, Badri Munir Sukoco, Indrianawati Usman, and David Ahlstrom. 2025. “Balancing Exploration-Exploitation Renewal and Indonesian Firm Performance: Do CEO Power and TMT Diversity Matter?” Humanities and Social Sciences Communications 12 (1): 1872. https://doi.org/10.1057/s41599-025-06139-2.
  • [9]McKenny, Aaron F., Herman Aguinis, Jeremy C. Short, and Aaron H. Anglin. 2018. “What Doesn’t Get Measured Does Exist: Improving the Accuracy of Computer-Aided Text Analysis.” Journal of Management 44 (7): 2909–33. https://doi.org/10.1177/0149206316657594.
  • [10]Pollach, Irene. 2012. “Taming Textual Data: The Contribution of Corpus Linguistics to Computer-Aided Text Analysis.” Organizational Research Methods 15 (2): 263–87. https://doi.org/10.1177/1094428111417451.
  • [11]Short, Jeremy C., Aaron F. McKenny, and Shane W. Reid. 2018. “More Than Words? Computer-Aided Text Analysis in Organizational Behavior and Psychology Research.” Annual Review of Organizational Psychology and Organizational Behavior 5 (1): 415–35. https://doi.org/10.1146/annurev-orgpsych-032117-104622.
  • [12]Loughran, Tim, and Bill McDonald. 2024. “Measuring Firm Complexity.” Journal of Financial and Quantitative Analysis 59 (6): 2487–514. https://doi.org/10.1017/s0022109023000716.
  • [13]Dyer, Travis, Mark Lang, and Lorien Stice-Lawrence. 2017. “The Evolution of 10-K Textual Disclosure: Evidence from Latent Dirichlet Allocation.” Journal of Accounting and Economics 64 (2-3): 221–45. https://doi.org/10.1016/j.jacceco.2017.07.002.
  • [14]Loukas, Lefteris, Manos Fergadiotis, Ion Androutsopoulos, and Prodromos Malakasiotis. 2021. “EDGAR-CORPUS: Billions of Tokens Make the World Go Round.” Proceedings of the Third Workshop on Economics and Natural Language Processing, 13–18. https://doi.org/10.18653/v1/2021.econlp-1.2.
  • [15]Gentzkow, Matthew, Bryan Kelly, and Matt Taddy. 2019. “Text as Data.” Journal of Economic Literature 57 (3): 535–74. https://doi.org/10.1257/jel.20181020.