Computational Finance Approaches to Detecting Information Asymmetry in Corporate Disclosures
Keywords:
corporate disclosure, information asymmetry, computational finance, natural language processing, anomaly detection, explainable artificial intelligence, regulatory technology, algorithmic fairnessAbstract
Corporate disclosure is the central mechanism through which public firms communicate value-relevant information to external stakeholders, but disclosure narratives are also strategic artifacts shaped by managerial discretion. Language choice, structural ordering, emphasis, and omission can preserve information asymmetry even within mandatory reporting regimes. This paper develops a systems-level analysis of computational finance approaches for detecting information asymmetry in corporate disclosures. It examines how textual analysis, natural language processing, and machine learning transform unstructured filings, risk-factor sections, earnings calls, and regulatory submissions into measurable signals of semantic deviation, tone, specificity, and anomaly. The discussion moves beyond isolated model evaluation to consider representation design, detection architecture, interpretability, deployment cadence, and human oversight as interconnected components of a disclosure monitoring infrastructure. In doing so, the paper analyzes structural trade-offs among prediction accuracy, fairness, robustness, regulatory legitimacy, and operational sustainability. It draws on evidence from accounting, finance, machine learning, and algorithmic governance to show that effective detection systems must be understood as socio-technical infrastructures rather than purely statistical instruments. The analysis concludes with implications for regulators, audit institutions, and platform designers seeking to reduce information asymmetry without creating new forms of opacity, bias, or systemic fragility.
References
1. Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. Journal of Finance, 66(1), 35-65.
2. Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. Journal of Finance, 62(3), 1139-1168.
3. Li, F. (2010). The information content of forward-looking statements in corporate filings—A naïve Bayesian machine learning approach. Journal of Accounting Research, 48(5), 1049-1102.
4. Campbell, J. L., Chen, H., Dhaliwal, D. S., Lu, H. M., & Steele, L. B. (2014). The information content of mandatory risk factor disclosures in corporate filings. Review of Accounting Studies, 19(1), 396-455.
5. Hope, O.-K., Hu, D., & Lu, H. (2016). The benefits of specific risk-factor disclosures. Review of Accounting Studies, 21(4), 1005-1045.
6. Bao, Y., & Datta, A. (2014). Simultaneously discovering and quantifying risk types from textual risk disclosures. Management Science, 60(6), 1371-1391.
7. Kogan, S., Levin, D., Routledge, B. R., Sagi, J. S., & Smith, N. A. (2009). Predicting risk from financial reports with regression. Proceedings of the North American Chapter of the Association for Computational Linguistics Human Language Technologies Conference, 272-280.
8. Hanley, K. W., & Hoberg, G. (2010). The information content of IPO prospectuses. Review of Financial Studies, 23(7), 2821-2864.
9. Hoberg, G., & Phillips, G. (2016). Text-based network industries and endogenous product differentiation. Journal of Political Economy, 124(5), 1423-1465.
10. Bodnaruk, A., Loughran, T., & McDonald, B. (2015). Using 10-K text to gauge financial constraints. Journal of Financial and Quantitative Analysis, 50(4), 623-646.
11. Gentzkow, M., Kelly, B., & Taddy, M. (2019). Text as data. Journal of Economic Literature, 57(3), 535-574.
12. Jiang, F., Lee, J., Martin, X., & Zhou, G. (2019). Manager sentiment and stock returns. Journal of Financial Economics, 132(1), 126-149.
13. Sun, F., He, S., Wang, R., Ke, L., Shen, H., & Liao, Q. (2026). Modeling Structural Deviation in 10-K Risk Factors: A Semantic Anomaly Detection and Explainable AI Approach. Risks, 14(4), 87.
14. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008.
15. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
16. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610-623.
17. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1-35.
18. Thelwall, M. (2018). Gender bias in sentiment analysis. Online Information Review, 42(1), 45-59.
19. Erel, I., Stern, L. H., Tan, C., & Weisbach, M. S. (2021). Selecting directors using machine learning. Review of Financial Studies, 34(7), 3226-3264.
20. Amel-Zadeh, A., & Serafeim, G. (2018). Why and how investors use ESG information: Evidence from a global survey. Financial Analysts Journal, 74(3), 87-103.
21. Christensen, H. B., Hail, L., & Leuz, C. (2021). Mandatory CSR and sustainability reporting: Economic analysis and literature review. Review of Accounting Studies, 26(3), 1176-1248.
22. Dietterich, T. G. (2000). Ensemble methods in machine learning. In Multiple Classifier Systems (pp. 1-15). Springer.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Global Financial Analytics Research Review
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
