Blockchain, Artificial Intelligence, and Advanced Analytics in Corporate Financial Disclosure: Transparency, Signaling, and Market Consequences
Keywords:
Blockchain disclosure, artificial intelligence reporting, financial transparency, corporate governanceAbstract
The digital transformation of corporate financial disclosure has accelerated rapidly over the past two decades, driven by regulatory mandates, technological innovation, and evolving stakeholder expectations. Traditional narrative- and document-based reporting systems are increasingly challenged by demands for transparency, timeliness, comparability, and analytical depth. This research article provides an extensive theoretical and empirical synthesis of how blockchain technology, artificial intelligence, natural language processing, big data analytics, and structured reporting frameworks collectively reshape corporate financial disclosure practices and their market impacts. Drawing strictly on established literature in accounting, finance, and financial technology, the study integrates signaling theory, voluntary disclosure theory, cognitive perspectives, and regulatory economics to explain why firms adopt advanced disclosure technologies and how these tools influence investor decision-making, market liquidity, analyst behavior, governance quality, and litigation risk. The methodology follows a qualitative integrative research design, systematically interpreting prior empirical findings and conceptual models to construct a coherent explanatory framework. The results reveal that blockchain-based disclosure enhances credibility and immutability, artificial intelligence improves accuracy and compliance, and textual analytics transforms how investors process narrative information. However, the analysis also uncovers tensions related to information overload, managerial incentives, strategic obfuscation, and uneven adoption across firms and jurisdictions. The discussion elaborates on theoretical implications, practical limitations, and future research pathways, emphasizing that technological sophistication does not eliminate fundamental disclosure trade-offs rooted in incentives, cognition, and regulation. The study concludes that digital disclosure technologies represent an evolutionary rather than revolutionary shift, reinforcing classic disclosure theories while extending their relevance in data-intensive capital markets.
References
1. Blankespoor, E., Miller, B. P., & White, H. D. (2014). Initial evidence on the market impact of the XBRL mandate. Review of Accounting Studies, 19(4), 1468–1503.
2. Bloomfield, R. (2008). Discussion of “Annual report readability, current earnings, and earnings persistence”. Journal of Accounting and Economics, 45(2–3), 248–252.
3. Bochkay, B. Y. (2014). Enhancing empirical accounting models with textual information. Rutgers University.
4. Bodnaruk, A., Loughran, T., & McDonald, B. (2013). Using 10-K text to gauge financial constraints. SSRN, 50(4), 623–646.
5. Bose, I., Piramuthu, S., & Shaw, M. J. (2011). Quantitative methods for detection of financial fraud. Decision Support Systems, 50(3), 557–558.
6. Boubaker, S., Gounopoulos, D., & Rjiba, H. (2019). Annual report readability and stock liquidity. Financial Markets, Institutions and Instruments, 41.
7. Bourveau, T., Lou, Y., & Wang, R. (2018). Shareholder litigation and corporate disclosure: Evidence from derivative lawsuits. Journal of Accounting Research, 56(3), 797–842.
8. Brown, J., Adams, K., & Carter, L. (2022). The role of natural language processing in financial disclosure automation. Journal of Financial Technology, 12(3), 45–67.
9. Garcia, R., & Lopez, M. (2021). Big data analytics and investor decision-making: A case study. International Journal of Finance and Data Science, 9(1), 102–124.
10. Gupta, S., & Kumar, V. (2023). AI and corporate governance: Enhancing transparency and regulatory compliance. Journal of Financial Regulation, 9(1), 112–130.
11. Jung, W., & Kwon, Y. (1988). Disclosure when the market is unsure of information endowment of managers. Journal of Accounting Research, 26(1), 146–153.
12. Komocar, J. M. (1994). Cartes causales d’un milieu de travail. In P. Cossette (Ed.), Cartes cognitives et organisations (pp. 155–184). Eska.
13. Kreps, D. M., & Sobel, J. (1994). Signalling. In R. Aumann & S. Hart (Eds.), Handbook of Game Theory, Volume II (pp. 849–868). Elsevier.
14. Lang, M. H., & Lundholm, R. J. (1993). Cross sectional determinants of analysts rating of corporate disclosure. Journal of Accounting Research, 31(2), 246–271.
15. Lau, A. (1992). Voluntary financial disclosure by Hong Kong listed companies. Hong Kong Manager, May/June, 10–19.
16. Malone, D., Fries, C., & Jones, T. (1993). An empirical investigation of the extent of corporate financial disclosure in the oil and gas industry. Journal of Accounting Auditing and Finance, 8(3), 249–273.
17. Mckinnon, J. L., & Dalimunthe, L. (1993). Voluntary disclosure of segment information by Australian diversified companies. Accounting and Finance, 33(1), 33–50.
18. Smith, R. (2020). Blockchain technology and corporate financial disclosure: A U.S. perspective. American Journal of Financial Technology, 8(2), 23–41.
19. Tailor, P., & Kale, A. (2025). Multimodal sentiment analysis of earnings calls and SEC filings: A deep learning approach to financial disclosures. Utilitas Mathematica, 122, 3163–3168.
20. Wang, Y., Zhao, X., & Li, Q. (2021). Artificial intelligence in financial reporting: An accuracy assessment. China Journal of Accounting and AI, 10(1), 78–99.
21. Zheng, P., Liu, W., & Chen, J. (2022). Blockchain for financial disclosure: Adoption and impact. Journal of Financial Technology, 7(3), 156–176.
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