The Strategic Gap: How AI-Driven Timing and Complexity Shape Investor Trust in the Age of Digital Agents

Paper: arXiv 2602.17895

Authors: Krishna Neupane

Abstract

Traditional models of market efficiency assume that equity prices incorporate information based on content alone, often neglecting the structural influence of reporting timing and cadence. This study introduces the Autonomous Disclosure Regulator, a multi-node AI framework designed to audit the intersection of disclosure complexity and filing unpredictability. Analyzing a population of 484,796 regulatory filings, the research identifies a structural Strategic Gap: a state where companies use confusing language and unpredictable timing to slow down how fast the market learns the truth by 60%. The results demonstrate a fundamental computational asymmetry in contemporary capital markets. While investors are now good at spotting “copy-paste” text, they remain vulnerable to strategic timing that obscures structural deterioration. The framework isolates 39 high-priority failures where the convergence of dense text and temporal surprises facilitated significant information rent extraction by insiders. By implementing a recursive agentic audit, the system identifies a cumulative welfare recovery potential of over 360% and demonstrates near-perfect resilience against technical data interruptions. The study concludes by proposing a transition toward an agentic regulatory state, arguing that as information integration costss rise, infrastructure must evolve from passive data repositories into active auditing nodes capable of real-time synthesis to preserve market integrity.

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper introduces a highly novel multi-node AI framework (Autonomous Disclosure Regulator) to audit corporate disclosure timing and complexity across nearly 500,000 filings. While it relies heavily on applied machine learning and natural language processing rather than deep theoretical mathematical proofs, its empirical scale and unique agentic perspective on market microstructure make it a strong contribution.

Research Flowchart

  flowchart TD
    A[Research Goal: Assess how disclosure complexity & filing timing impact market learning and trust] --> B[Data Inputs: 484,796 Regulatory Filings]
    B --> C[Methodology: Autonomous Disclosure Regulator<br><i>Multi-node AI Framework</i>]
    C --> D[Computational Process: Recursive Agentic Audit<br><i>Analyzes text density & temporal surprise</i>]
    D --> E[Key Finding: Strategic Gap<br><i>Confusing language & unpredictable timing slow market learning by 60%</i>]
    D --> F[Key Outcomes: 39 High-Priority Failures &<br>360%+ Cumulative Welfare Recovery Potential]

Cited References (17)

1. Discussion of “disclosure processing costs, investors’ information choice, and equity market outcomes: A review” (2020) — link
2. New Evidence on the Relation between the Enterprise Multiple and Average Stock Returns (2011) — link
3. Implications of rational inattention (2003) — link
4. The pricing of discretionary accruals (1996) — link
5. Continuous Auctions and Insider Trading (1985) — link
6. The Market for “Lemons”: Quality Uncertainty and the Market Mechanism (1970)
7. “Generative AI in Financial Reporting.” Financial Reporting (2025)
8. Redacted Disclosure (2017) — link

0 of 17 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.