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

Krishna Neupane

TL;DR

This work reevaluates price discovery in a digital, high-velocity disclosure environment by introducing the Autonomous Disclosure Regulator (ADR), a four-node agentic auditing framework that processes nearly half a million SEC filings to detect and quantify the Informedness Gap. It shows that a Strategic Gap emerges when high semantic complexity combines with low temporal predictability, reducing price discovery velocity by about 60% and enabling insider rent extraction; the framework identifies 39 high-priority cases and reports a potential welfare recovery of around 360% under agentic vigilance. By operationalizing Node A–D with semantic embeddings, temporal syntax, state persistence, and recursive search, the ADR shifts oversight from passive repositories to active, real-time auditing, advocating an Agentic Regulatory State. The findings imply that standardizing disclosure cadence and enhancing machine-readability can significantly improve market integrity, countering computational asymmetry in modern capital markets. The work combines transformer-based semantic auditing, Blackwell informativity, Ramsey-style regulatory recursion, and SHAP-based interpretability to map and mitigate complex information frictions in regulatory data streams.

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.

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

TL;DR

This work reevaluates price discovery in a digital, high-velocity disclosure environment by introducing the Autonomous Disclosure Regulator (ADR), a four-node agentic auditing framework that processes nearly half a million SEC filings to detect and quantify the Informedness Gap. It shows that a Strategic Gap emerges when high semantic complexity combines with low temporal predictability, reducing price discovery velocity by about 60% and enabling insider rent extraction; the framework identifies 39 high-priority cases and reports a potential welfare recovery of around 360% under agentic vigilance. By operationalizing Node A–D with semantic embeddings, temporal syntax, state persistence, and recursive search, the ADR shifts oversight from passive repositories to active, real-time auditing, advocating an Agentic Regulatory State. The findings imply that standardizing disclosure cadence and enhancing machine-readability can significantly improve market integrity, countering computational asymmetry in modern capital markets. The work combines transformer-based semantic auditing, Blackwell informativity, Ramsey-style regulatory recursion, and SHAP-based interpretability to map and mitigate complex information frictions in regulatory data streams.

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.
Paper Structure (42 sections, 12 equations, 2 figures, 11 tables)

This paper contains 42 sections, 12 equations, 2 figures, 11 tables.

Table of Contents

  1. Introduction
  2. Related Literature
  3. From Lexical Dictionaries to Semantic Embeddings
  4. Re-characterizing Integration Costs as an Agentic Pivot
  5. Information Asymmetry as a Strategic Choice
  6. Theoretical Framework
  7. The Three Pillars of Agentic Velocity
  8. The Phase Shift toward Agentic Vigilance
  9. Node A: Semantic Interface and the Exhaustiveness Frontier
  10. Node B: Temporal Syntax and Rational Inattention
  11. Node C: Optimal Supervision and State Management
  12. Node D: Information Integrity and Recursive Search
  13. Methodology: The Agentic Disclosure Reasoner
  14. Node B: Temporal Syntax and Agentic Propensity
  15. Node C: LangGraph Orchestration and State Persistence
  16. ...and 27 more sections

Figures (2)

  • Figure 1: The Structural Distribution of Information Frictions: Interaction Between Disclosure Complexity and Reporting Unpredictability.
  • Figure 2: The Multiplier Effect of Disclosure Frictions: Interaction Between Reporting Unpredictability ($\Phi$) and Semantic Friction ($Z_{\text{Comp}}$).