The Strategic Gap: How AI-Driven Timing and Complexity Shape Investor Trust in the Age of Digital Agents
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866908842882760704 |
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| author | Neupane, Krishna |
| author_facet | Neupane, Krishna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_17895 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The Strategic Gap: How AI-Driven Timing and Complexity Shape Investor Trust in the Age of Digital Agents Neupane, Krishna Computational Finance General Finance 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. |
| title | The Strategic Gap: How AI-Driven Timing and Complexity Shape Investor Trust in the Age of Digital Agents |
| topic | Computational Finance General Finance |
| url | https://arxiv.org/abs/2602.17895 |