Paper: arXiv 2507.07037

Authors: Yimin Du, Guolin Tang

Abstract

We develop a theoretical framework for understanding how cognitive load affects information processing in financial markets and test it using exogenous variation in disclosure complexity. Our model distinguishes between attention allocation and cognitive processing capacity, showing that complex information creates differential effects across investor types. Using a comprehensive dataset of corporate disclosures and a novel identification strategy based on regulatory changes, we find that cognitive load significantly impairs price discovery, with effects concentrated among less sophisticated investors. A one-standard-deviation increase in cognitive complexity reduces information incorporation speed by 18% and increases mispricing duration by 23%. We provide evidence for three theoretical mechanisms: selective attention, processing errors, and strategic complexity. Our findings suggest that cognitive constraints create systematic inefficiencies in financial markets, with important implications for disclosure regulation and market design.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a strong combination of theoretical framework modeling cognitive load and empirical validation using exogenous regulatory shocks (XBRL). It clearly defines the mechanisms and tests them using established readability metrics and diff-in-diff econometrics. While the empirical text cuts off at the end of the provided excerpt, the methodology and identification strategy are well-designed and rigorous.

Research Flowchart

  flowchart TD
    A[Research Goal: Assess Impact of Cognitive Load & Complexity on Price Discovery] --> B[Model Development: Distinguish Attention Allocation vs. Processing Capacity]
    B --> C[Data & Identification: Corporate Disclosures + Regulatory Exogenous Variations]
    C --> D[Empirical Analysis: Measure Information Speed & Mispricing Duration]
    D --> E[Key Mechanisms: Selective Attention, Processing Errors, Strategic Complexity]
    E --> F[Outcomes: +1 SD Complexity → 18% Slower Info Incorporation & 23% Longer Mispricing]

Cited References (58)

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  3. The Effects of Reporting Complexity on Small and Large Investor Trading (2010)
  4. Measuring Readability in Financial Disclosures (2014)
  5. Does It Add Up? Early Evidence on the Data Quality of XBRL Filings to the SEC (2011)
  6. Does Information-Processing Cost Affect Firm-Specific Information Acquisition? Evidence from XBRL Adoption (2016)
  7. Are XBRL Files Being Accessed? Evidence from the SEC EDGAR Log File Dataset (2018) — link
  8. The Use of EDGAR Filings by Investors (2017)
and 50 more
  1. The Determinants and Consequences of Information Acquisition via EDGAR (2015)
  2. Disclosure Processing Costs, Investors’ Information Choice, and Equity Market Outcomes: A Review (2020)
  3. How Does Information Processing Efficiency Relate to Investment Efficiency? Evidence from XBRL Adoption (2021) — link
  4. Interactive Data to Improve Financial Reporting, Release No. 33-9002 (2009)
  5. EDGAR Log File Data Sets (2017)
  6. Inline XBRL Filing of Tagged Data, Release No. 33-10514 (2018) — link
  7. EDGAR Application Programming Interfaces (2025) — link
  8. In Search of Attention (2011)
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  25. The Effect of First Wave Mandatory XBRL Reporting across the Financial Information Environment (2012)
  26. Does XBRL Adoption Reduce Information Asymmetry? (2011)
  27. Text as Data (2019)
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  29. Differential Access to Price Information in Financial Markets (2016)
  30. Individual Investors and Financial Disclosure (2013)
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  32. How Does Readability Influence Investors’ Judgments? Consistency of Benchmark Performance Matters (2015)
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  37. The Information Content of Mandatory Risk Factor Disclosures in Corporate Filings (2014)
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  39. Long-Run Growth of Financial Data Technology (2020)
  40. Data Abundance and Asset Price Informativeness (2018)
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  47. Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks — → 2503.16974
  48. The Memorization Problem: Can We Trust LLMs ’ Economic Forecasts? — → 2504.14765
  49. Can AI Be Trusted with Financial Data? (2026)
  50. AI -Equipped Institutional Investors, XBRL Standardization, and the Reach of Structured-Data Disclosure Mandates (2026) — link

2 of 58 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.