Paper: arXiv 2511.15364

Authors: Ke Wu, Baozhong Yang, Zhenkun Ying, Dexin Zhou

Abstract

We show that while anonymization effectively obscures firm identity, it significantly reduces the power of textual understanding, thereby diminishing models’ ability to extract meaningful economic signals from financial texts. This information loss is particularly severe when numerical and object entities are removed from texts and is amplified in texts characterized by high linguistic uncertainty and firm specificity. Importantly, in the setting of sentiment extraction from earnings call transcripts, we find that information loss induced by anonymization is more pervasive and severe than the effects of look-ahead bias, suggesting that the costs of anonymization may outweigh its benefits in certain financial applications.

Complexity vs Empirical Score

  • Math Complexity: 2.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This paper presents a timely and empirically rigorous critique of using text anonymization to mitigate look-ahead bias in LLMs. The authors execute a comprehensive set of empirical tests across earnings call transcripts and news headlines, demonstrating that anonymization causes severe information loss that outweighs its benefits. While mathematical derivations are minimal, the statistical methodology, cross-model comparisons, and practical insights for NLP in finance are exceptional.

Research Flowchart

  flowchart TD
    A["Research Goal: Evaluate impact of text anonymization on NLP model performance in finance"] --> B["Data Inputs: Earnings Call Transcripts & Equities Data"]
    B --> C["Methodology: Apply Anonymization<br><i>(Remove numerical & object entities)</i>"]
    C --> D["Computational Process: Sentiment Extraction & Predictive Signal Testing"]
    D --> E["Comparison: Anonymization Information Loss vs. Look-Ahead Bias"]
    E --> F["Key Outcomes: Anonymization causes severe information loss,<br>exceeding look-ahead bias impact"]

Cited References (35)

  1. Entity Neutering (2025)
  2. Global Business Networks (2025)
  3. When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks (2011)
  4. The News in Earnings Announcement Disclosures: Capturing Word Context Using LLM Methods (2025)
  5. Lookahead Bias in Pretrained Language Models (2024)
  6. The Memorization Problem: Can We Trust LLMs ’ Economic Forecasts? (2025)
  7. StoriesLM : A Family of Language Models with Time-Indexed Training Data (2024)
  8. Large Language Models: An Applied Econometric Framework (2026)
and 27 more
  1. Geoeconomic Pressure (2025)
  2. Economic Representations (2026)
  3. ChatGPT and Corporate Policies — → 2409.17933
  4. Generative AI , Managerial Expectations, and Economic Activity — → 2410.03897
  5. Caution Ahead: Numerical Reasoning and Look-Ahead Bias in AI Models (2026)
  6. Chronologically Consistent Large Language Models — → 2502.21206
  7. Assessing Look-Ahead Bias in Stock Return Predictions Generated by GPT Sentiment Analysis — → 2309.17322
  8. Wisdom or Whims? Investor Opinions, Trading Strategies, and Market Outcomes (2026)
  9. Driven to Distraction: Extraneous Events and Underreaction to Earnings News (2009)
  10. More Than Words: Quantifying Language to Measure Firms’ Fundamentals (2008)
  11. Measuring Mutual Fund Performance with Characteristic-Based Benchmarks (1997)
  12. Enhancing Investment Decisions with Sentiment Analysis: A Probabilistic Ranking Framework (2026)
  13. Narrative Asset Pricing: Interpretable Systematic Risk Factors from News Text (2023)
  14. Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models (2026)
  15. The Colour of Finance Words (2023)
  16. Expected Returns and Large Language Models (2022)
  17. Conference Calls and Information Asymmetry (2004)
  18. An Empirical Examination of Conference Calls as a Voluntary Disclosure Medium (1999)
  19. Does Public Financial News Resolve Asymmetric Information? (2010)
  20. Measuring Credit Risk Using Qualitative Disclosure (2021)
  21. Financial Ratios and the Probabilistic Prediction of Bankruptcy (1980)
  22. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy (1968)
  23. Detecting Lookahead Bias in LLM Forecasts — → 2512.23847
  24. Beliefs That Predict Returns and Beliefs That Attract Flows: Policy Insights and Sentiment Catering in Mutual Funds (2026)
  25. In the Fed’s Mind (2026)
  26. Is There Information in Corporate Acquisition Plans? (2025)
  27. Text Is All You Need: Asset Pricing Without Returns (2026)

5 of 35 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.