Anonymization and Information Loss

2025-11-19

AI Summary

Financial text anonymization—widely adopted to safeguard corporate privacy—may critically impair models’ semantic understanding of economic signals, particularly in high-uncertainty, firm-specific texts such as earnings call transcripts. Method: The authors conduct empirical analysis using de-identification (removing numeric values and named entities) and systematically evaluate its impact on sentiment extraction tasks. Contribution/Results: Anonymization induces more pervasive and severe information loss than forward-looking bias; performance degradation is especially pronounced in fine-grained semantic parsing scenarios. This study provides the first quantitative assessment of the semantic cost of anonymization in financial NLP, challenging the prevailing assumption of its default applicability. It offers both theoretical grounding and methodological caution for reconciling privacy preservation with analytical utility in financial language processing.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Privacy
• Data Mining & Knowledge Management: Semantic Web

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

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.

Problem

• Anonymization reduces textual understanding power in financial analysis
• Removing numerical entities causes severe economic signal degradation
• Anonymization costs outweigh benefits in earnings call sentiment extraction

Innovation

• Anonymization reduces textual understanding power
• Information loss worsens with entity removal
• Anonymization costs outweigh benefits financially