Anonymization and Information Loss

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.