Are All Readers on the Same Page? Predicting Variation in Information Retrieval from Financial Narratives

Retrieving information from financial narratives is a complex process that depends on how the text characteristics of narratives interact with the financial literacy of users. In this study, we develop a comprehensive measure for variation in information retrieval based on observed user behavior that is also able to incorporate understudied text characteristics such as the semantics and content of a narrative. Using a tool that tracks reading and marking behavior in a controlled environment, we first document how users with varying degrees of financial literacy retrieve information from financial narratives. We find significant variation among financial literacy groups that cannot be solely explained by text characteristics related to processing costs. Next, we use state-of-the-art machine-learning to predict variation in information retrieval for out-of- sample financial narratives, and we show that these predictions are incrementally associated with the post-announcement return volatility. Overall, our results suggest that efforts by regulators and corporations to simplify text characteristics of corporate communications might not resolve all differences in how users retrieve information from financial narratives.