Vague Knowledge: Evidence from Analyst Reports
This addresses the challenge of representing subjective expectations in finance, offering incremental insights into the role of language in conveying non-quantifiable information.
The paper tackles the problem of how vague knowledge about future payoffs is communicated, finding that textual tone in analyst reports predicts forecast errors and revisions, with stronger effects under vaguer language, higher uncertainty, and analyst busyness.
People in the real world often possess vague knowledge of future payoffs, for which quantification is not feasible or desirable. We argue that language, with differing ability to convey vague information, plays an important but less-known role in representing subjective expectations. Empirically, we find that in their reports, analysts include useful information in linguistic expressions but not numerical forecasts. Specifically, the textual tone of analyst reports has predictive power for forecast errors and subsequent revisions in numerical forecasts, and this relation becomes stronger when analyst's language is vaguer, when uncertainty is higher, and when analysts are busier. Overall, our theory and evidence suggest that some useful information is vaguely known and only communicated through language.