Vague Knowledge: Evidence from Analyst Reports

TL;DR

This paper demonstrates that analyst reports contain valuable predictive information conveyed through language, especially vaguer expressions, which can forecast errors and revisions in numerical forecasts.

Contribution

It highlights the importance of linguistic cues in analyst reports as a source of useful information about vague knowledge not captured by numerical forecasts.

Findings

Textual tone predicts forecast errors and revisions.

Vaguer language correlates with higher uncertainty and busier analysts.

Language conveys valuable information not captured by numbers.

Abstract

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.