Expectations Formation with Fat-tailed Processes: Evidence from Sales Forecasts

TL;DR

This paper investigates how sales forecast errors relate to past revisions, revealing non-linear reactions to news, and proposes a fat-tailed process model to explain these dynamics and their impact on stock returns.

Contribution

It introduces a novel framework modeling sales growth with fat-tailed processes and linear forecasting rules, capturing non-linear forecast error behaviors.

Findings

Forecasters underreact to typical news

Overreact to significant news

Model aligns with observed sales and return data

Abstract

We empirically analyze a large sample of firm sales growth expectations. We find that the relationship between forecast errors and lagged revision is non-linear. Forecasters underreact to typical (positive or negative) news about future sales, but overreact to very significant news. To account for this non-linearity, we propose a simple framework, where (1) sales growth dynamics have a fat-tailed high frequency component and (2) forecasters use a simple linear rule. This framework qualitatively fits several additional features of data on sales growth dynamics, forecast errors, and stock returns.