A Quantitative Analysis of Distortions in Managerial Forecasts
65 Pages Posted: 14 Jun 2018 Last revised: 20 Feb 2024
There are 3 versions of this paper
A Quantitative Analysis of Distortions in Managerial Forecasts
A Quantitative Analysis of Distortions in Managerial Forecasts
A Quantitative Analysis of Distortions in Managerial Forecasts
Date Written: February 19, 2024
Abstract
This paper quantifies the economic costs of distortions in managerial forecasts. We exploit a long panel of managerial forecast errors and investment decisions of a representative sample of Italian firms. Our analysis relies on three key features of the data: (1) on average, sales forecasts are unconditionally unbiased and the standard deviation of sales forecast errors is 17.0% (2) managerial forecasts are conditionally biased, i.e., errors are autocorrelated with an AR(1) coefficient of 0.331, suggesting underreaction (3) while investment responds significantly to sales-forecast, the elasticity of firms’ capital stock to sales forecast is significantly smaller than one, at 0.485. We quantitatively interpret these findings through the lens of standard Q-model with distorted expectations. Distortions in expectation reduce firm value by 1.5%, and lower aggregate productivity by 0.76%. These findings remain unchanged when we introduce additional frictions (e.g., fixed adjustment costs, financial constraints) and behavioral biases (e.g., optimism).
Keywords: Expectation Formation; Misallocation; Corporate Investment
JEL Classification: D61; D84
Suggested Citation: Suggested Citation