On the relation between forecast precision and trading profitability of financial analysts

TL;DR

This paper models the relationship between financial analysts' forecast accuracy and their trading profitability, revealing that the connection is complex and influenced by market dynamics and information correlation.

Contribution

It provides a novel analytical framework using multivariate Gaussian models to characterize analysts' payoffs and explores the non-monotonic relationship between forecast precision and profitability.

Findings

Forecast accuracy and profitability relationship is non-monotonic.

Market equilibrium results in zero expected payoff for all participants.

Correlation between forecasts affects expected payoffs depending on signal accuracy.

Abstract

We analyze the relation between earning forecast accuracy and expected profitability of financial analysts. Modeling forecast errors with a multivariate Gaussian distribution, a complete characterization of the payoff of each analyst is provided. In particular, closed-form expressions for the probability density function, for the expectation, and, more generally, for moments of all orders are obtained. Our analysis shows that the relationship between forecast precision and trading profitability need not to be monotonic, and that, for any analyst, the impact on his expected payoff of the correlation between his forecasts and those of the other market participants depends on the accuracy of his signals. Furthermore, our model accommodates a unique full-communication equilibrium in the sense of Radner (1979): if all information is reflected in the market price, then the expected payoff of all market participants is equal to zero.