Information Acquisition and Disclosure in Forecasting Contests

Abstract

We analyze a model in which competing analysts strategically acquire and disclose information in forecasting contests. In acquiring information, analysts face a tradeoff—while more precise information improves their forecast accuracy and the likelihood of winning the contest, it also increases their conditional signal correlation making differentiation to win the contest harder. In equilibrium, analysts do not make truthful forecasts if contest rewards are sufficiently high. Increasing contest competitiveness, by scaling up contest rewards, generally encourages information acquisition, but can discourage information acquisition by the inefficient analyst, with a high information cost, to the point of not acquiring information at all. Stronger competition can reduce price efficiency at higher reward levels. Analysts with identical information acquisition cost can become differently informed and use opposing forecasting strategies.

Keywords: Tournament; competition; analyst forecasts; ranking

JEL Classification: D82; D83; M41