An international study on the impact of corruption on analysts’ forecasts

This study investigates the impact of corruption on financial analysts’ forecast accuracy and forecast uncertainty. I employ an international sample of 71 countries classified into two subsamples of advanced economies and emerging and developing economies to capture underlying heterogeneity. The empirical methodology involves a dynamic panel analysis that tests for persistence while it controls for endogeneity. The main finding shows that corruption increases forecast uncertainty and reduces forecast accuracy, though some variability is observed across countries. Given the complexities of the underlying relationships, I also investigate two other channels through which corruption could affect analysts’ forecasts using interactions terms with earnings management and rule of law. Earnings management amplifies the negative impact of corruption on forecast accuracy, whereas the rule of law mitigates it. Robustness analysis further demonstrates that anti-corruption policy interventions, such as enhancing legal enforcement and providing greater freedom, improve analysts’ forecast accuracy.