This paper quantifies the amount of noise and bias in analysts' forecast of corporate earnings at various horizons. We … next decompose the relative accuracy of these forecasts into three components: (i) noise, (ii) bias and (iii) analysts … both noise and bias are increase linearly. We then show most existing models lack a mechanism to account for these facts …

We derive general, yet simple, sharp bounds on the size of the omitted variable bias for a broad class of causal … how the bound on the bias depends only on the additional variation that the latent variables create both in the outcome … variables (in explaining treatment and outcome variation) are sufficient to place overall bounds on the size of the bias …

demonstrate that machine learning (ML) can automatically satisfy these conditions due to its inherent inductive bias toward …-state multiplicity. Additionally, the inductive bias provides a foundation for modeling forward-looking behavioral agents with self …

additional tests, some of which utilize data on adverse health events, suggests that the forecast biases are at least partly due …

cross-sectional variation. On average, the bias increases in the forecast horizon, and analysts revise their expectations …. We show that analyst expectations are on average biased upwards, and that this bias exhibits substantial time-series and …