Prior research suggests that managers often fail to complete announced open market repurchase (OMR) plans due to uncertainty in the firm's future cash flows. Although high-ability managers should manage cash flows more effectively to complete share reacquisitions, there is little evidence to...

Biased forecasts, particularly the inadequate adjustment from current values and excessive clustering, are increasingly explained as resulting from anchoring. However, experiments presented in support of this interpretation lack economic conditions, particularly monetary incentives, feedback for...

We construct measures of uncertainty and its dispersion exploiting the heterogeneity of a large set of model predictions. The approach is forward-looking, can be computed in real-time, and can be applied at any frequency. We illustrate the methodology with expected shortfall predictions of...

We use machine learning to predict stock returns at forward horizons from 1 month ahead to 120 months ahead. Stock return predictability declines with the forecast horizon; it follows an asymptotic exponential decay process consisting of a permanent component (c. 20 bp/month) and a transient...

To study coordination in complex social systems such as financial markets, the authors introduce a new prediction market set-up that accounts for fundamental uncertainty. Nonetheless, the market is designed so that its total value is known, and thus its rationality can be evaluated. In two...