In many applications, an anomaly detection system presents the most anomalous data instance to a human analyst, who then must determine whether the instance is truly of interest (e.g. a threat in a security setting). Unfortunately, most anomaly detectors provide no explanation about why an instance was considered anomalous, leaving the analyst with no guidance about where to begin the investiga...

We propose a regression-based method for combining analyst forecasts to improve forecasting efficiency. This significantly reduces the bias in earnings forecasts, and generates that consistently outperform consensus over time across firms of different characteristics. Incorporating firm-level macroeconomic information model further improves performance. Forecasting gains increase with dispersio...

Abstract We show that analyst behavior changes in response to a randomly assigned shock exogenously varies the timeliness and cost of accessing mandatory disclosures cross-section investors: analysts reduce coverage issue less optimistic, more accurate, bold, informative forecasts. Our evidence indicates strategic component their behavior: are stronger among with incentives like affiliated or r...