This study speaks to investment academics and practitioners by describing and analyzing the population of return predictive signals (RPS) publicly identified during the period 1970-2010. Our supraview brings to light a number of new facts about the population of RPS, including that more than 330...

We selectively review recent advancements in research on predictive models of earnings and returns. We discuss why applying statistical, econometric, and machine learning advancements to forecasting earnings and returns presents difficult challenges. In the context of these challenges, we...

We selectively review recent advancements in research on predictive models of earnings and returns. We discuss why applying statistical, econometric, and machine learning advancements to forecasting earnings and returns presents difficult challenges. In the context of these challenges, we...

We selectively review recent advancements in research on predictive models of earnings and returns. We discuss why applying statistical, econometric, and machine learning advancements to forecasting earnings and returns presents difficult challenges. In the context of these challenges, we...

We investigate whether the adoption of the Current Expected Credit Loss (CECL) standard by U.S. banks affects three properties of financial analysts’ loan loss provision forecasts: accuracy, dispersion, and coverage. We find that CECL adoption is associated with reduced accuracy and coverage,...