Advanced analytics is a booming area in the data management industry and a hot research topic. Almost all toolkits that implement machine learning (ML) algorithms assume that the input is a single table, but most relational datasets are not stored as single tables due to normalization. Thus, analysts often join tables to obtain a denormalized table. Also, analysts typically ignore any functiona...

I examine the relationship between analysts’ use of public information and the performance of their stock recommendations. I construct a measure of analysts’ reliance on public information (RPI) based on the sensitivity of their recommendation revisions to quantitative variables that are known to predict returns. I find a strong negative relationship between analysts’ past RPI and their future ...

In May 2004, with the war for talent in high gear, Groysberg and colleagues from Harvard Business School wrote in these pages about the risks of hiring star performers away from competitors. After studying the fortunes of more than 1,000 star stock analysts, they found that when a star switched companies, not only did his performance plunge, so did the effectiveness of the group he joined and t...

Relying on the well-established theoretical result that uncertainty has a common and an idiosyncratic component, we propose a new measure of earnings forecast uncertainty as the sum of dispersion among analysts and the variance of mean forecast errors estimated by a GARCH model. The new measure is based on both common and private information available to analysts at the time they make their for...

Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high quality forecasts – especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges,...