Policymakers fear artificial intelligence (AI) will disrupt labor markets, especially for high-skilled workers. We investigate this concern using novel, task-specific data for security analysts. Exploiting variation in AI's power across stocks, we show analysts with portfolios that are more...

We study how information sharing within an organization affects individual performance. We look at situations in which the same analyst, while working at the same broker, covers multiple mergers and acquisitions (M&As), in particular the acquirer prior to the M&A and the merged firm thereafter....

Using a novel dataset, we show that components of firms' GAAP earnings stemming from ancillary business activities or transitory shocks are significant in frequency and magnitude. These components have grown over time and are dispersed across various sections of the 10-K. Excluding them from...

This paper examines return predictability when the investor is uncertain about the right state variables. A novel feature of the model averaging approach used in this paper is to account for finite-sample bias of the coefficients in the predictive regressions. Drawing on an extensive...