We provide a new framework for using text as data in empirical models. The framework identifies salient information in unstructured text that can control for multidimensional heterogeneity among assets. We demonstrate the efficacy of the framework by reexamining principal-agent problems in residential real estate markets. We show that the agent-owned premiums reported in the extant literature dissipate when the salient textual information is included. The results suggest the previously reported agent-owned premiums suffer from an omitted variable bias, which prior studies incorrectly ascribed to market distortions associated with asymmetric information.

In this paper, we use call option prices to identify synergies and news from merger and acquisition (M&A) transaction announcements. We find that M&A announcements result in large and approximately equal gains to the bidder and the target on average, with the combined gains being large enough to justify the premium paid to target shareholders. On average, M&A announcements release good news about targets, but bad news about bidders. This suggests that market prices understate true synergy gains, and helps reconcile the generally negative market-based evidence on value-creation in takeovers with their continued prominence in everyday business strategy.