This paper develops a theoretical framework to shed light on variation in credit rating standards over time and across asset classes. Ratings issued by credit rating agencies serve a dual role: they provide information to investors and are used to regulate institutional investors. We show that introducing rating-contingent regulation that favors highly rated securities may increase or decrease rating informativeness, but unambiguously increases the volume of highly rated securities. If the regulatory advantage of highly rated securities is sufficiently large, delegated information acquisition is unsustainable, since the rating agency prefers to facilitate regulatory arbitrage by inflating ratings. Our model relates rating informativeness to the quality distribution of issuers, the complexity of assets, and issuers' outside options. We reconcile our results with the existing empirical literature and highlight new, testable implications, such as repercussions of the Dodd-Frank Act.

Cash-and stock-financed takeover bids induce strikingly different target revaluations. We exploit detailed data on unsuccessful takeover bids between 1980 and 2008, and we show that targets of cash offers are revalued on average by +15% after deal failure, whereas stock targets return to their pre-announcement levels. The differences in revaluation do not revert over longer horizons. We find no evidence that future takeover activities or operational changes explain these differences. While the targets of failed cash and stock offers are both more likely to be acquired over the following eight years than matched control firms, no differences exist between cash and stock targets, neither in the timing nor in the value of future offers. Similarly, we cannot detect differential operational policies following the failed bid. Our results are most consistent with cash bids revealing prior undervaluation of the target. We reconcile our findings with the opposite conclusion in earlier literature (Bradley, Desai, and Kim, 1983) by identifying a look-ahead bias built into their sample construction. $ This paper benefited significantly from the comments of Harry DeAngelo, our referee, and discussions by Nihat Aktas, Audra Boone, Matthew Rhodes-Kropf, and Pavel Savor, as well as the detailed input from Javed Ahmed, Dirk Jenter, and Marlena Lee. We also acknowledge helpful comments by

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