The stated goals of the SEC are to protect investors, maintain orderly markets and facilitate capital formation. These goals can be achieved with very light regulation if, as assumed by traditional economic theory, investors process information costlessly and protect themselves from informational disadvantages, and firms optimally balance the costs and benefits of committing to make their reports reliable. A growing body of research demonstrates that light regulation fails to achieve the SEC 's goals, because investors find information processing costly and fail to protect themselves. After reviewing theory and prior evidence, I discuss new lessons learned from Jiang, Petroni, and Wang ( ), who show that Pink Sheets ® reduced the liquidity of firms with low reporting quality and increased the liquidity of firms with high reporting quality, merely by highlighting the quality of their listed firms’ disclosure. While the Pink Sheets ® innovation might have occurred through many causal channels, all of them entail a violation of costless processing and self‐protection, and lead to the conclusion that this lightly regulated market did not initially meet the stated goals of the SEC . I conclude by arguing that markets can achieve the SEC 's goals only if they exhibit a particularly strong version of “dynamic” market efficiency, which requires that each individual trade on the path to even incomplete revelation occurs at the then‐optimal price. Because dynamic efficiency is unlikely, we should stop being surprised to see evidence that lightly regulated markets fall short on key dimensions. Instead, we should use our well‐developed understanding of market inefficiency to guide regulation.

In the published proceedings of the first Journal of Accounting Research Conference, Vatter [1966] lamented that “Gathering direct and original facts is a tedious and difficult task, and it is not surprising that such work is avoided.” For the fiftieth JAR Conference, we introduce a framework to help researchers understand the complementary value of seven empirical methods that gather data in different ways: prestructured archives, unstructured (“hand‐collected”) archives, field studies, field experiments, surveys, laboratory studies, and laboratory experiments. The framework spells out five goals of an empirical literature and defines the seven methods according to researchers’ choices with respect to five data gathering tasks. We use the framework and examples of successful research studies in the financial reporting literature to clarify how data gathering choices affect a study's ability to achieve its goals, and conclude by showing how the complementary nature of different methods allows researchers to build a literature more effectively than they could with less diverse approaches to gathering data.