[We reexamine long-term abnormal returns for portfolios sorted on governance characteristics. Firms with strong shareholder rights and firms with weak shareholder rights differ from the population of firms and from each other in how they cluster across industries. Using well-specified tests under this industry clustering, we find statistically zero long-term abnormal returns for portfolios sorted on governance. Our results have important implications for interpreting studies that link governance to firm value and stock returns, demonstrate the importance of the coarseness of industry definitions in financial research, and shed light on addressing statistical problems created by industry clustering in samples.]

Operating performance and stock return results imply that managers who commit fraud anticipate large stock price declines if they were to report truthfully, which would cause greater losses for managerial stockholdings than for options because of differences in convexity. Fraud firms have significantly greater incentives from unrestricted stockholdings than control firms do, and unrestricted stockholdings are their largest incentive source. Our results emphasize the importance of the shape and vesting status of incentive payoffs in providing incentives to commit fraud. Fraud firms also have characteristics that suggest a lower likelihood of fraud detection, which implies lower expected costs of fraud.

We reexamine long-term abnormal returns for portfolios sorted on governance characteristics. Firms with strong shareholder rights and firms with weak shareholder rights differ from the population of firms and from each other in how they cluster across industries. Using well-specified tests under this industry clustering, we find statistically zero long-term abnormal returns for portfolios sorted on governance. Our results have important implications for interpreting studies that link governance to firm value and stock returns, demonstrate the importance of the coarseness of industry definitions in financial research, and shed light on addressing statistical problems created by industry clustering in samples.

Using trade and quote data from the NYSE, we examine the relation between dealer attention, dealer revenue, and the probability of informed trade. We find that dealer revenue net of losses to better-informed traders in NYSE stocks is positively related to the speed at which quotes adjust to full information levels. The speed of quote adjustment is faster for stocks with greater dealer attention, as measured by a stock’s relative prominence at its post and panel location on the NYSE floor. The level of dealer attention in turn is positively related to a stock’s probability of information-based trading. The results are consistent with a theoretical model we derive in which dealers trade multiple securities and must optimally allocate their limited attention to monitoring order flow to minimize losses to better-informed traders.