Applying Fishburn's [4] conditions for convex stochastic dominance, exact linear programming algorithms are proposed and implemented for assigning discrete return distributions into the first‐ and second‐order stochastic dominance optimal sets. For third‐order stochastic dominance, a superconvex stochastic dominance approach is defined which allows classification of choice elements into superdominated, mixed, and superoptimal sets. For a choice set of 896 security returns treated previously in the literature, 454, 25, and 13 distributions are in the first‐, second‐, and third‐order convex stochastic dominance optimal sets, respectively. These optimal sets compare with admissible first‐, second‐, and third‐order stochastic dominance sets of 682, 35, and 19 distributions, respectively. The applicability of superconvex stochastic dominance for continuous distributions defined over a bounded interval is then shown. The difficulties in identifying the elements of the superdominated set for distributions defined over the entire real line are demonstrated in the determination of the dominated choices for a set of normally distributed mutual fund returns previously examined by Meyer [9]. Specifically, we find that the dominated set determined by Meyer is too large.

Although the increase in non-GAAP earnings metrics has drawn unfavorable attention from regulators and standard setters, it can provide valuable experience for investors. We investigate whether experience with non-GAAP earnings metrics influences investors’ pricing of non-GAAP exclusions. We measure experience as the frequency with which managers or analysts provide non-GAAP earnings over the prior eight quarters and find that experience aids in the pricing of non-GAAP exclusions. Absent prior experience with non-GAAP earnings metrics, investors appear to overestimate the persistence of exclusions at the earnings announcement, which corrects in the following months. Cross-sectional tests suggest that experience facilitates investors’ pricing of non-GAAP exclusions by reducing their information processing costs.

Using a global sample of multinational corporations (MNCs) and their foreign subsidiaries, we find that repatriation taxes impair subsidiary-level investment efficiency. Consistent with internal agency conflicts between the central management of the MNC and the manager of the foreign subsidiary being the driver, we show that this effect is concentrated in subsidiaries with high information asymmetry and in subsidiaries that are weakly monitored. Quasi-natural experiments in the U.K. and Japan establish a causal relationship for our findings and suggest that a repeal of repatriation taxes increases subsidiary-level investment efficiency while reducing the level of investment. Our paper provides timely empirical evidence to inform expectations for the effects of a recent change to the U.S. international tax law that eliminated repatriation taxes from most of the future foreign earnings of U.S. MNCs. JEL Classifications: H21; H25; F23; G31. Data Availability: Data are available from the public sources cited in the text.

Developing models to detect financial statement fraud involves challenges related to (1) the rarity of fraud observations, (2) the relative abundance of explanatory variables identified in the prior literature, and (3) the broad underlying definition of fraud. Following the emerging data analytics literature, we introduce and systematically evaluate three data analytics preprocessing methods to address these challenges. Results from evaluating actual cases of financial statement fraud suggest that two of these methods improve fraud prediction performance by approximately 10 percent relative to the best current techniques. Improved fraud prediction can result in meaningful benefits, such as improving the ability of the SEC to detect fraudulent filings and improving audit firms' client portfolio decisions.

Calls from practice suggest that audit committee members with industry expertise can improve audit committee effectiveness. Nevertheless, regulators and the extant literature have focused on the financial expertise of the audit committee. We posit that audit committee industry knowledge is valuable because accounting guidance, estimates, and oversight of the external auditor are often linked to a company's operations within a particular industry. Taking a holistic view, we examine two measures of financial reporting quality (financial restatements and discretionary accruals) and two measures of external auditor oversight (audit and nonaudit fees). As predicted, we find that audit committee members who are both accounting and industry experts perform better than those with only accounting expertise. We also find that in certain instances, supervisory experts who are also industry experts perform better than supervisory experts alone. Overall, these results suggest that industry expertise, when combined with accounting expertise, can improve the effectiveness of the audit committee in monitoring the financial reporting process. Data Availability: All data are gathered from publicly available sources.

We examine the impact on share prices of firms whose CEOs and CFOs certify their financial statements under oath, pursuant to the administrative order issued by the SEC on June 27, 2002. We hypothesize that (1) the certification provides assurance to investors by making disclosure more credible and by reducing information asymmetry between owners and management, and (2) the assurance value of certification is reflected in the stock price of the certifying company. Overall, the empirical results are consistent with our hypotheses. We observe, on average, positive abnormal returns for firms whose CEOs/CFOs certified their financial statements by August 14, 2002. Based on an analysis of bid-ask spreads, certifying firms experienced a significant decline in information asymmetry after certification. In cross-sectional analyses, we find abnormal returns are positively associated with firms that were under investigation, that used Andersen as their auditor, and that practiced aggressive revenue recognition.

This paper considers the costs of reducing consumption of a good by making its production illegal and punishing apprehended illegal producers. We use illegal drugs as a prominent example. We show that the more inelastic either demand for or supply of a good is, the greater the increase in social cost from further reducing its production by greater enforcement efforts. So optimal public expenditures on apprehension and conviction of illegal suppliers depend not only on the difference between the social and private values from consumption but also on these elasticities. When demand and supply are not too elastic, it does not pay to enforce any prohibition unless the social value is negative. We also show that a monetary tax could cause a greater reduction in output and increase in price than optimal enforcement against the same good would if it were illegal, even though some producers may go underground to avoid a monetary tax. When enforcement is costly, excise taxes and quantity restrictions are not equivalent.

We analyze a unique data set on multiunit auctions, which contains the actual demand schedules of the bidders as well as the auction awards in over 400 Swedish Treasury auctions. First, we document that bidders vary their prices, bid dispersion, and the quantity demanded in response to increased uncertainty at the time of bidding. Second, we find that bid shading can be explained by a winner’s curse–driven model in which each bidder submits only one bid, despite the fact that the bidders in our data set use much richer bidding strategies. Third, we explore the extent to which the received theories of multiunit auctions are able to offer insights into the bidder behavior we observe. Our empirical evidence is consistent with some of the predictions of the models of auctions that emphasize private information, the winner’s curse and the champion’s plague. While the models of multiunit auctions serve as useful guideposts, our empirical findings also point to several new areas of research in multiunit auctions that are of policy and theoretical interest.

This paper examines the EPA's decision to cancel or continue the registrations of cancer-causing pesticides that went through the special review process between 1975 and 1989. Despite claims to the contrary, our analysis indicates that the EPA indeed balanced risks against benefits in regulating pesticides: Risks to human health or the environment increased the likelihood that a particular pesticide use was canceled by the EPA; at the same time, the larger the benefits associated with a particular use, the lower was the likelihood of cancellation. Intervention by special-interest groups was also important in the regulatory process. Comments by grower organizations significantly reduced the probability of cancellation, whereas comments by environmental advocacy groups increased the probability of cancellation. Our analysis suggests that the EPA is fully capable of weighing benefits and costs when regulating environmental hazards; however, the implicit value placed on health risks--$35 million per applicator cancer case avoided--may be considered high by some persons.