Auditing, Stratified Sampling, Substantive test, Stochastic modeling

Exploiting a unique identification strategy based on inaccurate news analytics, we document an effect of news analytics on the market independent of the informational content of the news. We show that news analytics speed up the stock price and trading volume response to articles, but reduce liquidity. Inaccurate news analytics lead to small price distortions that are corrected quickly. The market impact of news analytics is greatest for press releases, as news analytics exhibit a particular skill in “seeing through” the positive spin of press releases. Furthermore, we provide evidence that high-frequency traders rely on the information from news analytics for directional trading on company-specific news. Received: May 17, 2018; Editorial decision: June 14, 2019 by Editor: Thierry Foucault. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

In 1982, Dimensional Fund Advisors launched a mutual fund intended to capture the returns of small-cap stocks. The ‘9–10 Fund’ is based on the CRSP 9–10 Index, an index of small-cap stocks constituting the ninth and tenth deciles of NYSE market capitalization, although the 9–10 Fund incorporates investment rules and a trading strategy that are aimed at minimizing the potentially excessive trade costs associated with such illiquid stocks. As a result, the 9–10 Fund provided a 2.2% annual premium over the 9–10 Index for the 1982–1995 period. We show that both the investment rules and the trade strategy components of the Fund’s design contribute significantly to this return difference.

This paper examines the change in the average level of moral development over a 7.5‐year period of promotion, attrition, and survival in five Big 6 firms. The study improves upon previous cross‐sectional studies that found decreases in the average level of moral development at the senior manager and partner levels, which has been referred to as the “inverted‐U” phenomenon. Problems with these studies that limit the generalizability of their findings include their cross‐sectional nature and samples that usually come from one or two firms. Over a 7.5‐year period, we found that the participating Big 6 firms retained auditors with higher average levels of moral development (measured using the defining issues test), while those with lower average levels left the firms. The average level of moral development for new partners was at least as high as the group from which they came. This research suggests that the concern about Big 6 firms retaining a higher proportion of auditors with lower moral development may be an artifact of research design.

This study extends prior research on the average level of moral development in public accounting by examining five large accounting firms and three staff levels. The research is important because it highlights the need to include auditors from several firms in research designs, provides evidence of differences in moral development among public accounting firms, and profiles the professions' average level of moral development for three levels. The data are from 494 managers and seniors (204 females and 290 males) from five Big Six firms. Using the Defining Issues Test (Rest 1979a) to measure moral development, several results were noted. First, the results indicate a difference in the average level of moral development among firms, suggesting that use of subjects from only one firm inhibits the generalizability of findings regarding moral development. Second, female managers are at a significantly higher average level of moral development than male managers. In fact, the average scores for male managers fell between those expected for senior high school and college students. The data suggest that a greater percentage of high‐moral‐development males and low‐moral‐development females are leaving public accounting than their respective opposites. These results indicate that the profession has retained, through advancement, males who are potentially less sensitive to the ethical implications of various issues. The analysis also indicates that Kohlberg's (1969) theory of moral development is not biased towards the thought processes of males because female auditors did not score lower on the Defining Issues Test.

The asymptotic refinements attributable to the block bootstrap for time series are not as large as those of the nonparametric iid bootstrap or the parametric bootstrap. One reason is that the independence between the blocks in the block bootstrap sample does not mimic the dependence structure of the original sample. This is the join-point problem. In this paper, we propose a method of solving this problem. The idea is not to alter the block bootstrap. Instead, we alter the original sample statistics to which the block bootstrap is applied. We introduce block statistics that possess join-point features that are similar to those of the block bootstrap versions of these statistics. We refer to the application of the block bootstrap to block statistics as the block–block bootstrap. The asymptotic refinements of the block–block bootstrap are shown to be greater than those obtained with the block bootstrap and close to those obtained with the nonparametric iid bootstrap and parametric bootstrap.

Several predetermined variables that reflect levels of bond and stock prices appear to predict returns on common stocks of firms of various sizes, long-term bonds of various default risks, and default-free bonds of various maturities. The returns on small-firm stocks and low-grade bonds are more highly correlated in January than in the rest of the year with previous levels of asset prices, especially prices of small-firm stocks. Seasonality is found in several conditional risk measures, but such seasonality is unlikely to explain, and in some cases is opposite to, the seasonal found in mean returns.

How do regional productivity shocks or transportation infrastructure improvements affect aggregate welfare? In a general class of spatial equilibrium models, we provide a formula for aggregate welfare changes, decomposed into terms associated with (i) technology [Fogel (1964), Railroads and American Economic Growth (Baltimore: Johns Hopkins Press), Hulten (1978) “Growth Accounting with Intermediate Inputs”, The Review of Economic Studies, 45, 511–518], (ii) spatial dispersion of marginal utility, (iii) fiscal externalities, (iv) technological externalities, and (v) redistribution. We further use this decomposition to derive a general formula for optimal spatial transfers and show that, whenever optimal transfers are in place, the technology term alone captures the aggregate welfare effects of technological shocks. We apply our framework to study welfare gains from improving the US highway network. We find that changes in the spatial dispersion of marginal utility are as important as technological externalities in accounting for the deviations from the Fogel-Hulten benchmark to assess welfare gains.

Missing data are a common challenge facing empirical researchers. This paper presents a general GMM framework and estimator for dealing with missing values of an explanatory variable in linear regression analysis. The GMM estimator is efficient under assumptions needed for consistency of linear-imputation methods. The estimator, which also allows for a specification test of the missingness assumptions, is compared to existing linear imputation, complete data, and dummy variable methods commonly used in empirical research. The dummy variable method is generally inconsistent even when data are missing completely at random, and the dummy variable method, when consistent, can be less efficient than the complete data method.