The purpose of this paper is to evaluate whether the expanded requirements of SAS No. 59 (A/CPA [1988]), which requires auditors to actively evaluate and report on a client's going-concern status for the coming year, have allowed investors to make more accurate ex ante assessments of firms that eventually file for bankruptcy. We extend Chen and Church [1996] (hereafter CC), who conclude that SAS No. 34 (AICPA [1981]) "subject to" going-concern opinions have information value because they reduce the surprise associated with bankruptcy announcements. We hypothesize that if SAS No. 59 has achieved what was intended, going-concern opinions issued under SAS No. 59 should further reduce investor surprise at bankruptcy announcements. While we do not believe SAS No. 59 was issued for the specific purpose of helping users to predict bankruptcy, we do suggest that the increased auditor responsibility and improved communication should provide users with information that is of relatively higher quality. This argument is based on a number of important differences between SAS No. 34 and SAS No. 59.

Measurement error in unexpected earnings is recognized as a source of bias in examinations of the relation between earnings and returns. Reverse regression procedures are commonly used as a means of coping with this bias. This study examines the properties of reverse regression procedures in multi-interacted variable settings with a specific focus on the earnings response coefficient (ERC) analysis of Collins and Kothari (J. Account. Econom. 11 (1989) 143.). It shows that both conventional reverse regression techniques and novel techniques employed by Collins and Kothari are not robust. It also demonstrates how reverse regression techniques can be successfully employed in such settings using non-interacted-variable designs.

Many recent empirical studies have concluded that analysts' earnings forecasts are optimistic on average. In this paper, we attempt to undo the effect of one potential source of optimistic bias in analysts' earnings forecasts. Assuming forecasts come from a truncated normal distribution, we estimate the “true” population mean using maximum likelihood. We find that our estimates of earnings are more accurate and less biased than standard measures of sample mean and median. However, we do not find a closer relationship between excess market returns and forecast errors from our maximum likelihood estimate than from the sample mean. This may suggest that the market does not fully incorporate analysts' incentives in generating expectations about future earnings.

Conditions are derived for the consistency of kernel estimators of the covariance matrix of a sum of vectors of dependent heterogeneous random variables, which match those of the currently best-known conditions for the central limit theorem, as required for a unified theory of asymptotic inference. These include finite moments of order no more than 2 + for > 0, trending variances, and variables which are near-epoch dependent on a mixing process, but not necessarily mixing. The results are also proved for the case of sample-dependent bandwidths.

In this paper we analyze the monitoring role of occupational pension funds in the UK. We argue that because of their objectives, structure and overall share holding, occupational pension funds are likely to have more incentives to monitor companies in which they hold large stakes than other financial institutions. By comparing companies in which these funds hold large stakes with a control group of companies listed on the London Stock Exchange, we show that occupational pension funds hold large stakes over a long-time period mainly in small companies. However, the value added by these funds is negligible and their holdings do not lead companies to comply with the Code of Best Practice or outperform their industry counterparts. Overall, our results suggest that occupational pension funds are not effective monitors.

This paper introduces a theory of market incompleteness based on the information transmission role of prices and its adverse impact on the provision of insurance in financial markets. We analyse a simple security design model in which the number and payoff of securities are endogenous. Agents have rational expectations and differ in information, endowments, and attitudes toward risk. When markets are incomplete, equilibrium prices are typically partially revealing, while full relevation is attained with complete markets. The optimality of complete or incomplete markets depends on whether the adverse selection effect (the unwillingness of agents to trade risks when they are informationally disadvantaged) is stronger or weaker than the Hirshleifer effect (the impossibility of trading risks that have already been resolved), as new securities are issued and prices reveal more information. When the Hirshleifer effect dominates, an incomplete set of securities is preferred by all agents, and generates a higher volume of trade.

An important group of traders in the foreign exchange market is governments who often adhere to a foreign exchange rate policy of occasional interventions with otherwise floating rates. In this article we provide a theoretical model and empirical evidence that government foreign exchange interventions create significant adverse selection problems for dealers. In particular, our model shows that the adverse selection component of the foreign exchange spread is positively related to the variance of unexpected intervention and that expected intervention has no impact on the spread. After controlling for inventory and order processing costs, we find that bid-ask spreads increase with U.S. dollar and German deutsche mark foreign exchange rate intervention during the period 1976–1994. Furthermore, when the intervention is decomposed into expected and unexpected components, we find a statistically and economically significant increase in spreads with the variance of unexpected intervention, while expected intervention has no significant impact on spreads.

Using quarterly data to estimate state-level matching functions, we obtain point estimates that are slightly higher than are found with national gross flows data, likely because of inherent differences in the data sources. We also estimate matching functions separately by the source of the new hire, and show that the results are consistent with the assumptions of endogenous job search by the employed and a preference for employed applicants by firms. Thus, care must be taken in interpreting empirical matching functions, which are likely a reduced-form combination of a structural matching function and a job competition model.