The effect of national governance codes on firm disclosure practices : evidence from analyst earnings forecasts

This study examines whether voluntary national governance codes have a significant effect on company disclosure practices. Two direct effects of the codes are expected: 1) an overall improvement in company disclosure practices, which is greater when the codes have a greater emphasis on disclosure; and 2) a leveling out of disclosure practices across companies (i.e., larger improvements in companies that were previously poorer disclosers) due to the codes new comply-or-explain requirements. The codes are also expected to have an indirect effect on disclosure practices through their effect on company governance practices. The results show that the introduction of the codes in eight East Asian countries has been associated with lower analyst forecast error and a leveling out of disclosure practices across companies. The codes are also found to have an indirect effect on company disclosure practices through their effect on board independence. This study shows that a regulatory approach to improving disclosure practices is not always necessary. Voluntary national governance codes are found to have both a significant direct effect and a significant indirect effect on company disclosure practices. In addition, the results indicate that analysts in Asia do react to changes in disclosure practices, so there is an incentive for small companies and family-owned companies to further improve their disclosure practices.

Impact of the Corporate Law Economic Reform Program Act 1999 on initial public offering prospectus earnings forecasts

Commencing 13 March 2000, the Corporate Law Economic Reform Program Act 1999 (Cth) introduced changes to the regulation of corporate fundraising in Australia. In particular, it effected a reduction in the litigation risk associated with initial public offering prospectus disclosure.We find that the change is associated with a reduction in forecast frequency and an increase in forecast value relevance, but not with forecast error or bias. These results confirm previous findings that changes in litigation risk affect the level but not the quality of disclosure. They also suggest that the reforms’ objectives of reducing fundraising costs while improving investor protection, have been achieved.

Error and variance bounds on sigmoidal neurons with weight and input errors

Bounds on the expectation and variance of errors at the output of a multilayer feedforward neural network with perturbed weights and inputs are derived. It is assumed that errors in weights and inputs to the network are statistically independent and small. The bounds obtained are applicable to both digital and analogue network implementations and are shown to be of practical value.

The distribution of the sample minimum-variance frontier

In this paper, we present a finite sample analysis of the sample minimum-variance frontier under the assumption that the returns are independent and multivariate normally distributed. We show that the sample minimum-variance frontier is a highly biased estimator of the population frontier, and we propose an improved estimator of the population frontier. In addition, we provide the exact distribution of the out-of-sample mean and variance of sample minimum-variance portfolios. This allows us to understand the impact of estimation error on the performance of in-sample optimal portfolios. Key Words: minimum-variance frontier; efficiency set constants; finite sample distribution

Modeling strategies in longitudinal data analysis: Covariate, variance function and correlation structure selection

A modeling paradigm is proposed for covariate, variance and working correlation structure selection for longitudinal data analysis. Appropriate selection of covariates is pertinent to correct variance modeling and selecting the appropriate covariates and variance function is vital to correlation structure selection. This leads to a stepwise model selection procedure that deploys a combination of different model selection criteria. Although these criteria find a common theoretical root based on approximating the Kullback-Leibler distance, they are designed to address different aspects of model selection and have different merits and limitations. For example, the extended quasi-likelihood information criterion (EQIC) with a covariance penalty performs well for covariate selection even when the working variance function is misspecified, but EQIC contains little information on correlation structures. The proposed model selection strategies are outlined and a Monte Carlo assessment of their finite sample properties is reported. Two longitudinal studies are used for illustration.