Comments on ‘Jointness of growth determinants’

We consider the measures of jointness proposed by Doppelhofer and Weeks ( 2009 ) and Strachan ( 2009 ) in the context of variable selection. Using the general criteria suggested in Ley and Steel ( 2007 ), we identify some shortcomings of these measures, which are illustrated with empirically relevant example cases. We argue that careful examination of the properties of any jointness measure is critical before using it to inform decisions, and favour the use of the measures proposed in Ley and Steel ( 2007 ). Copyright © 2009 John Wiley & Sons, Ltd.     

On the effect of prior assumptions in Bayesian model averaging with applications to growth regression

We consider the problem of variable selection in linear regression models. Bayesian model averaging has become an important tool in empirical settings with large numbers of potential regressors and relatively limited numbers of observations. We examine the effect of a variety of prior assumptions on the inference concerning model size, posterior inclusion probabilities of regressors and on predictive performance. We illustrate these issues in the context of cross‐country growth regressions using three datasets with 41–67 potential drivers of growth and 72–93 observations. Finally, we recommend priors for use in this and related contexts. Copyright © 2009 John Wiley & Sons, Ltd.     

A re-examination of international portfolio diversification based on evidence from leveraged bootstrap methods

This article investigates the issue of international portfolio diversification with respect to the three largest financial markets in the world—namely the US, Japan and the UK. In addition to making use of traditional portfolio analysis, we also suggest a procedure to calculate bootstrap correlation coefficients that can take into account the dynamic structure between the markets as measured by bootstrapped causality tests. Weekly data is used. The results from the first approach are supporting international diversification. The bootstrapped causality tests provide additional empirical support for this conclusion since the size of the causal effects is negligible and the bootstrap correlations are similar as the standard ones.     

Estimating time-varying variances and covariances via nearest neighbour multivariate predictions: applications to the NYSE and the Madrid Stock Exchange Index

In this article, we present a technique to obtain the time-varying covariance matrix for several time series for nearest neighbour predictors. To illustrate the use of this technique, we analyse the time-varying variances and correlations between the daily returns on two equity stock market indexes, the New York Stock Exchange and the Madrid Stock Exchange Index.     

Integrated reporting and capital markets in an international setting: The role of financial analysts

This study investigates the interplay between integrated reporting (IR) and capital markets. In particular, building on voluntary disclosure and information processing theories, we hypothesize and empirically find that IR adoption improves analysts' ability to make accurate earnings forecasts. Whereas previous studies focus on the South African context, we rely on an international sample that also allows us to study the moderating effect of the corporate governance regime (shareholder or stakeholder oriented). The results suggest that IR improves analysts' ability to make accurate predictions to a larger extent in North America than in Europe, and we derive interesting insights on the much‐debated nature of IR. This study offers valuable insights to policy makers interested in improving disclosure practices in the financial market.     

Determinants of cross-border M&As in Latin America

This article analyzes the determinants of cross-border M&As in the Latin American region during the period 1998-2004. Using a unique dataset of 868 Mergers and Acquisitions (M&A) events, the study focuses attention on the effect of macroeconomic and investor protection conditions in the countries where the companies reside over the likelihood of these companies participating in a cross-border M&A transaction. The study considers the effect of company-specific variables in the likelihood of going cross-border. Univariate analysis and logistic regressions strongly support the idea that better economic and business-friendly conditions in the countries where the target operates, increase the likelihood of cross-border merger. Results show that not only is the business environment in the target country important but also in the bidder country. Lower levels of property rights protection in the acquirer country negatively affect the likelihood of a cross-border deal. Finally, the likelihood of a cross-border merger increases when the target faces higher cost of funding than the acquirer's.     

Derived versus full name brand extensions

Most brand extension studies follow the assumption that brand extensions use the full original parent brand name (e.g., Oral-B tooth brush may extend to Oral-B dental floss). However, some companies use derived brand names in their brand extension strategies (e.g. Nestea Iced Tea). This study explores the advantages and disadvantages of derived brand extensions compared to full name extensions. The study examines the importance of target market effects on the evaluation of both brand extension strategies. Findings support the idea that derived brand names leverage parent brand evaluations and protect parent brand from extension failures.