Characterization of the Row Geometric Mean Ranking with a Group Consensus Axiom

An axiomatic approach is applied to the problem of extracting a ranking of the alternatives from a pairwise comparison ratio matrix. The ordering induced by row geometric mean method is proved to be uniquely determined by three independent axioms, anonymity (independence of the labelling of alternatives), responsiveness (a kind of monotonicity property) and aggregation invariance, which requires the preservation of group consensus, that is, the pairwise ranking between two alternatives should remain unchanged if unanimous individual preferences are combined by geometric mean.     

Inflation,real economic growth and unemployment expectations: an empirical analysis based on the ECB survey of professional forecasters

Expectations are at the centre of modern macroeconomic theory and policymakers. In this article, we examine the predictive ability and the consistency properties of macroeconomic expectations using data of the European Central Bank (ECB) Survey of Professional Forecasters (SPF). In particular, we provide evidence on the properties of forecasts for three key macroeconomic variables: the inflation rate, the growth rate of real gross domestic product and the unemployment rate.     

The practice of collective bargaining in the private sector in Quebec (Canada): the changing ‘rules of the game’

Drawing on 45 semi‐structured interviews with union negotiators active in the Quebec private sector, this article shows that local bargaining practices, despite their plurality, have tended to change following major trends. It also reveals, more fundamentally, a redefinition of the ‘rules of the game’. The transformation and stability of these social rules, which are much more focused on the needs of employers, have tended to weaken collective bargaining as a tool for industrial democracy.     

The Effect of Media Coverage on the Pricing of Seasoned Equity Offerings

This paper examines the role of media coverage and investor attention on the outcomes of seasoned equity offerings (SEOs). I use an archive of Thomson Reuters news articles to proxy for firm visibility and investor attention. I find that the volumes of news articles prior to the offerings are positively associated with the offer price discounts of SEOs. Furthermore, the volumes of news articles are negatively associated with the cumulative abnormal returns three days around the SEOs. I conclude that the costs of equity increase with media coverage prior to SEOs. Overall, the evidence is consistent with the hypothesis that media coverage affects investors' information processing in SEOs. Copyright © 2018 ASAC. Published by John Wiley & Sons, Ltd.     

Foreign direct investment subsidy in a dynamic stochastic general equilibrium model with heterogeneous firms

This study analyzes the macroeconomic impacts of subsidies to attract multinational corporations when firms are determining whether to enter or how to serve foreign markets. We show that a small FDI subsidy scheme induces consumption gains and delivers short‐term welfare improvement for the FDI host country if firms differ in productivity. However, the subsidy generates a new problem and results in the wealth reallocation effect, leading to welfare deterioration for the host country in the long run. Moreover, we find that a subsidy program induces a welfare improvement for the host country if it is offered to all domestic producers instead of foreign producers only in the host country.     

Information and Knowledge as Antecedents of Consumer Attitudes and Intentions to Buy and Recommend Fair-Trade Products

The authors propose and empirically test a causal model to understand how the availability of fair-trade information and consumer knowledge about this issue affect consumers’ attitudes and intentions toward fair-trade products. The model is built upon the attitude-behavior paradigm and the premises of agency theory. It is tested through structural equation modeling with a sample of 292 Spanish consumers. The findings are that consumers do not have good knowledge about fair trade and that this is significantly determined by the lack of information about this in the market. It is also observed that consumers’ perceptions about the availability of fair-trade information have negative effects on their concern about this issue and that such information as is available is not effective in reducing consumer skepticism. The research represents an extension of previous fair-trade literature because the role of information and communication in improving consumer attitudes and buying intentions has rarely been explored in the case of ethical products.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

How capital markets assess the credibility and accuracy of CSR reporting: Exploring the effects of assurance quality and CSR restatement issuance

This research examines how the credibility and accuracy of corporate social responsibility (CSR) disclosures enhance market confidence by exploring their effect on the cost of capital. How do capital markets react to higher-quality assurance of CSR reporting? Can the hypothetical reduction in the cost of capital that assurance quality produces be explained by restatements of previous information given in these CSR reports? We explore the relationship between assurance quality, as a proxy for credibility and market confidence, and the mediating effect of CSR restatements as indicators of accuracy. We propose regression models with a sample of CSR reports issued by European companies. Building upon the signalling and legitimacy theories, the results support the positive effect of higher assurance quality statements on market confidence by confirming a decrease in the cost of capital; assurance quality gives an indication of credibility to the capital market and restatements related to methodological updates signal accuracy. The reduction in the cost of capital brought on by higher assurance quality appears to be determined by the issuance of CSR restatements, which create legitimacy for both client companies and assurance providers. CSR reporting and assurance have reached a greater level of maturity as demonstrated by the evolution of these practices and financial market perceptions.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.