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.     

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.     

Periodic strategies in optimal execution with multiplicative price impact

We study the optimal execution problem with multiplicative price impact in algorithmic trading, when an agent holds an initial position of shares of a financial asset. The interselling decision times are modeled by the arrival times of a Poisson process. The criterion to be optimized consists in maximizing the expected net present value of the gains of the agent, and it is proved that an optimal strategy has a barrier form, depending only on the number of shares left and the level of the asset price.     

Evolution of indigenous tourism among the Lacandon of Chiapas: an application of Weaver’s model

ABSTRACT

In this paper, Weaver’s six-stage indigenous tourism model is applied to the Lacandon Maya (Hach Winik) of Chiapas, Mexico. Based on a comprehensive review of the anthropological and historical literature on this indigenous group, combined with longitudinal ethnographic and collaborative research performed with tourism entrepreneurs, the Lacandon tourism experience is assessed from the pre-European period until present. By analysing a case study of indigenous tourism in Mexico, a developing country in another geographical region and with a different colonial past, this work supplements Weavers’ perspective. The results show that the fourth and fifth stages of Weaver’s model coincide in this case study, while the sixth stage is still incomplete. Although the Lacandon case has its peculiarities and bearing in mind that several different factors should be considered in the Latin American context, the model proves to be an interesting tool for indigenous tourism analysis in developing countries.     

Determinants of Fair Trade Product Purchase Intention of Dutch Consumers According to the Extended Theory of Planned Behaviour

The study reported in this paper investigated the determinants of fair trade (FT) product purchase intention among Dutch consumers according to the extended Theory of Planned Behaviour and determined whether the effects of those determinants differ between male and female consumers. To test the various research hypotheses, an online survey with 499 respondents from a Dutch research panel was employed. Results of the multi-group analysis using a structural equation modelling approach reveal that FT product purchase intention of both male and female consumers are predicated on moral obligation and self-identity. The impact of subjective norm on purchase intention is statistically significant for male consumers only. Analyses reveal that, indeed, the impact of subjective norm on FT product purchase intention is moderated by consumers’ gender.