Using Bayesian variable selection methods to choose style factors in global stock return models

This paper investigates the presence of global style factors in global equity investment. To this end, we apply Bayesian variable selection methods from the statistics literature to give guidance in the decision to include/omit factors in a global (linear factor) stock return model. Once we have accounted for country and sector, it is possible to see which style or styles best explains current asset returns. This study does not find compelling evidence for global styles as useful explanatory factors in a fixed parameter regression model, once country and sector have been accounted for.     

The new ecological paradigm and responses to climate change in China

The purpose of this study was to develop and evaluate a Chinese-Mandarin version of the revised new ecological paradigm (NEP-R) scale. In a sample of 515 Mandarin-speaking Chinese nationals, we first assessed the factor structure and internal consistency of the NEP-R and assessed its validity by examining associations with global warming risk perceptions and mitigation behavior. Respondents completed the NEP-R scale, together with measures of risk perception and mitigation behavior. Exploratory and confirmatory factor analyses indicated that a two-factor solution, reflecting ecocentric and anthropocentric worldviews, best fit the data. Multi-group path analysis revealed that respondents with stronger ecocentric and weaker anthropocentric worldviews perceived more risks associated with global warming. In turn, respondents who perceived more risks reported engaging in more global warming mitigation behaviors. But importantly, the path between risk perceptions and behavior was significantly stronger for highly educated respondents than for less educated respondents, suggesting that education may represent an important strategy for bridging the gap between perceived risks about global warming and action.     

The angel investment decision: insights from Australian business angels

While much has been written about the investment criteria of business angels, few studies explore why these particular criteria are important to them. The Australian context has a diverse range of actors along with complex jurisdictional arrangements, making for an interesting background for investigation of the angel finance phenomenon. We examine 12 business angels in the rapidly changing Australian context and use nine corroborating participants to validate responses and identify four key drivers – personal experience, trust, the need to contribute and realistic expectations – that influence business angels during the initial investment process.     

L'innovation dans les services à forte intensité de connaissances (SFIC)†

This article examines the determinants of innovation in knowledge intensive business services (KIBS), generally hypothesizing that differences in the effect of the determinants of innovation depend on the type of innovation developed within KIBS. The study results are based on estimates of two econometric models using data from a survey of 392 firms in the province of Ontario, Canada.     

Real‐time forecast combinations for the oil price

Baumeister and Kilian (Journal of Business and Economic Statistics, 2015, 33(3), 338–351) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and other real‐time variables. Mindful of the importance of Brent crude oil as a global price benchmark, we extend consideration to the North Sea‐based measure and update the evaluation sample to 2017:12. We model the oil price futures curve using a factor‐based Nelson–Siegel specification estimated in real time to fill in missing values for oil price futures in the raw data. We find that the combined forecasts for Brent are as effective as for other oil price measures. The extended sample using the oil price measures adopted by Baumeister and Kilian yields similar results to those reported in their paper. Also, the futures‐based model improves forecast accuracy at longer horizons.     

Sim-heuristics low-carbon technologies’ selection framework for reducing costs and carbon emissions of heavy goods vehicles

ABSTRACT

UK logistics fleets face increasing competitive pressures due to volatile fuel prices and the small profit margins in the industry. By reducing fuel consumption, operational costs and carbon emissions can be reduced. While there are a number of technologies that can reduce fuel consumption, it is often difficult for logistics companies to identify which would be the most beneficial to adopt over the medium and long terms. With a myriad of possible technology combinations, optimising the vehicle specification for specific duty cycles requires a robust decision-making framework. This paper combines simulated truck and delivery routes with a metaheuristic evolutionary algorithm to select the optimal combination of low-carbon technologies that minimise the greenhouse gas emissions of long-haul heavy goods vehicles during their lifetime cost. The framework presented is applicable to other vehicles, including road haulage, waste collection fleets and buses by using tailored parameters in the heuristics model.