The trade-offs between economic efficiency and job creation in olive grove smallholdings

Cooperation among farmers is an effective alternative to reduce production costs in smallholdings of traditional olive groves and increase their low profitability. However, an increase in efficiency reduces working hours and the labour required for the management of farms. This fact can lead to negative effects in rural areas, especially considering the importance of the social aspect of the crop. Therefore, to contribute to the global discussion on the transition towards more sustainable farming, it is important to know, ex ante, what level of employment is generated by olive groves and what could be lost under more competitive and efficient scenarios. The purpose of the work is to quantify the expected impact on employment by the implementation of cooperative management methods in order to generate true and accurate information that can be used in the design of future policies that aim to increase the economic profitability of small olive farms. The results make clear that this way of managing farms, by increasing the efficiency of agricultural work, has a significant negative impact on employment. However, this management formula does encourage the professionalisation of the sector by generating quality employment, in addition to contributing to the preservation of small farms and the socio-cultural and territorial benefits they generate.     

Brexit: Tracking and disentangling the sentiment towards leaving the EU

On 23 June, 2016, the UK held a referendum to decide whether to stay in the European Union or leave. The uncertainty surrounding the outcome of this referendum had major consequences for public policy, investment decisions, and currency markets. We discuss some of the subtleties involved in smoothing and disentangling poll data in light of the problem of tracking the dynamics of the intention to Brexit, and propose a multivariate singular spectrum analysis method that produces trendlines on the unit simplex. The trendline yield via multivariate singular spectrum analysis is shown to resemble that of local polynomial smoothing, and singular spectrum analysis presents the nice feature of disentangling the dynamics directly into components that can be interpreted as changes in public opinion or sampling error. The merits and disadvantages of some different approaches for obtaining smooth trendlines on the unit simplex are contrasted, in terms of both local polynomial smoothing and multivariate singular spectrum analysis.     

Crime prediction by data-driven Green’s function method

We develop an algorithm that forecasts cascading events, by employing a Green’s function scheme on the basis of the self-exciting point process model. This method is applied to open data of 10 types of crimes happened in Chicago. It shows a good prediction accuracy superior to or comparable to the standard methods which are the expectation–maximization method and prospective hotspot maps method. We find a cascade influence of the crimes that has a long-time, logarithmic tail; this result is consistent with an earlier study on burglaries. This long-tail feature cannot be reproduced by the other standard methods. In addition, a merit of the Green’s function method is the low computational cost in the case of high density of events and/or large amount of the training data.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

Can governance help in making an IPO “successful”? New evidence from Europe

This paper investigates the determinants of a “successful” IPO from a corporate governance perspective upon a representative sample of European listings from 2000 to 2015. We use an extensive dataset of market performance, financial data, and corporate governance characteristics to run the investigation. Differently from previous studies, our analysis embraces both a short‐term perspective and a medium–long‐term perspective, where the board of directors seems to perform different tasks, moving from a value creation to a value protection strategy. Among the others, we find that board size, board independence, and their qualifications, together with their experience in other boards, are associated with a positive performance of the IPO in a short‐term horizon and in the medium–long‐term period, although significant differences emerge among those time perspectives.     

Managers’ exploration activities and individual unlearning: the mediating role of learning orientation and reflection

Abstract

The main goal of this study was to investigate the effects of managers’ exploration activities on learning orientation, reflection, and unlearning of team members. A questionnaire survey was conducted among 115 employees in 23 teams from a Japanese pharmaceutical company. The results of multi-level analyses suggest that managers’ exploration activities had a direct influence on team members’ learning orientations, which subsequently had a positive influence on their unlearning, with and without the mediation of reflection. These findings suggest that a subordinate’s unlearning is driven by managers’ activities through motivational and cognitive processes. The theoretical and practical implications of the findings are discussed.     

Environmental policies,national culture,and stock price crash risk: Evidence from renewable energy firms

This study investigates the impact of country‐level environmental performance and national culture on the stock price crash risk of renewable energy firms. Employing a large sample of 626 renewable energy firms across 31 countries, we find a significant nonlinear relationship between country‐level environmental performance and crash risk. National culture dimensions are found to strongly predict the crash risk of renewable energy firms, particularly after the global financial crisis. On the contrary, national culture dimensions and environmental policies are observed to not exert any significance in explaining the crash risk of fossil fuel firms. Our results are robust with respect to alternative measures of stock price crash risk and the endogeneity of national culture dimensions. Overall, the findings of this paper contribute to the environmental economics literature by providing new evidence regarding the role of societal and environmental factors in explaining the stock price crash risk of energy firms.     

Resurrecting the size effect: Evidence from a panel nonlinear cointegration model for the G7 stock markets

Firm size is known to be an important factor affecting stock returns. This study proposes a panel threshold cointegration model to investigate the impact of the size effect on stock returns for the panel of G7 countries: Canada, France, Germany, Italy, Japan, the U.K., and the U.S. over the period 1991:1–2012:12. The empirical analysis is based upon the nonlinear cointegration framework using the asymmetric ARDL cointegration methodology (Shin et al., 2011). This methodological approach permits a much richer degree of flexibility in the dynamic adjustment process toward equilibrium, than in the classical linear model. Our findings indicate the presence of asymmetric adjustment around a unique long-run equilibrium. In particular, the empirical analysis provides evidence of asymmetric effects between stock returns and the size effect, while controlling for the book-to-market ratio and the price-to-earnings ratio.     

Rumor Clarification and Stock Returns: Do Bull Markets Behave Differently from Bear Markets?

This paper analyzes the effects of official rumor clarification on Chinese stock returns under different market conditions. The results show that the average cumulative abnormal return after the clarification event is significantly positive in a bull market, and significantly negative in a bear market. The results are robust across various types of rumors, including rumors of mergers and acquisitions, asset restructuring, and positive changes in a firm's operations. Moreover, in both bull and bear markets, investors are unable to distinguish between rumors that prove true and those that prove false, or between strong and weak rumor denial. Furthermore, investors are also unable to adjust their strategies accordingly.