Over the last few decades, growing attention to the topic of social responsibility has affected financial markets and institutional authorities. Indeed, recent environmental, social, and financial crises have inevitably led regulators and investors to take into account the sustainable investing issue; however, the question of how Environmental, Social, and Governance (ESG) criteria impact financial portfolio performances is still open. In this work, we examine a multi-objective optimization model for portfolio selection, where we add to the classical Mean-Variance analysis a third non-financial goal represented by the ESG scores. The resulting optimization problem, formulated as a convex quadratic programming, consists of minimizing the portfolio variance with parametric lower bounds on the levels of the portfolio expected return and ESG. We provide here an extensive empirical analysis on five datasets involving real-world capital market indexes from major stock markets. Our empirical findings typically reveal the presence of two behavioral patterns for the 16 Mean-Variance-ESG portfolios analyzed. Indeed, over the last fifteen years we can distinguish two non-overlapping time windows on which the inclusion of portfolio ESG targets leads to different regimes in terms of portfolio profitability. Furthermore, on the most recent time window, we observe that, for the US markets, imposing a high ESG target tends to select portfolios that show better financial performances than other strategies, whereas for the European markets the ESG constraint does not seem to improve the portfolio profitability.

We analyse the anomalously warm summer months in the Mediterranean region using the 850 hPa temperature, T850, extracted from the ERA-40 reanalysis, in order to find how these anomalies are related to the anomaly of the jet stream over the Euro-Atlantic area. In this region, the westerly jet has two main branches: the Scandinavian jet and the Mediterranean jet, which, in summer, are at a meridional minimum distance. In addition, we analyse the heat waves in the Central Mediterranean in the last half century using the temperature observations collected at fifty surface stations distributed throughout the Italian peninsula and its two major islands, and we relate these events to the position of the Mediterranean jet. We find that, when these two jets are almost aligned, there is a streak of the Mediterranean jet over the Alps and, to the south of them, an anticyclonic vorticity aloft, which forces a strong subsidence and an adiabatic warming of the troposphere over the Mediterranean. This configuration is also a characteristic of anomalously warm spells over the basin. While, when the Mediterranean jet resides further south and along the northern rim of Africa, its meridional distance from the Scandinavian jet is relatively large, the vorticity over the Mediterranean is cyclonic and this region is relatively cool. The tropospheric temperature difference between these two configurations (Mediterranean jet over the Alps and jet over North Africa) is of the order of 3 °C. Since the correlation between the observed temperatures over Italy and the ERA-40 T850 in the Central Mediterranean is more than 90%, and more than 60% in the T850 of the entire Mediterranean basin, we conclude that the heat waves over Italy are representative of exceptionally warm episodes over the Central Mediterranean, and that a warm spell over Italy is a country-scale symptom of warm spells in the Mediterranean basin. Copyright © 2006 Royal Meteorological Society.

A large number of portfolio selection models have appeared in the literature since the pioneering work of Markowitz. However, even when computational and empirical results are described, they are often hard to replicate and compare due to the unavailability of the datasets used in the experiments. We provide here several datasets for portfolio selection generated using real-world price values from several major stock markets. The datasets contain weekly return values, adjusted for dividends and for stock splits, which are cleaned from errors as much as possible. The datasets are available in different formats, and can be used as benchmarks for testing the performances of portfolio selection models and for comparing the efficiency of the algorithms used to solve them. We also provide, for these datasets, the portfolios obtained by several selection strategies based on Stochastic Dominance models (see "On Exact and Approximate Stochastic Dominance Strategies for Portfolio Selection" (Bruni et al. [2])). We believe that testing portfolio models on publicly available datasets greatly simplifies the comparison of the different portfolio selection strategies.