The present technological revolution, characterized by the pervasive and growing presence of robots, automation, Artificial Intelligence and machine learning, is going to transform societies and economic systems. However, this is not the first technological revolution humankind has been facing, but it is probably the very first one with such an accelerated diffusion pace involving all the industrial sectors. Studying its mechanisms and consequences (will the world turn into a jobless society or not?), mainly considering the labor market dynamics, is a crucial matter. This paper aims at providing an updated picture of main empirical evidence on the relationship between new technologies and employment both in terms of overall consequences on the number of employees, tasks required, and wage/inequality effect.

The wide-ranging academic literature on corporate governance in the banking sector includes only a few studies on bank ownership and, specifically, on the comparative power of shareholders within the corporate structure. This paper reports an investigation into the presence of multiple large shareholders and their influence on profitability and risk in the long-term, considering a sample of 697 U.S. and European listed commercial banks from 2008 to 2018. It was found that the number of large and institutional shareholders has a positive impact on profitability, but no effect on risk. However, long-term ownership by multiple large shareholders contributes to decreasing risk in banks.

Risk behavior can be capricious and may vary from month to month. We study 62 clients of a private bank in Northern Italy. The individuals are of special interest for several reasons. As active traders, they manage the value-at-risk (VaR) of a portion of their wealth portfolios. In addition, they act alone, i.e., without input from a financial adviser. Based on VaR-statistics, we find that, in general, the subjects become more risk-averse after suffering losses and more risk-seeking after experiencing gains. The monthly gains and losses that alter investor risk behavior represent true changes in wealth but are “on paper” only, i.e., not immediately realized. Our results allow several interpretations, but they are not at odds with a house money effect, or the possibility that overconfident investors trade on illusions. Rapidly shifting risk behavior in fast response to unstable circumstances weakens individual risk tolerance as a deep parameter and key construct of finance theory.

This paper explores the employment impact of innovation activity, taking into account both R&D expenditures and embodied technological change (ETC). We use a novel panel dataset covering 265 innovative Italian firms over the period 1998-2010. The main outcome from the proposed fixed effect estimations is a labor-friendly nature of total innovation expenditures; however, this positive effect is barely significant when the sole in-house R&D expenditures are considered and fades away when ETC is included as a proxy for innovation activities. Moreover, the positive employment impacts of innovation activities and R&D expenditures are totally due to firms operating in high-tech industries and large companies, while no job-creation due to technical change is detectable in traditional sectors and SMEs.

This paper develops an approach based on Gram–Charlier-like expansions for modeling financial series to take in due account features such as leptokurtosis. A Gram–Charlier-like expansion adjusts the moments of interest of a given distribution via its own orthogonal polynomials. This approach, formerly adopted for univariate series, is here extended to a multivariate context by means of spherical densities. Previous works proposed the Gram–Charlier of the multivariate Gaussian, obtained by using Hermite polynomials. This work shows how polynomial expansions of an entire class of spherical laws can be worked out with the aim of obtaining a wide set of leptokurtic multivariate distributions. A Gram–Charlier-like expansion is a distribution characterized by an additional parameter with respect to the parent spherical law. This parameter, which measures the increase in kurtosis due to the polynomial expansion, can be estimated so as to make the resulting distribution capable of describing the empirical kurtosis found in the data. An application of the Gram–Charlier-like expansions to a set of financial assets proves their effectiveness in modeling multivariate financial series and assessing risk measures, such as the value at risk and the expected shortfall.