This paper is closed access until 16th October 2019.Bank branch efficiency measurements range from simple ratio and standard regression analyses to more complex frontier approaches, each with specific strengths and weaknesses. However, in isolation, the indexes that these approaches generate fail to capture the multidimensional nature of bank branch efficiency. This paper develops a three-step procedure that enables combining the strengths of the existing approaches. We begin by taking the widest number of efficiency indexes proposed in literature (step 1), reduce the redundant information through a collinearity analysis (step 2), and categorize bank branches into efficiency classes through a clustering procedure (step 3). We test our approach on 23 branches of an Italian regional bank. The results show that this three-step approach is able to provide a multidimensional view of efficiency based on indexes employing a wide range of theoretical and methodological approaches with different ways of conceiving (intermediation vs. production) and measuring (stock, vs. flow, or physical measures) bank outputs and inputs, different definitions of efficiency (technical and cost efficiency), and different measurement approaches (ratios, standard regression analysis and frontier functions). The resulting efficiency ranking is consistent with those that univariate indexes generate and provides a balanced evaluation of branch efficiency when such indexes produce contradictory indications

In the last few years, financial managers, supervisors and policy makers have been collectively engaged on the role of the banking and financial sector in relation to environmental and sustainability issues. The efficiency level that characterizes the production process of banks and financial intermediaries is strictly interconnected with sustainability so that, from a long term perspective, cost and profit efficiency reveals to be one of the most important premises for sustainability. This work aimed to analyze the level of efficiency achieved by European banking groups to verify if some key drivers of efficiency deriving from strategic choices outlined by literature on single banks (i.e., size, number of branches, cost income ratio and geographical location) are the same when considering banking groups. The study is focused on the period 2011–2016 that is characterized by, at least, two critical events that occurred after the sub-prime crisis. The level of efficiency of each banking group was measured with the Stochastic Frontier Approach and then the units were clustered into three homogeneous sets. Observing these clusters, the results show that the groups belonging to some specific countries appear to be more efficient with respect to others. Moreover, the banking groups characterized by many branches and a high level of cost income ratio exhibit a medium level of efficiency. Eventually, the biggest banking groups are the least efficient.

Using a unique dataset of 50 listed companies that meet the majority of the OECD requirements for social impact investments, we construct a social impact finance stock index and investigate how investing in social impact firms can contribute to portfolio risk-return performance. We build portfolios with three different methodologies (naïve, Markowitz mean-variance optimization, GARCH-copula model), and we study the performance in terms of returns, Sharpe ratio, utility, and forecast premium based on a constant relative risk aversion function for investors with different levels of risk aversion. Consistent with the idea that social impact investment can improve portfolio risk-return performance, the results of our macro asset allocation analysis show the importance of a large fraction of investor portfolios’ stake committed to social impact investments.