Stefania Funari

Measuring the performance of ethical mutual funds: a DEA approach

The solidarity and social responsibility features that characterize the ethical mutual funds satisfy the fulfillment of humanitarian aims, but may lower the investment profitability. Hence, when we measure the performance of ethical mutual funds, we cannot disregard the ethical component. In this contribution, we propose a performance indicator that considers the expected return, the investment risk, the ethical component and the subscription and redemption costs together. The performance measure proposed is obtained using a data envelopment analysis (DEA) approach, which allows one to measure the relative efficiency of decision-making units in the presence of a multiple input–multiple output structure. The DEA performance indicator for ethical funds can be computed with different models, according to the nature of the ethical indicator that characterizes the socially responsible funds. In particular, a DEA categorical variable model seems appropriate.

An interval portfolio selection problem based on regret function

Abstract Different approaches, besides the traditional Markowitz’s model, have been proposed in the literature to analyze portfolio selection problems. Among them we can cite the possibilistic portfolio models, which treat the expected return rates of the securities as fuzzy or possibilistic variables, instead of random variables. Such models, which are based on possibilistic mathematical programming, describe the uncertainty of the real world as ambiguity and vagueness, rather than stochasticity. Actually, another way to treat the uncertainty in decision making problems consists of assuming that the data are not well defined, but are able to vary in given intervals. Interval analysis is thus appropriate to handle the imprecise input data. In this paper we consider a portfolio selection problem in which the prices of the securities are treated as interval variables. In order to deal with such an interval portfolio problem, we propose the adoption of a minimax regret approach based on a regret function.

Constant and Variable Returns to Scale DEA Models for Socially Responsible Investment Funds

In order to evaluate the performance of socially responsible investment (SRI) funds, we propose some models which use data envelopment analysis (DEA) and can be computed in all phases of the business cycle. These models focus on the most crucial elements of an investment in mutual funds.

DEA models with a constant input for SRI mutual funds with an application to European and Swedish funds

Data envelopment analysis (DEA) allows one to take into account the degree of social responsibility of mutual funds, together with financial risk and return. This contribution proposes some DEA models in which the input and output variables are focused on the main determinants of investments in socially responsible investing (SRI) mutual funds. Unlike other DEA models, a constant initial capital and the final value of the investment are considered; this ensures the positivity of all variables, even during financial crises. The initial capital deposited by an investor is assumed to be equal for all funds, so that we have a constant input. The implications of the presence of a constant input in DEA models are studied, which have important consequences for the analysis of the performance of mutual funds, in particular with regard to the type of returns to scale. The models proposed are applied to the European data to evaluate the performance of SRI mutual funds in the period June 2006 to June 2009. Moreover, a specific analysis compares the performance of SRI and non-SRI mutual funds, in order to determine if SRIs require a sacrifice in terms of financial rewards. Finally, a more detailed investigation is carried out for the Swedish SRI mutual funds.

An evolutionary approach to preference disaggregation in a MURAME-based creditworthiness problem

HighlightsFirst application of the particle swarm optimization (PSO) to preference disaggregation problems in a MUlticriteria Ranking MEthod (MURAME) framework.Reformulation of the involved constrained optimization problem in terms of penalized unconstrained optimization problem for the PSO implementation.Application to large real credit scoring and credit ranking problems. In this paper, we propose to use an evolutionary methodology in order to determine the values of the parameters for implementing the MUlticriteria RAnking MEthod (MURAME). The proposed approach has been designed for dealing with a creditworthiness evaluation problem faced by an important north-eastern Italian bank needing to score and/or to rank firms (which act as alternatives) applying for a loan. The point of the matter, known as preference disaggregation, consists in finding the MURAME parameters which minimize the inconsistency between the MURAME evaluations of given alternatives and those properly revealed by the decision maker (DM). To find a numerical solution of the involved mathematical programming problem, we adopt an evolutionary algorithm based on the particle swarm optimization (PSO), which is an iterative metaheuristics grounded on swarm intelligence. The obtained results show a high consistency between the MURAME outputs produced by the PSO-based solution algorithm and the actual scoring/ranking of the applicants provided by the bank (which acts as the DM).

DEA Performance Assessment of Mutual Funds

The objectives of this paper are manyfold. First we present a comprehensive review of the literature of DEA models for the performance assessment of mutual funds. Then we discuss the problem of the presence of negative returns in DEA modeling for mutual funds and we identify a DEA model that is financially justified and tackles the issue of negative returns in a natural way. Moreover, we present an empirical application on real market data, considering different risk measures. We consider also different holding periods, which include both a period of financial crisis and one of financial recovery. Moreover, we compare the results of the DEA performance measure with those obtained with traditional financial indicators.

Chain store against manufacturers: Regulation can mitigate market distortion

Contemporary domination of chain-stores in retailing is modeled, perceiving a monopolistic retailer as a market leader. A myriad of her suppliers compete in a monopolistic competitive sector, displaying quadratic consumers’ preferences for a differentiated good. The leader announces her markup before the suppliers choose their prices/quantities. She may restrict the range of suppliers or allow for free entry. Then, a market distortion, stemming from double marginalization and excessive variety would be softened whenever the government allows the retailer to apply an entrance fee to the suppliers, or/and per-quantity sales subsidies (doing the opposite to usual Russian regulation).

Particle Swarm Optimization for Preference Disaggregation in Multicriteria Credit Scoring Problems

In this paper we deal with the problem of preference disaggregation in credit scoring problems developed by using multicriteria analysis. In order to determine the values of the parameters that characterize the preference model of the decision maker, we adopt Particle Swarm Optimization, which is a biologically-inspired heuristics based on swarm intelligence. We test the ability of PSO to find the optimal values of the parameters on a real data set provided by an Italian bank.

The Role of Fund Size and Returns to Scale in the Performance of Mutual Funds

In this contribution we investigate the effects of the size of mutual funds on their performance by using a Data Envelopment Analysis (DEA) approach. We discuss the role of fund size in the performance evaluation and wonder whether it is appropriate to include size information among the input/output variables of the DEA models. Moreover, we analyze the nature of returns to scale in mutual fund performance and investigate whether returns to scale are constant, increasing or decreasing in a set of European mutual funds.

Building a Global Performance Indicator to Evaluate Academic Activity Using Fuzzy Measures

The aim of this note is to provide a global performance index that allows to evaluate the performance of each faculty member and which is able to consider the multidimensional nature of the academic activity in terms of research, teaching and other activities that academics should/might exercise. In order to model also the case in which there could be synergic and redundant connections among the different areas of the academic activity, we propose to use fuzzy measures and the Choquet integral as an aggregator of the different components.