Blanca Pérez-Gladish

Fuzzy compromise programming for portfolio selection

The aim of this paper is to solve a portfolio selection problem using Sharpes single index model in a soft framework. Estimations of subjective or imprecise future beta for every asset can be represented through fuzzy numbers constructed on the basis of statistical data and the relevant knowledge of the financial analyst; the model, therefore, works with data that contain more information than any classical model and dealing with it does not involve a great extra computational effort. In order to solve the portfolio selection problem we have formulated a Fuzzy Compromise Programming problem. For this task we have introduced the fuzzy ideal solution concept based on soft preference and indifference relationships and on canonical representation of fuzzy numbers by means of their @a-cuts. The accuracy between the ideal solution and the objective values is evaluated handling the fuzzy parameters through their expected intervals and using discrepancy between fuzzy numbers in our analysis. A major feature of this model is its sensitivity to the analysts opinion as well as to the decision-makers preferences. This allows interaction with both when it comes to design the best portfolio.

Socially responsible investment: A multicriteria approach to portfolio selection combining ethical and financial objectives

In a context of Socially Responsible Investment (SRI), this paper deals with portfolio selection for investors interested in ethical policies. In the opportunity set there are ethical assets and other assets which are not characterized as ethical. Two goals are considered, the traditional financial goal in the classical utility theory under uncertainty and an ethical goal in the same utility framework. A new financial-ethical bi-criteria model is proposed with absolute risk aversion coefficients and targets depending on the investor’s ethical profile. This approach is relevant as an increasing number of mutual funds are becoming interested in SRI strategies. From the proposed model, an actual case on green investment is developed. Concerning this case (without generalizing to other contexts), an analysis of the numerical results shows that efficient portfolios obtained by the traditional E-V model outperform the strong green portfolios in terms of expected return and risk, but this does not significantly occur with weak green investment.

Profiling Socially Responsible Investors: Australian Evidence

With the aid of an online survey, the purpose of this study is to examine financial preferences; social, environmental and ethical concerns; and socio-demographic characteristics of Australian socially responsible (SR) investors. The study advances knowledge of SR investors’ profiles and their motivations when making investment decisions. Based on a sample of 145 investors, our findings suggest that SR investors seek financial return as well as non-financial benefits. Social conscience and social health issues, as opposed to environmental issues, are relevant to investors. Interestingly, investor risk tolerance is a relatively unimportant factor in the choice of SR investments. Finally, in terms of socio-demographics, SR investors tend to be middle-aged, be middle-income professionals and have tertiary qualifications.

An AHP-based approach to mutual funds' social performance measurement

Socially responsible investors have both, financial as well as non-financial goals in investment decision-making. Several methods for ranking mutual funds based on financial performance have been developed; nonetheless, ranking based on non-financial performance is rather underdeveloped. The aim of this article is to present a ranking method for mutual funds, based on their socially responsible performance. The suggested ranking method could complement financial information and help socially responsible mutual fund managers, individual and institutional investors in their portfolio selection process. The results reveal, after comparing the rank obtained with the proposed method with rankings derived from other socially responsible measurements based on just one decision criterion, that an integrated framework using multiple criteria decision analysis (MCDA) techniques could help the investor in selecting a suitable socially responsible mutual funds portfolio, because the consideration of several criteria reflect more precisely the multiple dimensions of this decision making problem.

Selecting the optimum portfolio using fuzzy compromise programming and Sharpe's single-index model

Different approaches besides the traditional Markowitzs model have been proposed in the literature to analyze portfolio selection problems. Among them, Compromise Programming (CP) is a suitable multiobjective programming technique which allows the handling of several objectives in those situations in which the existence of a high level of conflict between criteria does not permit the simultaneous optimization of all the considered objectives. When objectives and constraints are in an imprecise environment Fuzzy CP arises as a suitable solving method. Imprecision will be quantified by means of fuzzy numbers that represent the continuous possibility distributions for fuzzy parameters and hence place a constraint on the possible values the parameters may assume. In this paper a new Fuzzy Compromise Programming approach is proposed based on the obtaining of the minimum fuzzy distance to the fuzzy ideal solution of the portfolio selection problem. Once this fuzzy distance has been obtained the second step consists of finding a crisp decision vector, an optimal portfolio, implying a fuzzy distance to the ideal solution the more accurate as possible to the fuzzy minimum distance previously obtained.

Synthetic indicators of mutual funds’ environmental responsibility: An application of the Reference Point Method

Socially Responsible Investing (SRI) is broadly defined as an investment process that integrates not only financial but also social, environmental, and ethical (SEE) considerations into investment decision making. SRI has grown rapidly around the world in the last decades. In the last years, given the causes of the 2008 financial crisis, ethical, social, environmental and governance concerns have become even more relevant investment decision criteria. However, while a diverse set of models have been developed to support investment decision-making based on financial criteria, models including also social responsibility criteria are rather scarce.

Selecting Portfolios Given Multiple Eurostoxx-Based Uncertainty Scenarios: A Stochastic Goal Programming Approach from Fuzzy Betas

Abstract We deal with the buy-and-hold choice of fund portfolios by considering multiple states of nature (future market scenarios). These states are associated with goals in the sense that the investor pursues to optimize a classical financial objective function as much as possible whatever the states of nature. As this classical function is very cumbersome for handling, a satisficing proxy is used. This proxy is Stochastic Goal Programming (SGP), a recent uncertainty multiobjective model characterized as follows: (a) it relies on Von Neumann and Morgensterns-Arrows Eu(R) principles in a framework of bounded rationality; (b) its moderate computational burden allows easy application to large scale problems. In SGP, the variability matrices of goals are aggregated by Arrows risk aversion coefficients. Concerning the case study, the states of nature are Eurostoxx market index scenarios defined from time series. As an opportunity set of assets, we use a large set of funds managed by an international consultancy. Potential returns on each fund are related to each scenario by using betas. As eliciting betas can be made from different samples leading to different results, we use fuzzy logic to decide among these different results in a framework of imprecision/uncertainty. Our approach is new as it combines SGP and fuzzy tools.

Fuzzy Extended Lexicographic Goal Programming

Goal Programming (GP) is perhaps the most widely used approach in the field of multicriteria decision making The major advantage of the GP model is its great flexibility which enables the decision maker to easily incorporate numerous variations of constraints and goals. Romero provides an unifying basis for GP and multiple objective programming approaches, Extended Lexicographic Goal Programming (ELGP) which is a rather general GP structure encompassing Archimedean and MINMAX (Tchebychev) GP variants as particular cases. In this work we propose the use of this general primary estructure (ELGP) for the resolution of fuzzy multiobjective programming problems.

A new approach of Romero’s extended lexicographic goal programming: fuzzy extended lexicographic goal programming

Goal programming (GP) is perhaps one of the most widely used approaches in the field of multicriteria decision making. The major advantage of the GP model is its great flexibility which enables the decision maker to easily incorporate numerous variations on constraints and goals. Romero provides a general structure, extended lexicographic goal programming (ELGP) for GP and some multiobjective programming approaches. In this work, we propose the extension of this unifying framework to fuzzy multiobjective programming. Our extension is carried out by several methodologies developed by the authors in the fuzzy GP approach. An interval GP model has been constructed where the feasible set has been defined by means of a relationship between fuzzy numbers. We will apply this model to our fuzzy extended lexicographic goal programming (FELGP). The FELGP is a general primary structure with the same advantages as Romero’s ELGP and moreover it has the capacity of working with imprecise information. An example is given in order to illustrate the proposed method.

Fuzzy Degree of Geographic Appropriateness for Social Impact Investing

Impact investing is an investment practice that is characterized by the explicit intentionality of attaining a social impact and the requisite of report and measure this impact in a transparent way. The investment decision making process has two main stages. In the first stage, filters are applied regarding four critical issues: target geography, impact theme, asset class and target return category. In this phase, the set of possible investment alternatives are determined based on their appropriateness for impact investment in terms of those four essential aspects. In a second stage, efficient portfolios are obtained taking into account financial criteria (maximizing expected return, minimizing risk) and trying to maximize the social impact of the portfolio of investments. In this chapter, we will focus on the establishment of the target geography for the impact investment proposing a fuzzy indicator of the appropriateness of a geographic area in terms of impact investment. This indicator will be based on Soft Computing techniques which are an attractive tool given the imprecise, ambiguous and uncertain nature of data related to social impact investment.