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FUZZY ECONOMIC REVIEW

ISSN (print) 1136-0593 · ISSN (online) 2445-4192

Volume IX, Number 1. May 2004

Computing competencies value through a fuzzy real option model

Lorella Cannavacciuolo, Luigi Iervolino, Luca Iandoli, Giuseppe Zollo. University of Naples Federico II

This paper presents a fuzzy real option model for the estimation of the value of an investment in individual compe-tencies. The use of the real option technique is justified on the basis of the following hypotheses: i) either deve-loping or acquiring a competency is a financial investment; ii) the value…
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Solving fuzzy goal programming problems

M. Jiménez. Universidad del País Vasco

M. Arenas, A. Bilbao, M.V. Rodríguez Uría. Universidad de Oviedo

In standard goal programming (GP) it is assumed that the decision maker (DM) is able to accurately determine goal values. This is unrealistic. Imprecise DM aspiration can be expressed through fuzzy sets whose membership functions represent the DM´s degree of satisfaction. When membership functi-ons are nonlinear, the model becomes a…
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Currency crises analyzed by type-i fuzzy system modelling

Ibrahim Özkan, Okan Aktan. Hacettepe University

I.B. Türksen. University of Toronto

Currency crises in developing countries have been an attracting point for economists for the last couple of decades. Different analyses and me-thods have been employed to capture the timing and the effects of crises on foreign exchange markets. In this paper, we propose a new approach to the analysis. This…
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A downside risk approach for the portfolio selection problem with fuzzy returns

Teresa León, Vicente Liern, Paulina Marco, Enriqueta Vercher. Universitat de València

José Vicente Segura. Universidad Miguel Hernández

This paper presents a new possibilistic programming approach to the portfolio selection problem. It is based on two issues: the approximation of the rates of return on securities by means of fuzzy numbers of trapezoidal form, for which we use the interval-valued ex-pectation defined by Dubois and Prade (1987), and…
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Prediction of insolvency in non-life insurance companies using support vector machines, genetic algorithms and simulated annealing

María Jesús Segovia-Vargas. Universidad Complutense de Madrid

Sancho Salcedo-Sanz. Universidad Carlos III de Madrid and The University of Birmingham

Carlos Bousoño-Calzón. Universidad Carlos III de Madrid

In this paper we propose an approach to predict insolvency of non-life insurance companies based on the application of Support Vector Machines (SVMs), hybridized with two global search heuristics: a Genetic Algorithm (GA) and a Simulated Annealing (SA). A SVM is used to classify firms as failed or non-failed, whereas…
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