Treffer: Minimizing loss probability bounds for portfolio selection

Title:
Minimizing loss probability bounds for portfolio selection
Authors:
GOTOH, Jun-Ya, TAKEDA, Akiko
Source:
European journal of operational research. 217(2):371-380
Publisher Information:
Amsterdam: Elsevier, 2012.
Publication Year:
2012
Physical Description:
print, 1/4 p
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Programmation mathématique, Mathematical programming, Théorie du risque. Assurance, Risk theory. Actuarial science, Sélection et gestion de portefeuilles, Portfolio theory, Analyse risque, Risk analysis, Análisis riesgo, Apprentissage probabilités, Probability learning, Aprendizaje probabilidades, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Borne erreur, Error bound, Limite error, Borne inférieure, Lower bound, Cota inferior, Borne supérieure, Upper bound, Cota superior, Bourse valeurs, Stock exchange, Bolsa valores, Classification à vaste marge, Vector support machine, Máquina ejemplo soporte, Dérivée fractionnaire, Fractional derivative, Derivada fracionario, Finance, Finanzas, Fonction rationnelle, Rational function, Función racional, Garantie contre risque, Warranty, Garantía contra riesgo, Gestion financière, Financial management, Administración financiera, Gestion portefeuille, Portfolio management, Gestión cartera, Gestion risque, Risk management, Gestión riesgo, Marché valeurs, Stock markets, Minimisation, Minimization, Minimización, Modèle empirique, Empirical model, Modelo empírico, Modélisation, Modeling, Modelización, Optimisation sous contrainte, Constrained optimization, Optimización con restricción, Programmation fractionnaire, Fractional programming, Programación fraccionaria, Programmation non convexe, Non convex programming, Programación no convexa, Quantile, Cuantila, Sélection portefeuille, Portfolio selection, Selección cartera, CVaR (conditional value-at-risk), Portfolio optimization, SVM (support vector machine)
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Industrial and Systems Engineering, Chuo University, 2-13-27 Kasuga, Bunkyo-ku, Tokyo 112-8551, Japan
Department of Administration Engineering, Keio University, 3-14-1 Hiyoshi, Kahoku, Yokohama, Kanagawa 223-8522, Japan
ISSN:
0377-2217
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=25627363
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Operational research. Management
Accession Number:
edscal.25627363
Database:
PASCAL Archive

Weitere Informationen

In this paper, we derive a portfolio optimization model by minimizing upper and lower bounds of loss probability. These bounds are obtained under a nonparametric assumption of underlying return distribution by modifying the so-called generalization error bounds for the support vector machine, which has been developed in the field of statistical learning. Based on the bounds, two fractional programs are derived for constructing portfolios, where the numerator of the ratio in the objective includes the value-at-risk (VaR) or conditional value-at-risk (CVaR) while the denominator is any norm of portfolio vector. Depending on the parameter values in the model, the derived formulations can result in a nonconvex constrained optimization, and an algorithm for dealing with such a case is proposed. Some computational experiments are conducted on real stock market data, demonstrating that the CVaR-based fractional programming model outperforms the empirical probability minimization.