Abstract
Due to increasing complexity and non-convexity of financial engineering problems, biologically inspired heuristic algorithms gained significant importance especially in the area of financial decision optimization. In this paper, the stochastic scenario-based risk-return portfolio optimization problem is analyzed and solved with an evolutionary computation approach. The advantage of applying this approach is the creation of a common framework for an arbitrary set of loss distribution-based risk measures, regardless of their underlying structure. Numerical results for three of the most commonly used risk measures conclude the paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Brabazon, A., O’Neill, M.: Biologically inspired algorithms for financial modelling. In: Natural Computing Series, Springer, Heidelberg (2006)
Markowitz, H.M.: Portfolio selection. The. Journal of Finance 7(1), 77–91 (1952)
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys 35(3), 268–308 (2003)
Streichert, F., Ulmer, H., Zell, A.: Evolutionary algorithms and the cardinality constrained portfolio selection problem. In: Selected Papers of the International Conference on Operations, pp. 253–260. Springer, Heidelberg (2003)
Schlottmann, F., Mitschele, A., Seese, D.: A multi-objective approach to integrated risk management. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 692–706. Springer, Heidelberg (2005)
Subbu, R., Bonissone, P., Eklund, N., Bollapragada, S., Chalermkraivuth, K.: Multiobjective financial portfolio design: a hybrid evolutionary approach. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1722–1729. IEEE Press, New York (2005)
Gomez, M.A., Flores, C.X., Osorio, M.A.: Hybrid search for cardinality constrained portfolio optimization. In: GECCO 2006. Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 1865–1866. ACM Press, New York (2006)
Lin, D., Li, X., Li, M.: A genetic algorithm for solving portfolio optimization problems with transaction costs and minimum transaction lots. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 808–811. Springer, Heidelberg (2005)
Maringer, D.: Portfolio Management with Heuristic Optimization. In: Advances in Computational Management Science, Springer, Heidelberg (2005)
Artzner, P., Delbaen, F., Eber, J.M., Heath, D.: Coherent measures of risk. Mathematical Finance 9(3), 203–228 (1999)
McNeil, A.J., Frey, R., Embrechts, P.: Quantitative risk management. In: Princeton Series in Finance, Princeton University Press, Princeton (2005)
Krokhmal, P., Palmquist, J., Uryasev, S.: Portfolio optimization with conditional value-at-risk objective and constraints. The Journal of Risk 4(2), 11–27 (2002)
Streichert, F., Ulmer, H., Zell, A.: Comparing discrete and continuous genotypes on the constrained portfolio selection problem. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 1239–1250. Springer, Heidelberg (2004)
Streichert, F., Ulmer, H., Zell, A.: Evaluating a hybrid encoding and three crossover operators on the constrained portfolio selection problem. In: Congress on Evolutionary Computation CEC 2004, vol. 1, pp. 932–939. IEEE Press, New York (2004)
Rockafellar, R.T., Uryasev, S.: Optimization of Conditional Value-at-Risk. The. Journal of Risk 2(3), 21–41 (2000)
Uryasev, S.: Conditional Value-at-Risk: Optimization algorithms and applications. Financial Engineering News 14, 1–5 (2000)
Gilli, M., Këllezi, E., Hysi, H.: A data-driven optimization heuristic for downside risk minimization. The. Journal of Risk 8(3), 1–18 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hochreiter, R. (2007). An Evolutionary Computation Approach to Scenario-Based Risk-Return Portfolio Optimization for General Risk Measures. In: Giacobini, M. (eds) Applications of Evolutionary Computing. EvoWorkshops 2007. Lecture Notes in Computer Science, vol 4448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71805-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-540-71805-5_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71804-8
Online ISBN: 978-3-540-71805-5
eBook Packages: Computer ScienceComputer Science (R0)