Journalartikel

Autorenliste: Dueck, G; Winker, P

Jahr der Veröffentlichung: 1992

Seiten: 159-178

Zeitschrift: Applied Stochastic Models and Data Analysis

Bandnummer: 8

Heftnummer: 3

DOI Link: https://doi.org/10.1002/asm.3150080306

Verlag: Wiley

Abstract: 

The paper studies the design of optimal (bond) portfolios taking into account various possible utility functions of an investor. The most prominent model for portfolio optimization was introduced by Markowitz. A real solution in this model can be achieved by quadratic programming routines for mean-variance analysis. Of course, there are many reasons for an investor to prefer other utility criteria than return/variance of return in the Markowitz model. In the last few years, many efficient multiple purpose optimization heuristics have been invented for the needs in optimizing telephone nets, chip layouts, job shop scheduling etc. Some of these heuristics have essential advantages: they are extremely flexible and very easy to implement on computers. One example of such an algorithm is the threshold-accepting algorithm (TA). TA is able to optimize portfolios under nearby arbitrary constraints and subject to nearly every utility function. In particular, the utility functions need neither to be convex, differentiable nor ‘smooth’ in any sense. We implemented TA for bond portfolio optimization with different utility criteria. The algorithms and computational results are presented. Under various utility functions, the ‘best’ portfolios look surprisingly different and have quite different qualities. Thus, for a portfolio manager it might be useful to provide himself with such a ‘multiple-taste’ optimizer in order to be able easily to readjust it according to his own personal utility considerations.

Harvard-Zitierstil: Dueck, G. and Winker, P. (1992) New concepts and algorithms for portfolio choice, Applied Stochastic Models and Data Analysis, 8(3), pp. 159-178. https://doi.org/10.1002/asm.3150080306

APA-Zitierstil: Dueck, G., & Winker, P. (1992). New concepts and algorithms for portfolio choice. Applied Stochastic Models and Data Analysis. 8(3), 159-178. https://doi.org/10.1002/asm.3150080306