A stochastic programming approach to multicriteria portfolio optimization

Date

2013-10-01

Author

Sakar, Ceren Tuncer
Köksalan, Mustafa Murat

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

241
views

0
downloads

We study a stochastic programming approach to multicriteria multi-period portfolio optimization problem. We use a Single Index Model to estimate the returns of stocks from a market-representative index and a random walk model to generate scenarios on the possible values of the index return. We consider expected return, Conditional Value at Risk and liquidity as our criteria. With stocks from Istanbul Stock Exchange, we make computational studies for the two and three-criteria cases. We demonstrate the tradeoffs between criteria and show that treating these criteria simultaneously yields meaningful efficient solutions. We provide insights based on our experiments.

Subject Keywords

Portfolio optimization, Stochastic programming, Market efficiency, Multicriteria, Liquidity, Conditional value at risk

URI

https://hdl.handle.net/11511/51522

Journal

JOURNAL OF GLOBAL OPTIMIZATION

DOI

https://doi.org/10.1007/s10898-012-0005-2

Collections

Department of Industrial Engineering, Article

Suggestions

OpenMETU
Core

An evolutionary algorithm for multiple criteria problems Soylu, Banu; Köksalan, Murat; Department of Industrial Engineering (2007) In this thesis, we develop an evolutionary algorithm for approximating the Pareto frontier of multi-objective continuous and combinatorial optimization problems. The algorithm tries to evolve the population of solutions towards the Pareto frontier and distribute it over the frontier in order to maintain a well-spread representation. The fitness score of each solution is computed with a Tchebycheff distance function and non-dominating sorting approach. Each solution chooses its own favorable weights accordin...
An Efficient Metaheuristic Algorithm for Engineering Optimization: SOPT Hasançebi, Oğuzhan (2012-06-01) Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems so far. In the present study, a simple optimization (SOPT) algorithm with two main steps; namely exploration and exploitation, is provided for practical applications. Aside from a reasonable rate of convergence attained, the ease in its implementation and dependency on few parameters only are among the advantageous characteristics of the proposed SOPT algorithm. The efficienc...
An Advanced evolutionary programming method for mechanical system design: feasibility enhanced particle swarm optimization Hasanoğlu, Mehmet Sinan; Dölen, Melik; Department of Mechanical Engineering (2019) Constrained optimization problems constitute an important fraction of optimization problems in mechanical engineering domain. It is not rare for these problems to be highly-constrained where a specialized approach that aims to improve constraint satisfaction level of the whole population as well as finding the optimum is deemed useful especially when the objective functions are very costly. This dissertation introduces a new algorithm titled Feasibility Enhanced Particle Swarm Optimization (FEPSO) to handle...
A Stochastic Maximum Principle for a Markov Regime-Switching Jump-Diffusion Model with Delay and an Application to Finance Savku, Emel; Weber, Gerhard Wilhelm (2018-11-01) We study a stochastic optimal control problem for a delayed Markov regime-switching jump-diffusion model. We establish necessary and sufficient maximum principles under full and partial information for such a system. We prove the existence-uniqueness theorem for the adjoint equations, which are represented by an anticipated backward stochastic differential equation with jumps and regimes. We illustrate our results by a problem of optimal consumption problem from a cash flow with delay and regimes.
Optimum design of steel frames using stochastic search techniques based on natural phenomena: A review Saka, M. P. (2007-09-21) Recent developments in optimization techniques that deal with finding the solution of combinatorial optimization problems has provided steel designers with new capabilities. These new optimization techniques use nature as a source of inspiration to develop new procedures for solving complex engineering problems. Among these, evolutionary algorithms mimic evolutionary biology and make use of the principle of the survival of the fittest to establish a numerical search algorithm. In the immune system algorithm...

Citation Formats

C. T. Sakar and M. M. Köksalan, “A stochastic programming approach to multicriteria portfolio optimization,” JOURNAL OF GLOBAL OPTIMIZATION, pp. 299–314, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/51522.