ABSTRACT
A portfolio optimization problem involves optimal allocation of finite capital to a series of assets to achieve an acceptable trade-off between profit and risk in a given investment period. In the paper, the extended Markowitz's mean-variance portfolio optimization model is studied with some practical constraints. We introduce a new operator and an adaptive strategy for improving the performance of the multi-dimensional mapping algorithm (MDM) proposed specially for the portfolio optimization. Experimental results show that the modification is efficient on tackling large-scale portfolio problems.
- T-J Chang, Nigel Meade, John E Beasley, and Yazid M Sharaiha. 2000. Heuristics for cardinality constrained portfolio optimisation. Computers & Operations Research 27, 13 (2000), 1271--1302. Google ScholarDigital Library
- Yi Chen, Aimin Zhou, Rongfang Zhou, Peng He, Yong Zhao, and Lihua Dong. 2017. An Evolutionary Algorithm with a New Coding Scheme for Multi-objective Portfolio Optimization. In Asia-Pacific Conference on Simulated Evolution and Learning. Springer, 97--109.Google ScholarCross Ref
- Khin Lwin, Rong Qu, and Graham Kendall. 2014. A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization. Applied Soft Computing 24 (2014), 757--772. Google ScholarDigital Library
- H. Markowitz. 1952. Portfolio selection. The Journal of Finance 7, 1 (Mar 1952), 77--91.Google Scholar
- Antonin Ponsich, Antonio Lopez Jaimes, and Carlos A Coello Coello. 2013. A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications. IEEE Transactions on Evolutionary Computation 17, 3 (2013), 321--344. Google ScholarDigital Library
- Rainer Storn and Kenneth Price. 1997. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization 11, 4 (1997), 341--359. Google ScholarDigital Library
- Qingfu Zhang and Hui Li. 2007. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on evolutionary computation 11, 6 (2007), 712--731. Google ScholarDigital Library
- An evolutionary algorithm with a new operator and an adaptive strategy for large-scale portfolio problems
Recommendations
Multi-objective heuristic algorithms for practical portfolio optimization and rebalancing with transaction cost
Graphical abstractDisplay Omitted
Highlights- A tri-objective portfolio selection model is proposed with risk, return and transaction costs as objectives.
AbstractPortfolio optimization is the process of allocating capital among a universe of assets to achieve better risk–return trade-off. Due to the dynamic nature of financial markets, the portfolio needs to be rebalanced to retain the desired ...
Large-Scale Loan Portfolio Selection
We consider the problem of optimally selecting a large portfolio of risky loans, such as mortgages, credit cards, auto loans, student loans, or business loans. Examples include loan portfolios held by financial institutions and fixed-income investors as ...
A Fast Converging Evolutionary Algorithm for Constrained Multiobjective Portfolio Optimization
Evolutionary Multi-Criterion OptimizationAbstractPortfolio optimization is a well-known problem in the domain of finance with reports dating as far back as 1952. It aims to find a trade-off between risk and expected return for the investors, who want to invest finite capital in a set of ...
Comments