Large-scale portfolio optimization using multiobjective dynamic mutli-swarm particle swarm optimizer

Portfolio optimization problems involve selection of different assets to invest so that the investor is able to maximize the overall return and minimize the overall risk. The complexity of an asset allocation problem increases with the increasing number of assets available for investing. When the number of assets/stocks increase to several hundred, it is difficult for classical method to optimize (construct a good portfolio). In this paper, the Multi-objective Dynamic Multi-Swarm Particle Swarm Optimizer is employed to solve a portfolio optimization problem with 500 assets (stocks). The results obtained by the proposed method are compared several other optimization methods. The experimental results show that this approach is efficient and confirms its potential to solve the large scale portfolio optimization problem.

[1]  Jing J. Liang,et al.  Dynamic Multi-Swarm Particle Swarm Optimization for Multi-objective optimization problems , 2012, 2012 IEEE Congress on Evolutionary Computation.

[2]  A. Schmidt.  Modern Financial Markets , 2011 .

[3]  Ponnuthurai N. Suganthan,et al.  Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection , 2010, Inf. Sci..

[4]  Chang-Chun Lin,et al.  Genetic algorithms for portfolio selection problems with minimum transaction lots , 2008, Eur. J. Oper. Res..

[5]  Abdullah Al Mamun,et al.  A realistic approach to evolutionary multiobjective portfolio optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[6]  Rahib Hidayat Abiyev,et al.  Fuzzy portfolio selection using genetic algorithm , 2007, Soft Comput..

[7]  Multifractal and R/S Analysis of Protein Structure , 2007, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007).

[8]  Wei Chen,et al.  Improved Particle Swarm Optimization for Realistic Portfolio Selection , 2007, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007).

[9]  Chieh-Yow Chianglin.  Applications of Genetic Algorithm to Portfolio Optimization with Practical Transaction Constraints , 2006, JCIS.

[10]  Massimo Guidolin,et al.  Asset Allocation Under Multivariate Regime Switching , 2006 .

[11]  José Antonio Lozano,et al.  A multiobjective approach to the portfolio optimization problem , 2005, 2005 IEEE Congress on Evolutionary Computation.

[12]  Laura Diosan,et al.  A multi-objective evolutionary approach to the portfolio optimization problem , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[13]  Jing J. Liang,et al.  Dynamic multi-swarm particle swarm optimizer , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[14]  Yves Crama,et al.  Simulated annealing for complex portfolio selection problems , 2003, Eur. J. Oper. Res..

[15]  Jürgen Teich,et al.  Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO) , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[16]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[17]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[18]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[19]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[20]  Tapabrata Ray,et al.  A Swarm Metaphor for Multiobjective Design Optimization , 2002 .

[21]  Jaroslava Hlouskova,et al.  The efficient frontier for bounded assets , 2000, Math. Methods Oper. Res..

[22]  Yazid M. Sharaiha,et al.  Heuristics for cardinality constrained portfolio optimisation , 2000, Comput. Oper. Res..

[23]  Maria Grazia Speranza,et al.  A heuristic algorithm for a portfolio optimization model applied to the Milan stock market , 1996, Comput. Oper. Res..

[24]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[25]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[26]  George Chow,et al.  Portfolio Selection Based on Return, Risk, and Relative Performance , 1995 .

[27]  Marco Tomassini,et al.  Distributed Genetic Algorithms with an Application to Portfolio Selection Problems , 1995, ICANNGA.

[28]  Andrea G. B. Tettamanzi,et al.  An Evolutionary Algorithm For Portfolio Selection In A Downside Risk Framework , 1995 .

[29]  Andrea G. B. Tettamanzi,et al.  A genetic approach to portfolio selection , 1993 .

[30]  Harry M. Markowitz,et al.  The Founders of Modern Finance: Their Prize-Winning Concepts and 1990 Nobel Lectures , 1991 .

[31]  H. Konno,et al.  Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market , 1991 .

[32]  Jong-Shi Pang,et al.  A New and Efficient Algorithm for a Class of Portfolio Selection Problems , 1980, Oper. Res..

[33]  W. Sharpe.  Portfolio Theory and Capital Markets , 1970 .