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Stock Portfolio Selection Using Chemical Reaction Optimization
Abstract:Stock portfolio selection is a classic problem in finance, and it involves deciding how to allocate an institution-s or an individual-s wealth to a number of stocks, with certain investment objectives (return and risk). In this paper, we adopt the classical Markowitz mean-variance model and consider an additional common realistic constraint, namely, the cardinality constraint. Thus, stock portfolio optimization becomes a mixed-integer quadratic programming problem and it is difficult to be solved by exact optimization algorithms. Chemical Reaction Optimization (CRO), which mimics the molecular interactions in a chemical reaction process, is a population-based metaheuristic method. Two different types of CRO, named canonical CRO and Super Molecule-based CRO (S-CRO), are proposed to solve the stock portfolio selection problem. We test both canonical CRO and S-CRO on a benchmark and compare their performance under two criteria: Markowitz efficient frontier (Pareto frontier) and Sharpe ratio. Computational experiments suggest that S-CRO is promising in handling the stock portfolio optimization problem.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1077120Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1576
 H. Markowitz, "Portfolio selection," Journal of Finance, vol. 7, no. 12, pp. 77-91, 1952.
 H. Markowitz, "Portfolio selection: efficient diversification of investments," New York: Wiley, 1959.
 W. F. Sharpe, "Mutual fund performance," Journal of Business, vol. 39, no. 1, pp. 119-138, 1966.
 J. K. Sengupta, "Portfolio decisions as games," International Journal of Systems Science, vol. 20, no. 8, pp. 1323-1334, 1989.
 B. K. Stone, "A linear programming formulation of the general portfolio selection problem," Journal of Financial and Quantitative Analysis, vol. 8, no. 4, pp. 621-636, 1973.
 G. D. Tollo, and A. Roli, "Metaheuristics for the portfolio selection problem," International Journal of Operations Research, vol. 5, no. 1, pp. 13-35, 2008.
 K. J. Oh, T. Y. Kim, S. H. Min and H. Y. Lee, "Portfolio algorithm based on portfolio beta using genetic algorithm," Expert Systems with Applications, vol. 30, no. 3, pp. 527-534, 2006.
 S. M. Wang, J. C. Chen, H. M. Wee, and K. J. Wang, "Non-linear stochastic optimization using genetic algorithm for portfolio selection," International Journal of Operations Research, vol. 3, no. 1, pp. 16-22, 2006.
 Y. Crama, and M. Schyns, "Simulated annealing for complex portfolio selection problem," European Journal of Operational Research, vol. 150, no. 3, pp. 546-571, 2003.
 G. Kendall, and Y. Su, "A particle swarm optimization approach in the construction of optimal risky portfolios," in Proc. of the 23rd LASTED International Multi-Conference on Artificial Intelligence and Applications, pp. 140-145, Innsbruck, Austria, 2005.
 R. Armananzas, and J. A. Lozano, "A multiobjective approach to the portfolio optimization problem," in Proc. of IEEE Congress on Evolutionary Computation (CEC), pp. 1388-1395, Edinburgh, UK, 2005.
 A. Y. S. Lam and V. O. K. Li, "Chemical-Reaction-Inspired Metaheuristic for Optimization," IEEE Transactions on Evolutionary Computation, vol. 14, no. 3, pp. 381-399, June 2010.
 J. Xu, A. Y.S. Lam, and V. O.K. Li, "Chemical reaction optimization for task scheduling in grid computing," IEEE Transactions on Parallel and Distributed Systems (TPDS), 18 Jan. 2011.
 J. Xu, A. Y. S. Lam, and V. O. K. Li, "Chemical reaction optimization for the grid scheduling problem," in Proc. of IEEE Int-l Conf. on Commun. (ICC2010), May 2010.
 A. Y. S. Lam, J. Xu, and V. O. K. Li, "Chemical reaction optimization for population transition in peer-to-peer live streaming," in Proc. of IEEE Congress on Evolutionary Computation, July 2010.
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