The Decision Support of the Securities Portfolio Composition Based on the Particle Swarm Optimization
- 10.2991/itids-19.2019.50How to use a DOI?
- swarm intelligence, particle swarm optimization, multi-objective optimization task, securities portfolio, index-entropic risk measures
Stochastic behavioral methods are becoming increasingly widespread for optimization tasks solving. One of these methods is the particle swarm optimization (PSO). The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. Its effectiveness and efficiency has rendered it a valuable metaheuristic approach in various scientific fields where complex optimization problems appear. Investment activity in the stock market is an area where the use of optimization techniques is required. The volatility and unpredictability of future stock prices create a situation of uncertainty for decision making in the financial market. Investors are forced to solve a multi-criteria optimization problem when forming an effective securities portfolio. This article proposes to use PSO to compile an optimal securities portfolio based on combined entropy risk index indicators. The portfolio consisted of 8 shares of Russian companies, the shares of each stock in the portfolio were found using particle swarm optimization. The efficiency of the modified algorithm is investigated, coefficients are proposed, and the results of a computational experiment are obtained.
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Efim Bronshtein AU - Olga Kondrateva PY - 2019/05 DA - 2019/05 TI - The Decision Support of the Securities Portfolio Composition Based on the Particle Swarm Optimization BT - Proceedings of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019) PB - Atlantis Press SP - 279 EP - 284 SN - 1951-6851 UR - https://doi.org/10.2991/itids-19.2019.50 DO - 10.2991/itids-19.2019.50 ID - Bronshtein2019/05 ER -