Intellectual decision-making support algorithm on the securities portfolio formation based of the swarm intelligence

The article discusses the issue of the need for intellectual support for decision-making when managing of the securities portfolio forming process. Modern systems for making investors decisions are based on the classical portfolio management theory and supposed the market efficiency requirement fulfillment, but the modern stock market, both domestic and global, cannot satisfy this condition. For effective decisions, it is necessary to use new methods and models of the portfolio management. To find the optimal portfolio, a modified particle swarm method is used and its advantages are investigated, among which the reduction in the number of objective function computations by 34% or more. The proposed algorithm for intelligent decision support makes it possible of choice according to three aspects: under the method of determining the model parameters, under the financial risk assessment model and under the optimal portfolio structure. The knowledge base contains a database and a precedents base, a base of rules includes indicators of the financial risk assessment model effectiveness aggregated for all precedents. The growth of the precedents base makes it possible to increase the authenticity of the risk measure efficiency assessment and makes it possible to form (or adapt) production rules.

1. Kondrateva O.V. Application of indexed-entropic risk measures in decision support systems for security portfolio management. Proc. of the 3rd international conference on intelligent technologies for information processing and management. 2015:159-162.

2. Bronshtein E.M., Kondrateva O.V. Security Portfolio Management Based on Combined Entropic Risk Measures. Journal of Computer and Systems Sciences International. 2013;52(5):837–841. https://doi.org/10.1134/S1064230713050043

3. Bronshtein E.M., Kondrateva O.V. The Decision Support of the Securities Portfolio Composition Based on the Particle Swarm Optimization. Proc. of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support. 2019;166:279-284. https://doi.org/10.2991/itids-19.2019.50.

4. Beni G., Wang J. Swarm Intelligence in Cellular Robotic Systems. Robots and biological systems: towards a new bionics? Proc. of the NATO Advanced Workshop. 1989:703-712. doi.org/10.1007/978-3-642-58069-7_38

5. Kennedy J., Mendes R. Population structure and particle swarm performance. Proc. of the 2002 Congress on Evolutionary Computation. doi:10.1109/CEC.2002.1004493

6. Shi Y., Eberhart R. A modified particle swarm optimizer. Proc. of the IEEE International Conference on Evolutionary Computation Proceedings, 1998:69–73. doi:10.1109/ICEC.1998.699146

7. Riesbeck C.K., Schank R. Inside Case-based Reasoning. Erlbaum. 1989:423.

8. Alterman R. Panel discussion on case representation. Proc. of the Second Workshop on Case-Based Reasoning.1989.

9. David B.S. Principles for case representation in a case based aiding system for lesson planning. Proc. of the Workshop on Case-Based Reasoning.1991.

10. Varshavskiy P.R., Eremeev A.P. Modeling Case-Based Reasoning in Intelligent Decision Support Systems. Artificial Intelligence and Decision Making. 2009;2:45-57.

11. Aamodt A., Plaza E. Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Artificial Intelligence Communications.1994;7(1):39–59. doi:10.3233/AIC-1994-7104