A Novel Portfolio Optimization Model Based on Pattern Recognition Algorithms

DOI

10.2991/iemss-17.2017.141How to use a DOI?

Keywords

multi-criteria unbiased optimization, Particle swarm optimization, Artificial Intelligence, genetic algorithm .

Abstract

There are two types of modern portfolio theory: narrow and broad. A narrowly conceived portfolio theory is often referred to by the name Markowitz portfolio theory. Often generalized portfolio theory, in addition to classical portfolio theory, and various alternative portfolio theories, include the capital asset pricing model and an effective securities market theory of capital markets. At the same time, because the traditional EMH cannot explain market anomalies, the various portfolio theories are have been challenged by behavioral financial theory. This study uses pattern recognition algorithms to improve the traditional portfolio optimization models. By utilizing improved particle swarm optimization algorithms, a hybrid optimization model based on this would be the result of the experiment, compared with that based on the traditional neural network model for quantitative model effect. Particle swarm optimization algorithms were originally generated to produce graphical simulations of flocks of birds and other such unpredictable movements. The developmental basis of the algorithms used in this experiment are observations of animal social behavior, demonstrating that sharing of information society provides groups with an evolutionary advantage. By joining near the speed of matching, taking multidimensional search into account, and according to speeds and distances, we can thus form the original version of particle swarm optimization. After introducing the parameters of inertia weight to better control the development of swarm behavior, we have produced the standard version. This study will focus on analysis to improve the process of generating particle swarm optimization models.

Copyright

© 2017, 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/).

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TY  - CONF
AU  - Gongbo Tan
AU  - Yan Wang
AU  - Jun Xiong
PY  - 2017/04
DA  - 2017/04
TI  - A Novel Portfolio Optimization Model Based on Pattern Recognition Algorithms
BT  - Proceedings of the 2017 International Conference on Innovations in Economic Management and Social Science (IEMSS 2017)
PB  - Atlantis Press
SP  - 711
EP  - 717
SN  - 2352-5428
UR  - https://doi.org/10.2991/iemss-17.2017.141
DO  - 10.2991/iemss-17.2017.141
ID  - Tan2017/04
ER  -