Neural networks, financial trading and the efficient markets hypothesis
ACSC '02 Proceedings of the twenty-fifth Australasian conference on Computer science - Volume 4
Designing Trading Agents for an Artificial Market with a Multi-Objective Genetic Algorithm
ICCIMA '01 Proceedings of the Fourth International Conference on Computational Intelligence and Multimedia Applications
An effective use of crowding distance in multiobjective particle swarm optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A multi-objective evolutionary approach to the portfolio optimization problem
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Designing safe, profitable automated stock trading agents using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The Optimal Multi-objective Optimization Using PSO in Blind Color Image Fusion
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
Molecular docking with multi-objective Particle Swarm Optimization
Applied Soft Computing
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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Stock traders consider several factors in making decisions. They also differ in the importance they attach to each of these objectives. This requires a tool that can provide an optimal tradeoff among different objectives, a problem aptly solved by a multiobjective optimization (MOO) system. However, the application of MOO to stock trading is very limited when compared with its existing applications in the fields of stock modeling and prediction, portfolio selection and portfolio optimization. Similarly, only a few real life applications have been proposed for multiobjective particle swarm optimization(MOPSO), an MOO algorithm based on particle swarm optimization which has experienced an increased popularity in recent years. In this paper, we present an application of MOO, specifically, of MOPSO, to stock trading. The system, using historical end-of-day market data, utilizes the trading signals from a set of financial technical indicators in order to develop a trading rule which is optimized for two objective functions, namely, Sharpe ratio and percent profit. The performance of the system was compared to the performance of the technical indicators and the market itself. The results show that the system performed well against the 5 technical indicators under study, outperforming them in terms of both objective functions in 3 training and testing periods. The system also performed competitively against the market. The system provided a diversity of solutions for the two objective functions and is found to be robust and fast. These results show the potential of the system as a tool for making stock trading decisions.