Neural networks, financial trading and the efficient markets hypothesis
ACSC '02 Proceedings of the twenty-fifth Australasian conference on Computer science - Volume 4
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Improving Technical Analysis Predictions: An Application of Genetic Programming
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
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
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
An effective use of crowding distance in multiobjective particle swarm optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
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
Genetic algorithms to optimise the time to make stock market investment
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
Evaluation of stock trading performance of students using a web-based virtual stock trading system
Computers & Mathematics with Applications
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Hybrid Kansei-SOM model using risk management and company assessment for stock trading
Information Sciences: an International Journal
Combining technical trading rules using particle swarm optimization
Expert Systems with Applications: An International Journal
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Stock traders consider several factors or objectives in making decisions. Moreover, they 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 multi-objective optimization (MOO) system. This paper aims to investigate the application of multi-objective optimization to end-of-day historical stock trading. We present a stock trading system that uses multi-objective particle swarm optimization (MOPSO) of financial technical indicators. Using end-of-day market data, the system optimizes the weights of several technical indicators over two objective functions, namely, percent profit and Sharpe ratio. The performance of the system was compared to the performance of the technical indicators, the performance of the market, and the performance of another stock trading system which was optimized with the NSGA-II algorithm, a genetic algorithm-based MOO method. The results show that the system performed well on both training and out-of-sample data. In terms of percent profit, the system outperformed most, if not all, of the indicators under study, and, in some instances, it even outperformed the market itself. In terms of Sharpe ratio, the system consistently performed significantly better than all the technical indicators. The proposed MOPSO system also performed far better than the system optimized by NSGA-II. The proposed 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.