Building Market Timing Strategies Using Trend Representative Testing and Computational Intelligence Metaheuristics

Mohamed, Ismail; Otero, Fernando E. B.

doi:10.1007/978-3-030-70594-7_2

Cited by 1 publication

(1 citation statement)

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“…At the end of algorithm's run, the non-dominated archive is return as the discovered Pareto set. The λ-PSO S and λ-PSO SP extend the λ-PSO algorithm with a Stochastic State Update and Pruning mechanisms [6], [13]. When pruning is enabled, the resultant algorithm is labeled λ-PSO SP , otherwise the resultant algorithm is labeled λ-PSO S .…”

Section: Algorithmsmentioning

confidence: 99%

A Performance Study of Multiobjective Particle Swarm Optimization Algorithms for Market Timing

Mohamed

Otero

2022

2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr)

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Market timing is the issue of deciding when to buy or sell a given asset on a financial market. As one of the core issues of algorithmic trading systems, designers of such systems have turned to computational intelligence methods to aid them in this task. In our previous work, we introduced a number of Particle Swarm Optimization (PSO) algorithms to compose strategies for market timing using a novel training and testing methodology that reduced the likelihood of overfitting and tackled market timing as a multiobjective optimization problem. In this paper, we provide a detailed analysis of these multiobjective PSO algorithms and address two limitations in the results presented previously. The first limitation is that the PSO algorithms have not been compared to well-known algorithms or market timing techniques. This is addressed by comparing the results obtained against NSGA-II and MACD, a technique commonly used in market timing strategies. The second limitation is that we have no insight regarding diversity of the Pareto sets returned by the algorithms. We address this by using RadViz to visualize the Pareto sets returned by all the algorithms, including NSGA-II and MACD. The results show that the multiobjective PSO algorithms return statistically significantly better results than NSGA-II and MACD. We also observe that the multiobjective PSO SP algorithm consistently displayed the best spread in its returned Pareto sets despite not having any explicit diversity promoting measures.

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Section: Algorithmsmentioning

confidence: 99%