A stock selection strategy using fuzzy neural networks

Wong, Fu-Lung; Wang, P.Z.

doi:10.1016/0925-2312(91)90026-8

Cited by 10 publications

(1 citation statement)

References 3 publications

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“…In [3], the authors developed stock classification model using the fuzzy clustering algorithm called fuzzy c-means. Among other studies, it is worth mentioning [6] where the theory of genetic algorithm was utilized in the financial market, also in [7] the authors use neural networks.…”

Section: Introductionmentioning

confidence: 99%

Pattern Classification on Complex System Using Modified Gustafson- Kessel Algorithm

Aguiar¹,

Filho²,

Koki³

2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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This work has been focused in the application of Gustafson-Kessel Algorithm in a complex system through a methodology proposed. The complex system here considered will be the financial market. So, the main objective of this paper is to classify objects in two patterns: winner and loser. The methodology is based on application of a method of clustering called Modified Gustafson-Kessel (MGK) in some open companies of the transportation sector and energy sector. Results shows that the use of MGK can better separate the promising actions from the non-promising ones with more precision due to its covariance matrix that can be change for generate the best separability among clusters. This produces a new tool for analysis of the dynamic of stock market with the main aim of given support to investor in make decision.

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Section: Introductionmentioning

confidence: 99%