MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

Mousavi, Somayeh; Esfahanipour, Akbar; Zarandi, M. H. Fazel

doi:10.1016/j.asoc.2015.05.021

Cited by 13 publications

(7 citation statements)

References 63 publications

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“…This study proposes a hybrid genetic fuzzy system to select the best stocks for considering in the portfolio composition. The TSK fuzzy rule-based systems have shown good capability for modeling the nonlinear dynamic systems in many fields including the short-term stock trend prediction [69][70][71]. In this paper, we intend to evaluate the performance of TSK systems in the stock ranking and selection problem over the longer investment horizons.…”

Section: Stock Selection Based On the Fundamental Analysismentioning

confidence: 99%

“…Due to a large number of fundamental variables as the input variables, the most influential subset of variables are selected by stepwise regression analysis. Stepwise regression analysis has been used successfully for variable selection in the stock market forecasting [69][70][71]79]. This technique either adds the variables onward or removes the variables backward to find the best combination of independent variables for forecasting the dependent variable.…”

Section: Please Insertmentioning

confidence: 99%

“…The quality of the TSK system heavily depends on the partitioning of the input space [80]. We determine the fuzzy sets for the input variables using the modified Adeli-Hung algorithm (AHA) [71]. This algorithm includes two stages.…”

Section: Generating Tsk Fuzzy Rule-based Systems For Stock Selectionmentioning

confidence: 99%

“…These stages are clearly explained in [82]. In the modified version of AHA, the input variables are partitioned individually, and the membership functions are determined for individual variables [71]. The modified AHA assigns symmetric triangular fuzzy sets to the input variables,…”

Section: Generating Tsk Fuzzy Rule-based Systems For Stock Selectionmentioning

confidence: 99%

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A modular Takagi-Sugeno-Kang (TSK) system based on a modified hybrid soft clustering for stock selection

Mousavi¹,

Esfahanipour²,

Zarandi³

2019

Scientia Iranica

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A modular Takagi-Sugeno-Kang (TSK) system based on a modified hybrid soft clustering for stock selection This study presents a new hybrid intelligent system with ensemble learning for stock selection using the fundamental information of companies. The system uses the selected financial ratios of each company as the input variables and ranks the candidate stocks. Due to the different characteristics of the companies from different activity sectors, modular system for stock selection may show a better performance in comparison with an individual system. Here, a hybrid soft clustering algorithm is proposed to eliminate the noise and partition the input data set into more homogeneous overlapped subsets. The proposed clustering algorithm benefits from the strengths of the fuzzy, possibilistic and rough clustering to develop a modular system. An individual Takagi-Sugeno-Kang (TSK) system is extracted from each subset using an artificial neural network and genetic algorithm. To integrate the outputs of the individual TSK systems, a new weighted ensemble strategy is proposed. The performance of the proposed system is evaluated among 150 companies listed on Tehran Stock Exchange (TSE) regarding information coefficient, classification accuracy and appreciation in stock price. The experimental results show that the proposed modular TSK system significantly outperforms the single TSK system as well as the other ensemble models using different decomposition and combination strategies.

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Section: Stock Selection Based On the Fundamental Analysismentioning

confidence: 99%

Section: Please Insertmentioning

confidence: 99%

Section: Generating Tsk Fuzzy Rule-based Systems For Stock Selectionmentioning

confidence: 99%

Section: Generating Tsk Fuzzy Rule-based Systems For Stock Selectionmentioning

confidence: 99%

See 2 more Smart Citations

A modular Takagi-Sugeno-Kang (TSK) system based on a modified hybrid soft clustering for stock selection

Mousavi¹,

Esfahanipour²,

Zarandi³

2019

Scientia Iranica

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“…O trabalho utiliza a Programação Genética Multi-Gene para criação e ajuste da base de regras de um Sistema Fuzzy Genético de Pittsburg. Já em [Mousavi et al 2015] um modelo de PG multiarvoresé empregado para melhorar a acurácia de um Sistema Fuzzy de Takagi-Sugeno para mapeamento dinâmico de portfólios.…”

Section: Introductionunclassified

Rede Neo-Fuzzy-Neuron com Programação Genética Aplicada em Problemas de Previsão e Identificação de Sistemas Não-Lineares

Brás¹,

Silva²

2019

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2019)

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Este artigo propõe dois algoritmos baseados em Programação Genética (PG) para aprendizado em redes Neo-Fuzzy Neuron (NFN). As abordagens utilizam a PG para gerar as regras associadas a cada entrada, criando e ajustando as funções de pertinência, e um método baseado no Gradiente para ajuste de pesos. Os modelos propostos são avaliados e comparados com modelos alternativos em problemas de previsão e identificação de sistemas não lineares. Os resultados obtidos mostram que os modelos propostos são promissores e competitivos com modelos alternativos do estado da arte.

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A novel fuzzy mathematical model for an integrated supply chain planning using multi-objective evolutionary algorithm

2020

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MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

Cited by 13 publications

References 63 publications

A modular Takagi-Sugeno-Kang (TSK) system based on a modified hybrid soft clustering for stock selection

A modular Takagi-Sugeno-Kang (TSK) system based on a modified hybrid soft clustering for stock selection

Rede Neo-Fuzzy-Neuron com Programação Genética Aplicada em Problemas de Previsão e Identificação de Sistemas Não-Lineares

A novel fuzzy mathematical model for an integrated supply chain planning using multi-objective evolutionary algorithm

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