Integration of genetic algorithm with artificial neural network for stock market forecasting

Sharma, Dinesh K.; Hota, H. S.; Brown, Kate; Handa, Richa

doi:10.1007/s13198-021-01209-5

Cited by 43 publications

(19 citation statements)

References 36 publications

(34 reference statements)

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“…On the other side, CNN and DL could perform better when working with a large amount of data [9] [10]. Use the global search capability of the GA to evolve the CNN weights for the histopathological breast image classification problem [11], train CNN model using the BreakHis dataset images as input and three different optimization approaches. In research paper [11], GA-based classifier performs almost as powerfully as the Adam optimizer, with a negligible difference.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…Use the global search capability of the GA to evolve the CNN weights for the histopathological breast image classification problem [11], train CNN model using the BreakHis dataset images as input and three different optimization approaches. In research paper [11], GA-based classifier performs almost as powerfully as the Adam optimizer, with a negligible difference. The batch size is equal to 32, the other two algorithms have the best accuracy, and when the batch size is equal to 128, GA has scored best accuracy.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…The main advantages of hybridized GA with other methods, [7] [12] [13] are better solution quality, better efficiency, a guarantee of feasible solutions, and optimized control parameters. Hybridization of intelligent techniques for an effective predictive model is essential as reported, [11]. The accuracy of the GA and ANN hybrid model has greater than the single Back propagation neural network (BPANN).…”

Section: Genetic Algorithmmentioning

confidence: 99%

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Impact of Genetic Algorithm for the Diagnosis of Breast Cancer: Literature Review

Balcha¹,

Woldie²

2023

AID

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In recent research from the total number of new cancer cases in Africa about 29.46% and in Ethiopia 31.85% are breast cancer cases. 25.84% of all cancer related death is from breast cancer. One of the challenges in the treatment of breast cancer is early detection. Researchers agreed that, improving the preventive mechanism of breast cancer is an early predicting and detecting model. Research efforts are continuing to present different solution approaches using advanced techniques of Artificial intelligence (AI), Machine learning (ML), Deep Learning (DL), and Computational Intelligence as well. A genetic algorithm is a hyper-parameter optimization algorithm that belongs to the class of evolutionary algorithms. Genetic Algorithm (GA) is used for complex search spaces for search and optimization. This reviewed literature paper shows the positive effect of GA in the diagnosis of breast cancer on AI algorithms.

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Section: Genetic Algorithmmentioning

confidence: 99%

Section: Genetic Algorithmmentioning

confidence: 99%

Section: Genetic Algorithmmentioning

confidence: 99%

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Impact of Genetic Algorithm for the Diagnosis of Breast Cancer: Literature Review

Balcha¹,

Woldie²

2023

AID

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“…4) Genetic manipulation. Genetic operation includes selection operation, crossover operation and mutation operation [16][17]. The selection operation is to sort the fitness of heavy fist individuals from large to small, and the ones with good fitness will be retained.…”

Section: Grey Correlation Bp Modelmentioning

confidence: 99%

Deformation prediction of mining slope based on grey correlation analysis and GA-BP model

Chen

Cai

Huang

et al. 2023

International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022)

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In order to improve the prediction accuracy of the slope deformation prediction method in mining area, aiming at the problems of many disaster causing factors of slope deformation and BP neural network is easy to fall into local minimum value, a prediction method based on the combination of genetic algorithm and BP neural network based on grey correlation analysis is proposed. The grey correlation analysis method is used to screen the main influencing factors, and the factors with high correlation degree are used as input indexes to simplify the network structure. Then, the genetic algorithm is used to optimize the BP neural network to establish the GA-BP model, and finally the prediction is compared. The results show that the grey correlation analysis can further improve the consistency between the predicted value and the real value, and the model can accurately predict the slope deformation. The research results have important auxiliary reference significance for mine safety production.

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“…The findings of the study reveal that the proposed methodology for selecting the significant variables of the ANN model outperformed the model with set of input factors taken from the literature. Sharma, Hota, Brown, and Handa (2022) introduced a hybrid methodology by combining the Genetic Algorithm (GA) and ANN model. The finding of the study the proposed model was better in forecasting stock indices in comparison to conventional BPNN.…”

Section: Literature Reviewmentioning

confidence: 99%

Stock market prediction, COVID-19 pandemic and neural networks: an SCG algorithm application

Goel

Som

2023

ECON

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PurposeThis study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011–February 2020) and during the COVID-19 (March 2020–June 2021).Design/methodology/approachSecondary data on macroeconomic variables and Nifty 50 index spanning a period of last ten years starting from 2011 to 2021 have been from various government and regulatory websites. Also, an artificial neural network (ANN) model was trained with the scaled conjugate gradient algorithm for predicting the National Stock exchange's (NSE) flagship index Nifty 50.FindingsThe findings of the study reveal that Scaled Conjugate Gradient (SCG) algorithm achieved 96.99% accuracy in predicting the Indian stock market in the pre-COVID-19 scenario. On the contrary, the proposed ANN model achieved 99.85% accuracy in during the COVID-19 period. The findings of this study have implications for investors, portfolio managers, domestic and foreign institution investors, etc.Originality/valueThe novelty of this study lies in the fact that are hardly any studies that forecasts the Indian stock market using artificial neural networks in the pre and during COVID-19 periods.

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Integration of genetic algorithm with artificial neural network for stock market forecasting

Cited by 43 publications

References 36 publications

Impact of Genetic Algorithm for the Diagnosis of Breast Cancer: Literature Review

Impact of Genetic Algorithm for the Diagnosis of Breast Cancer: Literature Review

Deformation prediction of mining slope based on grey correlation analysis and GA-BP model

Stock market prediction, COVID-19 pandemic and neural networks: an SCG algorithm application

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