Stock price predictive analysis : An application of hybrid barnacles mating optimizer with artificial neural network

Zuriani, Mustaffa and Mohd Herwan, Sulaiman (2023) Stock price predictive analysis : An application of hybrid barnacles mating optimizer with artificial neural network. International Journal of Cognitive Computing in Engineering, 4. pp. 109-117. ISSN 2666-3074. (Published)

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Artificial Neural Network (ANN) is an effective machine learning technique for addressing regression tasks. Nonetheless, the performance of ANN is highly dependent on the values of its parameters, specifically the weight and bias. To improve its predictive generalization, it is crucial to optimize these parameters. In this study, the Barnacles Mating Optimizer (BMO) is employed as an optimization tool to automatically optimize these parameters. As a relatively new optimization algorithm, it has been shown to be effective in addressing various optimization problems. The proposed hybrid predictive model of BMO-ANN is tested on time series data of stock price using six selected inputs to predict the next day’ closing prices. Evaluated based on Mean Square Error (MSE) and Root Mean Square Error (RMSPE), the proposed BMO-ANN exhibits significant superiority over the other identified hybrid algorithms. Additionally, the difference in means between BMO-ANN and other identified hybrid algorithms was found to be statistically significant, with a significance level of 0.05%.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Artificial neural network; Barnacles mating optimizer; Machine learning; Optimization; Stock price prediction; Time series prediction
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Computing
Faculty of Electrical and Electronic Engineering Technology
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 28 Aug 2023 08:14
Last Modified: 28 Aug 2023 08:14
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