Stock price predictive analysis: An application of hybrid barnacles mating optimizer with artificial neural network
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 t...
| Main Authors: | , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado: |
KeAi Communications Co.
2023
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| Subjects: | |
| Acceso en liña: | http://umpir.ump.edu.my/id/eprint/41492/1/Stock%20price%20predictive%20analysis.pdf |
| _version_ | 1825815695454109696 |
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| author | Zuriani, Mustaffa Mohd Herwan, Sulaiman |
| author_facet | Zuriani, Mustaffa Mohd Herwan, Sulaiman |
| author_sort | Zuriani, Mustaffa |
| collection | UMP |
| description | 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%. |
| first_indexed | 2024-09-25T03:50:08Z |
| format | Article |
| id | UMPir41492 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-09-25T03:50:08Z |
| publishDate | 2023 |
| publisher | KeAi Communications Co. |
| record_format | dspace |
| spelling | UMPir414922024-06-06T05:21:14Z http://umpir.ump.edu.my/id/eprint/41492/ Stock price predictive analysis: An application of hybrid barnacles mating optimizer with artificial neural network Zuriani, Mustaffa Mohd Herwan, Sulaiman QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering 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%. KeAi Communications Co. 2023-06 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/41492/1/Stock%20price%20predictive%20analysis.pdf 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. 109 -117. ISSN 2666-3074. (Published) https://doi.org/10.1016/j.ijcce.2023.03.003 https://doi.org/10.1016/j.ijcce.2023.03.003 |
| spellingShingle | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Zuriani, Mustaffa Mohd Herwan, Sulaiman Stock price predictive analysis: An application of hybrid barnacles mating optimizer with artificial neural network |
| title | Stock price predictive analysis: An application of hybrid barnacles mating optimizer with artificial neural network |
| title_full | Stock price predictive analysis: An application of hybrid barnacles mating optimizer with artificial neural network |
| title_fullStr | Stock price predictive analysis: An application of hybrid barnacles mating optimizer with artificial neural network |
| title_full_unstemmed | Stock price predictive analysis: An application of hybrid barnacles mating optimizer with artificial neural network |
| title_short | Stock price predictive analysis: An application of hybrid barnacles mating optimizer with artificial neural network |
| title_sort | stock price predictive analysis an application of hybrid barnacles mating optimizer with artificial neural network |
| topic | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/41492/1/Stock%20price%20predictive%20analysis.pdf |
| work_keys_str_mv | AT zurianimustaffa stockpricepredictiveanalysisanapplicationofhybridbarnaclesmatingoptimizerwithartificialneuralnetwork AT mohdherwansulaiman stockpricepredictiveanalysisanapplicationofhybridbarnaclesmatingoptimizerwithartificialneuralnetwork |