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Malaysian Daily Stock Prediction Analysis Using Supervised Learning Algorithms

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dc.contributor.authorHazirah Halulen_US
dc.contributor.authorKarmila Hanim Kamilen_US
dc.date.accessioned2024-05-30T02:07:47Z
dc.date.available2024-05-30T02:07:47Z
dc.date.issued2022
dc.date.submitted2023-2-4
dc.descriptionVol. 8 No. 2 (2022)en_US
dc.description.abstractNowadays, Machine Learning (ML) plays a significant role in the economy, especially in the stock trading strategy. However, there is an inadequate extensive data analysis using various ML methods. Previous findings usually focus on the forecasting stock index or selecting a limited number of stocks with restricted features. Therefore, the contribution of this paper focused on evaluating different supervised learning algorithms, namely Logistic Regression (LR), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB), on a big dataset from 28 stocks in Bursa Malaysia. By setting their parameter along and using Walk-Forward Analysis (WFA) method, the trading signal was evaluated based on Accuracy Rate, Precision Rate, Recall Rate, and F1 Score. For stock trading strategies in Malaysia in particular, the findings of this study show that SVM has a better performance compared to LR and XGB in time series forecasting. The ML algorithms have values ranging from 53% to 66% for Accuracy Rate (AR), Recall Rate (RR), and F1 Score (F1). In addition, SVM has the highest Precision Rate (PR) of 73% among the ML algorithms.en_US
dc.identifier.citationHalul, H., & Kamil, K. H. (2022). Malaysian Daily Stock Prediction Analysis Using Supervised Learning Algorithms. Malaysian Journal of Science Health & Technology, 8(2), 31–37. https://doi.org/10.33102/2022229en_US
dc.identifier.doi10.33102/2022229
dc.identifier.epage43
dc.identifier.issn2601-0003
dc.identifier.issue2
dc.identifier.other372-13
dc.identifier.spage37
dc.identifier.urihttps://mjosht.usim.edu.my/index.php/mjosht/article/view/229
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/15477
dc.identifier.volume8
dc.language.isoenen_US
dc.publisherUSIM Pressen_US
dc.relation.ispartofMalaysian Journal of Science, Health & Technology (MJoSHT)en_US
dc.subjectMachine Learning, Supervised Learning Classifier, Walk-Forward Analysis, time series forecastingen_US
dc.titleMalaysian Daily Stock Prediction Analysis Using Supervised Learning Algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication

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