Short-Term Stock Price Prediction Based on Single and Stacking Machine Learning Models

Chia Yean Lim ()
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Chia Yean Lim: School of Computer Sciences, Universiti Sains Malaysia, 11800, Minden, Malaysia Author-2-Name: Wenchuan Sun Author-2-Workplace-Name: School of Computer Sciences, Universiti Sains Malaysia, 11800, Minden, Malaysia Author-3-Name: Fengqi Guo Author-3-Workplace-Name: CITIC Securities, 150000, Harbin, China Author-4-Name: Sau Loong Ang Author-4-Workplace-Name: Department of Computing and Information Technology, Tunku Abdul Rahman University of Management and Technology, Penang Branch, 11200, Tanjung Bungah, Malaysia Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:

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Abstract: " Objective - As the investment environment improves, individuals are increasingly eager to invest their idle funds. Securities companies have become the preferred choice for buying financial products. The current accuracy of stock predictions relies on the comprehensive models used by each securities company, including stock market trading, data, and stock pricing models. However, securities companies have not adequately explored a single suitable model for stock predictions and have rarely assessed the effectiveness of stacking and ensemble methods in improving these predictions. Methodology - This research first explored and proposed the best single-stock prediction model. Next, it combined four individual prediction models to create a stacking model. Findings - The comparison between the single and stacking models demonstrated that the stacking model's prediction accuracy exceeded that of the single model. Therefore, it is recommended that securities companies adopt a stacking-type prediction model to forecast share prices for their investment customers. Novelty - Using a stacking model could improve the accuracy of stock price predictions for investment managers, help users make better decisions, and ultimately enhance the company's earnings by delivering more accurate investment outcomes. Type of Paper - Empirical"

Keywords: Long short-term memory; random forest model; stacking model; stock prediction; support vector machine; XGBoost model. (search for similar items in EconPapers)
JEL-codes: F17 F47 (search for similar items in EconPapers)
Pages: 7
Date: 2026-03-31
New Economics Papers: this item is included in nep-cfn, nep-cmp, nep-fmk and nep-for
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Published in Global Journal of Business and Social Science Review, Volume 14, Issue 1

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Persistent link: https://EconPapers.repec.org/RePEc:gtr:gatrjs:gjbssr674

DOI: 10.35609/gjbssr.2026.14.1(2)

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