Article: Optimising AdaBoost with bio-inspired algorithms for stock market prediction: a comparative study on Hang Seng index Journal: International Journal of Internet Protocol Technology (IJIPT) 2025 Vol.18 No.3 pp.150 - 162 Abstract: This study presented a new integrated forecasting model for improving the accuracy of stock price predictions for the Hang Seng Index. Using the ensemble learning theory, this research constructs the core modelling engine based on Adaptive Boosting (AdaBoost) is fine-tuned using three metaheuristic optimisation approaches: Particle Swarm Optimisation, Antlion Optimisation and Biogeography-Based Optimisation. These algorithms are based on nature search for optimising AdaBoost hyperparameters such as the number of estimators, learning rate and complexity of the base learner to improve generalisation and minimise prediction errors. The BBO-AdaBoost was the most superior one. A data set including daily HSI stock prices (open, high, low, close) and volume were obtained from 2015 to 2023. The data were min-max normalised and were split into 80:20 for training and testing. The results show that the BBO-AdaBoost achieved the best result with the least MAPE of 2.29%, outperforming both the stand-alone AdaBoost and other hybrid models. Inderscience Publishers - linking academia, business and industry through research

Title: Optimising AdaBoost with bio-inspired algorithms for stock market prediction: a comparative study on Hang Seng index

Authors: Cuicui Feng; Kai Huang; Wendi Wang; Xian Yu

Addresses: Guangxi Natural Resources Vocational and Technical College, Chongzuo, Guangxi, China ' Guangxi Natural Resources Vocational and Technical College, Chongzuo, Guangxi, China ' Guangxi Natural Resources Vocational and Technical College, Chongzuo, Guangxi, China ' Guangxi Natural Resources Vocational and Technical College, Chongzuo, Guangxi, China

Abstract: This study presented a new integrated forecasting model for improving the accuracy of stock price predictions for the Hang Seng Index. Using the ensemble learning theory, this research constructs the core modelling engine based on Adaptive Boosting (AdaBoost) is fine-tuned using three metaheuristic optimisation approaches: Particle Swarm Optimisation, Antlion Optimisation and Biogeography-Based Optimisation. These algorithms are based on nature search for optimising AdaBoost hyperparameters such as the number of estimators, learning rate and complexity of the base learner to improve generalisation and minimise prediction errors. The BBO-AdaBoost was the most superior one. A data set including daily HSI stock prices (open, high, low, close) and volume were obtained from 2015 to 2023. The data were min-max normalised and were split into 80:20 for training and testing. The results show that the BBO-AdaBoost achieved the best result with the least MAPE of 2.29%, outperforming both the stand-alone AdaBoost and other hybrid models.

Keywords: stock price prediction; Hang Seng index; adaptive boosting; biogeography-based optimisation; particle swarm optimisation; antlion optimisation; hybrid forecasting model; machine learning; financial market forecasting; time series analysis.

DOI: 10.1504/IJIPT.2025.148574

International Journal of Internet Protocol Technology, 2025 Vol.18 No.3, pp.150 - 162

Received: 13 Apr 2025
Accepted: 20 May 2025
Published online: 12 Sep 2025 *

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Title: Optimising AdaBoost with bio-inspired algorithms for stock market prediction: a comparative study on Hang Seng index

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