Title: Time series prediction of stock market movements by utilising the BRO-SVR hybrid model: a case study of the Shanghai Stock Exchange

Authors: Weishuang Xu; Daming Li; Siqi Hao

Addresses: School of Accountancy, Shandong Youth University of Political Science, Jinan, Shandong, China ' Qilu Normal University, Jinan, Shandong, China ' University of Science Malaysia, Jalan Universiti, Gelugor, Pulau Pinang, Malaysia

Abstract: Predicting future stock prices is challenging due to numerous uncontrollable factors. However, data-driven methods have enabled more accurate forecasts despite inherent uncertainties. Traditionally, such forecasts relied on technical and fundamental indicators. With advancements in Machine Learning (ML), prediction accuracy and accessibility have significantly improved. This study presents a novel approach that integrates the Battle Royale Optimisation (BRO) algorithm with an enhanced support vector regression model to predict stock prices. Applied to data from the Shanghai Stock Exchange, the proposed model demonstrated high prediction accuracy. It consistently outperformed existing methods, significantly improving time series forecasting of stock values. High R²-values during both training and testing phases confirm the model's robustness. These results indicate that the BRO-optimised model effectively manages stock market volatility and serves as a reliable tool for analysts and investors seeking accurate and consistent financial forecasting.

Keywords: stock price prediction; SVR; support vector regression; BRO; battle royale optimisation; SSE; Shanghai Stock Exchange; ML; machine learning; metaheuristic algorithms; time series forecasting; prediction accuracy metrics.

DOI: 10.1504/IJIPT.2026.153845

International Journal of Internet Protocol Technology, 2026 Vol.19 No.2, pp.65 - 76

Received: 15 Apr 2025
Accepted: 23 May 2025
Published online: 28 May 2026 *

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