Author(s)

Zohre Ghabussi

Corresponding Author

Zohre Ghabussi

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 Optimization (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- optimized 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, Support Vector Regression (SVR), Battle Royale Optimization (BRO), Shanghai Stock Exchange (SSE), Machine Learning (ML), Metaheuristic Algorithms, Time Series Forecasting, Prediction Accuracy Metrics.