Stock Price Forecasting of PT Bank Rakyat Indonesia (Persero) Tbk Using the Support Vector Regression Method

Abstract

Stock price forecasting is an important activity in the capital market because stock price movements tend to be nonlinear and volatile over time. PT Bank Rakyat Indonesia (Persero) Tbk (BBRI) is a blue-chip stock with high liquidity and strong fundamentals, making it an appropriate subject for forecasting research. This study aims to predict BBRI’s stock price using the Support Vector Regression (SVR) method, which is known for its ability to model nonlinear relationships and minimize overfitting. The data used consist of BBRI’s daily closing prices from January 2020 to December 2024. Before modeling, the data were normalized using the Min–Max method and divided into training and testing sets with an 80:20 ratio.The initial baseline model employed an SVR with a linear kernel. The model was then optimized using the Radial Basis Function (RBF) kernel through Grid Search Optimization combined with time-series cross-validation to determine the best parameter combination. Optimal parameters were selected based on the lowest Root Mean Square Error (RMSE). The results show that the SVR RBF model outperformed the linear model in capturing the nonlinear patterns of BBRI’s stock price. During testing, the optimized model achieved an RMSE of 0.022054, indicating high predictive accuracy. The optimized SVR model was subsequently used to forecast stock prices for the next period and demonstrated relatively stable yet dynamic price movements. Overall, the findings confirm that the SVR method is effective and reliable for stock price forecasting and can serve as a valuable reference for investors and future financial research.