Prediction of Central Asia Bank’s Stock Price using Support Vector Regression Method - Dide Guna Jayaswara; Isnandar Slamet; Yuliana Susanti (Proceeding International Conference on Religion, Science and Education, 2023)
The capital market in Indonesia is a developing market that is very vulnerable to global economic conditions and world capital markets. There is a term mostly found in the capital market, namely investment. Investment is a form of delaying consumption from the present to the future where there is a risk of uncertainty. In capital market investment, it is necessary to analyse stock price predictions to find out stock prices in the future by using historical stock prices because stock prices change quickly from time to time, making the shareholder immediately decide when the shares should be sold or retained. This study aims to obtain stock price predictions and model implementation and determine the model’s accuracy in predicting a stock’s movement to benefit from each stock price that changes from time to time or can be referred to as time series data. Therefore, we need a method that can predict stock prices. In this study, the predicted stocks are BCA stock data using the SVR algorithm with linear and RBF kernels. SVR has advantages in making accurate stock price predictions and can overcome overfitting problems. From several studies conducted, SVR provides optimal results for stock price prediction analysis. Based on the results of the research for BCA shares, it can be concluded that the SVR algorithm has an excellent predictive accuracy value with a linear kernel function with the help of a grid search using a k-fold Cross-validation of 3, which has an R-square value of training data of 93.79% and an R- square data testing is 92.98%, Mean Absolute Percentage Error (MAPE) training data is 0.2340, and MAPE data testing is 0.1021, Root Mean Square Error (RMSE) training data is 0.0597, and RMSE data testing is 0.0499. This algorithm shows that the SVR method is suitable for predicting stock prices.