Developing support vector regression model to forcast stock prices of mining companies in Indonesia

Abstract

The modern era as it is now the world of stock investment is in great demand by investors, both long-term and short-term stock investments. Stock investment provides many benefits for investors. To get large profits, investors need to do an analysis in stock investments to predict the price of the shares to be purchased. Very volatile stock price movements make it difficult for investors to predict stock prices. The main hope of investors is to benefit from each price that changes from time to time or can be referred to as time series data. Data mining is a process of extracting large information from a data by collecting, using data, historical patterns of data relationships, and relationships in large data sets. Support vector regression has advantages in making accurate stock price predictions and can overcome the problem of overfitting by itself. PTBA, and ITMG are the leading coal mining companies in Indonesia, so many people want to invest in the company. ADRO, PTBA, and ITMG stock price prediction analysis using support vector regression algorithm has good predictive accuracy values, including. PTBA stock price have an R-square value of 97.9% in the RBF kernel and linear with MAPE respectively of 2,465 and 2,480. And for ITMG stock price it has an R-square accuracy of 94.3% in the RBF kernel and linear with MAPE respectively 5.874 and 5.875. These results indicate that the SVR method is best used for forecasting stock prices.