TY - JOUR
T1 - Application of Support Vector Regression in Indonesian Stock Price Prediction with Feature Selection Using Particle Swarm Optimisation
AU - Rustam, Zuherman
AU - Kintandani, Puteri
N1 - Funding Information:
*is research was financially supported by the Indonesia Ministry of Research and Higher Education, with a PDUPT 2018 research grant scheme (ID number 389/UN2.R3.1/ HKP05.00/2018).
Publisher Copyright:
© 2019 Zuherman Rustam and Puteri Kintandani.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Stock investing is one of the most popular types of investments since it provides the highest return among all investment types; however, it is also associated with considerable risk. Fluctuating stock prices provide an opportunity for investors to make a high profit. We can see the movement of groups of stock prices from the stock index, which is called Jakarta Composite Index (JKSE) in Indonesia. Several studies have focused on the prediction of stock prices using machine learning, while one uses support vector regression (SVR). Therefore, this study examines the application of SVR and particle swarm optimisation (PSO) in predicting stock prices using stock historical data and several technical indicators, which are selected using PSO. Subsequently, a support vector machine (SVM) was applied to predict stock prices with the technical indicator selected by PSO as the predictor. The study found that stock price prediction using SVR and PSO shows good performances for all data, and many features and training data used by the study have relatively low error probabilities. Thereby, an accurate model was obtained to predict stock prices in Indonesia.
AB - Stock investing is one of the most popular types of investments since it provides the highest return among all investment types; however, it is also associated with considerable risk. Fluctuating stock prices provide an opportunity for investors to make a high profit. We can see the movement of groups of stock prices from the stock index, which is called Jakarta Composite Index (JKSE) in Indonesia. Several studies have focused on the prediction of stock prices using machine learning, while one uses support vector regression (SVR). Therefore, this study examines the application of SVR and particle swarm optimisation (PSO) in predicting stock prices using stock historical data and several technical indicators, which are selected using PSO. Subsequently, a support vector machine (SVM) was applied to predict stock prices with the technical indicator selected by PSO as the predictor. The study found that stock price prediction using SVR and PSO shows good performances for all data, and many features and training data used by the study have relatively low error probabilities. Thereby, an accurate model was obtained to predict stock prices in Indonesia.
UR - http://www.scopus.com/inward/record.url?scp=85065615566&partnerID=8YFLogxK
U2 - 10.1155/2019/8962717
DO - 10.1155/2019/8962717
M3 - Article
AN - SCOPUS:85065615566
VL - 2019
JO - Modelling and Simulation in Engineering
JF - Modelling and Simulation in Engineering
SN - 1687-5591
M1 - 8962717
ER -