Application of Support Vector Regression in Indonesian Stock Price Prediction with Feature Selection Using Particle Swarm Optimisation

Zuherman Rustam,Puteri Kintandani

doi:10.1155/2019/8962717

Zuherman Rustam, Puteri Kintandani

Open Access

https://doi.org/10.1155/2019/8962717

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Abstract

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.

Highlights

A stock is a sign of ownership of a company and indicates that a shareholder holds a share of the company’s assets and earnings [1]
Whether increase or decrease, of the stock prices of all securities listed on the Indonesia Stock Exchange (IDX). is is an important concern for investors since Jakarta Composite Index (JKSE) affects the attitudes of investors regarding whether to buy, hold, or sell their shares
Experimental Results e experimental results of stock price prediction using support vector regression (SVR) and particle swarm optimisation (PSO) showed good performances for all the data that were used, and many features and training data used by the study had relatively small normalised mean squared error (NMSE) values, which averaged below 0.1. e data used are JKSE and real estate stock data, comprising Alam Sutera Reality Tbk (ASRI), Agung Podomoro Land Tbk (APLN), and Serpong Damai Tbk (BSDE)

Summary

Zuherman Rustam and Puteri Kintandani

Fluctuating stock prices provide an opportunity for investors to make a high profit. Several studies have focused on the prediction of stock prices using machine learning, while one uses support vector regression (SVR). Erefore, 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. A support vector machine (SVM) was applied to predict stock prices with the technical indicator selected by PSO as the predictor. E 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. Ereby, an accurate model was obtained to predict stock prices in Indonesia

Introduction

Literature Review

Technical indicator

Data training

Conclusions