Application of support vector regression for Jakarta stock composite index prediction with feature selection using laplacian score

Zuherman Rustam, Khadijah Takbiradzani

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Researchers and investors have been searching for accurate model to predict the stock value. An accurate model prediction could gain profits for investors. According to Indonesia Stock Exchange, stock is becoming one of the most popular financial instrument in Indonesia. Investors take the smaller sample called index that represent the whole because it would be too complicated to record every single security that trades in the country. There are many stock indices in the world, one of them, is Jakarta Composite Index (JKSE). One of the benefits of following the stock indices value is to reduce the loss in investment. Thus, this paper is focused in supervised learning method to solve regression problem, Support Vector Machines for Regression (SVR). There are fourteen technical indicators calculated in this paper. Laplacian score will be calculated for each fourteen technical indicators. Laplacian score is calculated to mirror the locality preserving power. Support Vector Machines for Regression (SVR) with feature selection using Laplacian Score is the proposed methodology with Jakarta Compostie Index (JKSE) are considered as input data. The best model is the prediction model with thirteen features and 30% training data which has value of Normalized Mean Squared Error (NMSE) is 1.30691E-07.

Original languageEnglish
Pages (from-to)88-97
Number of pages10
JournalJournal of Theoretical and Applied Information Technology
Volume97
Issue number1
Publication statusPublished - 15 Jan 2019

Keywords

  • Jakarta Composite Index (JKSE)
  • Laplacian score
  • Stock price trend prediction
  • Support Vector Machines for Regression (SVR)

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