Application of SVM-KNN using SVR as feature selection on stock analysis for Indonesia stock exchange

D. A. Puspitasari, Zuherman Rustam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Stocks are known as high-risk and high-return investments. Forecasting stock prices movement is the challenging problem for researchers and financial analysts. Support Vector Machines (SVM) with K Nearest Neighbor (KNN) approach will be applied to forecast stock prices of a listed company in Indonesia Stock Exchange (IDX). The stock data are collected from January 2013 to December 2016. First, this paper used feature selection method to select important indicators from thirteen technical indicators using Support Vector Regression (SVR). Second, the stock data are classified using SVM to represent profit or loss and the output helps to find the best nearest neighbor from the training set. Next, stock prices are forecasted using KNN. The performance of this model is computed using Root Mean Square Error (RMSE) and relative error. In this case, the experiment result shows that three indicators selected from feature selection present good prediction capability and the accuracy for close prices prediction is 93.33 % accurately.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
EditorsRatna Yuniati, Terry Mart, Ivandini T. Anggraningrum, Djoko Triyono, Kiki A. Sugeng
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735417410
DOIs
Publication statusPublished - 22 Oct 2018
Event3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 - Bali, Indonesia
Duration: 26 Jul 201727 Jul 2017

Publication series

NameAIP Conference Proceedings
Volume2023
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
CountryIndonesia
CityBali
Period26/07/1727/07/17

Keywords

  • KNN
  • SVM
  • feature selection
  • forecasting
  • stocks

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