Stock Price Movement Prediction Using Technical Analysis and Sentiment Analysis

Tommy Wijaya Sagala, Mei Silviana Saputri, Rahmad Mahendra, Indra Budi

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

Abstract

This study aims to predict stock price movement using combination of technical analysis and sentiment analysis. When conducting stock transactions, the traders consider not only market activities but also the sentiments expressed within information reported in media. We build the classifier to categorize the price quotes into one of three classes: "up", "down", and "constant". We conduct the experiment with several algorithms, i.e. Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naïve Bayes. The results of our empirical study is that the highest accuracy achieved from the method combining features from historical data and online media sentiment, on 5 days trading window using the SVM algorithm.

Original languageEnglish
Title of host publicationAPIT 2020 - 2020 2nd Asia Pacific Information Technology Conference
PublisherAssociation for Computing Machinery
Pages123-127
Number of pages5
ISBN (Electronic)9781450376853
DOIs
Publication statusPublished - 17 Jan 2020
Event2nd Asia Pacific Information Technology Conference, APIT 2020 - Bali Island, Indonesia
Duration: 17 Jan 202019 Jan 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2nd Asia Pacific Information Technology Conference, APIT 2020
Country/TerritoryIndonesia
CityBali Island
Period17/01/2019/01/20

Keywords

  • Classification
  • Price
  • Sentiment Analysis
  • Stocks
  • Technical Analysis
  • Trader

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