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Stock Price Movement Prediction Using Technical Analysis and Sentiment Analysis

Published: 29 March 2020 Publication History

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.

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Cited By

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  • (2023)The Correlation of Headline News Sentiment and Stock Return During Dividend Period2023 International Conference on Digital Business and Technology Management (ICONDBTM)10.1109/ICONDBTM59210.2023.10327342(1-6)Online publication date: 2-Aug-2023
  • (2022)Mid-Price Prediction Using Online Kernel Adaptive FilteringEmerging Technologies for Computing, Communication and Smart Cities10.1007/978-981-19-0284-0_51(701-714)Online publication date: 20-Apr-2022
  • (2021)Multivariate and Online Prediction of Closing Price Using Kernel Adaptive FilteringComputational Intelligence and Neuroscience10.1155/2021/64000452021Online publication date: 1-Jan-2021
  • Show More Cited By

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  1. Stock Price Movement Prediction Using Technical Analysis and Sentiment Analysis

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    APIT '20: Proceedings of the 2020 2nd Asia Pacific Information Technology Conference
    January 2020
    185 pages
    ISBN:9781450376853
    DOI:10.1145/3379310
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 29 March 2020

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    Author Tags

    1. Classification
    2. Price
    3. Sentiment Analysis
    4. Stocks
    5. Technical Analysis
    6. Trader

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    View all
    • (2023)The Correlation of Headline News Sentiment and Stock Return During Dividend Period2023 International Conference on Digital Business and Technology Management (ICONDBTM)10.1109/ICONDBTM59210.2023.10327342(1-6)Online publication date: 2-Aug-2023
    • (2022)Mid-Price Prediction Using Online Kernel Adaptive FilteringEmerging Technologies for Computing, Communication and Smart Cities10.1007/978-981-19-0284-0_51(701-714)Online publication date: 20-Apr-2022
    • (2021)Multivariate and Online Prediction of Closing Price Using Kernel Adaptive FilteringComputational Intelligence and Neuroscience10.1155/2021/64000452021Online publication date: 1-Jan-2021
    • (2021)Financial Forecasting of Stock Market Using Sentiment Analysis and Data AnalyticsIntelligent Sustainable Systems10.1007/978-981-16-6369-7_38(423-430)Online publication date: 17-Dec-2021

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