Stock Prices Prediction using the Title of Newspaper Articles with Korean Natural Language Processing

Hyungbin Yun, Ghudae Sim, Junhee Seok

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    20 Citations (Scopus)

    Abstract

    Non-quantitative data have a significant impact on the financial market as well as quantitative data. In this paper, we propose CNN model of stock price prediction using Korean natural language processing. In the case of Korean natural language processing research was not actively performed compared to English language. We converted Korean sentences into nouns and vectorized them using skip-grams to extract the characteristics of the words. Then, the vectorized word sentence was used as input data of the CNN model to predict the stock price after 5 days of trading day. Most models have more than 50% prediction accuracy for stock price up and down. The highest accuracy of the model was about 53%. Since the result is not considerable but meaningful, it shows the possibility of developing the stock price prediction model through Korean natural language processing in the future.

    Original languageEnglish
    Title of host publication1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages19-21
    Number of pages3
    ISBN (Electronic)9781538678220
    DOIs
    Publication statusPublished - 2019 Mar 18
    Event1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 - Okinawa, Japan
    Duration: 2019 Feb 112019 Feb 13

    Publication series

    Name1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019

    Conference

    Conference1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
    Country/TerritoryJapan
    CityOkinawa
    Period19/2/1119/2/13

    Bibliographical note

    Funding Information:
    This work was supported by the National Research Foundation of Korea grant (NRF-2017R1C1B2002850) and Korea University grant (K1822271) as well as a grant from Mirae Asset Global Investment. Correspondence should be addressed to [email protected].

    Publisher Copyright:
    © 2019 IEEE.

    Keywords

    • Korean natural language processing
    • artificial neural network
    • convolution neural network
    • skip-gram
    • stock price prediction

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Computer Science Applications
    • Artificial Intelligence

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