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

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
CountryJapan
CityOkinawa
Period19/2/1119/2/13

Fingerprint

Processing
Financial markets

Keywords

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

ASJC Scopus subject areas

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

Cite this

Yun, H., Sim, G., & Seok, J. (2019). Stock Prices Prediction using the Title of Newspaper Articles with Korean Natural Language Processing. In 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 (pp. 19-21). [8668996] (1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAIIC.2019.8668996

Stock Prices Prediction using the Title of Newspaper Articles with Korean Natural Language Processing. / Yun, Hyungbin; Sim, Ghudae; Seok, Junhee.

1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 19-21 8668996 (1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019).

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

Yun, H, Sim, G & Seok, J 2019, Stock Prices Prediction using the Title of Newspaper Articles with Korean Natural Language Processing. in 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019., 8668996, 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 19-21, 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, Okinawa, Japan, 19/2/11. https://doi.org/10.1109/ICAIIC.2019.8668996
Yun H, Sim G, Seok J. Stock Prices Prediction using the Title of Newspaper Articles with Korean Natural Language Processing. In 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 19-21. 8668996. (1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019). https://doi.org/10.1109/ICAIIC.2019.8668996
Yun, Hyungbin ; Sim, Ghudae ; Seok, Junhee. / Stock Prices Prediction using the Title of Newspaper Articles with Korean Natural Language Processing. 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 19-21 (1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019).
@inproceedings{97522fec95a942ca8d404ad849ccaf5f,
title = "Stock Prices Prediction using the Title of Newspaper Articles with Korean Natural Language Processing",
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.",
keywords = "artificial neural network, convolution neural network, Korean natural language processing, skip-gram, stock price prediction",
author = "Hyungbin Yun and Ghudae Sim and Junhee Seok",
year = "2019",
month = "3",
day = "18",
doi = "10.1109/ICAIIC.2019.8668996",
language = "English",
series = "1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "19--21",
booktitle = "1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019",

}

TY - GEN

T1 - Stock Prices Prediction using the Title of Newspaper Articles with Korean Natural Language Processing

AU - Yun, Hyungbin

AU - Sim, Ghudae

AU - Seok, Junhee

PY - 2019/3/18

Y1 - 2019/3/18

N2 - 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.

AB - 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.

KW - artificial neural network

KW - convolution neural network

KW - Korean natural language processing

KW - skip-gram

KW - stock price prediction

UR - http://www.scopus.com/inward/record.url?scp=85063875504&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063875504&partnerID=8YFLogxK

U2 - 10.1109/ICAIIC.2019.8668996

DO - 10.1109/ICAIIC.2019.8668996

M3 - Conference contribution

AN - SCOPUS:85063875504

T3 - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019

SP - 19

EP - 21

BT - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

ER -