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
N1 - 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 jseok14@korea.ac.kr.
Publisher Copyright:
© 2019 IEEE.
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 - Korean natural language processing
KW - artificial neural network
KW - convolution neural network
KW - skip-gram
KW - stock price prediction
UR - http://www.scopus.com/inward/record.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.
T2 - 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
Y2 - 11 February 2019 through 13 February 2019
ER -