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 language | English |
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Title of host publication | 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 19-21 |
Number of pages | 3 |
ISBN (Electronic) | 9781538678220 |
DOIs | |
Publication status | Published - 2019 Mar 18 |
Event | 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 - Okinawa, Japan Duration: 2019 Feb 11 → 2019 Feb 13 |
Publication series
Name | 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 |
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Conference
Conference | 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 |
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Country/Territory | Japan |
City | Okinawa |
Period | 19/2/11 → 19/2/13 |
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