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Comparison of Models for Stock Price Prediction Based on Keyword Search Volume According to the Social Acceptance of Artificial Intelligence
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Yujung Cho (Department of Big Data Analytics, Kyung Hee University)
Sohn Kwonsang (School of Management, Kyung Hee University)
Kwon Ohbyung (School of Management, Kyung Hee University)
Vol. 27, No. 1, Page: 103 ~ 128
Keywords
Artificial Intelligence, Social Acceptance, Stock Price Prediction, Multivariate Time Series, Keyword Search Volume, LSTM, VAR
Abstract
Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.
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인공지능의 사회적 수용도에 따른 키워드 검색량 기반 주가예측모형 비교연구
조유정 (경희대학교)
손권상 (경희대학교)
권오병 (경희대학교)
Keywords
인공지능, 사회적 수용도, 주가예측, 다변량 시계열, 키워드 검색량, LSTM, VAR
Abstract
최근 주식의 수익률과 거래량을 설명하는 주요 요인으로서 투자자의 관심도와 주식 관련 정보 전파의 영향력이 부각되고 있다. 또한 인공지능과 같은 혁신 신기술을 개발보급하거나 활용하려는 기업의 경우 거시환경 및 시장 불확실성 때문에 기업의 미래 주식 수익률과 주식 변동성을 예측하기 어렵다는 문제를 가지고 있다. 이는 인공지능 활성화의 장애요인 으로 인식되고 있다. 따라서 본 연구의 목적은 인공지능 관련 기술 키워드의 인터넷 검색량을 투자자의 관심 척도로 사용 하여, 기업의 주가 변동성을 예측하는 기계학습 모형을 제안하는 것이다. 이를 위해 심층신경망 LSTM(Long Short-Term Memory)과 벡터자기회귀(Vector Autoregression)를 통해 주식시장을 예측하고, 기술의 사회적 수용 단계에 따라 키워드 검색량을 활용한 주가예측 성능 비교를 통해 기업의 투자수익 예측이나 투자자들의 투자전략 의사결정을 지원하는 주가 예측모형을 구축하였다. 또한 인공지능 기술의 세부하위 기술에 대한 분석도 실시하여 기술 수용단계에 따른 세부기술 키워드 검색량의 변화를 살펴보고 세부기술에 대한 관심도가 주식시장 예측에 미치는 영향을 살펴보았다. 이를 위해 본 연구에서는 인공지능, 딥러닝, 머신러닝 키워드를 선정하여, 2015년 1월 1일부터 2019년 12월 31일까지 5년간의 인터넷 주별 검색량 데이터와 코스닥 상장 기업의 주가 및 거래량 데이터를 수집하여 분석에 활용하였다. 분석 결과 인공지능 기술에 대한 키워드 검색량은 사회적 수용 단계가 진행될수록 증가하는 것으로 나타났고, 기술 키워드를 기반으로 주가예 측을 하였을 경우 인식(Awareness)단계에서 가장 높은 정확도를 보였으며, 키워드 별로 가장 좋은 예측 성능을 보이는 수 용 단계가 다르게 나타남을 확인하였다. 따라서 기술 키워드를 활용한 주가 예측 모델 구축을 위해서는 해당 기술의 하위 기술 분류를 고려할 필요가 있다. 본 연구의 결과는 혁신기술을 기반으로 기업의 투자수익률을 예측하기 위해서는 기술에 대한 대중의 관심이 급증하는 인식 단계를 포착하는 것이 중요하다는 점을 시사한다. 또한 최근 금융권에서 선보이고 있는 빅데이터 기반 로보어드바이저(Robo-advisor) 등 투자 의사 결정 지원 시스템 개발 시 기술의 사회적 수용도를 세분화 하여 키워드 검색량 변화를 통해 예측 모델의 정확도를 개선할 수 있다는 점을 시사하고 있다.
Cite this article
JIIS Style
Cho, Y., S. Kwonsang, and K. Ohbyung, "Comparison of Models for Stock Price Prediction Based on Keyword Search Volume According to the Social Acceptance of Artificial Intelligence", Journal of Intelligence and Information Systems, Vol. 27, No. 1 (2021), 103~128.

IEEE Style
Yujung Cho, Sohn Kwonsang, and Kwon Ohbyung, "Comparison of Models for Stock Price Prediction Based on Keyword Search Volume According to the Social Acceptance of Artificial Intelligence", Journal of Intelligence and Information Systems, vol. 27, no. 1, pp. 103~128, 2021.

ACM Style
Cho, Y., Kwonsang, S., and Ohbyung, K., 2021. Comparison of Models for Stock Price Prediction Based on Keyword Search Volume According to the Social Acceptance of Artificial Intelligence. Journal of Intelligence and Information Systems. 27, 1, 103--128.
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@article{Cho:JIIS:2021:836,
author = {Cho, Yujung and Kwonsang, Sohn and Ohbyung, Kwon},
title = {Comparison of Models for Stock Price Prediction Based on Keyword Search Volume According to the Social Acceptance of Artificial Intelligence},
journal = {Journal of Intelligence and Information Systems},
issue_date = {March 2021},
volume = {27},
number = {1},
month = Mar,
year = {2021},
issn = {2288-4866},
pages = {103--128},
url = {},
doi = {},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Artificial Intelligence, Social Acceptance, Stock Price Prediction, Multivariate Time Series, Keyword Search Volume, LSTM and VAR },
}
%0 Journal Article
%1 836
%A Yujung Cho
%A Sohn Kwonsang
%A Kwon Ohbyung
%T Comparison of Models for Stock Price Prediction Based on Keyword Search Volume According to the Social Acceptance of Artificial Intelligence
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 27
%N 1
%P 103-128
%D 2021
%R
%I Korea Intelligent Information System Society