온라인 언급이 기업 성과에 미치는 영향 분석|RISS 상세보기

다국어 초록 (Multilingual Abstract)

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, ‘energy/chemical’, ‘consumer goods for living’ and ‘consumer discretionary’ showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as ‘information technology’ and ‘shipbuilding/transportation’ industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as ‘Kangwon Land’, ‘KT & G’ and ‘SK Innovation’ showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as ‘Young Poong’, ‘LG’, ‘Samsung Life Insurance’, and ‘Doosan’ had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

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참고문헌 (Reference)

1 조혜진, "주식 뉴스 콘텐츠를 활용한 오피니언마이닝 기반의 OAR 감성사전 알고리즘 기법" 한국정보기술학회 13 (13): 111-119, 2015

2 유은지, "주가지수 방향성 예측을 위한 주제지향 감성사전 구축 방안" 한국지능정보시스템학회 19 (19): 95-110, 2013

3 김승우, "오피니언 분류의 감성사전 활용효과에 대한 연구" 한국지능정보시스템학회 20 (20): 133-148, 2014

4 김영민, "소셜 미디어 감성분석을 통한 주가 등락 예측에 관한 연구" 엘지씨엔에스 13 (13): 59-70, 2014

5 김유신, "뉴스와 주가 : 빅데이터 감성분석을 통한 지능형 투자의사결정모형" 한국지능정보시스템학회 18 (18): 143-156, 2012

6 조은경, "감성 분석 연구의 현황과 말뭉치에 기반한 사례 분석 - 영화평 자료를 중심으로 -" 언어과학회 (61) : 259-282, 2012

7 Bollen, J, "Twitter Mood Predicts the Stock Market" 2 (2): 1-8, 2011

8 Evangelopoulos, N., "The Dual Micro/Macro Informing Role of Social Network Sites: Can Twitter Macro Messages Help Predict Stock Prices?" 15 : 247-268, 2012

9 Lee, J., "Recent Advances and Trends in Predictive Manufacturing Systems in Big Data Environment" 1 (1): 38-41, 2013

10 Kim, D. S., "Public Opinion Sensing and Trend Analysis on Social Media: A Study on Nuclear Power on Twitter" 9 (9): 373-384, 2014