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사회과학
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예술체육학
복합학
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Citeasy
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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2015.8
- 수록면
- 1,457 - 1,476 (20page)
이용수
초록· 키워드
Although stock price forecasting has been a traditional topic in the research domain of investment decision making, there have been many difficulties in forecasting stock price due to the unexpected rapid changes in stock prices. Recently, many researchers attempted to analyze sentiment in SNS data or news data to forecast stock price, but these researches have limitations that they used only one of sentiment data or KOSPI (Korea Composite Stock Price index) data in forecasting stock price. The aim of this paper is to propose new domain-specific sentiment dictionaries on stock price by using sentiment analysis, and acquire daily sentiment indices by analyzing the sentiment of news articles, and then use both of the sentiment data and KOSPI data together as input for data mining model for daily KOSPI forecasting, and finally improve the accuracy of forecasting the direction of KOSPI. TF-IDF weight was considered in building sentiment dictionaries and calculating daily sentiment indices by using domain-specific sentiment dictionaries. Our empirical result showed that in particular, a K-NN model with KOSPI and the sentiment data calculated by using both TF-IDF weights-based sentiment dictionary and the weights of news article itself in each news article data, had the accuracy of 68% and outperformed any other models in validation data.
#Stock Price Forecasting
#Data Mining
#Opinion Mining
#Sentiment Analysis
#Sentiment Dictionary
#Weights
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목차
- 〈Abstract〉
- 1. Introduction
- 2. Literature review
- 3. Research model
- 4. Empirical results
- 5. Conclusion and suggestions
- References
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