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http://dx.doi.org/10.7838/jsebs.2020.25.4.061

Stock Price Prediction Using Sentiment Analysis: from "Stock Discussion Room" in Naver  

Kim, Myeongjin (Department of Industrial Engineering(ITM program), Seoul National University of Science and Technology)
Ryu, Jihye (Department of Industrial Engineering(ITM program), Seoul National University of Science and Technology)
Cha, Dongho (Headquarter of Multi-Solution, KB Asset Management)
Sim, Min Kyu (Department of Industrial Engineering, Seoul National University of Science and Technology)
Publication Information
The Journal of Society for e-Business Studies / v.25, no.4, 2020 , pp. 61-75 More about this Journal
Abstract
The scope of data for understanding or predicting stock prices has been continuously widened from traditional structured format data to unstructured data. This study investigates whether commentary data collected from SNS may affect future stock prices. From "Stock Discussion Room" in Naver, we collect 20 stocks' commentary data for six months, and test whether this data have prediction power with respect to one-hour ahead price direction and price range. Deep neural network such as LSTM and CNN methods are employed to model the predictive relationship. Among the 20 stocks, we find that future price direction can be predicted with higher than the accuracy of 50% in 13 stocks. Also, the future price range can be predicted with higher than the accuracy of 50% in 16 stocks. This study validate that the investors' sentiment reflected in SNS community such as Naver's "Stock Discussion Room" may affect the demand and supply of stocks, thus driving the stock prices.
Keywords
High-Frequency Financial Data; Text Mining; Sentimental Analysis; Deep Neural Networks;
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Times Cited By KSCI : 16  (Citation Analysis)
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