As the rapid development of internet and the booming of financial
market in China, the study of extracting the
emotional state of netizens from financial public opinions and using it for
quantitative investment analysis has drawn a lot of attention. Because of the limitation of datasets scale, quantitative investment analysis based on financial
public opinion has some unsolved problems in the research of financial analysis,
such as the results cannot predict the stock
price in real stock markets. Based on the long-short-term memory network in
deep learning, the proposes study combined with the theory of herding effect in
behavioral finance, this paper designs an emotional classification model for
netizens’ comments on social media, interpret emotional state transaction of
netizens through sentiment analysis, forming an investor’s emotional states’ transfer model, and
incorporating the emotional states as a factor into the stock price-forecasting
model at last. The results show that the investor’s emotional states have a significant impact on stock price volatility. This stock price
forecasting method based on sentiment analysis also provides a new technical
path for quantitative investment analysis in the financial market.
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