[Objective] This paper aims to study the impacts of media coverage on stock market. [Methods] We used the LSTM deep neural networks to evaluate the sentiments of the online news, forum posts and blogs from leading financial websites. Then, we established autoregressive distributed lag model and panel regression model to test the relationship between media information sentiments and stock market performance from the perspectives of macro market and individual stocks. [Results] (I) In the short term, the positive and negative sentiments significantly changed the stock prices and led to overreaction. In the longer term, the stock market reversed. (II) There were a negative relationship between sentiment volatility/discrepancy and stock prices, and a U-shaped nonlinear correlation between sentiment discrepancy and trading. (III) Investors reacted more immediately and strongly to positive sentiments, and the rational correction of this overreaction was slower than those of the negative information. (IV) High discrepancy of sentiments led to more over-trading than high consensus. [Limitations] The accuracy of sentiment analysis needs to be improved with more complex models. [Conclusions] Our research provides theoretical, methodological and practical implications for financial supervision and regulation.
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Yonghua Cen,Zhihao Tan,Chengyao Wu. Impacts of Financial Media Information on Stock Market: An Empirical Study of Sentiment Analysis. Data Analysis and Knowledge Discovery, 2019, 3(9): 98-114.
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