Recent works have shown that social media platforms are able to influence thetrends of stock price movements. However, existing works have majorly focusedon the U.S. stock market and lacked attention to certain emerging countriessuch as China, where retail investors dominate the market. In this regard, asretail investors are prone to be influenced by news or other social media,psychological and behavioral features extracted from social media platforms arethought to well predict stock price movements in the China's market. Recentadvances in the investor social network in China enables the extraction of suchfeatures from web-scale data. In this paper, on the basis of tweets fromXueqiu, a popular Chinese Twitter-like social platform specialized forinvestors, we analyze features with regard to collective sentiment andperception on stock relatedness and predict stock price movements by employingnonlinear models. The features of interest prove to be effective in ourexperiments.
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