Investor Sentiment from Guba Messages and the Predictability of Stock Extreme Returns

Abstract

Abstract:

With the widespread popularity of Internet, the impact of investor sentiment based on the Internet platform on the stock market has injected new vitality into the relationship between the sentiment and the stock market. This paper first classifies guba (online stock forum) messages by using the Bayes classification algorithm, develops a comprehensive index to measure investor sentiment from three dimensions:"tone" based on the quality information, "exposure" based on quantity information and "attention" based on the intensity information. On that basis, this paper makes a research deep into the relationship between investor sentiment and extreme returns. The results show that investor sentiment index based on Bayes classification algorithm has more advantages in explaining the change trend of Shanghai index. Further evidence indicates that the effects of investor sentiment on the different trend of extreme returns are asymmetric. Specifically, there is a significant predictability for the positive extreme returns. The conclusion can provide advices for investors to invest and for regulators to optimize the market construction.

Key words: guba messages, Bayes classification algorithm, investor sentiment, extreme returns

Jin Xiu, Jiang Shangwei, Yuan Ying. Investor Sentiment from Guba Messages and the Predictability of Stock Extreme Returns[J]. , 2018, 30(7): 16-25.

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