Using Fine-grained Emotion Computing Model to Analyze the Interactions between Netizens Sentiments and Stock Returns

摘要：

　　From the perspective of behavioural finance, this paper combines the fine-grained sentiment calculation with the stock market econometric model to explore the interactions between netizens sentiments and stock returns, analyze the differences in the influences of various emotions expressed by netizens on the stock market.First, it constructs a sentiment dictionary for the financial field;then, it calculates the emotion values contained in the text corpus, and constructs a textual sentiment classifier based on the recurrent neural network, calculates the emotion value and establishes the daily netizen sentiment index;and finally, it builds an econometric model to study the interactions between the netizen sentiment index and the stock returns.The results show that this model improves the accuracy of sentiment classification, reduces the number of iterations and saves computing resources;and that the netizen sentiment index, especially, disgust and like, has significant effects on the stock price changes and transaction volumes, while on the other hand, the listed companys stock returns data has no reverse effect on the netizen sentiment index.

关键词： Lexicon construction Sentiment analysis Emotion computing Stock returns Big data

作者: Meng-xuan Zhu Wei Zhang Meng Wang Sui-xi Kong Jian Wang

作者单位: School of Information, Central University of Finance and Economics, Beijing, 100081, China

会议类型: 国际会议

会议名称: The Eighteenth Wuhan International Conference on E-Business(第18届武汉电子商务国际会议)

会议地点: 武汉

会议语种:英文

页码: 206-214

在线出版日期: 2019-05-24（万方平台首次上网日期，不代表论文的发表时间）