The Stock Market Reaction To Different Types Of Financial Information On Social Internet

With technological advancements that cultivate vibrant creation,sharing and collaboration among Web users,investors can rapidly obtain more valuable and timely information. Meanwhile,the adaption of user engagement in media effectively magnifies the information in the news. With such rapid information influx,investor decisions tend to be influenced by peer and public emotions. An effective methodology to quantitatively analyze the mechanism of information percolation and its degree of impact on stock markets has yet to be explored. In this article,we propose a quantitative media-aware trading strategy to investigate the media impact on stock markets. Our main findings are that(1)Fundamental information of firm-specific news articles can enrich the knowledge of investors and affect their trading activities;(2) Public sentiments cause emotional fluctuations in investors and intervene in their decision making and the media impact on firms varies according to firm characteristics and article content.In this paper,we focus on Weibo,which is the social network is an excellent source for gathering the emotions of people. There are thousands of Weibo posted in every second and every Weibo that may contain a variety of user's emotions. The users' collective emotional behaviors are with great impacts on today's societies, so it is good to find groups for society management based on users' emotional behavior. This article focuses on analyzing multivariate emotional behavior of users in social network and the goal is to cluster the users from a fully new perspective-emotions. The following tasks are completed: firstly,the multivariate emotion of Chinese Weibo with vector is analyzed and multivariate time series to describe the user's emotional behavior are constructed. Secondly, considering principal component analysis in similarity and distance similarity,the similarity of the multivariate emotion time series is measured. The contribution could be summarized as follows: groups of users though different emotions in social network are discovered. The emotional fluctuation and intensity of users are considered as well. Experiment in clustering effectively illustrates the emotional behavior characteristics of the users in different groups.