Exploring the Linear and Nonlinear Causality Between Internet Big Data and Stock Markets

In the era of big data, stock markets are closely connected with Internet big data from diverse sources. This paper makes the first attempt to compare the linkage between stock markets and various Internet big data collected from search engines, public media and social media. To achieve this purpose, a big data-based causality testing framework is proposed with three steps, i.e., data crawling, data mining and causality testing. Taking the Shanghai Stock Exchange and Shenzhen Stock Exchange as targets for stock markets, web search data, news, and microblogs as samples of Internet big data, some interesting findings can be obtained. 1) There is a strong bi-directional, linear and nonlinear Granger causality between stock markets and investors’ web search behaviors due to some similar trends and uncertain factors. 2) News sentiments from public media have Granger causality with stock markets in a bi-directional linear way, while microblog sentiments from social media have Granger causality with stock markets in a unidirectional linear way, running from stock markets to microblog sentiments. 3) News sentiments can explain the changes in stock markets better than microblog sentiments due to their authority. The results of this paper might provide some valuable information for both stock market investors and modelers.