Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5102741 | Physica A: Statistical Mechanics and its Applications | 2017 | 17 Pages |
Abstract
In this work, we propose a novel framework for causal inference that does not require any assumption about a particular parametric form of the model expressing statistical relationships among the variables of the study and can effectively control a large number of observed factors. We apply our method in order to estimate the causal impact that information posted in social media may have on stock market returns of four big companies. Our results indicate that social media data not only correlate with stock market returns but also influence them.
Related Topics
Physical Sciences and Engineering
Mathematics
Mathematical Physics
Authors
Fani Tsapeli, Mirco Musolesi, Peter Tino,