Social media has proven to be a highly cost-effective alternative to traditional monitored mechanisms for detecting and predicting public opinion trends in financial market environments. In this project, we draw on our long-standing research experience in collaboration with DataStory (a social media market research lab) to develop initiatives in two directions. Firstly, to build a data collection capability for real-time public opinion data from news media, public forums, blogs, social networks, and other user-generated content platforms. Secondly, to use computational social science methods (combining social science theory of public opinion and machine learning algorithms) to mine the collected opinion data and derive predictive models of financial public opinion dynamics. Finally, we will produce and commercialize a financial public opinion monitoring system based on the resulting predictive models coupled with constantly updated social media data.

This financial public opinion monitoring system will potentially serve a variety of institutional clients, including investment institutions, consulting firms/think tanks, policy makers, academic research units, financial media firms, NGOs, etc. The system can be commercialized in two formats: syndicated products such as daily or weekly trends reports on a subscription-fee basis, and customized services such as live-monitoring visualizers tailored made for specific clients. We will launch the products and services first in Hong Kong, and gradually expand to other parts of the world.