Dr. Jason Thatcher’s students were lucky enough to be part of the first Clemson University class to take advantage of the university’s new social media command center. Dr. Thatcher encouraged his students to form groups and tackle a research project using social media and Salesforce Radian6.

1. Case Study / Clemson University
Clemson University Students use social media to
predict foreign exchange markets
The Challenge The Result
Dr. Jason Thatcher’s students were lucky enough Out of the 58 trades made by the Radian6 powered
to be part of the first Clemson University class social algorithm, only 13 moved in the opposite
to take advantage of the university’s new social direction – a prediction rate of 77%. The group
media command center. Dr. Thatcher encouraged started with $5,000 in a demo brokerage account
his students to form groups and tackle a research leveraged at 50:1 and let it trade over the course
project using social media and Salesforce Radian6. of seven weeks. At the end of the seven weeks,
The students in the class were thrilled at the the students had earned over $44,000 – a 784%
assignment and spent the next several months increase. The group is on track to have over one
creating some very interesting and innovative million dollars in the account before the end of June.
research projects. One group decided to tackle
currency exchange markets; they had their money The students and their professor are thrilled with
on the fact that social media could help them the results. Their project confirms there really is
reliably predict major daily movements in the EUR/ wisdom in crowds and that social media offers
USD currency pair. The students hoped to better great potential in terms of business and research
understand how the wisdom of a crowd could play applications.
a role in the foreign exchange market.
The Approach
The group of five students – Scott Cole, Kyle
LePrevost, Brett Smentek, Paul Smith and James
Kaplanges – worked together to first understand
how people were talking about the markets online,
how they could best monitor these conversations,
and the keywords that they should use when
setting up their Radian6 topic profile. Once they
had an understanding of the conversations, they
created an algorithm that looks for large spikes in
social data in either direction (buying or selling).
They then built, tested and implemented an
automated trading platform based on their live
Radian6 data. Then it was time to see how their
predictions stacked up.
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