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Students at competition

Another impressive podium finish for Guelph business students at undergraduate finance competition

U of G business students from the Guelph Student Investment Council (GSIC) attended Battle on Bay 2019 over the weekend, an undergraduate finance competition hosted by the Ryerson University Finance Society.

Attending the competition for the first time, University of Guelph’s GSIC students had an impressive showing that saw them place third overall, taking home a $1,000 prize.

The team consisted of:

Haley and Luka at QFAC 2018

U of G business student among winning team at Queen’s finance conference

Two University of Guelph business students from the Guelph Student Investment Council (GSIC) were invited to attend the Queen's Finance Association Conference (QFAC) in Toronto at the Omni King Edward Hotel.

BComm students Haley DeBruyne and Luka Vulicevic represented the University of Guelph at the conference, which has become a well-recognized undergraduate conference in the finance industry with sponsors including Morgan Stanley, Goldman Sachs and other New York IB firms.

Chair's Honour Reception - Fall 2017

On October 4th, the Department of Economics and Finance held its Annual Chair's Honour Reception to recognize and congratulate many of the department's undergraduate students. The exclusive event was attended by students who are on the Chair's Honour List. These students are registered in one of the Department's majors and have achieved a cumulative average of 80% or higher. Members of the newly created Finance Honours Society were also invited.

The Systematic Linking of Historical Records - Workshop

Faculty and graduate students are invited to a workshop "The Systematic Linking of Historical Records" that brings together researchers from Economics, Computing Science and History. Some papers at the workshop will explore new linking techniques, other papers will discuss metrics to assess the quality of the linked data, and yet other papers will illustrate what can be learned from a methodologically-sensitive use of the linked data.

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