What Strategy Will Pay Off in the University Challenge?

When investment teams face off, who will use data most effectively?

This is exactly what graduate students will learn in the Peeptrade University Challenge when student teams, each managing a $10,000 portfolio funded by Peeptrade, compete for prime results. This challenge, which begins October 3rd and ends December 2,  gives students the actual experience of investment and risk management while also raising money for charities that the students have chosen.

Come December 3, teams from these participating university business schools will present final results and defend their investments before a panel of experts: 

  • University of Chicago 
  • Princeton
  • London Business School
  • INSEAD
  • University of California−Berkeley
  • MIT’s Sloan School of Management
  • Georgetown University
  • UNC’s Kenan-Flagler Business School
  • Dartmouth 

No aspect of this competition is more important than this: “How will different teams use data to drive their investment strategies, and what data will they use?” This takes on special significance in light of ramped-up discussion in the finance industry about the role of data.

Is data a new form of “high-potential capital”? A recent Forbes article authored by Oracle executive Di Seghposs says exactly that. The thinking is summed up this way: “Data has become at least as important to modern commerce as cash assets, inventories, facilities, and intellectual property. In some business models, it’s the only form of capital.”

Today’s business school students, take heed: in its April 2016 “Modern Finance Experience” conference, Oracle (joined by American Express, GE, etc.) emphasized the need to make reliable data the spear-tip of asset management. The argument is compelling. In a digitized world where new business models spring up like mushrooms in a damp forest, the finance function must shift “from governance to guidance.”

In her article, Seghposs adds this thought: “Finance leaders should consider adopting new predictive planning and analytics tools, including visualization tools, to quickly identify important signals within the data.” The Peeptrade Investment Challenge puts future finance leaders to the test at a time when new predictive tools are available. With what toolkits will universities’ teams meet the challenge?

In The Future of Finance, Matt Bell reports on conversations with Tim Gaumer, global director of fundamental research at Thomson Reuters. “We searched keywords that could predict financial distress and we came up with a predictive model,” says Gaumer.

Gaumer offers an example of big data’s X-ray capability: “Models like this that use big data managed to predict, a year ago, that U.S company Peabody Energy would be in a dire financial state, and in April 2016 the company filed for Chapter 11 bankruptcy.” The moral of the story: “Big data was able to predict a bad outlook, long before human analysts and credit agencies did.”

So, the heat is on: new data tools allow competing asset managers to see the landscape they navigate in new ways. It will be very interesting, come December 3, to learn what data tools were used—and how—by the students of some of the world’s best business schools.

Blog by: Ali Bakir, Peeptrade Co-Founder & Director of Business Development

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