For many years, Wall Street investors have used
sophisticated software like artificial neural networks to gain a trading
advantage. These software tools use a range of data inputs and historical
trends to predict stock prices.
But the cattle market is a different beast. “The
software tools used to predict the stock market fail miserably if you apply them
to cattle futures,” says Jordan Baumeister. She worked the past year with
fellow computer science majors Trevor Borman and Dustin Reff to build models
that could better predict the cattle and corn markets in an effort to offer
commodity traders an edge. The team used artificial intelligence and data
science to create mathematical models to predict future market trends and
provide a comparison for anomalies, like droughts or floods, using historical
data analytics.
“Our overall goal was to optimize the risk versus
reward tradeoff that shows up when you exchange these contracts on the futures
market,” says Reff.
To achieve this goal, the students had to rely on
decades of previous work.
A long history of success
In 1993, Todd Gagne was a student at Mines
developing his own software programs when he crossed paths with Ron Ragsdale,
who ranched on 55,000 acres of rolling prairie near the confluence of the Belle
Fourche and Cheyenne Rivers.
Ragsdale came to ranching following a successful
career in law along with a ...