Taking Emotion Out of the Equation

Human emotions and financial markets are a dangerous combination. It’s the job of James Wilkinson (MSAI ‘22) to help Citi separate the two.  

Wilkinson is the London-based vice president of algorithmic trading at the third largest U.S. banking institution. As part of his role, he implements artificial intelligence (AI) to develop profitable trading strategies for corporate bonds.  

To that task, he brings the lessons he learned from Northwestern Engineering's Master of Science in Artificial Intelligence (MSAI) program. Wilkinson said he found value in all his MSAI classes, but one in particular stood out.  

“My generative models class with Professor Bryan Pardo was instrumental to my current understanding of AI,” he said. “It gave me a deep intuition of almost all of the cutting edge models in the field and a knowledge of the history of the field to boot.”  

Algorithmic trading aims to take emotions out of trading and allow for faster, more profitable trades than humans can accomplish alone. Human emotions have been blamed for everything from the stock market crash that led to the Great Depression to the “irrational exuberance” that drove unprofitable internet stocks in the 1990s.  

Those emotions also are said to be the source of “tulip mania,” which gets its name from the Dutch Golden Age when contract prices for a single bulb reached extraordinarily high levels – as much as 10 times the annual income of a skilled artisan – before crashing in February 1637 when people realized it was, in fact, a common flower.   

Wilkinson said he brings a scientific, experimental approach to his study of the inner-workings of financial markets.  

“I enjoy studying them through a bottom-up, data driven approach, developing and testing hypotheses that improve my understanding of them, and being able to test my knowledge directly through the medium of trading,” he said.  

Wilkinson came to the United States from his native London, where he worked full-time for Goldman Sachs. He said he was attracted to the MSAI program because of its course offerings and quality faculty.  

“I was very keen on studying AI,” he said. “I also wanted to escape London for a couple of years to see somewhere new.”  

But when the opportunity presented itself as he neared graduation to return to London, Wilkinson was ready to go home. The draw of family and friends, as well as the opportunity with Citi, was greater than any offer he received in the United States, he said.  

And so Wilkinson applies the knowledge he gained from the MSAI program to his work with Citi everyday. Those lessons have proven invaluable, he said.  

“I use a lot of data analysis day-to-day, and knowing how to translate this into machine learning modeling is extremely important,” he said. “It has helped me gain the confidence needed to use some of the advanced models in the field in practice by knowing when they apply to certain problems.”  

Because of how the MSAI program prepared Wilkinson to do the work he is now doing, he strongly recommends it to prospective students.  

His advice?  

“Take advantage of the opportunity to code,” he said. “Practice this skill as much as possible to build your confidence.”