Two disciplines familiar to econometricians, factor analysis of equities returns and machine learning, have grown up alongside each other. Used in tandem, these fields of study can build effective investment-management tools, according to City University of Hong Kong's Guanhao Feng (a graduate of Chicago Booth's PhD Program), Booth's Nicholas Polson, and Booth PhD candidate Jianeng Xu. The researchers set out to determine whether they could create a deep-learning model to automate the management of a portfolio built on buying stocks that are expected to rise and short selling those that are expected to fall, known as a long-short strategy. They created a machine-learning algorithm that built a long-short equity portfolio from the top and bottom 20 percent of a 3,000-stock universe. They ranked the equities using the five-factor model of Chicago Booth's Eugene F. Fama and Dartmouth's Kenneth R. French.

ASK 100 students what they want from an MBA programme and you're likely to get 100 different answers. However, ask them what they want more of, and trends are easier to discern. At the Kellogg School of Management at Northwestern University, a survey of the current class earlier this year asked what students wanted to learn more about. "It has rapidly consumed a lot of mental real estate with our MBA students," says Brian Uzzi, who teaches a course on AI to MBAs at Kellogg. AI has become a key tool for businesses in all industries.