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.