The use of AI in trading has long been advocated, with robotraders now accounting for a growing share of all trades. The progress made is illustrated by a recent study from North Carolina State University’s Poole College of Management, which shows how AI can be used to help traders meet specific risk and return goals across a large portfolio featuring hundreds of different assets.

“We wanted to know whether we could use machine learning to improve the Sharpe Ratio in order to get better information on what to buy, sell, or keep in your portfolio to enhance your portfolio performance over periods of 6-12 months,” the researchers explain. “This work shows that we can.”

Measuring trade-offs

The Sharpe Ratio is a commonly used means of measuring the various trade-offs investors make in their portfolios between the scale of returns and the risks associated with the stocks in the portfolio.

Figuring this trade-off out is obviously complicated when there are hundreds, even thousands of assets in a portfolio. This is where the researchers believe AI can come in. They wanted to test whether AI could successfully incorporate a wide range of financial factors.

“Managing a portfolio that contains hundreds of assets is challenging,” they explain. “It can contain a variety of stocks and commodities, most of which are related to each other in some way. How do you handle a dynamic matrix that is this complicated? We set out to train an AI program to account for a wide variety of factors with the ultimate goal of achieving a specific Sharpe Ratio—and we did it.

“It’s important to note that there is no ‘correct’ Sharpe Ratio—it will vary depending on how much risk an investor is comfortable with. But we’ve been able to train our AI to achieve whatever Sharpe Ratio target you’ve established for your portfolio, over the course of 6-12 months. We’ve demonstrated this in both simulations and in real-world practice.”