How Self-Driving-Car Technology Can Help Machines Trade Stocks

Traders have long used price charts to predict future investment returns. Patterns such as the “head and shoulders,” “double top,” or “ascending triangle” form a visual shorthand for investors looking for repeatable shapes and figures that suggest profitable trades. Now artificial intelligence, using the same pattern-recognition techniques that factor into programming self-driving cars, is proving adept at technical investing as well.

University of Chicago PhD student Jingwen Jiang, Yale’s Bryan Kelly, and Chicago Booth’s Dacheng Xiu built a machine learning–based program that can recognize the patterns in stock-price charts and use them to generate profitable trading strategies. The findings support the idea that visual representations of stock prices contain valuable and actionable information.

“We have heard that traders can translate price charts into signals,” Xiu says. “Our findings suggest that the shape of the data contains enough information for trading. This justifies what technical traders do.”

Jiang, Kelly, and Xiu find that using past prices and momentum, expressed graphically, can generate profitable trading strategies over short time periods. The effect wanes the longer a position is held. As the holding periods grow to months and quarters, fundamental issues such as financial reporting and corporate events begin to hold more sway over future prices. They find that their chart-reading ML system’s predictions delivered higher returns with less risk than trades made using other methods for predicting returns.

The A.I. methodology the researchers employ is called a convolutional neural network (CNN). It’s the same type of ML that programm¬ers are using to help autonomous vehicles identify pedestrians, stop signs, and sidewalk curbs. Its performance in recognizing investment patterns suggests that computers can reliably predict returns without relying on the intuitions, hunches, and judgments that human investors develop over years of trading.

“A technical trader uses prior knowledge to define patterns,” Xiu says. “A CNN has no prior knowledge. Without using any existing chart, I am going to ask the CNN to learn from the price curve to extract useful information for prediction.”

The findings also suggest that a CNN can make useful predictions even when data sets are incomplete or small. The researchers find that they can use patterns observed in data-rich US-based markets to make predictions about stock-price performance in foreign markets with shorter track records.

Suppose you have two data sets, Xiu says. One is high quality and can be used to train a computer to recognize a cat. The other, more limited one contains data about tigers. Essentially, the researchers find that they can use lower-level features associated with both cats and tigers, primarily learned from the first data set, to teach a computer to recognize a tiger, with little or even no information from the second data set.

Through this transfer learning, the researchers say, a CNN system might be able to analyze newer and emerging asset classes if they share features with more mature markets.