Financial markets are governed by a combination of rational and irrational forces, statistical probabilities and "animal spirits." It takes fluency in both to understand the market, let alone beat it. Yet market actors, including asset traders, now frequently use machine-learning techniques to help generate predictions of future asset prices.
Scholars such as Bo Hu, assistant professor of finance at the Costello College of Business at George Mason University, are researching how these machine-learning tools are changing the decision-making processes that move the market, for better or worse.
The subject of Hu's recent paper in Management Science is a well-known machine-learning technique called LASSO (least absolute shrinkage and selection operator), which has been widely adopted by financial practitioners since its introduction in 1996 by statistician Robert Tibshirani.
"If you look at the original paper, it describes an approach created by adding a regularization penalty to the least-squares regression method," Hu says. Translation: "The power of LASSO is that it can screen out (i.e., penalize) weak signals while capturing stronger, potentially profitable ones. A LASSO-type trading strategy involves an 'inactive zone' for smaller-scale activity, in which the trading strategy is to do nothing."
