Can We Learn to Beat the Best Stock
Allan Borodin, Ran El-Yaniv and Vincent Gogan
Journal of Artificial Intelligence Research
A novel algorithm for actively trading stocks is presented. While traditional expert advice and ``universal'' algorithms
(as well as standard technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirical results on historical markets provide strong evidence that this type of technical trading can ``beat the market'' and moreover, can beat the best stock in the market. In doing so we utilize a new idea for smoothing critical parameters in the context of expert learning.