PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Can We Learn to Beat the Best Stock
Allan Borodin, Ran El-Yaniv and Vincent Gogan
Journal of Artificial Intelligence Research Volume 21, pp. 579-594, 2004.

Abstract

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:229
Deposited By:Ran El-Yaniv
Deposited On:23 November 2004