Game-theoretic optimal portfolios
Management Science
The weighted majority algorithm
Information and Computation
Universal Portfolios With and Without Transaction Costs
Machine Learning - Special issue: computational learning theory, COLT '97
Algorithms for portfolio management based on the Newton method
ICML '06 Proceedings of the 23rd international conference on Machine learning
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Universal linear prediction by model order weighting
IEEE Transactions on Signal Processing
Universal Piecewise Linear Prediction Via Context Trees
IEEE Transactions on Signal Processing - Part II
Universal portfolios with side information
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
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We consider the sequential portfolio investment problem. Building on results in signal processing, machine learning, and other areas, we combine the insights of Cover and Ordentlich's side information portfolio with those of Blum and Kalai's transaction costs algorithm to construct one that performs well under transaction costs while taking advantage of side information. We introduce factor graphs as a computational tool for analysis and design of universal (low regret) algorithms, and develop our algorithm with this insight. Finally, we demonstrate that, in contrast to other algorithms, our portfolio performs well over the full range of costs.