Sampling and integration of near log-concave functions
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
A Comparison of New and Old Algorithms for a Mixture EstimationProblem
Machine Learning - Special issue on the eighth annual conference on computational learning theory, (COLT '95)
Random walks and an O*(n5) volume algorithm for convex bodies
Random Structures & Algorithms
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Universal Portfolios With and Without Transaction Costs
Machine Learning - Special issue: computational learning theory, COLT '97
Efficient algorithms for universal portfolios
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Universal data compression and portfolio selection
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
On-line algorithms for combining language models
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Universal portfolios with side information
IEEE Transactions on Information Theory
Solving convex programs by random walks
Journal of the ACM (JACM)
Algorithms for portfolio management based on the Newton method
ICML '06 Proceedings of the 23rd international conference on Machine learning
The geometry of logconcave functions and sampling algorithms
Random Structures & Algorithms
Logarithmic regret algorithms for online convex optimization
Machine Learning
CORN: Correlation-driven nonparametric learning approach for portfolio selection
ACM Transactions on Intelligent Systems and Technology (TIST)
Meta optimization and its application to portfolio selection
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Regret minimization algorithms for pricing lookback options
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
Pricing exotic derivatives using regret minimization
SAGT'11 Proceedings of the 4th international conference on Algorithmic game theory
Logarithmic regret algorithms for online convex optimization
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection
ACM Transactions on Knowledge Discovery from Data (TKDD)
Online portfolio selection: A survey
ACM Computing Surveys (CSUR)
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A constant rebalanced portfolio is an investment strategy that keeps the same distribution of wealth among a set of stocks from day to day. There has been much work on Cover's Universal algorithm, which is competitive with the best constant rebalanced portfolio determined in hindsight (Cover, 1991, Helmbold et al, 1998, Blum and Kalai, 1999, Foster and Vohra, 1999, Vovk, 1998, Cover and Ordentlich, 1996a, Cover, 1996c). While this algorithm has good performance guarantees, all known implementations are exponential in the number of stocks, restricting the number of stocks used in experiments (Helmbold et al, 1998, Cover and Ordentlich, 1996a, Ordentlich and Cover, 1996b, Cover, 1996c, Blum and Kalai, 1999). We present an efficient implementation of the Universal algorithm that is based on non-uniform random walks that are rapidly mixing (Applegate and Kannan, 1991, Lovasz and Simonovits, 1992, Frieze and Kannan, 1999). This same implementation also works for non-financial applications of the Universal algorithm, such as data compression (Cover, 1996c) and language modeling (Chen et al, 1999).