On-line portfolio selection

Erik Ordentlich · Thomas M. Cover

41 citations0/0 files storedConference on Learning Theory

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Advanced Bandit Algorithms Research Financial Markets and Investment Strategies Risk and Portfolio Optimization

Abstract

We find an on-line portfolio selection strategy which minimizes the worst case regret with respect to the growth rate of wealth achieved by the best off-line optimized constant rebalanced portfolio.The resulting regret is compared to that of the infinite horizon Dirichlet-weighted universal portfolio.Both strategies track the performance of the best off-line constant rebalanced portfolio to first order in the exponent.1

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1996Universal portfolios with side information

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2016Online Portfolio Selection Strategy Based on Combining Experts’ Advice 2005Improved Second-Order Bounds for Prediction with Expert Advice

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