The Cost of Achieving the Best Portfolio in Hindsight
Mathematics of Operations Research
Universal Portfolios With and Without Transaction Costs
Machine Learning - Special issue: computational learning theory, COLT '97
Potential-Based Algorithms in On-Line Prediction and Game Theory
Machine Learning
On the Competitive Theory and Practice of Portfolio Selection (Extended Abstract)
LATIN '00 Proceedings of the 4th Latin American Symposium on Theoretical Informatics
Universal portfolios with side information
IEEE Transactions on Information Theory
Algorithms for portfolio management based on the Newton method
ICML '06 Proceedings of the 23rd international conference on Machine learning
Regret Minimization Under Partial Monitoring
Mathematics of Operations Research
The communication complexity of uncoupled nash equilibrium procedures
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Supermartingales in Prediction with Expert Advice
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
IEEE Transactions on Signal Processing
Supermartingales in prediction with expert advice
Theoretical Computer Science
From external to internal regret
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Online portfolio selection: A survey
ACM Computing Surveys (CSUR)
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This paper extends the game-theoretic notion of internal regret to the case of on-line potfolio selection problems. New sequential investment strategies are designed to minimize the cumulative internal regret for all possible market behaviors. Some of the introduced strategies, apart from achieving a small internal regret, achieve an accumulated wealth almost as large as that of the best constantly rebalanced portfolio. It is argued that the low-internal-regret property is related to stability and experiments on real stock exchange data demonstrate that the new strategies achieve better returns compared to some known algorithms.