Empirical Bayes stock market portfolios
Advances in Applied Mathematics
Elements of information theory
Elements of information theory
Journal of the ACM (JACM)
Online computation and competitive analysis
Online computation and competitive analysis
The statistical adversary allows optimal money-making trading strategies
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
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
When a decision tree learner has plenty of time
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
IEEE Transactions on Signal Processing
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
Risk-Sensitive online learning
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection
ACM Transactions on Knowledge Discovery from Data (TKDD)
How much is it worth to know the future in online conversion problems?
Discrete Applied Mathematics
Online portfolio selection: A survey
ACM Computing Surveys (CSUR)
Robust median reversion strategy for on-line portfolio selection
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.