Growth optimal investment strategies
Growth optimal investment strategies
Game-theoretic optimal portfolios
Management Science
Universal sequential learning and decision from individual data sequences
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
COLT '96 Proceedings of the ninth 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
The Journal of Machine Learning Research
Simulated Annealing in Convex Bodies and an 0*(n4) Volume Algorithm
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Internal Regret in On-Line Portfolio Selection
Machine Learning
Efficient algorithms for online decision problems
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Can we learn to beat the best stock
Journal of Artificial Intelligence Research
Logarithmic regret algorithms for online convex optimization
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Efficient learning algorithms for changing environments
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Factor graphs for universal portfolios
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
CORN: Correlation-driven nonparametric learning approach for portfolio selection
ACM Transactions on Intelligent Systems and Technology (TIST)
Common component analysis for multiple covariance matrices
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Meta optimization and its application to portfolio selection
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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)
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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We experimentally study on-line investment algorithms first proposed by Agarwal and Hazan and extended by Hazan et al. which achieve almost the same wealth as the best constant-rebalanced portfolio determined in hindsight. These algorithms are the first to combine optimal logarithmic regret bounds with efficient deterministic computability. They are based on the Newton method for offline optimization which, unlike previous approaches, exploits second order information. After analyzing the algorithm using the potential function introduced by Agarwal and Hazan, we present extensive experiments on actual financial data. These experiments confirm the theoretical advantage of our algorithms, which yield higher returns and run considerably faster than previous algorithms with optimal regret. Additionally, we perform financial analysis using mean-variance calculations and the Sharpe ratio.