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
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Efficient algorithms for universal portfolios
The Journal of Machine Learning Research
Algorithms for portfolio management based on the Newton method
ICML '06 Proceedings of the 23rd international conference on Machine learning
Efficient algorithms for online convex optimization and their applications
Efficient algorithms for online convex optimization and their applications
Efficient learning algorithms for changing environments
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Can we learn to beat the best stock
Journal of Artificial Intelligence Research
Universal portfolios with side information
IEEE Transactions on Information 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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Machine learning techniques have been adopted to select portfolios from financial markets in some emerging intelligent business applications. In this article, we propose a novel learning-to-trade algorithm termed CORrelation-driven Nonparametric learning strategy (CORN) for actively trading stocks. CORN effectively exploits statistical relations between stock market windows via a nonparametric learning approach. We evaluate the empirical performance of our algorithm extensively on several large historical and latest real stock markets, and show that it can easily beat both the market index and the best stock in the market substantially (without or with small transaction costs), and also surpass a variety of state-of-the-art techniques significantly.