Stock market volatility and regime shifts in returns
Information Sciences: an International Journal
Enhancing Q-learning for optimal asset allocation
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Machine Learning
Risk-Sensitive Reinforcement Learning
Machine Learning
Stock Trading System Using Reinforcement Learning with Cooperative Agents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Two-Phase Stock Trading System Using Distributional Differences
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
A Multi-agent Q-learning Framework for Optimizing Stock Trading Systems
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A hybrid genetic-neural architecture for stock indexes forecasting
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
Dynamic Asset Allocation for Stock Trading Optimized by Evolutionary Computation
IEICE - Transactions on Information and Systems
On the convergence of stochastic iterative dynamic programming algorithms
Neural Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Neural Networks
Regression methods for pricing complex American-style options
IEEE Transactions on Neural Networks
Learning to trade via direct reinforcement
IEEE Transactions on Neural Networks
Empirical analysis of an on-line adaptive system using a mixture of Bayesian networks
Information Sciences: an International Journal
Towards constraint optimal control of greenhouse climate
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Hessian matrix distribution for Bayesian policy gradient reinforcement learning
Information Sciences: an International Journal
Coordination control of greenhouse environmental factors
International Journal of Automation and Computing
Reinforcement learning algorithms with function approximation: Recent advances and applications
Information Sciences: an International Journal
Automated trading with performance weighted random forests and seasonality
Expert Systems with Applications: An International Journal
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Stock trading is an important decision-making problem that involves both stock selection and asset management. Though many promising results have been reported for predicting prices, selecting stocks, and managing assets using machine-learning techniques, considering all of them is challenging because of their complexity. In this paper, we present a new stock trading method that incorporates dynamic asset allocation in a reinforcement-learning framework. The proposed asset allocation strategy, called meta policy (MP), is designed to utilize the temporal information from both stock recommendations and the ratio of the stock fund over the asset. Local traders are constructed with pattern-based multiple predictors, and used to decide the purchase money per recommendation. Formulating the MP in the reinforcement learning framework is achieved by a compact design of the environment and the learning agent. Experimental results using the Korean stock market show that the proposed MP method outperforms other fixed asset-allocation strategies, and reduces the risks inherent in local traders.