Abstract-The portfolio management for trading in the stock market poses a challenging stochastic control problem of significant commercial interests to finance industry. To date, many researchers have proposed various methods to build an intelligent portfolio management system that can recommend financial decisions for daily stock trading. Many promising results have been reported from the supervised learning community on the possibility of building a profitable trading system. More recently, several studies have shown that even the problem of integrating stock price prediction results with trading strategies can be successfully addressed by applying reinforcement learning algorithms. Motivated by this, we present a new stock trading framework that attempts to further enhance the performance of reinforcement learning-based systems. The proposed approach incorporates multiple Q-learning agents, allowing them to effectively divide and conquer the stock trading problem by defining necessary roles for cooperatively carrying out stock pricing and selection decisions. Furthermore, in an attempt to address the complexity issue when considering a large amount of data to obtain long-term dependence among the stock prices, we present a representation scheme that can succinctly summarize the history of price changes. Experimental results on a Korean stock market show that the proposed trading framework outperforms those trained by other alternative approaches both in terms of profit and risk management.Index Terms-Financial prediction, intelligent multiagent systems, portfolio management, Q-learning, stock trading.

Abstract. This paper presents a reinforcement learning framework for stock trading systems. Trading system parameters are optimized by Qlearning algorithm and neural networks are adopted for value approximation. In this framework, cooperative multiple agents are used to efficiently integrate global trend prediction and local trading strategy for obtaining better trading performance. Agents communicate with others sharing training episodes and learned policies, while keeping the overall scheme of conventional Q-learning. Experimental results on KOSPI 200 show that a trading system based on the proposed framework outperforms the market average and makes appreciable profits. Furthermore, in view of risk management, the system is superior to a system trained by supervised learning.

Abstract. We study a stock trading method based on dynamic bayesian networks to model the dynamics of the trend of stock prices. We design a three level hierarchical hidden Markov model (HHMM). There are five states describing the trend in first level. Second and third levels are abstract and concrete hidden Markov models to produce the observed patterns. To train the HHMM, we adapt a semi-supervised learning so that the trend states of first layer is manually labelled. The inferred probability distribution of first level are used as an indicator for the trading signal, which is more natural and reasonable than technical indicators. Experimental results on representative 20 companies of Korean stock market show that the proposed HHMM outperforms a technical indicator in trading performances.

No abstract

Abstract. Given the pattern-based multi-predictors of the stock price, we study a method of dynamic asset allocation to maximize the trading performance. To optimize the proportion of asset to be allocated to each recommendations of the predictors, we design an asset allocator called meta policy in the Q-learning framework. We utilize both the information of each predictor's recommendations and the ratio of the stock fund over the asset to efficiently describe the state space. The experimental results on Korean stock market show that the trading system with the proposed asset allocator outperforms other systems with fixed asset allocation methods. This means that reinforcement learning can bring synergy effects to the decision making problem through exploiting supervised-learned predictors.