Optimal Asset Allocation Using Reinforcement Learning: A Case Study
In recent years, a new learn-to-invest framework using direct investment performance optimization techniques has emerged and is gradually gaining recognition as a promising framework for develiping intelligent investment systems. This methodology continues earlier efforts in which similar investment problems are formulated from the standpoint of traditional dynamic programming and stochastic control. In this paper, we propose to train an S&P 500/ T-bill asset allocation system by optimizing the utility function directly through reinforcement learning techniques. The preseuted novel approach is theoretically appealin due to the fact that it is a one-step optimization process and it does not require any intermediate steps, such as making forecasts or labeling desired investments. The simulation results demonstrate the effectiveness of this asset allocation strategy.
H. Li et al., "Optimal Asset Allocation Using Reinforcement Learning: A Case Study," Intelligent Systems Through Artificial Neural Networks Smart Engineering Systems Design, American Society of Mechanical Engineers (ASME), Jan 2005.
Engineering Management and Systems Engineering
Keywords and Phrases
T-Bill Asset Allocation System; Forcasting; Investments; Learn-To-Invest Framework
Article - Conference proceedings
© 2005 American Society of Mechanical Engineers (ASME), All rights reserved.
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