Open AccessProceedings Article
Optimal Asset Allocation using Adaptive Dynamic Programming
Ralph Neuneier
- Vol. 8, pp 952-958
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TLDR
Asset allocation is formalized as a Markovian Decision Problem which can be optimized by applying dynamic programming or reinforcement learning based algorithms and is shown to be equivalent to a policy computed by dynamic programming.Abstract:
In recent years, the interest of investors has shifted to computerized asset allocation (portfolio management) to exploit the growing dynamics of the capital markets. In this paper, asset allocation is formalized as a Markovian Decision Problem which can be optimized by applying dynamic programming or reinforcement learning based algorithms. Using an artificial exchange rate, the asset allocation strategy optimized with reinforcement learning (Q-Learning) is shown to be equivalent to a policy computed by dynamic programming. The approach is then tested on the task to invest liquid capital in the German stock market. Here, neural networks are used as value function approximators. The resulting asset allocation strategy is superior to a heuristic benchmark policy. This is a further example which demonstrates the applicability of neural network based reinforcement learning to a problem setting with a high dimensional state space.read more
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References
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Book
Dynamic Programming
TL;DR: The more the authors study the information processing aspects of the mind, the more perplexed and impressed they become, and it will be a very long time before they understand these processes sufficiently to reproduce them.
Learning to Solve Markovian Decision Processes
TL;DR: This dissertation establishes a novel connection between stochastic approximation theory and RL that provides a uniform framework for understanding all the different RL algorithms that have been proposed to date and highlights a dimension that clearly separates all RL research from prior work on DP.