scholarly journals An actor-critic-based portfolio investment method inspired by benefit-risk optimization

2018 ◽  
Vol 12 (4) ◽  
pp. 351-360
Author(s):  
Lili Tang
Keyword(s):  
Optimal Investment ◽  
Securities Market ◽  
Time Segment ◽  
Curse Of Dimension ◽  
Risk Optimization ◽  
Optimal Value ◽  
Markov Decision ◽  
Markov Decision Model ◽  
Set Up ◽  
Maximal Benefit

How to get maximal benefit within a range of risk in securities market is a very interesting and widely concerned issue. Meanwhile, as there are many complex factors that affect securities’ activity, such as the risk and uncertainty of the benefit, it is very difficult to establish an appropriate model for investment. Aiming at solving the curse of dimension and model disaster caused by the problem, we use the approximate dynamic programming to set up a Markov decision model for the multi-time segment portfolio with transaction cost. A model-based actor-critic algorithm under uncertain environment is proposed, where the optimal value function is obtained by iteration on the basis of the constrained risk range and a limited number of funds, and the optimal investment of each period is solved by using the dynamic planning of limited number of fund ratio. The experiment indicated that the algorithm could get a stable investment, and the income could grow steadily.

scholarly journals First-order sensitivity of the optimal value in a Markov decision model with respect to deviations in the transition probability function

2020 ◽  
Vol 92 (1) ◽  
pp. 165-197 ◽  
Author(s):  
Patrick Kern ◽  
Axel Simroth ◽  
Henryk Zähle
Keyword(s):  
Optimization Problems ◽  
Transition Probabilities ◽  
Broad Class ◽  
Transition Probability ◽  
Mathematical Finance ◽  
Model Complexity ◽  
First Order ◽  
Optimal Value ◽  
Markov Decision ◽  
Markov Decision Model

Abstract Markov decision models (MDM) used in practical applications are most often less complex than the underlying ‘true’ MDM. The reduction of model complexity is performed for several reasons. However, it is obviously of interest to know what kind of model reduction is reasonable (in regard to the optimal value) and what kind is not. In this article we propose a way how to address this question. We introduce a sort of derivative of the optimal value as a function of the transition probabilities, which can be used to measure the (first-order) sensitivity of the optimal value w.r.t. changes in the transition probabilities. ‘Differentiability’ is obtained for a fairly broad class of MDMs, and the ‘derivative’ is specified explicitly. Our theoretical findings are illustrated by means of optimization problems in inventory control and mathematical finance.

scholarly journals Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources

2019 ◽  
Vol 9 (13) ◽  
pp. 2744
Author(s):  
Wu ◽  
Xie ◽  
Chen ◽  
Tang
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Power Control ◽  
Summation Method ◽  
Source Node ◽  
Hybrid Energy ◽  
Conflicting Objectives ◽  
Markov Decision ◽  
Wireless Relay ◽  
Markov Decision Model

The Hybrid energy supply (HES) wireless relay system is a new green network technology, where the source node is powered by the grid and relay is powered by harvested renewable energy. However, the network’s performance may degrade due to the intermittent nature of renewable energy. In this paper, our purpose is to minimize grid energy consumption and maximize throughput. However, improving the throughput requires increasing the transmission power of the source node, which will lead to a higher grid energy consumption. Linear weighted summation method is used to turn the two conflicting objectives into a single objective. Link assignment and a power control strategy are adopted to maximize the total reward of the network. The problem is formulated as a discrete Markov decision model. In addition, a backwards induction method based on state deletion is proposed to reduce the computational complexity. Simulation results show that the proposed algorithm can effectively alleviate performance degradation caused by the lack of renewable energy, and present the trade-off between energy consumption and throughput.

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