NDLI: Resource allocation with stochastic optimal control approach

Content Provider	Springer Nature Link
Author	Valian, Haleh Jafari, Mohsen A. Golmohammadi, Davood
Copyright Year	2014
Abstract	A control-theoretic decision making system is proposed for an agent (decision maker) to “optimally” allocate and deploy his/her resources over time among a dynamically changing list of opportunities (e.g., financial assets), in an uncertain market environment. The solution is a sequence of actions with the objective of optimizing total reward function. This control-theoretic approach is unique in a sense that it solves the problem at distinct time epochs over a finite time horizon and strategies are discovered directly. Rather than basing a decision making system on forecasts or training via a reinforcement learning algorithm using current state data, we train our system via a Q-learning algorithm using Geometric Brownian Motion as an asset price function. While the above problem is quite general, we focus solely on the problem of dynamic financial portfolio management with the objective of maximizing the expected utility for a given risk level. The performance functions that we consider for our system are realized mean return, drawdown and standard deviation. We find that our model achieves a better return and drawdown compared to a known market index as a benchmark.
Starting Page	625
Ending Page	641
Page Count	17
File Format	PDF
ISSN	02545330
Journal	Annals of Operations Research
Volume Number	239
Issue Number	2
e-ISSN	15729338
Language	English
Publisher	Springer US
Publisher Date	2014-06-20
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Decision system Optimization Resource allocation Stochastic Operation Research/Decision Theory Combinatorics Theory of Computation
Content Type	Text
Resource Type	Article
Subject	Management Science and Operations Research

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