Paper Info
Title
Enhancing Q-learning for optimal asset allocation
Abstract
This paper enhances the Q-learning algorithm for optimal asset allocation proposed in (Neuneier, 1996 [6]). The new formulation simplifies the approach by using only one value-function for many assets and allows model-free policy-iteration. After testing the new algorithm on real data, the possibility of risk management within the framework of Markov decision problems is analyzed. The proposed methods allows the construction of a multi-period portfolio management system which takes into account transaction costs, the risk preferences of the investor, and several constraints on the allocation.
Year
Venue
Keywords
1997
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
optimal asset allocation,enhancing q-learning
DocType
Volume
ISSN
Conference
10
1049-5258
ISBN
Citations 
PageRank 
0-262-10076-2
16
1.48
References 
Authors
4
1
Name
Order
Citations
PageRank
Ralph Neuneier125646.89