TY - GEN
T1 - Policy Gradient for Ratio Optimization
T2 - 56th Annual Conference on Information Sciences and Systems, CISS 2022
AU - Suttle, Wesley A.
AU - Koppel, Alec
AU - Liu, Ji
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We consider policy gradient methods for ratio optimization problems by way of an illustrative case study: maximizing the Omega ratio of a financial portfolio. We propose a general framework for ratio optimization in sequential decision-making problems, explore the notion of hidden quasiconcavity in such problems, and propose an actor-critic algorithm for the Omega ratio problem. Our central contribution is to show that the algorithm converges almost surely to (a neighborhood of) a global optimum and to demonstrate its performance in practice.
AB - We consider policy gradient methods for ratio optimization problems by way of an illustrative case study: maximizing the Omega ratio of a financial portfolio. We propose a general framework for ratio optimization in sequential decision-making problems, explore the notion of hidden quasiconcavity in such problems, and propose an actor-critic algorithm for the Omega ratio problem. Our central contribution is to show that the algorithm converges almost surely to (a neighborhood of) a global optimum and to demonstrate its performance in practice.
KW - portfolio optimization
KW - quasiconcave programming
KW - reinforcement learning
UR - https://www.scopus.com/pages/publications/85128721552
U2 - 10.1109/CISS53076.2022.9751163
DO - 10.1109/CISS53076.2022.9751163
M3 - Conference contribution
AN - SCOPUS:85128721552
T3 - 2022 56th Annual Conference on Information Sciences and Systems, CISS 2022
SP - 281
EP - 286
BT - 2022 56th Annual Conference on Information Sciences and Systems, CISS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 March 2022 through 11 March 2022
ER -