Name
Title | ||
|---|---|---|
Continuous-time reinforcement learning approach for portfolio management with time penalization |
Abstract | ||
|---|---|---|
•Proposes a new continuous-time RL algorithm for solving the portfolio problem.•Considers an actor/critic reinforcement learning architecture.•Provides a new solution characterized by transaction costs and time penalization.•Employs a proximal optimization novel approach involving time penalization.•Estimates the transition rate matrices and rewards. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.eswa.2019.03.055 | Expert Systems with Applications |
Keywords | Field | DocType |
Portfolio,Reinforcement learning,Transaction costs,Continuous-time,Markov chains | Mathematical optimization,Ergodicity,Computer science,Lagrange multiplier,Project portfolio management,Markov chain,Portfolio,Transition rate matrix,Artificial intelligence,Arbitrage,Machine learning,Reinforcement learning | Journal |
Volume | ISSN | Citations |
129 | 0957-4174 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mauricio García-Galicia | 1 | 1 | 0.35 |
| Alin A. Carsteanu | 2 | 2 | 1.04 |
| Julio B. Clempner | 3 | 91 | 20.11 |