| Full text | |
| Author(s): |
Pereira Neto, Eduardo Lopes
;
Freire, Valdinei
;
Delgado, Karina Valdivia
;
MartinezVillasenor, L
;
HerreraAlcantara, O
;
Ponce, H
;
CastroEspinoza, FA
Total Authors: 7
|
| Document type: | Journal article |
| Source: | ADVANCES IN SOFT COMPUTING, MICAI 2020, PT I; v. 12468, p. 13-pg., 2020-01-01. |
| Abstract | |
In the Portfolio Management problem the agent has to decide how to allocate the resources among a set of stocks in order to maximize his gains. This decision-making problem is modeled by some researchers through Markov decision processes (MDPs) and the most widely used criterion in MDPs is maximizing the expected total reward. However, this criterion does not take risk into account. To deal with risky issues, risk sensitive Markov decision processes (RSMDPs) are used. To the best of our knowledge, RSMDPs and more specifically RSMDPs with exponential utility function have never been applied to handle this problem. In this paper we introduce a strategy to model the Portfolio Management problem focused on day trade operations in order to enable the use of dynamic programming. We also introduce a measure based on Conditional Value-at-Risk (CVaR) to evaluate the risk attitude. The experiments show that, with our model and with the use of RSMDPs with exponential utility function, it is possible to change and interpret the agent risk attitude in a very understandable way. (AU) | |
| FAPESP's process: | 18/11236-9 - Markov decision process and risk |
| Grantee: | Karina Valdivia Delgado |
| Support Opportunities: | Regular Research Grants |