| Full text | |
| Author(s): |
Sbruzzi, Elton F.
;
Leles, Michel C. R.
;
Nascimento, Cairo L., Jr.
;
IEEE
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 12TH ANNUAL IEEE INTERNATIONAL SYSTEMS CONFERENCE (SYSCON2018); v. N/A, p. 6-pg., 2018-01-01. |
| Abstract | |
In this paper, we introduce a novel method to select the components of a portfolio of securities. This method is based on a reinforcement learning technique known as learning automata. Several heuristic solutions for the portfolio weights selection problem have been introduced in literature. The point is that these applications assumes that portfolio components are given. The difference of our work is that we propose some heuristic in order to select the portfolio components instead of the weights. In terms of heuristic, we propose learning automata because its ability to solve complex systems such as a the optimal portfolio components. We test the use of learning automata in terms of financial indicators optimization. Our findings show that our proposed method improves the portfolio optimization performance in terms of accuracy and computational effort. (AU) | |
| FAPESP's process: | 17/20248-8 - Employing computational intelligence techniques and Big Data analytics in a multi-agent system experiment of finance |
| Grantee: | Michel Carlo Rodrigues Leles |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 16/04992-6 - Employing computational intelligence techniques and Big Data analytics in a multi-agent system experiment of finance |
| Grantee: | Cairo Lúcio Nascimento Júnior |
| Support Opportunities: | Research Grants - eScience and Data Science Program - Regular Program Grants |