Dissertação
Estudo de aplicação de técnicas de aprendizado por reforço no problema de otimização de portfólio
Fecha
2022-04-28Autor
Victor Marcius Magalhães Pinto
Institución
Resumen
The portfolio management problem, focus of this work, consists of determining the
optimal asset allocation within a wallet, in order to maximize (or minimize) one
or more objectives. These objectives are usually related to risk and return metrics. In financial economics literature, this problem has been solved using portfolio
optimization models, such as Markowitz, CAPM and Black Litterman, which are
executed for each instant when portfolio rebalancing is necessary. This decision
process, incremental and under uncertainty, can be seen as a Markovian decision
process, which makes modeling under reinforcement learning paradigm attractive,
this being a recent trend discussed in machine learning literature. This work aims
to investigate the use of reinforcement learning technics in portfolio optimization
problem. A literature review is realized, with its main learnings. In a case study, a
portfolio asset weight control problem is modeled as a Markovian decision process,
and reinforcement learning algorithms are used to optimize it. The implementations are made in an incremental way, aiming to demonstrate the logic behind
these algorithms developments. Finally, the model’s behavior is compared with
Markowitz based strategies, and the result shows that these approaches hold good
performances, and have a promising use for this kind of problem.