Thesis etd-02022021-234333 |
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Thesis type
Tesi di laurea magistrale
URN
etd-02022021-234333
Thesis title
Stochastic Optimal Control and Machine Learning Techniques for Portfolio Optimization problems
Department
INGEGNERIA DELL'INFORMAZIONE
Course of study
INGEGNERIA ROBOTICA E DELL'AUTOMAZIONE
Supervisors
.
relatore Landi, Alberto
relatore Di Persio, Luca
relatore Trevisan, Dario
relatore Di Persio, Luca
relatore Trevisan, Dario
Keywords
- applications to portfolio optimization problems
- machine learning
- stochastic optimal control
Graduation session start date
25/02/2021
Availability
Withheld
Release date
25/02/2091
Abstract (Inglese)
Abstract (Italiano)
In the first part of the thesis, it is given an introduction to the most important concepts and results employed in stochastic optimal control problems. We provided the derivation of the Dynamic Programming principle, Bellman principle and HJB equation. After that, we presented an approach that employs stochastic optimal control methods to portfolio optimization problems.
In the second part of the thesis, we focused on machine learning and reinforcement learning techniques, presenting two different machine learning models to approach the portfolio optimization task
In the second part of the thesis, we focused on machine learning and reinforcement learning techniques, presenting two different machine learning models to approach the portfolio optimization task
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