Portfolio management and Deep learning: Reinforcement learning and Transformer applied to stock market data
Marco Gullotto
Portfolio management and Deep learning: Reinforcement learning and Transformer applied to stock market data.
Rel. Enrico Bibbona, Patrizia Semeraro. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2021
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Abstract
This thesis, developed during an internship in Add-For S.p.A., is a research thesis in the Fintech field to design a new financial strategy in “Portfolio management and selection”. The task is to make investment decisions based on strategies that ensure maximum profit for each investment period. It is a complicated optimization problem that aims to find the best actions to select the most profitable assets over a period of time. This task is challenging due to the difficulties in representing asset price series as these are non-stationary and exhibit noise and fluctuations. Deep learning has been used to address this problem, but the final results are rather poor and not applicable to real problems.
The goal of this thesis is to study the state of the art in the portfolio management and selection field, with particular emphasis on the use of Transformer architecture and reinforcement learning (RL), and the application of these models to real problems
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