Deep Reinforcement Learning for Portfolio Optimization
Gioele Scaletta
Deep Reinforcement Learning for Portfolio Optimization.
Rel. Luca Cagliero, Jacopo Fior. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2024
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Abstract
The imperative task of managing assets amid uncertainty by exploiting market inefficiencies carries significant implications for financial investors. Portfolio management, involving asset selection, allocation, and monitoring, aims to maximize returns while mitigating risks, considering the specific financial goals, risk appetite, and investing time horizon preferences of each individual or institution. Indeed, the risk-return trade-off, central to portfolio optimization, hinges on the investor's preferences. Risk encompasses systematic and unsystematic risks, with diversification mitigating the latter. In imperfect markets, characterized by information asymmetries and frictions, investors hope to exploit inefficiencies also created by their own psychology sometimes leading to irrational behaviour. In this environment, creating a customized and dynamically informed portfolio management strategy based on mathematical predictions of future asset prices often becomes unfeasible.
This thesis work focuses on automating the portfolio management task using Deep Reinforcement Learning (DRL)
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