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Mendes, D. A. & Maltez, F. (2023). Deep Reinforcement Learning for Investing: A Quantamental Approach for Portfolio Management. Deep Reinforcement Learning for Investing: A Quantamental Approach for Portfolio Management.

D. E. Mendes and F. A. Maltêz,  "Deep Reinforcement Learning for Investing: A Quantamental Approach for Portfolio Management", in Deep Reinforcement Learning for Investing: A Quantamental Approach for Portfolio Management, 2023

@unpublished{mendes2023_1765823675189,
	author = "Mendes, D. A. and Maltez, F.",
	title = "Deep Reinforcement Learning for Investing: A Quantamental Approach for Portfolio Management",
	year = "2023"
}

TY  - EJOUR
TI  - Deep Reinforcement Learning for Investing: A Quantamental Approach for Portfolio Management
T2  - Deep Reinforcement Learning for Investing: A Quantamental Approach for Portfolio Management
AU  - Mendes, D. A.
AU  - Maltez, F.
PY  - 2023
AB  - This study aims to evaluate how deep reinforcement learning (DRL) can improve financial
portfolio management. It also has a second goal of understanding if financial fundamental
features (e.g., revenue, debt, cash flow) improve model performance. After conducting a
literature review to establish the current state-of-the-art, the CRISP-DM method was followed:
1) Business understanding; 2) Data understanding; 3) Data preparation on two datasets, one
with market only features and another with also fundamental features; 4) Modeling – Advantage
Actor-Critic, Deep Deterministic Policy Gradient and Twin-delayed DDPG DRL models were
optimized; 5) Evaluation.
Models had a consistent sharpe ratio performance across datasets – average of 0.35 vs
0.30 for the baseline, in the test set. It is also demonstrated that fundamental features improved
model robustness and consistency. Hence, supporting the use of both DRL models and
quantamental investment strategies for portfolio managers to generate alpha while increasing
investor’s trust through higher transparency
ER  -