Risk-Adjusted Deep Reinforcement Learning for Portfolio Optimization: A Multi-reward Approach

Abstract Portfolio optimization is a widely studied topic in quantitative finance. Recent advances in portfolio optimization have shown promising capabilities of deep reinforcement learning algorithms to dynamically allocate funds across various potential assets to meet the objectives of prospective...

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Auteurs principaux:	Himanshu Choudhary, Arishi Orra, Kartik Sahoo, Manoj Thakur
Format:	Artigo
Langue:	Inglês
Publié:	Springer 2025-05-01
Collection:	International Journal of Computational Intelligence Systems
Sujets:	Deep reinforcement learning Portfolio optimization Convolutional neural network Proximal policy optimization Quantitative finance
Accès en ligne:	https://doi.org/10.1007/s44196-025-00875-8
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