MARKOWITZ MEETS BELLMAN: KNOWLEDGE-DISTILLED REINFORCEMENT LEARNING FOR PORTFOLIO MANAGEMENT

By Gang Hu, Ming Gu

Published 2024-05-08

Everscope rating

1499.5

Relevance to quantitative trading

9 / 10

Implementation complexity

8 / 10

Reproducibility

3 / 5

About this paper

Methodology: Knowledge Distilled Deep Deterministic Policy Gradient (KDD). Problem types: Portfolio Optimization, Reinforcement Learning.

arXiv:2405.05449 · Paper rankings

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