Deep Reinforcement Learning Agent for S&P 500 Stock Selection

Deep Reinforcement Learning Agent for S&P 500 Stock Selection

This study investigated the performance of a trading agent based on a convolutional neural network model in portfolio management. The results showed that with real-world data the agent could produce relevant trading results, while the agent’s behavior corresponded to that of a high-risk taker. The d...

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Bibliographic Details
Main Authors:	Tommi Huotari, Jyrki Savolainen, Mikael Collan
Format:	Article
Language:	English
Published:	MDPI AG 2020-11-01
Series:	Axioms
Subjects:	deep reinforcement learning portfolio selection convolutional neural network feature selection trading agent
Online Access:	https://www.mdpi.com/2075-1680/9/4/130

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