Study proposes a deep reinforcement learning stock trading strategy incorporating sentiment analysis
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Hrishikesh Singh, Aarti Sharma, and Millie Pant published a paper proposing a deep reinforcement learning stock trading method that integrates a sentiment analysis component with deep injection. They designed two types of agent strategies based on Actor-Critic: A2C and PPO for policy optimization only, and DDPG, TD3, and SAC incorporating Q-learning principles. The study claims that agents combining Q-learning principles, after deep injection of sentiment information, achieve significant and stable performance improvements in profit maximization and risk-averse trading.
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- arXiv q-finDeep Reinforcement Learning Methods for Stock Trading: Actor-Critic Agents with Rich Financial Context
This study proposes a deep reinforcement learning stock trading method that integrates a sentiment analysis component with deep injection. It designs two types of Actor-Critic agent strategies: A2C and PPO for policy optimization only, and DDPG, TD3, and SAC that incorporate Q-learning principles. The study finds that agents combining Q-learning, when deep-injected with sentiment information, achieve significant and stable performance improvements in profit maximization and risk-averse trading.
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