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What is Reinforcement Learning in Stock Trading about?

The paper explores the modeling of stock-market investors as reinforcement learning (RL) agents, analyzing data from 46 players in a financial market online game. It finds that RL, particularly Q-Learning, effectively captures investor behavior and decision-making processes, revealing that not all players are short-sighted. The study highlights the significance of personal experience and risk assessment in investment decisions, suggesting that RL can enhance our understanding of human financial behavior.

Author

1165090759

Language

EN