Once Burned, Twice Shy? The Effect of Stock Market Bubbles on Traders that Learn by Experience

2023 Winter Simulation Conference (WSC)(2023)

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摘要
We study how experience with asset price bubbles changes the trading strategies of reinforcement learning (RL) traders and ask whether the change in trading strategies helps to prevent future bubbles. We train the RL traders in a multi-agent market simulation platform, ABIDES, and compare the strategies of traders trained with and without bubble experience. We find that RL traders without bubble experience behave like short-term momentum traders, whereas traders with bubble experience behave like value traders. Therefore, RL traders without bubble experience amplify bubbles, whereas RL traders with bubble experience tend to suppress and sometimes prevent them. This finding suggests that learning from experience is a mechanism for a boom and bust cycle where the experience of a collapsing bubble makes future bubbles less likely for a period of time until the memory fades and bubbles become more likely to form again.
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关键词
Asset Pricing,Boom And Bust,Trading Strategies,Price Bubbles,Asset Bubbles,Test Session,Business Environment,Simulation Environment,Market Participants,Market Volatility,Types Of Agents,Multi-agent Systems,Bubble Formation,Fundamental Values,Huge Losses,Shapley Value,Trading Days,Ornstein-Uhlenbeck Process,Reinforcement Learning Agent,Bubble Burst,Order Book,Trained Agent,Large Volatility,Proximal Policy Optimization,Bubble Generation,State Space,Retail Investors,Stock Price,Stock Market
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