This paper examines the under/overreaction effect driven by sentiment belief in an artificial limit order market when agents are risk averse and arrive in the market with different time horizons. We employ agent-based modeling to build up an artificial stock market with order book and model a type of sentiment belief display over/underreaction by following a Bayesian learning scheme with a Markov regime switching between conservative bias and representative bias. Simulations show that when compared with classic noise belief without learning, sentiment belief gives rise to short-term intraday return predictability. In particular, under/overreaction trading strategies are profitable under sentiment beliefs, but not under noise belief. Moreover, we find that sentiment belief leads to significantly lower volatility, lower bid-ask spread, and larger order book depth near the best quotes but lower trading volume when compared with noise belief.

中文翻译：

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人工限价单市场中的投资者情绪

本文研究了当代理商厌恶风险并在不同时间范围内到达市场时，在人工限价订单市场中由情绪信念驱动的过度/过度反应效应。我们采用基于代理的建模方法来建立带有订单簿的人工股票市场，并通过遵循贝叶斯学习方案并在保守偏见和代表偏见之间进行马尔可夫体制转换来对一种情绪信念显示过度/反应不足的模型进行建模。模拟显示，与没有学习的经典噪声信念相比，情绪信念会带来短期盘中收益的可预测性。尤其是，过度/过度反应交易策略在情感信念下是有利可图的，但在噪音信念下却没有。此外，我们发现，情绪信念会导致大幅降低波动率，降低买卖差价，