A numerical agent-based spin model of financial markets, based on the Potts model from statistical mechanics, with a novel interpretation of the spin variable (as regards financial-market models) is presented. In this model, a value of the spin variable is only the agent’s opinion concerning current market situation, which he communicates to his nearest neighbors. Instead, the agent’s action (i.e., buying, selling, or staying inactive) is connected with a change of the spin variable. Hence, the agents can be considered as cunning in this model. That is, these agents encourage their neighbors to buy stocks if the agents have an opportunity to sell them, and the agents encourage their neighbors to sell stocks if the agents have a reversed opportunity. Predictions of the model are in good agreement with empirical data from various real-life financial markets. The model reproduces the shape of the usual and absolute-value autocorrelation function of returns as well as the distribution of times between superthreshold losses.

提出了一种基于数字主体的金融市场旋转模型，该模型基于统计力学中的Potts模型，并对旋转变量（就金融市场模型而言）进行了新颖的解释。在此模型中，旋转变量的值仅是代理商对当前市场状况的意见，并与最近的邻居进行沟通。相反，代理的行为（即购买，出售或保持不活动状态）与旋转变量的变化有关。因此，代理可以被认为是狡猾的在这个模型中。也就是说，如果代理商有机会出售股票，则这些代理商会鼓励其邻居购买股票，如果代理商具有逆转机会，则代理商会鼓励其邻居出售股票。该模型的预测与来自各种现实金融市场的经验数据高度吻合。该模型再现了通常的收益率绝对值自相关函数的形状以及超阈值损失之间的时间分布。