  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.
