Explaining stock return distributions via an agent-based model

US stock returns exhibit mixed Gaussian probabilistic features as well as nonlinear and complex dynamics, but existing agent-based models of stock markets have not focused on replicating or explaining all these phenomena jointly. In this paper, a new agent-based model of the stock market is proposed that can replicate and explain such phenomena jointly. In the new model, stocks are a claim to a dividend process determined by a hidden state process which follows a Markov chain. The model produces a probability distribution of stock returns that is mixed Gaussian, like the US stock market. Using a generalized multiscale entropy method, it is shown that the simulated returns have similar complexity and entropy properties to US stock returns for plausible parameter values. Sensitivity analyses show that the simulated stock price series generated by the model varies in a plausible manner with various underlying important parameters such as agent risk aversion, agent beliefs, the underlying stock dividend process, returns to risk-free assets and dividend transition probabilities.