Risk preference, forecasting accuracy and survival dynamics: Simulations based on a multi-asset agent-based artificial stock market ?

The relevance of risk preference and forecasting accuracy to the survival of investors is an issue that has recently attracted a number of recent theoretical studies. At one extreme, it has been shown that risk preference can be entirely irrelevant, and that in the long run what distinguishes the agents who survive from those who vanish is just their forecasting accuracy. Being in line with the market selection hypothesis, this theoretical result is, however, established mainly on the basis of Pareto optimal allocation. By using agent-based computational modeling, this paper extends the existing studies to an economy where adaptive behaviors are autonomous and complex heterogeneous, and where the economy is notorious for its likely persistent deviation from Pareto optimality. Specifically, a computational multi-asset artificial stock market corresponding to Blume and Easley (1992) and Sandroni (2000) is constructed and studied. Through simulation, we present results that contradict the market selection hypothesis. Among the eight types of agents considered in this model, only log-utility agents survive, and the rest are driven out, including even those who have superior forecasting accuracy. Nevertheless, when all the agents are of the same type, the wealth share is positively correlated to forecasting accuracy, and the market selection hypothesis is sustained, at least in a weak sense.