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|Title:||Risk Preference, Forecasting Accuracy and Survival Dynamics:Simulations Based on a Multi-Asset Agent-Based Artificial Stock Market|
|Issue Date:||2009-09-14 13:31:30 (UTC+8)|
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 dissertation 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 multiasset
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. Risk preference plays a key role in survivability. And agents who
have superior forecasting accuracy may be driven out just because of their risk preference.
Nevertheless, when all the agents are with the same preference, the wealth share is positively
correlated to forecasting accuracy, and the market selection hypothesis is sustained, at least
in a weak sense.
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