This paper presents the computational results obtained in strategy experiments in an artificial futures market with human agents. Participants submit their own trading agents and they receive the results of all the market participants in order to improve for the next round. After two rounds of experiments, simulations with only trading agents are run. Our computational results show that the time series data support so-called stylized facts in some aspects and that learning effects seem to bring the prices closer to a theoretical value. Market impacts of human and trading agents are also investigated.
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