Conventional market microstructure theory suggests that trading rules affect the prices observed in the market; the effect such trading rules have will be found in the statistical properties of the returns generated. Using market microstructure theory it is difficult to provide a comprehensive assessment of the impact the set of trading rules have as each element of these rules generally requires different models with different assumptions where combining these models into a single framework becomes nearly impossible. Furthermore, the behavioral assumptions required in these models make it difficult to distinguish between the impact on the prices of the trading rules and the behavior of traders.

To address these difficulties, we develop an agent-based model in which traders follow a simple trading protocol, not resembling optimizing behavior, thus being close to zero-intelligence traders first introduced in Gode/Sunder (1993). We let such traders interact in an artificial financial market using a wide range of trading rules that can commonly be found in financial markets. Using such a framework has been shown in Cliff/Bruten (1997) to be useful in analyzing the impact of trading rules on market outcomes. The trading rules we specifically investigate in our model are the price determination, tick size, priority rules, market transparency and market maker intervention.

We find that using our model we are able to reproduce the most commonly found stylized facts of financial market returns: absence of autocorrelation, volatility clustering, fat tails and multi-scaling. The details of these properties, however, depend on the trading rules used. When exploring these details, we find that in particular the fat tails of the returns are affected by the presence of informed traders, market maker intervention and tick size. Other stylized facts, like multi-scaling, are very sensitive to the trading rules.

Our findings do not only further the understanding of the origins of the stylized facts and their robustness to changes in the market structure, but have also implications for market design. Applying objective functions, e.g. the aim to minimize the volatility of the market, it is possible to obtain the optimal set of trading rules, given institutional restrictions, e.g. the number of informed traders.

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