Effect of moving averages in the tickwise tradings in the stock market

In the recent years the automatic generation of trading rules for stock and currency markets by means of Evolutionary Algorithms has become a popular game. Although, it is disputed whether or not such evolved trading rules are able to generate reliable profit on out-of-sample sets, especially if trading costs are considered. In this paper we focus on tickwise data and introduce a simple trading scheme based on Learning Classifier like action rules. These rules have only access to the most recent time series history and are thus only able to exploit the short term memory effects of tickwise data. Rather than searching for profitable trading rules on tickwise data, we first concentrate on evaluating the predictive properties of alternative indices, namely moving averages.