Financial returns and efficiency as seen by an artificial technical analyst
Previous research has shown that simple trading rules can be useful tools for evaluating financial models. Here we introduce trading rules which are selected by an artificially intelligent agent who learns from experience - an Artificial Technical Analyst. We show that the rules used by this agent can lead to the recognition of subtle regularities in return processes whilst suffering from lesser data-mining problems than other rules commonly used as model evaluation devices. The relationship between the efficiency of financial markets and the efficacy of technical analysis is investigated and it is shown that the Artificial Technical Analyst can be used to provide a quantifiable measure of market efficiency. The measure is applied to the DJIA daily index from 1962 to 1986 and it is shown that a quantification of efficiency based on the profits of an Artificial Technical Analyst can lead to interesting results concerning the behaviour of other investors.
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