✍ Scribed by Spyros Skouras
No coin nor oath required. For personal study only.
We introduce trading rules which are selected by an arti"cially intelligent agent who learns from experience * an Arti"cial Technical Analyst. These rules restrict the data-mining concerns associated with the use of &simple' technical trading rules as model evaluation devices and are good at recognising subtle regularities in return processes. The relationship between the e$ciency of "nancial markets and the e$cacy of technical analysis is investigated and it is shown that the Arti"cial Technical Analyst can be used to provide a quantitative measure of market e$ciency. We estimate this measure on the DJIA daily index from 1962 to 1986 and draw implications for the optimal behaviour of certain classes of investors. It is also shown that the structure of technical trading rules commonly used is consistent with utility maximisation for risk neutral agents and in a myopic sense even for risk-averse agents.
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