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Recent research has discovered two seemingly contradictory facts about U.S. intervention in foreign exchange markets. On the one hand, extrapolative technical trading rules trade against U.S. foreign exchange intervention and produce excess returns—returns in excess of nominal interest rates—during these periods, and U.S. intervention itself, is profitable over long periods. LeBaron (1996) and Szakmary and Mathur (1997) have shown that excess returns to technical trading rules are high during periods of central bank intervention and that the technical rules trade contrary to the direction of official intervention. Along the same lines, Neely and Weller (1997) have shown that trading rules constructed by genetic programs can use information on the direction of U.S. intervention to increase their excess returns in some exchange rates: When the Federal Reserve is buying dollars, traders following technical rules are usually selling dollars and profiting handsomely. Some—Dooley and Shafer (1983), Corrado and Taylor (1986), Sweeney

A genetic programming system for data mining trading rules out of past foreign exchange data is described. The system is tested on real data from the dollar/yen and dollar/DM markets, and shown to produce considerable excess returns in the dollar/yen market. Design issues relating to potential rule complexity and validation regimes are explored empirically. Keeping potential rules as simple as possible is shown to be the most important component of success. Validation issues are more complicated. Inspection of fitness on a validation set is used to cut-off search in hopes of avoiding overfitting. Additional attempts to use the validation set to improve performance are shown to be ineffective in the standard framework. An examination of correlations between performance on the validation set and on the test set leads to an understanding of how such measures can be marginally benificial; unfortunately, this suggests that further attemps to improve performance through...

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AI has long been applied to the problem of predicting financial markets.

Financial market developments over the last decade have greatly increased interest in the properties of high-frequency data. Stimulated by the search for greater arbitrage opportunities, which have been created or facilitated by innovations in computer technology, central banks are now

Abstract: The objective of this paper is describe a new paradigm for the trading of equities. In our formulation, the control corresponds to a feedback law which modulates the amount invested I(t) in stock over time. The controller also includes a saturation limit Imax corresponding to a limit on the value at risk. The admissible stock price evolution p(t) over time is modelled as a family P of uncertain inputs against which we seek robust returns. Motivated by the fact that back-testing of candidate trading strategies involves significant cost and effort associated with computational simulation over sufficiently diverse markets, our paradigm involves the notion of synthetic prices and some idealizations involving the volatility of prices and trading liquidity. Our point of view is that a robust performance certification in this somewhat idealized market setting serves as a filter to determine if a trading strategy is worthy of the considerable time and expense associated with full-scale back-testing. The paper also includes a description of a so-called saturation reset controller. This controller is used to illustrate how the model works in practice and the attainment of robustness objectives over various sub-classes of P.

An introduction to technical analysis. For more information, see Sewell (2001). 1