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We present a system for combining the different types of predictions given by a wide category of mechanical trading rules through statistical learning methods (boosting, and several model averaging methods like Bayesian or simple averaging methods). Statistical learning methods supply better out-of-sample results than most of the single moving average rules in the NYSE Composite Index from January 1993 to December 2002. Moreover, using a filter to reduce trading frequency, the filtered boosting model produces a technical strategy which, although it is not able to overcome the returns of the buy-and-hold (B&H) strategy during rising periods, it does overcome the B&H during falling periods and is able to absorb a considerable part of falls in the market. Copyright © 2008 John Wiley & Sons, Ltd.

SUMMARYWe propose a new test to detect chaotic dynamics, based on the stability of the largest Lyapunov exponent from different sample sizes. This test is applied to the data used in the single-blind controlled competition tests for non-linearity and chaos that were generated by Barnett et al. (1997), as well as to several other chaotic series. The results suggest that the new test is particularly effective when compared to other stochastic alternatives (both linear and non-linear). For large sample sizes the power of the test is one, although for small sample sizes it diminishes occasionally.

Institute of Applied Economics (IREA) in Barcelona was founded in 2005, as a research institute in applied economics. Three consolidated research groups make up the institute: AQR, RISK and GiM, and a large number of members are involved in the Institute. IREA focuses on four priority lines of investigation: (i) the quantitative study of regional and urban economic activity and analysis of regional and local economic policies, (ii) study of public economic activity in markets, particularly in the fields of empirical evaluation of privatization, the regulation and competition in the markets of public services using state of industrial economy, (iii) risk analysis in finance and insurance, and (iv) the development of micro and macro econometrics applied for the analysis of economic activity, particularly for quantitative evaluation of public policies.IREA Working Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. For that reason, IREA Working Papers may not be reproduced or distributed without the written consent of the author. A revised version may be available directly from the author.Any opinions expressed here are those of the author(s) and not those of IREA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions.

* This research was funded by the Spanish Ministry of Education (project ECO2010-21318). We thank the editor (Esther Ruiz) and two anonymous referees for their constructive suggestions. We would also like to thank

This article assesses the economic significance of the non-linear predictability of EMS exchange rates. To that end, and using daily data for nine EMS currencies covering the 1 January 1978-31 December 1994 period, it considers nearest- neighbour non-linear predictors, transforming their forecasts into a technical trading rule, whose profitability has been evaluated against the traditional moving average trading rules, considering both interest rates and transaction costs. The results suggest that in most cases, a trading rule based on a non-linear predictor outperforms the moving average, both in terms of returns and in terms of the ideal profit and the Sharpe ratio profitability indicators.

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