1993:1-1997:6, the trading rules perform poorer on both series, since only 15.19% (29.25%) generate a strictly positive net excess return for the CSCE (LIFFE) series. As can be seen in the tables, the results for the different subsets of technical trading rules do not differ from the complete set of trading rules for all periods.
Pound-Dollar exchange rate
For the full sample the trading rules do not show much economically significant forecasting power, with only 10.14% of the trading rules generating a strictly positive excess return net of 0.1% transaction costs. The same result is found for the first subperiod, with 9.32% generating a strictly positive net excess return. The trading rules seem to work better when they are applied to the Pound-Dollar exchange rate in the second subperiod, with 30.81% of the trading rules generating a strictly positive net excess return. In the third subperiod the strategies work badly and only 2.07% generate a strictly positive net excess return. Thus for all three data series it is found that the set of technical trading strategies performs poorly in the subperiod 1993:1-1997:6, when compared with the preceding period 83:1-92:12.Notice that, for example under the null hypothesis of a random walk, the net excess return of technical trading rules will be negative due to transaction costs. The fact that a large set of technical trading rules generates a strictly positive net excess return, especially for the LIFFE cocoa futures series, is therefore surprising and suggestive of economically significant profit opportunities. It is hard however, to evaluate the statistical significance of this observation. Therefore, in the next subsection we focus on the question whether the forecasting power of returns is statistically significant.