days. Further the table shows the percentage of strategies for which the difference in mean return of the data series during buy and sell days is significantly positive. Finally, the percentage of strategies for which the data series at the same time has a significantly positive mean return during buy days as well as a significantly negative mean return during sell days is shown. A correction is made for 0.1% transaction costs.
Table 2.9 shows in contrast to table 2.8 the percentage of strategies which generate statistically significant bad results, i.e. the percentage of strategies with a significantly negative mean excess return, with a significantly negative (positive) mean buy (sell) return, with a significantly negative mean buy-sell difference and the percentage of strategies which have a significantly negative mean buy return as well as a significantly positive mean sell return. These statistics are computed to test whether technical trading rules as a group show statistically significant bad forecasting power.
The tables lists only the results of one sided tests with a 10% significance level, the results for a 5% significance level are similar but of course weaker. For a 1% significance level most significant results disappear.
Cocoa futures series
For the full sample period the strategies applied to the CSCE cocoa series show hardly any statistically significant forecasting power. For example, the difference in mean return during buy and sell days is significantly positive only in 1.38% of the trading rules, whereas a significantly negative mean return during sell days occurs only in 5.92% of all strategies. Only in 0.3% of the cases the mean excess return is significantly positive, hence no significant profits could be made. For the LIFFE series on the other hand the results are remarkably different. For 26.58% of the strategies the mean buy-sell difference is significantly positive. In particular, the strategies seem to forecast the sell days very well, with more than half (50.53%) of all strategies having a significantly negative mean return during sell days. In contrast, the mean buy return is significantly positive only in 6.86% of all strategies. 13.86% of the strategies have a significantly positive mean excess return when applied to the LIFFE series. Looking at table 2.9 a lot of strategies perform statistically very bad when applied to the CSCE series, while the percentage of strategies that performs statistically badly is much less for the LIFFE series. Thus for the full sample the set of strategies applied to the LIFFE series shows a lot of economic significance, which is also statistically significant, and a lot of trading rules have a statistically significant forecasting power, i.e. they detect periods in which the data series rises and declines, while the percentage of trading rules which performs statistically badly is smaller than the percentage of trading rules which performs statistically good.
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