B shows the bootstrap results for testing whether the bad significance of the technical trading rules can be explained by the several null models. In the case of the structural break in trend model the results show again that the set of technical trading rules behaves statistically worse when applied to the simulated series than to the original data series. For example in 96% of the simulations the percentage of strategies with a significantly negative mean excess return is larger than the 5.9% found when the same strategies are applied to the original data series. Despite that the structural break in trend model can explain the statistically significant forecasting power of the trading rules, also this model cannot explain the good results found when testing for bad significance of the strategies in the original data series. Thus the original time series has characteristics which causes the trend-following technical trading techniques to show signs of forecasting power, most probably the characteristic of the strong change in direction of the price trend. However this characteristic is not the only explanation, because it cannot explain the relatively low percentage of trend-following technical trading techniques which performed statistically badly on the original time series.
2.7 Success and failure of technical trading
The technical trading strategies as a group show economic and statistically significant forecasting power when applied to the LIFFE cocoa series, especially in the period 1983:1-1987:12. On the other hand the same technical trading strategies show no sign of forecasting power when applied to the CSCE cocoa series in the same period. The futures contracts differ in their specification of quality, currency and place of delivery, but it is surprising that the difference in economic and statistical significance is so large. Why are these differences so pronounced?The daily CSCE cocoa returns show somewhat stronger autocorrelation in the first two lags than the LIFFE returns, which suggests more predictability. The variance of the CSCE series is slightly bigger across all subperiods than the variance of the LIFFE series, which may be an indication why trend-following rules have more difficulty in predicting the CSCE cocoa series. However, it seems that this somewhat higher variance cannot explain the large differences. For example, in the second subperiod, when the volatility is the strongest across all subperiods for both time series, the trading rules perform almost equally well on the CSCE and LIFFE cocoa futures prices and show forecasting power of the sell days for both series. Hence, there must be some other explanation for the differences of technical trading performance.
Figure 2.3 already showed that, in the period 1983:1-1987:12, the LIFFE and CSCE