when the complete set of technical trading strategies is applied to the LIFFE cocoa futures prices in the period 1983:1-1997:6. All the results presented are the fractions of simulation results that are larger than the results for the original data series. In panel A the fractions of the 500 bootstrapped time series are reported for which the percentage of trading rules with a significantly positive mean excess return, with a significantly positive mean buy return, with a significantly negative mean sell return, with a significantly positive mean buy-sell difference, and with a significantly positive mean buy as well as significantly negative mean sell return at a ten percent significance level using a one sided t-test is larger than the same percentage found when the same trading rules are applied to the original data series. Panel B on the other hand reports the bootstrap results for the bad significance of the trading rules. It shows the fraction of the 500 bootstrapped time series for which the percentage of trading rules with a bad significance is even larger than the percentage of trading rules with a bad significance at a 10% significance level using a one sided t-test when applied to the original data series.
For the cocoa series the mean excess return is approximately equal to the return on the futures position without correcting for the risk-free interest rate earned on the margin account, because
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rte =ln(1+rtf + |
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Post)-ln(1+rtf)
≈ rtf + |
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Post -rtf
= |
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Post.
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Therefore the mean excess return of a trading rule applied to the bootstrapped cocoa series is calculated as the mean return of the positions taken by the strategy, so that we don't need to bootstrap the risk-free interest rate.
We have already seen in table 2.8 that for 34.5% of the strategies the mean excess return is significantly positive in the first subperiod for the LIFFE cocoa futures series. The number in the column of the random walk results in the row tPerf>tc, which is 0.002, shows that for 0.2% of the 500 random walk simulations the percentage of strategies with a significantly positive mean excess return is larger than the 34.5% found when the strategies are applied to the original data series. This number can be thought of as a simulated ``p-value''. Hence the good results for the excess return found on the original data series cannot be explained by the random walk model. For 26.7% of the strategies the mean buy return is significantly positive. The fraction in the row tBuy>tc shows that in only 3.2% of the simulations the percentage of strategies with a significantly positive mean buy return is larger than the 26.7% found in the original data series. However, the fraction in the row tSell<-tc, shows that in 14% of the simulations the percentage of strategies with a significantly negative mean sell return is larger than the 39.5% of strategies with a significantly negative mean sell return when applied to the original data
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