data snooping. Hansen (2001) showed that White's RC is biased in the direction of one, caused by the inclusion of poor strategies. Because we compute p-values for both tests, we can investigate whether the two test procedures result in different inferences about forecasting ability of technical trading. If zero transaction costs are implemented, then we find for the mean return selection criterion that the RC and the SPA-test in some cases lead to different conclusions. The SPA-test finds in numerous cases that the best strategy does beat the buy-and-hold significantly after correction for data snooping and the inclusion of bad strategies. Thus the biased RC misguides the researcher in several cases by not rejecting the null. However, if as little as 0.10% costs per trade are implemented, then both tests lead for almost all data series to the same conclusion: the best technical trading strategy selected by the mean return criterion is not capable of beating the buy-and-hold benchmark after correcting for the specification search that is used to find the best strategy. In contrast, for the Sharpe ratio selection criterion we find totally different results. Now the SPA-test rejects its null for 30 data series in the case of zero transaction costs, while the RC rejects its null for only 10 data series. If transaction costs are increased further to even 1% per trade, then for approximately one third of the stocks analyzed, the SPA-test rejects the null of no superior predictive ability at the 10% significance level, while the RC rejects the null for only two data series. We find for the Sharpe ratio selection criterion large differences between the two testing procedures. Thus the inclusion of poor performing strategies for which the SPA-test is correcting, can indeed influence the inferences about the predictive ability of technical trading rules.
The results show that technical trading has forecasting power for a certain group of stocks listed in the AEX-index. Further the best way to select technical trading strategies is on the basis of the Sharpe ratio criterion. However the testing procedures are mainly done in sample. Therefore next we apply a recursive optimizing and testing method to test whether the best strategy found in a specification search during a training period shows also forecasting power during a testing period thereafter. For example, every month the best strategy from the last 6 months is selected to generate trading signals during that month. In total we examine 28 different training and testing period combinations. In the case of zero transaction costs the best recursive optimizing and testing procedure yields on average an excess return over the buy-and-hold of 32.23% yearly, if the best strategy in the training period is selected by the mean return criterion. Thus the best strategy found in the past continues to generate good results in the future. If 0.50% (1%) transaction costs are implemented, then the excess return decreases to 15.05% (8.02%). These are quite substantial numbers. Estimation of Sharpe-Lintner CAPMs shows that, after correction for 0.10% transaction costs and risk, the best recursive optimizing and