duces risk of trading. Next, a correction is made for data snooping by applying the RC and the SPA-test. If the mean return criterion is used for selecting the best strategy, then both tests lead for almost all data series to the same conclusion if as little as 0.10% transaction costs are implemented, namely that 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 select the best strategy. In contrast, if the Sharpe ratio selection criterion is used, then for one third of the data series the null of no superior forecasting power is rejected by the SPA-test, even after correction for 1% transaction costs. Thus in contrast to the findings for the stocks listed in the DJIA in Chapter 3, we find that technical trading has economically and statistically significant forecasting power for a group of stocks listed in the AEX-index, after a correction is made for transaction costs, risk and data snooping, if the Sharpe ratio criterion is used for selecting the best technical trading strategy. Finally, the recursive optimizing and testing method does show out-of-sample forecasting profits of technical trading. Estimation of Sharpe-Lintner CAPMs shows, after correction for 0.10% transaction costs, that the best recursive optimizing and testing method has statistically significant risk-corrected forecasting power for more than 40% of the data series examined. However, if transaction costs increase to 0.50% per trade, then for almost all data series the best recursive optimizing and testing procedure has no statistically significant risk-corrected forecasting power anymore. Thus only for sufficiently low transaction costs technical trading is economically and statistically significant for a group of stocks listed in the AEX-index.
In Chapter 5 the set of 787 trend-following technical trading strategies is applied to 50 local main stock market indices in Africa, North and South America, Asia, Europe, the Middle East and the Pacific, and to the MSCI World Index in the period January 1981 through June 2002. We consider the case of an US-based trader and recompute all profits in US Dollars. It is found that half of the indices could not even beat a continuous risk-free investment. However, as in Chapters 3 and 4 it is found for both selection criteria that for each stock market index a technical trading strategy can be selected that is capable of beating the buy-and-hold benchmark, also after correction for transaction costs. Furthermore, after implementing 1% costs per trade, still for half of the indices a statistically significant risk-corrected forecasting power is found by estimating CAPMs. If also a correction is made for data snooping, then we find as in Chapter 4 that both selection criteria yield different results. In the presence of 0.50% transaction costs the null hypothesis of no superior predictive ability of the best technical trading strategy selected by the mean return criterion over the buy-and-hold benchmark after correcting for the specification search is not rejected for most indices by both the RC and