that could be made by technical trading rules in US Dollars. For this second case we generate technical trading signals in two different ways. Firstly by using the local main stock market index in local currency and secondly by using the local main stock market index recomputed in US Dollars. Observed technical trading rule profits could be the reward for risk. Therefore we test by estimating a Sharpe-Lintner capital asset pricing model (CAPM) whether the best technical trading rule selected for each stock market index is also profitable after correction for risk. Both the local index and the MSCI World Index are used as the benchmark market portfolio in the CAPM estimation equation. If the technical trading rules show economically significant forecasting power after correction for risk and transaction costs, then further it is tested whether the best strategy found for each local main stock market index is indeed superior to the buy-and-hold benchmark after correction for data snooping. This chapter may therefore be seen as an empirical application of White's (2000) Reality Check and Peter Hansen's (2001) test for superior predictive ability. Further we test by recursively optimizing our technical trading rule set whether technical analysis shows true out-of-sample forecasting power.
In section 5.2 we list the local main stock market indices examined in this chapter and we show the summary statistics. We refer to the sections 3.3, 3.4 and 3.5 for the discussions on the set of technical trading rules applied, the computation of the performance measures and finally the problem of data snooping. Section 5.3 presents the empirical results of our study. In section 5.4 we test whether recursively optimizing and updating our technical trading rule set shows genuine out-of-sample forecasting ability. Finally, section 5.5 summarizes and concludes.