respectively 6, 2, 1 and 1 indices by the SPA-test. Hence we conclude, after a correction is made for transaction costs and data snooping, that the best strategy selected by the Sharpe ratio criterion is capable of beating the benchmark of a buy-and-hold strategy for approximately 25% of the indices analyzed. These results are mainly found for the Asian stock market indices, but also for some European stock market indices, such as Italy and Portugal.
5.4 A recursive out-of-sample forecasting approach
In section 3.7 we argued to apply a recursive out-of-sample forecasting approach to test whether technical trading rules have true out-of-sample forecasting power. For example, recursively at the beginning of each month it is investigated which technical trading rule performed the best in the preceding six months (training period) and this strategy is used to generate trading signals during the coming month (testing period). In this section we apply the recursive out-of-sample forecasting procedure to the main local stock market indices examined in this chapter.We define the training period on day t to last from t-Tr until and including t-1, where Tr is the length of the training period. The testing period lasts from t until and including t+Te-1, where Te is the length of the testing period. At the end of the training period the best strategy is selected by the mean return or Sharpe ratio criterion. Next, the selected technical trading strategy is applied in the testing period to generate trading signals. After the end of the testing period this procedure is repeated again until the end of the data series is reached. For the training and testing periods we use 28 different parameterizations of [Tr, Te] which can be found in Appendix B of Chapter 4.
If trading case 3 is applied, then in the case of 0.25% transaction costs, tables 5.13 and 5.14 show for the local main stock market indices some statistics of the best recursive optimizing and testing procedure, if the best strategy in the training period is selected by the mean return and Sharpe ratio criterion respectively. Because the longest training period is one year, the results are computed for the period 1983:1-2002:6. Tables 5.15A and 5.15B summarize the results for both selection criteria in the case of 0, 0.10, 0.25, 0.50, 0.75 and 1% costs per trade. In the second to last row of table 5.15A it can be seen that, if in the training period the best strategy is selected by the mean return criterion, then the excess return over the buy-and-hold of the best recursive optimizing and testing procedure is, on average, 37.72, 30.60, 21.41, 12.4, 7.05 and 4.47% yearly in the case of 0, 0.10, 0.25, 0.50, 0.75 and 1% costs per trade. If transaction costs increase, then the best recursive optimizing and testing procedure becomes less profitable. Good results, also