data series the best technical trading strategy is selected by the mean return or Sharpe ratio criterion. The advantage of the Sharpe ratio selection criterion over the mean return selection criterion is that it selects the strategy with the highest return/risk pay-off. Although for 12 stocks it is found that they could not even beat a continuous risk free investment, we find for both selection criteria that for each data series a technical trading strategy can be selected that is capable of beating the buy-and-hold benchmark, even after correction for transaction costs. For example, if the best strategy is selected by the mean return criterion, then on average, the best strategy beats the buy-and-hold benchmark with 152, 141, 135, 131, 127 and 124% yearly in the case of 0, 0.10, 0.25, 0.50, 0.75 and 1% transaction costs. However these extremely high numbers are mainly caused by IT and telecommunications related companies. If we discard these companies from the calculations, then still on average, the best strategy beats the buy-and-hold benchmark with 32, 22, 19, 17, 16 and 15% for the six different costs cases. These are quite substantial numbers.
The profits generated by the technical trading strategies could be the reward necessary to attract investors to bear the risk of holding the asset. To test this hypothesis we estimate Sharpe-Lintner CAPMs. For each data series the daily return of the best strategy in excess of the risk-free interest rate is regressed against a constant (α) and the daily return of the market-weighted AEX-index in excess of the risk-free interest rate. The coefficient of the last regression term is called β and measures the riskiness of the strategy relatively to buying and holding the market portfolio. If technical trading rules do not generate excess profits after correction for risk, then α should not be significantly different from zero. In the case of zero transaction costs it is found for the mean return as well as the Sharpe ratio criterion that for respectively 37 and 39 data series the estimate of α is significantly positive at the 10% significance level. Even if transaction costs are increased to 1% per trade, then we find for half of the data series that the estimate of α is still significantly positive. Moreover it is found that simultaneously the estimate of β is significantly smaller than one for many data series. Thus for both selection criteria we find for approximately half of the data series that in the presence of transaction costs the best technical trading strategies have forecasting power and even reduce risk.
An important question is whether the positive results found in favour of technical trading are due to chance or the fact that the best strategy has genuine superior forecasting power over the buy-and-hold benchmark. This is called the danger of data snooping. We apply White's (2000) Reality Check (RC) and Hansen's (2001) Superior Predictive Ability (SPA) test, to test the null hypothesis that the best strategy found in a specification search is not superior to the benchmark of a buy-and-hold if a correction is made for