trades for the AEX-index and almost all stocks, except for Gist Brocades, Stork, TPG and Unilever. However, if 0.25% costs per trade are calculated, then for 22 data series out of 51 the standard deviation ratio is larger than one. According to the efficient markets hypothesis it is not possible to exploit a data set with past information to predict future price changes. The excellent performance of the technical trading rules could therefore be the reward for holding a risky asset needed to attract investors to bear the risk. Since the technical trading rule forecasts only depend on past price history, it seems unlikely that they should result in unusual risk-adjusted profits. To test this hypothesis we regress Sharpe-Lintner capital asset pricing models (CAPMs)
rti-rtf=α + β (rtAEX-rtf) + εt.
(1)
Here
rti is the return on day
t of the best strategy applied to stock
i,
rtAEX is the return on day
t of the market-weighted AEX-index, which represents the market portfolio, and
rtf is the risk-free interest rate. The coefficient β measures the riskiness of the active technical trading strategy relatively to the passive strategy of buying and holding the market portfolio. If β is not significantly different from one, then it is said that the strategy has equal risk as a buying and holding the market portfolio. If β>1 (β<1), then it is said that the strategy is more risky (less risky) than buying and holding the market portfolio and that it therefore should yield larger (smaller) returns. The coefficient α measures the excess return of the best strategy applied to stock
i after correction of bearing risk. If it is not possible to beat a broad market portfolio after correction for risk and hence technical trading rule profits are just the reward for bearing risk, then α should not be significantly different from zero. Table 4.8A shows for the 0 and 0.50% transaction costs cases
3 the estimation results if for each data series the best strategy is selected by the mean return criterion. Estimation is done with Newey-West (1987) heteroskedasticity and autocorrelation consistent (HAC) standard errors. Table 4.10 summarizes the CAPM estimation results for all transaction cost cases by showing the number of data series for which significant estimates of α or β are found at the 10% significance level.
For example, for the best strategy applied to the AEX-index in the case of zero transaction costs, the estimate of α is significantly positive at the 1% significance level and is equal to 5.27 basis points per day, that is approximately 13.3% per year. The estimate of β is significantly smaller than one at the 5% significance level, which indicates that although the strategy generates a higher reward than simply buying and holding the index, it is less risky. If transaction costs increase to 1%, then the estimate of α decreases
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