Furthermore, we test whether the best strategies can beat the buy-and-hold benchmark significantly after correction for data snooping. This chapter may be seen as an empirical application of White's RC and Hansen's SPA-test. In addition we test by recursively optimizing our trading rule set whether technical analysis shows true out-of-sample forecasting power.
In section 3.2 we list the stock price data examined in this chapter and we show the summary statistics. Section 3.3 presents an overview of the technical trading rules applied to the stock price data. Section 3.4 describes which performance measures are used and how they are calculated. In section 3.5 the problem of data snooping is addressed and a short summary of White's RC and Hansen's SPA-test is presented. Section 3.6 shows the empirical results. In section 3.7 we test whether recursively optimizing and updating our technical trading rule set shows genuine out-of-sample forecasting ability. Finally section 3.8 concludes.
3.2 Data and summary statistics
The data series examined in this chapter are the daily closing levels of the Dow-Jones Industrial Average (DJIA) and the daily closing stock prices of 34 companies listed in the DJIA in the period January 2, 1973 through June 29, 2001. Table 3.1 lists the data series. The companies in the DJIA are the largest and most important in their industries. Prices are corrected for dividends, capital changes and stock splits. As a proxy for the risk-free interest rate we use daily data on US 3-month certificates of deposits. Several studies found that technical trading rules show significant forecasting power in the era until 1987 and no forecasting power anymore from then onwards. Therefore we split our data sample in two subperiods. Table 3.2 shows the summary statistics for the period 1973-2001 and the tables 3.3 and 3.4 show the summary statistics for the two subperiods 1973-1986 and 1987-2001. Because the first 260 data points are used for initializing the technical trading strategies, the summary statistics are shown from January 1, 1974. In the tables the first and second column show the names of the data series examined and the number of available data points. The third column shows the mean yearly effective return in percentage/100 terms. The fourth through seventh column show the mean, standard deviation, skewness and kurtosis of the logarithmic daily return. The eight column shows the t-ratio to test whether the mean logarithmic return is significantly different from zero. The ninth column shows the Sharpe ratio, that is the extra return over the risk-free interest rate per extra point of risk, as measured by the standard deviation. The tenth column shows the largest cumulative loss, that is the largest decline from a peak
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