1976-1990. Applying bootstrap techniques they conclude that the profits of the technical trading strategies cannot be explained by a random walk model or by autocorrelation in the data. LeBaron (1993) applies trading rules to exchange rates based on interest rate differentials, moving averages and volatility comparison and concludes that the trading rules tested have forecasting power.
Several authors have emphasized the danger of data snooping, meaning that if one searches long enough in a data set, there will always appear one trading strategy that seems to work. Many authors mitigate this problem by using only trading rules that are frequently used in financial practice or by reporting the robustness of their results across different subperiods. However, Sullivan, Timmermann and White (1999) noted that such trading strategies could be the result of survivorship bias, since the currently used trading rules in practice can be the result of a continuous search for the best strategy. Therefore they propose to use White's (2000) Reality Check bootstrap methodology to correct for data snooping. Sullivan et al. (1999) take the results of Brock et al. (1992) on the DJIA in the period 1897-1986 as starting point. They find that the results of Brock et al. (1992) are robust to data snooping in the period 1897-1986, but that in the period 1987-1997 the performance of the best trading rule is not significant when corrected for data snooping. Sullivan et al. (1999) show that the same results hold for a universe of 7846 trading rules and conclude that the worse performance of trading rules in the period 1987-1997 may be explained by a change of the market mechanism, e.g. an increase of market efficiency due to lower transaction costs and increased liquidity.
The present chapter is empirical and tests the profitability and predictability of objective trend-following technical trading techniques in the cocoa futures markets in the period 1983:1-1997:6. In order to avoid the problem of data snooping our approach is to test a large set of more than 5000 trading strategies, moving average, trading range break-out and filter rules, and to investigate the magnitude of the fraction generating strictly positive excess returns and statistically significant forecasting power. Cocoa futures contracts are traded at two different exchanges, namely at the Coffee, Sugar and Cocoa Exchange (CSCE) in New York and the London International Financial Futures Exchange (LIFFE). The results for the two cocoa futures contracts are strikingly different. When applied to the LIFFE cocoa futures prices, 58.3% of all trading rules generate strictly positive excess returns, even when correcting for transaction costs. Furthermore, a large set of trading rules exhibits statistically significant forecasting power of the LIFFE cocoa futures series, with e.g. 26.6% having significantly positive mean buy minus sell return; for the 5 year subperiod 1983:1-1987:12 even 46.7% of all trading rules has a significantly positive mean buy minus sell return. However, the same set of strategies performs poorly on the CSCE