For the period 1993:1-1997:12 we find that the forecasting power of the technical trading strategies applied to the cocoa futures prices and the Pound-Dollar exchange rate is much less than in the preceding period 1983:1-1992:12. This is in line with many papers that found that forecasting power of trading strategies tends to disappear in the 1990s.
Although the present chapter only documents the economic and statistical performance of technical trading rules applied to a single commodity market, some general conclusions that may be useful for other financial series as well are suggested by our case-study. First, in order to assess the success or failure of technical trading it is useful to test a large class of trading rules, as done in this chapter. A necessary condition for reliable success of technical trading seems to be that a large class of trading rules, not just a few, should work well. If only a few trading rules are successful this may simply be due to ``chance'' or to data snooping. It should also be emphasized that even if a large class of trading rules has statistically significant forecasting power this is not a sufficient condition for economically significant trading profits after correcting for transaction costs. An example is the Pound-Dollar exchange rate for which a large fraction of trading rules exhibits statistically significant forecasting power, but these trading rules hardly generate economic net profitability.
Our case-study of the cocoa futures series and the Pound-Dollar exchange rate series suggest a connection between the success or failure of technical trading rules and the trend and volatility of the corresponding series. When trends are weak and volatility is relatively high, technical trading does not have much forecasting power and therefore also cannot lead to economic profitability. Technical trading is unable to uncover these trends, because volatility is too high. When trends are weak but volatility is relatively low, technical trading rules can have statistically significant forecasting power without economically significant profitability. In that case, because volatility is low technical trading can still pick up the weak trends, but the changes in returns, although predictable, are too small to account for transaction costs. Finally, when trends are strong and volatility is relatively high, a large set of technical trading rules may have statistically significant forecasting power leading to economically significant profit opportunities. In that case, even though volatility is high the trends are strong enough to be picked up by technical trading. Moreover, since volatility is high, the magnitude of the (predictable) changes in returns is large enough to cover the transaction costs. We emphasize that this connection between predictive and economic performance of technical trading is suggestive and only documented by the market studied here. Further research, of interest from a theoretical as well as a practical viewpoint, is needed to uncover whether the success and failure of technical trading is explained by the relative magnitudes of trend and volatility.