futures prices through their behavior. The question ``Can cocoa futures prices be predicted by technical analysis?'' thus becomes important from a practitioner's viewpoint. This question is not only important to cocoa producers, but in general to producers of any commodity hedging price risk. If technical analysis has forecasting power and speculators take positions in the market on the basis of technical analysis, these speculators can affect market prices. Why should a (cocoa) producer go short in the futures market to hedge his price risk exposure if he knows that a lot of speculators in the market are buying long positions driving up the price? Knowledge of the behavior of speculators in the market may be useful to adapt a producers' price hedging strategy.
Until fairly recently, the academic literature has paid little attention to technical trading strategies. Until the 1980s the efficient markets hypothesis (EMH) was the dominating paradigm in finance, see e.g. Fama (1970) and Samuelson (1965). According to a strong form of the EMH, financial time series follow a random walk and are thus inherently unpredictable. All information is discounted in the prices already and prices will only adapt if new information becomes available. Because news arrives randomly, prices will move randomly. According to the EMH, financial time series are unpredictable and technical analysis is useless and cannot lead to statistically significant prediction or economically significant profits.
In the last decade however, technical analysis has regained the interest of many economic researchers. Several authors have shown that financial prices and returns are forecastable to some extent, either from their own past or from some other publicly available information, see e.g. Fama and French (1988), Lo and MacKinlay (1988, 1997, 1999) and Pesaran and Timmermann (1995, 2000). In particular, it has been shown that simple technical trading rules used in financial practice can generate positive profits and can have statistically significant forecasting power. For example Brock, Lakonishok and LeBaron (1992) test 26 simple technical trading rules on daily data of the Dow-Jones Industrial Average (DJIA) in the period 1897-1986. Each of the trading rules Brock et al. (1992) test generates higher returns during buy days, that is periods following buy signals, than during sell days, that is periods following sell signals. Further they find that returns following buy signals are less volatile than returns following sell signals. By applying bootstrap techniques they show that their results are not consistent with some popular null models like the random walk, the AR(1), the GARCH-in-mean and the exponential GARCH model. LeBaron (2000) performs the same analysis as Brock et al. (1992) for the period 1988-1999 and finds that trading rules perform much worse in this period, but that volatility remains different between buy and sell periods. Levich and Thomas (1993) test filter and moving-average trading rules on foreign currency futures prices in the period