There are three principles underlying technical analysis. The first is that all information is gradually discounted in the prices. Through the market mechanism the expectations, hopes, dreams and believes of all investors are reflected in the prices. A technical analyst argues that the best adviser you can get is the market itself and there is no need to explore fundamental information. Second, technical analysis assumes that prices move in upward, downward or sideways trends. Therefore most technical trading techniques are trend-following instruments. The third assumption is that history repeats itself. Under equal conditions investors will react the same leading to price patterns which can be recognized in the data. Technical analysts claim that if a pattern is detected in an early stage, profitable trades can be made.

In this thesis we confine ourselves to objective trend-following technical trading techniques which can be implemented on a computer. In this chapter we test in total 5350 technical trading strategies divided in three different groups: moving-average rules (in total 2760), trading range break-out (also called support-and-resistance) rules (in total 1990) and filter rules (in total 600). These strategies are also described by Brock, Lakonishok and LeBaron (1992), Levich and Thomas (1993) and Sullivan, Timmermann and White (1999). Lo, Mamaysky and Wang (2000) use non-parametric methods to implement other, geometrically based technical trading rules such as head-and-shoulder pattern formation. We use the parameterizations of Sullivan et al. (1999) as a starting point to construct our sets of trading rules. These parameterizations are presented in Appendix B. The strategies will be computed on the continuous cocoa time series and the Pound-Dollar exchange rate. If a buy (sell) signal is generated at the end of day t, we assume that a long (short) position is taken in the market at day t against the settlement price of day t.

2.3.1  The moving-average trading rule

Moving-average (MA) trading rules are the most commonly used and most commonly tested technical trading strategies. Moving averages are recursively updated averages of past prices. They yield insight in the underlying trend of a price series and also smooth out an otherwise volatile series. In this thesis we use equally weighted moving averages
MAtn=
1
n
n-1
∑
j=0
Pt-j,
where MAtn is the moving average at time t of the last n observed prices. Short (long) term trends can be detected by choosing n small (large). The larger n, the slower the MA adapts and the more the volatility is smoothed out. Technical analysts therefore refer to a MA with a large n as a slow MA and to a MA with a small n as a fast MA.
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