most popular technical trading rules is based on moving averages. A moving average is a recursively updated, for example daily, weekly or monthly, average of past prices. A moving average smoothes out erratic price movements and is supposed to reflect the underlying trend in prices. A buy (sell) signal is said to be generated at time t if the price crosses the moving average upwards (downwards) at time t. Figure 1.1 shows an example of a 200-day moving average applied to the Amsterdam Stock Exchange Index (AEX-index) in the period March 1, 1996 through July 25, 2002. The 200-day moving average is exhibited by the dotted line. It can be seen that the moving average follows the price at some distance. It changes direction after a change in the direction of the prices has occurred. By decreasing the number of days over which the moving average is computed, the distance can be made smaller, and trading signals occur more often. Despite that the 200-day moving-average trading rule is generating signals in some occasions too late, it can be seen that the trading rule succeeds in detecting large price moves that occurred in the index. In this thesis we will develop a technical trading rule set on the basis of simple trend-following trading techniques, such as the above moving-average strategy, as well as refinements with %-band-filters, time delay filters, fixed holding periods and stop-loss. We will test the profitability and predictability of a large class of such trading rules applied to a large number of financial asset price series.
Critiques on technical analysis
Technical analysis has been heavily criticized over the decades. One critique is that it trades when a trend is already established. By the time that a trend is signaled, it may already have taken place. Hence it is said that technical analysts are always trading too late.As noted by Osler and Chang (1995, p.7), books on technical analysis fail in documenting the validity of their claims. Authors do not hesitate to characterize a pattern as frequent or reliable, without making an attempt to quantify those assessments. Profits are measured in isolation, without regard for opportunity costs or risk. The lack of a sound statistical analysis arises from the difficulty in programming technical pattern recognition techniques into a computer. Many technical trading rules seem to be somewhat vague statements without accurately mathematically defined patterns. However Neftci (1991) shows that most patterns used by technical analysts can be characterized by appropriate sequences of local minima and/or maxima. Lo, Mamaysky and Wang (2000) develop a pattern recognition system based on non-parametric kernel regression. They conclude (p.1753): ``Although human judgment is still superior to most computational algorithms in