Pattern recognition
Academic research on the effectiveness of technical analysis in financial markets, as reviewed above, mainly implements filters, moving averages, momentum and support-and-resistance rules. These technical indicators are fairly easy to program into a computer. However the range of technical trading techniques is very broad and an important part deals with visual pattern recognition. The claim by technical analysts of the presence of geometric shapes in historical price charts is often criticized as being too subjective, intuitive or even vague. Levy (1971) was the first to examine 32 possible forms of five point chart patterns, i.e. a pattern with two highs and three lows or two lows and three highs, which are claimed to represent channels, wedges, diamonds, symmetrical triangles, (reverse) head-and-shoulders, triple tops, and triple bottoms. Local extrema are determined with the help of Alexander's (1961) filter techniques. After trading costs are taken into account it is concluded that none of the 32 patterns show any evidence of profitable forecasting ability in either bullish or bearish direction when applied to 548 NYSE securities in the period July 1964 through July 1969. Neftci (1991) shows that technical patterns can be fully characterized by using appropriate sequences of local minima and maxima. Hence it is concluded that any pattern can potentially be formalized. Osler and Chang (1995) were the first to evaluate the predictive power of head-and-shoulders patterns using a computer-implemented algorithm in foreign exchange rates. The features of the head-and-shoulders pattern are defined to be described by local minima and maxima that are found by applying Alexander's (1961) filter techniques. The pattern recognition algorithm is applied to six currencies (JPY, DEM, CD, SF, FF and BP against the USD) in the period March 1973 to June 1994. Significance is tested with the bootstrap methodology described by Brock et al. (1992) under the null of a random walk and GARCH model. It is found that the head-and-shoulders pattern had significant predictive power for the DEM and the JPY, also after correcting for transaction costs and interest rate differentials. Lo, Mamaysky and Wang (2000) develop a pattern recognition algorithm based on non-parametric kernel regression to detect (inverse) head-and-shoulders, broadening tops and bottoms, triangle tops and bottoms, rectangle tops and bottoms, and double tops and bottoms - patterns that are the most difficult to quantify analytically. The pattern recognition algorithm is applied to hundreds of NYSE and NASDAQ quoted stocks in the period 1962-1996. It is found that technical patterns do provide incremental information, especially for NASDAQ stocks. Further it is found that the most common patterns are double tops and bottoms, and (inverted) head-and-shoulders.24