Academic journal article Financial Management

A Test of Technical Analysis: Matching Time Displaced Generalized Patterns

Academic journal article Financial Management

A Test of Technical Analysis: Matching Time Displaced Generalized Patterns

Article excerpt

We use a least squares metric to match the return pattern of a target stock with that of an out-of-sample-twin. The twin with the smallest metric is found by a comprehensive period-by-period search of stocks in the Center for Research in Security Prices data set extending back to 1926. If technical analysis has value, targets of twins producing the highest returns in the twin postperiod should also have the highest performance in the target postperiod. Using a randomly selected sample of 66,000 return patterns, we find higher means for targets" corresponding to the highest returning twin quintile. We also use regressions to risk adjust target returns and find that twin returns in the postmatch period significantly predict risk-adjusted target returns.

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The historical price graph may be the most recognizable of all forms of information considered by the ordinary investor. However, financial academics, relying on the logic of the efficient markets theorem and exhaustive empirical investigations, largely contend that the information contained in past stock prices is of little value to the investor. Conversely, technical analysts embrace the idea that past patterns are informative. Malkiel (1981) states "... technical analysis is anathema to the academic world. We love to pick on it. Our bullying tactics are prompted by two considerations: (1) the method is patently false and (2) it easy to pick on. And while it may seem a bit unfair to pick on such a sorry target, just remember it is your money we are trying to save." Technical analysts counter that academic studies can never capture every nuance of the stock chart. In addition, if technical analysis is of no value, it is puzzling why it is still prevalent in the marketplace. Why would a rational economic agent engage in such activity and why would firms pay for this form of human capital? Lo, Mamaysky, and Wang (2000) characterize the difference between technical analysis and quantitative finance as follows: "Technical analysis is primarily visual, whereas quantitative finance is primarily algebraic and numerical." And "technical analysis has survived through the years, perhaps because its visual model of analysis is more conducive to human cognitions...." Menkhoff (2010) finds that the vast majority of 692 fund managers in five market (the United States, Germany, Switzerland, Italy, and Thailand) trading countries rely heavily on technical analysis. He concludes that "At a forecasting horizon of weeks, technical analysis is the most important form of analysis and up to this horizon it is thus more important than fundamental analysis."

A significant problem in technical analysis is the precise mathematical definition and prespecification of the patterns or rules. Neftci (1991) addresses the problem of the precise mathematical definition of technical rules and notes that any well defined rule must pass the test of being defined in Markov time. In short, at time t, the rule must give buy and sell signals without using information from times [tau] > t. Further, he notes that according to the Wiener-Kolmogorov prediction theory, vector autoregressions (VARs) should yield the optimal linear forecast. Hence, any forecast that improves on VARs must be based on nonlinear methods.

Another problem confronting technical analysis is that of data snooping. Data snooping occurs when the same data set is used repeatedly to investigate different models of pricing or selection rules. Since there are no equilibrium models of technical analysis, efforts to find profitable trading rules are necessarily ad hoc. Thus, the significance levels are suspect and almost certainly overstated. Sullivan, Timmermann, and White (1999) use White's (2000) reality check bootstrap methodology to develop data snooping adjustments in the context of technical analysis.

Our approach satisfies the Neftci (1991) criterion and largely avoids the issue of data snooping since we do not prespecify patterns or selection rules. …

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