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The Profitability of Technical Analysis: a Review

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The Profitability of Technical Analysis: a Review The Profitability of Technical Analysis: A Review by Cheol-Ho Park and Scott H. Irwin The Profitability of Technical Analysis: A Review by Cheol-Ho Park and Scott H. Irwin1 October 2004 AgMAS Project Research Report 2004-04 1 Cheol-Ho Park is a Graduate Research Assistant in the Department of Agricultural and Consumer Economics at the University of Illinois at Urbana-Champaign. Scott H. Irwin is the Laurence J. Norton Professor of Agricultural Marketing in the Department of Agricultural and Consumer Economics at the University of Illinois at Urbana- Champaign. Funding support from the Aurene T. Norton Trust is gratefully acknowledged. The Profitability of Technical Analysis: A Review Abstract The purpose of this report is to review the evidence on the profitability of technical analysis. To achieve this purpose, the report comprehensively reviews survey, theoretical and empirical studies regarding technical trading strategies. We begin by overviewing survey studies that have directly investigated market participants’ experience and views on technical analysis. The survey literature indicates that technical analysis has been widely used by market participants in futures markets and foreign exchange markets, and that about 30% to 40% of practitioners appear to believe that technical analysis is an important factor in determining price movement at shorter time horizons up to 6 months. Then we provide an overview of theoretical models that include implications about the profitability of technical analysis. Conventional efficient market theories, such as the martingale model and random walk models, rule out the possibility of technical trading profits in speculative markets, while relatively recent models such as noisy rational expectation models or behavioral models suggest that technical trading strategies may be profitable due to noise in the market or investors’ irrational behavior. Finally, empirical studies are surveyed. In this report, the empirical literature is categorized into two groups, “early” and “modern” studies, according to the characteristics of testing procedures. Early studies indicated that technical trading strategies were profitable in foreign exchange markets and futures markets, but not in stock markets before the 1980s. Modern studies indicated that technical trading strategies consistently generated economic profits in a variety of speculative markets at least until the early 1990s. Among a total of 92 modern studies, 58 studies found positive results regarding technical trading strategies, while 24 studies obtained negative results. Ten studies indicated mixed results. Despite the positive evidence on the profitability of technical trading strategies, it appears that most empirical studies are subject to various problems in their testing procedures, e.g., data snooping, ex post selection of trading rules or search technologies, and difficulties in estimation of risk and transaction costs. Future research must address these deficiencies in testing in order to provide conclusive evidence on the profitability of technical trading strategies. i The Profitability of Technical Analysis: A Review Table of Contents Introduction ………………………………………………………...…………………...……. 1 Survey Studies ……………………………………………………...…………………...……. 3 Theory ………………………………………………………………………………………... 5 The Efficient Markets Hypothesis ……………………………………………………...…. 5 The Martingale Model ………………………………………………………………...... 7 Random Walk Models ………………………..……………………………………..….. 8 Noisy Rational Expectations Models .…………………………………………..………... 9 Noise Traders and Feedback Models ………………………………………..…………….. 12 Other Models ………………………………………………………….…………………… 13 Summary of Theory ……………………………………………………………………….. 15 Empirical Studies …………………………………………………………………………….. 16 Technical Trading Systems ………………………………………………………………... 17 Dual Moving Average Crossover ………………………………………………………. 18 Outside Price Channel …………………………………………………………………... 18 Relative Strength Index …………………………………………………………………. 19 Alexander’s Filter Rule ………………………………………………………………... 20 Early Empirical Studies (1960-1987) ……………………………………………………... 20 Overview ………………………………………….…………………………………….. 20 Representative Early Studies ……………………………...……………………………. 21 Summary of Early Studies ………………………………...……………………………. 24 Modern Empirical Studies (1988-2004) …………………………………………………... 26 Overview ………………………………………………………………….…………….. 26 Representative Modern Studies …………………………………………...……………. 26 Standard Studies ……………………………………………………………...………. 26 Model-Based Bootstrap Studies …………………………………………….………... 28 Genetic Programming Studies ………………………………………………..……… 33 Reality Check Studies ……………………………………………..…………………. 37 Chart Pattern Studies …………………………………………..……………………... 41 Nonlinear Studies ………….…………………………………...…………………….. 43 Other Studies ………………………………………………...……………………….. 45 Summary of Modern Studies ………………………...……………...………………….. 46 Summary and Conclusion …..………………………………………………..………………. 49 References ……………………………………………………………………………………. 55 Tables and Figures …………………………………………………………………………… 71 ii The Profitability of Technical Analysis: A Review Introduction Technical analysis is a forecasting method of price movements using past prices, volume, and open interest.2 Pring (2002), a leading technical analyst, provides a more specific definition: “The technical approach to investment is essentially a reflection of the idea that prices move in trends that are determined by the changing attitudes of investors toward a variety of economic, monetary, political, and psychological forces. The art of technical analysis, for it is an art, is to identify a trend reversal at a relatively early stage and ride on that trend until the weight of the evidence shows or proves that the trend has reversed.” (p. 2) Technical analysis includes a variety of forecasting techniques such as chart analysis, pattern recognition analysis, seasonality and cycle analysis, and computerized technical trading systems. However, academic research on technical analysis is generally limited to techniques that can be expressed in mathematical forms, namely technical trading systems, although some recent studies attempt to test visual chart patterns using pattern recognition algorithms. A technical trading system consists of a set of trading rules that result from parameterizations, and each trading rule generates trading signals (long, short, or out of market) according to their parameter values. Several popular technical trading systems are moving averages, channels, and momentum oscillators. Since Charles H. Dow first introduced the Dow theory in the late 1800s, technical analysis has been extensively used among market participants such as brokers, dealers, fund managers, speculators, and individual investors in the financial industry.3 Numerous surveys indicate that practitioners attribute a significant role to technical analysis. For example, futures fund managers rely heavily on computer-guided technical trading systems (Irwin and Brorsen 1985; Brorsen and Irwin 1987; Billingsley and Chance 1996), and about 30% to 40% of foreign exchange traders around the world believe that technical analysis is the major factor determining exchange rates in the short-run up to six months (e.g., Menkhoff 1997; Cheung and Wong 2000; Cheung, Chinn, and Marsh 2000; Cheung and Chinn 2001). In contrast to the views of many practitioners, most academics are skeptical about technical analysis. Rather, they tend to believe that markets are informationally efficient and hence all available information is impounded in current prices (Fama 1970). In efficient markets, therefore, any attempts to make profits by exploiting currently available information are futile. In a famous passage, Samuelson (1965) argues that: 2 In futures markets, open interest is defined as “the total number of open transactions” (Leuthold, Junkus, and Cordier 1989). 3 In fact, the history of technical analysis dates back to at least the 18th century when the Japanese developed a form of technical analysis known as candlestick charting techniques. This technique was not introduced to the West until the 1970s (Nison 1991). “…there is no way of making an expected profit by extrapolating past changes in the futures price, by chart or any other esoteric devices of magic or mathematics. The market quotation already contains in itself all that can be known about the future and in that sense has discounted future contingencies as much as is humanly possible.” (p. 44) Nevertheless, in recent decades rigorous theoretical explanations for the widespread use of technical analysis have been developed based on noisy rational expectation models (Treynor and Ferguson 1985; Brown and Jennings 1989; Grundy and McNichols 1989; Blume, Easley, and O’Hara 1994), behavioral (or feedback) models (De Long et al. 1990a, 1991; Shleifer and Summers 1990), disequilibrium models (Beja and Goldman 1980), herding models (Froot, Scharfstein, and Stein 1992), agent-based models (Schmidt 2002), and chaos theory (Clyde and Osler 1997). For example, Brown and Jennings (1989) demonstrated that under a noisy rational expectations model in which current prices do not fully reveal private information (signals) because of noise (unobserved current supply of a risky asset) in the current equilibrium price, historical prices (i.e., technical analysis) together with current prices help traders make more precise inferences about past and present signals than do current prices alone (p. 527). Since Donchian (1960), numerous empirical studies have tested the profitability of technical trading rules
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