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Technical Analysis -- History and Methodology


"Technical analysis is the study of market action, primarily through the use of charts, for the purpose of forecasting future price trends."[1]

In its purest form, technical analysis considers only the actual price behavior of the market or instrument, based on the premise that price reflects all relevant factors before an investor becomes aware of them through other channels.

Technical analysis is widely used among traders and financial professionals, and some studies say its use is more widespread than is "fundamental" analysis in the foreign exchange market.[2][3] Academics such as Eugene Fama say the evidence for technical analysis is sparse and is refuted by the efficient market hypothesis,[4][5] yet some Federal Reserve and academic studies include evidence that supports technical analysis.[6][7][8] MIT finance professor Andrew Lo argues that "several academic studies suggest that technical analysis may well be an effective means for extracting useful information from market prices."[9] Burton Malkiel argues, "Technical analysis is an anathema to the academic world." He further argues that under the weak form of the efficient market hypothesis, "...you cannot predict future stock prices from past stock prices."[10]

General description

Technical analysts (or technicians) identify non-random price patterns and trends in financial markets and attempt to exploit those patterns [11] While technicians use various methods and tools, the study of price charts is primary. Technicians especially search for archetypal patterns, such as the well-known head and shoulders reversal pattern, and also study such indicators as price, volume, and moving averages of the price. Many technical analysts also follow indicators of investor psychology (market sentiment).

Essentially, technical analysis examines two areas of investing: the analysis of market "psych" (or sentiment), and the analysis of supply/demand (whether investors have the funds to support their hopes and fears). A bullish investor without funds cannot take the market higher.

Technicians seek to forecast price movements such that large gains from successful trades exceed more numerous but smaller losing trades, producing positive returns in the long run through proper risk control and money management.

There are several schools of technical analysis. Adherents of different schools (for example, candlestick charting, Dow Theory, and Elliott wave theory) may ignore the other approaches, yet many traders combine elements from more than one school. Technical analysts use judgment gained from experience to decide which pattern a particular instrument reflects at a given time, and what the interpretation of that pattern should be. Technical analysts may disagree among themselves over the interpretation of a given chart.

Technical analysis is frequently contrasted with fundamental analysis, the study of economic factors that some analysts say can influence prices in financial markets. Pure technical analysis holds that prices already reflect all such influences before investors are aware of them, hence the study of price action alone. Some traders use technical or fundamental analysis exclusively, while others use both types to make trading decisions.


The principles of technical analysis derive from the observation of financial markets over hundreds of years.[citation needed] The oldest known example of technical analysis was a method used by Japanese traders as early as the 18th century, which evolved into the use of candlestick techniques, and is today a main charting tool.[12][13]

Dow Theory is based on the collected writings of Dow Jones co-founder and editor Charles Dow, and inspired the use and development of modern technical analysis from the end of the 19th century. Modern technical analysis considers Dow Theory its cornerstone.[14]

Many more technical tools and theories have been developed and enhanced in recent decades, with an increasing emphasis on computer-assisted techniques.

Principles of technical analysis

Technicians say that a market's price reflects all relevant information, so their analysis looks more at "internals" than at "externals" such as news events. Price action also tends to repeat itself because investors collectively tend toward patterned behavior -- hence technicians' focus on identifiable trends and conditions.

Market action discounts everything

Based on the premise that all relevant information is already reflected by prices, technical analysts believe it is redundant to do fundamental analysis -- they say news and news events do not significantly influence price, and cite supporting research such as the study by Cutler, Poterba, and Summers titled "What Moves Stock Prices?"

On most of the sizable return days [large market moves the information that the press cites as the cause of the market move is not particularly important. Press reports on adjacent days also fail to reveal any convincing accounts of why future profits or discount rates might have changed. Our inability to identify the fundamental shocks that accounted for these significant market moves is difficult to reconcile with the view that such shocks account for most of the variation in stock returns. [15]

Prices move in trends

Technical analysts believe that prices trend. Technicians say that markets trend up, down, or sideways (flat). This basic definition of price trends is the one put forward by Dow Theory.[11]

An example of a security that had an apparent trend is AOL from November 2001 through August 2002. A technical analyst or trend follower recognizing this trend would look for opportunities to sell this security. AOL consistently moves downward in price. Each time the stock rose, sellers would enter the market and sell the stock; hence the "zig-zag" movement in the price. The series of "lower highs" and "lower lows" is a tell tale sign of a stock in a down trend.[16] In other words, each time the stock edged lower, it fell below its previous relative low price. Each time the stock moved higher, it could not reach the level of its previous relative high price.

Note that the sequence of lower lows and lower highs did not begin until August. Then AOL makes a low price that doesn't pierce the relative low set earlier in the month. Later in the same month, the stock makes a relative high equal to the most recent relative high. In this a technician sees strong indications that the down trend is at least pausing and possibly ending, and would likely stop actively selling the stock at that point.

History tends to repeat itself

Technical analysts believe that investors collectively repeat the behavior of the investors that preceded them. "Everyone wants in on the next Microsoft," "If this stock ever gets to $50 again, I will buy it," "This company's technology will revolutionize its industry, therefore this stock will skyrocket" -- these are all examples of investor sentiment repeating itself. To a technician, the emotions in the market may be irrational, but they exist. Because investor behavior does repeat itself so often, technicians believe that recognizable (and predictable) price patterns will develop on a chart.[11]

Technical analysis is not limited to charting, yet is always concerned with price trends. For example, many technicians monitor surveys of investor sentiment. These surveys gauge the attitude of market participants, specifically whether they are bearish or bullish. Technicians use these surveys to help determine whether a trend will continue or if a reversal could develop; they are most likely to anticipate a change when the surveys report extreme investor sentiment. Surveys that show overwhelming bullishness, for example, are evidence that an uptrend may reverse -- the premise being that if most investors are bullish they have already bought the market (anticipating higher prices). And because most investors are bullish and invested, one assumes that few buyers remain. This leaves more potential sellers than buyers, despite the bullish sentiment. This suggests that prices will trend down, and is an example of contrarian trading.


The Wall Street Journal Europe states "Whether technical analysis is really useful ... is a matter of some dispute on Wall Street. Some investors believe that it is impossible to forecast the market's ups and downs. Academic studies have shown that when most people, professionals and amateurs alike, try to move money in and out of stocks to beat market fluctuations, they tend to wind up with losses."[17] The same article shows how several technical analysts can simultaneously make contradictory predictions.

Lack of evidence

Critics of technical analysis include well known fundamental analysts. For example, Peter Lynch once commented, "Charts are great for predicting the past." Warren Buffett has said, "I realized technical analysis didn't work when I turned the charts upside down and didn't get a different answer" and "If past history was all there was to the game, the richest people would be librarians."[1]

Some academic studies say technical analysis has little predictive power, but other studies say it may produce excess returns. For example, measurable forms of technical analysis, such as non-linear prediction using neural networks, have been shown to occasionally produce statistically significant prediction results.[18] A Federal Reserve working paper[7] regarding support and resistance levels in short-term foreign exchange rates "offers strong evidence that the levels help to predict intraday trend interruptions," although the "predictive power" of those levels was "found to vary across the exchange rates and firms examined."

Cheol-Ho Park and Scott H. Irwin reviewed 95 modern studies on the profitability of technical analysis and said 56 of them find positive results, 20 obtain negative results, and 19 indicate mixed results: "Despite the positive evidence...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."[19]

However, a comprehensive study of the question by Amsterdam economist Gerwin Griffioen concludes that: "for the U.S., Japanese and most Western European stock market indices the recursive out-of-sample forecasting procedure does not show to be profitable, after implementing little transaction costs. Moreover, for sufficiently high transaction costs it is found, by estimating CAPMs, that technical trading shows no statistically significant risk-corrected out-of-sample forecasting power for almost all of the stock market indices."[5]

Efficient market hypothesis

The efficient market hypothesis (EMH) contradicts the basic tenets of technical analysis, by stating that past prices cannot be used to profitably predict future prices. Thus it holds that technical analysis cannot be effective. Economist Eugene Fama published the seminal paper on the EMH in the Journal of Finance in 1970, and said "In short, the evidence in support of the efficient markets model is extensive, and (somewhat uniquely in economics) contradictory evidence is sparse." [20] EMH advocates say that if prices quickly reflect all relevant information, no method (including technical analysis) can "beat the market." Developments which influence prices occur randomly and are unknowable in advance.

Technicians say that EMH ignores the way markets work, in that many investors base their expectations on past earnings, track record, etc. Because future stock prices can be strongly influenced by investor expectations, technicians claim it only follows that past prices influence future prices.[21] They also point to research in the field of behavioral finance, specifically that people are not the rational participants EMH makes them out to be. Technicians have long said that irrational human behavior influences stock prices, and that this behavior leads to predictable outcomes.[22] Author David Aronson says that the theory of behavioral finance blends with the practice of technical analysis:

By considering the impact of emotions, cognitive errors, irrational preferences, and the dynamics of group behavior, behavioral finance offers succinct explanations of excess market volatility as well as the excess returns earned by stale information strategies…. cognitive errors may also explain the existence of market inefficiencies that spawn the systematic price movements that allow objective TA [technical analysis] methods to work.[21]

EMH advocates reply that while individual market participants do not always act rationally (or have complete information), their aggregate decisions balance each other, resulting in a rational outcome (optimists who buy stock and bid the price higher are countered by pessimists who sell their stock, which keeps the price in equilibrium).[23] Likewise, complete information is reflected in the price because all market participants bring their own individual, but incomplete, knowledge together in the market.[23]

Random walk hypothesis

The random walk hypothesis may be derived from the weak-form efficient markets hypothesis, which is based on the assumption that market participants take full account of any information contained in past price movements (but not necessarily other public information). In his book A Random Walk Down Wall Street, Princeton economist Burton Malkiel said that technical forecasting tools such as pattern analysis must ultimately be self-defeating: "The problem is that once such a regularity is known to market participants, people will act in such a way that prevents it from happening in the future." [24]

Technicians say the EMH and Random Walk theories both ignore the realities of markets, in that participants are not completely rational (they can be greedy, overly risky, etc.) and that current price moves are not independent of previous moves (technicians point to charts similar to AOL above.)[16][25] Critics reply that one can find virtually any chart pattern after the fact, but that this does not prove that such patterns are predictable. Technicians maintain that both theories would also invalidate numerous other trading strategies such as index arbitrage, statistical arbitrage and many other trading systems.[21]


Globally, the industry is represented by The International Federation of Technical Analysts (IFTA). In the United States the industry is represented by two national organizations: the Market Technicians Association (MTA), and the American Association of Professional Technical Analysts (AAPTA). In Canada the industry is represented by the Canadian Society of Technical Analysts.

Use of technical analysis

Many traders say that trading in the direction of the trend is the most effective means to be profitable in financial or commodities markets. John W. Henry, Larry Hite, Ed Seykota, Richard Dennis, William Eckhardt, Victor Sperandeo, Michael Marcus and Paul Tudor Jones (some of the so-called Market Wizards in the popular book of the same name by Jack D. Schwager) have each amassed massive fortunes via the use of technical analysis and its concepts. George Lane, a technical analyst, coined one of the most popular phrases on Wall Street, "The trend is your friend!"

Many non-arbitrage algorithmic trading systems rely on the idea of trend-following, as do many hedge funds. A relatively recent trend, both in research and industrial practice, has been the development of increasingly sophisticated automated trading strategies. These often rely on underlying technical analysis principles (see algorithmic trading article for an overview).

Systematic trading and technical analysis

Neural networks

Since the early 90's when the first practically usable types emerged, artificial neural networks (ANNs) have rapidly grown in popularity. They are artificial intelligence adaptive software systems that have been inspired by how biological neural networks work. Their use comes in because they can learn to detect complex patterns in data. In mathematical terms, they are universal non-linear function approximators[26] [27] meaning that given the right data and configured correctly, they can capture and model any input-output relationships. This not only removes the need for human interpretation of charts or the series of rules for generating entry/exit signals but also provides a bridge to fundamental analysis as the variables used in fundamental analysis can be used as input.

In addition, as ANNs are essentially non-linear statistical models, their accuracy and prediction capabilities can be both mathematically and empirically tested. In various studies neural networks used for generating trading signals have significantly outperformed buy-hold strategies as well as traditional linear technical analysis methods.[28] [29] [30]

While the advanced mathematical nature of such adaptive systems have kept neural networks for financial analysis mostly within academic research circles, in recent years more user friendly neural network software has made the technology more accessible to traders.

Rule-based trading

Rule-based trading is an approach to make one's trading plans by strict and clear-cut rules. Unlike some other technical methods or most fundamental analysis, it defines a set of rules that determines all trades, leaving minimal discretion.

For instance, a trader might make a set of rules stating that he will take a long position whenever the price of a particular instrument closes above its 50-day moving average, and shorting it whenever it drops below.

Combining Technical Analysis with other Market Forecast Methods

John Murphy in his book "Technical Analysis of the Financial Markets", says that the principal sources of information available to technicians are price, volume and open interest. Other data, such as indicators and sentiment analysis are considered secondary.

However, many technical analysts reach outside pure technical analysis, combining other market forecast methods with their technical work. One such approach, known as Fusion Analysis [3002.html] overlays fundamental with technical analysis, in an attempt to improve portfolio manager performance. Another advocate for this approach is John Bollinger, who coined the term Rational Analysis as the intersection of technical analysis and fundamental analysis[capital growth letter.htm].

Technical analysis is also often combined with quantitative analysis and economics.For example, neural networks may be used to help identify intermarket relationships [[2]]. A few market forecasters combine financial astrology with technical analysis. Chris Carolan's article "Autumn Panics and Calendar Phenomenon," which won the Market Technicians Association Dow Award for best technical analysis paper in 1998, demonstrates how technical analysis and lunar cycles can be combined [[3]].

Investor and newsletter polls, and magazine cover sentiment indicators, are also used by technical and market analysts. [[4]]

Charting terms and indicators

Widely-known technical analysis concepts include:


  • Ichimoku Charts, Nicole Elliott, Harriman House, 2007
  • Getting Started in Technical Analysis, Jack D. Schwager, Wiley, 1999
  • New Concepts in Technical Trading Systems, J. Welles Wilder, Trend Research, 1978
  • Reminiscences of a Stock Operator, Edwin Lefevre, John Wiley & Sons Inc, 1994
  • Street Smarts, Connors/Raschke, 1995
  • Technical Analysis: The Complete Resource for Financial Market Technicians, Kirkpatrick/Dahlquist, 2007
  • Technical Analysis of Futures Markets, John J. Murphy, New York Institute of Finance, 1986
  • Technical Analysis of Stock Trends, 8th Edition (Hardcover), Robert D. Edwards, John Magee, W. H. C. Bassetti (Editor), American Management Association, 2001
  • Technical Analysis of the Financial Markets, John J. Murphy, New York Institute of Finance, 1999,
  • The Free E-Book of Technical Analysis, Wallstreetcourier, [5]
  • The Profit Magic of Stock Transaction Timing, J.M. Hurst, Prentice-Hall, 1972


  1. John J. Murphy, Technical Analysis of the Futures Markets (New York Institute of Finance, 1986), page 1.
  2. Taylor, Mark P., and Helen Allen (1992). "The Use of Technical Analysis in the Foreign Exchange Market," Journal of International Money and Finance, 11(3), 304–314.
  3. Cross, Sam Y. (1998). All About the Foreign Exchange Market in the United States, Federal Reserve Bank of New York chapter 11, pp. 113-115.
  4. Fama, Eugene (May 1970). "Efficient Capital Markets: A Review of Theory and Empirical Work," The Journal of Finance, v. 25 (2), pp. 383-417.,
  5. Griffioen, Technical Analysis in Financial Markets
  6. Brock, William, Josef Lakonishok and Blake Lebaron (1992). "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," The Journal of Finance, 47(5), pp. 1731-1764.
  7. Osler, Karen (July 2000). "Support for Resistance: Technical Analysis and Intraday Exchange Rates," FRBNY Economic Policy Review (abstract and paper here).
  8. Neely, Christopher J., and Paul A. Weller (2001). "Technical analysis and Central Bank Intervention," Journal of International Money and Finance, 20 (7), 94-70 (abstract and paper here).
  9. Lo, Andrew W., Harry Mamaysky and Jiang Wang (2000). "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, v. 55 (abstract and paper here), pp. 1705-1765.
  10. Burton Malkiel, A Random Walk Down Wall Street pp. 118, 139, 165
  11. John J. Murphy, Technical Analysis of the Financial Markets (New York Institute of Finance, 1999), pages 1-5,24-31.
  12. Nison, Steve (1991). Japanese Candlestick Charting Techniques, 15 -18. 
  13. Nison, Steve (1994). Beyond Candlesticks: New Japanese Charting Techniques Revealed, John Wiley and Sons, p. 14.
  14. Hill, Arthur. Dow Theory.
  15. David M. Cutler, James M. Poterba, Lawrence H. Summers, "What Moves Stock Prices?", NBER Working Paper #2538 (March 1988), pp 13-14.
  16. Kahn, Michael N. (2006). Technical Analysis Plain and Simple: Charting the Markets in Your Language, Financial Times Press, Upper Saddle River, New Jersey, p. 80.
  17. Browning, E.S.. "Reading market tea leaves", The Wall Street Journal Europe, Dow Jones, July 31, 2007, pp. 17-18. 
  18. Skabar, Cloete, Networks, Financial Trading and the Efficient Markets Hypothesis
  19. Cheol-Ho Park and Scott H. Irwin, What Do We Know about the Profitability of Technical Analysis? (March 2006).
  20. Eugene Fama, "Efficient Capital Markets: A Review of Theory and Empirical Work," The Journal of Finance, volume 25, issue 2 (May 1970), pp. 383-417.
  21. Aronson, David R. (2006). Evidence-Based Technical Analysis, Hoboken, New Jersey: John Wiley and Sons, pages 357, 355-356, 342.
  22. Prechter, Robert R., Jr., and Wayne D. Parker (2007). "The Financial/Economic Dichotomy in Social Behavioral Dynamics: The Socionomic Perspective," Journal of Behavioral Finance, vol. 8 no. 2 (abstract here), pp. 84-108.
  23. Clarke, J., T. Jandik, and Gershon Mandelker (2001). “The efficient markets hypothesis,” Expert Financial Planning: Advice from Industry Leaders, ed. R. Arffa, 126-141. New York: Wiley & Sons.
  24. Burton Malkiel, A Random Walk Down Wall Street, W. W. Norton & Company (April 2003) p. 168.
  25. Poser, Steven W. (2003). Applying Elliott Wave Theory Profitably, John Wiley and Sons, p. 71.
  26. K. Funahashi, On the approximate realization of continuous mappings by neural networks, Neural Networks vol 2, 1989
  27. K. Hornik, Multilayer feed-forward networks are universal approximators, Neural Networks, vol 2, 1989
  28. R. Lawrence. Using Neural Networks to Forecast Stock Market Prices
  29. B.Egeli et al. Stock Market Prediction Using Artificial Neural Networks
  30. M. Zeki. Neural Network Applications in Stock Market Predictions - A Methodology Analysis

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