Technical analysis

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 v • d • e 

In finance, technical analysis is a security analysis discipline for forecasting the direction of prices through the study of past market data, primarily price and volume.^[1]

Contents

• 1 History
• 2 General description
• 3 Characteristics
• 4 Principles
  • 4.1 Market action discounts everything
  • 4.2 Prices move in trends
  • 4.3 History tends to repeat itself
• 5 Industry
• 6 Use
• 7 Systematic trading
  • 7.1 Neural networks
  • 7.2 Rule-based trading
• 8 Combination with other market forecast methods
• 9 Charting terms and indicators
  • 9.1 Types of charts
  • 9.2 Concepts
  • 9.3 Overlays
  • 9.4 Price-based indicators
  • 9.5 Volume-based indicators
• 10 Empirical evidence
  • 10.1 Efficient market hypothesis
    • 10.1.1 Random walk hypothesis
• 11 See also
• 12 Notes
• 13 Books
• 14 External links

[edit] History

The principles of technical analysis derive from the observation of financial markets over hundreds of years.^[2] The oldest known hints of technical analysis appear in Joseph de la Vega'saccounts of the Dutch markets in the 17th century. In Asia, the oldestexample of technical analysis is thought to be a method developed by Homma Munehisa during early 18th century which evolved into the use of candlestick techniques, and is today a main charting tool.^[3][4]

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 fromthe end of the 19th century. Other pioneers of analysis techniquesinclude Ralph Nelson Elliott, William Delbert Gann and Richard Wyckoff who developed their respective techniques in the early 20th century.

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

This section does not cite any references or sources.
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[edit] General description

While fundamental analysts examine earnings, dividends, newproducts, research and the like, technical analysts examine whatinvestors think about those developments and whether or not investorshave the wherewithall to back up their opinions; these two concepts arecalled psych (psychology) and supply/demand. In the M = P/E equation,technicians assess M, the multiple investors do/may pay - if they havethe money - for the fundamentals they envision. They employ manytechniques, one of which is the use of charts. Using charts, technicalanalysts seek to identify price patterns and trends in financial markets and attempt to exploit those patterns.^[5]While technicians use various methods and tools, the study of pricecharts is but one. Other techniques are the use of indicators of thestate of psych - are investors bullish or bearish? - and are theywilling to spend money to back up their beliefs. A spent-out bullcannot move the market higher, and vice versa; investors need to knowwhich they are facing. In the end, stock prices are only what investorsthink; determining what they think is every bit as critical as anearnings estimate.

Technicians especially search for archetypal price chart patterns, such as the well-known head and shoulders or double top reversal patterns, study indicators, moving averages, and look for forms such as lines of support, resistance, channels, and more obscure formations such as flags, pennants, balance days and cup and handle patterns.

Technical analysts also extensively use indicators, some of whichare mathematical transformations of price, often including volume.These indicators are used to help determine whether an asset istrending, and if it is, its price direction and probablility ofcontinuation. Technicians also look for relationships betweenprice/volume indices and market indicators. Examples include the relative strength index, and MACD. Other avenues of study include correlations between changes in options (implied volatility)and put/call ratios with price. Also important are sentiment indicatorssuch as Put/Call ratios, bull/bear ratios, short interest and ImpliedVolatility, etc.

There are several techniques of technical analysis. Adherents of different techniques (for example, candlestick charting, Dow Theory, and Elliott wave theory)may ignore the other approaches, yet many traders combine elements frommore than one technique. Some technical analysts use subjectivejudgment to decide which pattern a particular instrument reflects at agiven time, and what the interpretation of that pattern should be.Others employ a strictly mechanical or systematic approach to patternidentification and interpretation.

Technical analysis is frequently contrasted with fundamental analysis, the study of economicfactors that influence the way investors price financial markets.Technical analysis holds that prices already reflect all suchinfluences before investors are aware of them. Uncovering thoseinfluences is what technical indicators are designed to do, imperfectas they may be. Understandably, fundamental indicators are equally not100% accurate either. Some traders use technical or fundamentalanalysis exclusively, while others use both types to make tradingdecisions which conceivably is the most rational approach.

Users of technical analysis are most often called technicians ormarket technicians. Some prefer the term technical market analyst orsimply market analyst. An older term, chartist, is sometimes used, butas the discipline has expanded and modernized, the use of the termchartist has become less popular, as it is only one aspect of technicalanalysis.^{[citation needed]}

[edit] Characteristics

Technical analysis employs models and trading rules based on price and volume transformations, such as the relative strength index, moving averages, regressions, inter-market and intra-market price correlations, cycles or, classically, through recognition of chart patterns.

Technical analysis stands in contrast to the fundamental analysisapproach to security and stock analysis. Technical analysis "ignores"the actual nature of the company, market, currency or commodity and isbased solely on "the charts," that is to say price and volumeinformation, whereas fundamental analysis does look at the actual factsof the company, market, currency or commodity. For example, any largebrokerage, trading group, or financial institution will typically haveboth a technical analysis and fundamental analysis team.

Technical analysis is widely used among traders and financialprofessionals, and is very often used by active day traders, marketmakers, and pit traders. In the 1960s and 1970s it was widely dismissedby academics. In a recent review, Irwin and Park^[6]reported that 56 of 95 modern studies found it produces positiveresults, but noted that many of the positive results were rendereddubious by issues such as data snooping so that the evidence in support of technical analysis was inconclusive; it is still considered by many academics to be pseudoscience.^[7] Academics such as Eugene Fama say the evidence for technical analysis is sparse and is inconsistent with the weak form of the efficient-market hypothesis.^[8][9] Users hold that even if technical analysis cannot predict the future, it helps to identify trading opportunities.^[10]

In the foreign exchange markets, its use may be more widespread than fundamental analysis.^[11][12]While some isolated studies have indicated that technical trading rulesmight lead to consistent returns in the period prior to 1987,^{[13][14][15][16]} most academic work has focused on the nature of the anomalous position of the foreign exchange market.^[17] It is speculated that this anomaly is due to central bank intervention.^[18]Recent research suggests that combining various trading signals into aCombined Signal Approach may be able to increase profitability andreduce dependence on any single rule.^[19]

[edit] Principles

Stock chart showing levels of support (4,5,6, 7, and 8) and resistance(1, 2, and 3); levels of resistance tend to become levels of supportand vice versa.

Technicians say that a market's price reflects all relevantinformation, so their analysis looks at the history of a security'strading pattern rather than external drivers such as economic,fundamental and news events. Price action also tends to repeat itselfbecause investors collectively tend toward patterned behavior – hencetechnicians' focus on identifiable trends and conditions.

[edit] Market action discounts everything

Based on the premise that all relevant information is alreadyreflected by prices, pure technical analysts believe it is redundant todo 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]...theinformation that the press cites as the cause of the market move is notparticularly important. Press reports on adjacent days also fail toreveal any convincing accounts of why future profits or discount ratesmight have changed. Our inability to identify the fundamental shocksthat accounted for these significant market moves is difficult toreconcile with the view that such shocks account for most of thevariation in stock returns.^[20]

[edit] Prices move in trends

See also: Market trend

Technical analysts believe that prices trend directionally, i.e.,up, down, or sideways (flat) or some combination. The basic definitionof a price trend was originally put forward by Dow Theory.^[5]

An example of a security that had an apparent trend is AOL fromNovember 2001 through August 2002. A technical analyst or trendfollower recognizing this trend would look for opportunities to sellthis security. AOL consistently moves downward in price. Each time thestock rose, sellers would enter the market and sell the stock; hencethe "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.^[21]In other words, each time the stock moved lower, it fell below itsprevious relative low price. Each time the stock moved higher, it couldnot reach the level of its previous relative high price.

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

[edit] History tends to repeat itself

Technical analysts believe that investors collectively repeat thebehavior of the investors that preceded them. "Everyone wants in on thenext Microsoft," "If this stock ever gets to $50 again, I will buy it,""This company's technology will revolutionize its industry, thereforethis stock will skyrocket" – these are all examples of investorsentiment repeating itself. To a technician, the emotions in the marketmay be irrational, but they exist. Because investor behavior repeatsitself so often, technicians believe that recognizable (andpredictable) price patterns will develop on a chart.^[5]

Technical analysis is not limited to charting, but it alwaysconsiders price trends. For example, many technicians monitor surveysof investor sentiment. These surveys gauge the attitude of marketparticipants, specifically whether they are bearish or bullish.Technicians use these surveys to help determine whether a trend willcontinue or if a reversal could develop; they are most likely toanticipate a change when the surveys report extreme investor sentiment.Surveys that show overwhelming bullishness, for example, are evidencethat an uptrend may reverse – the premise being that if most investorsare bullish they have already bought the market (anticipating higherprices). And because most investors are bullish and invested,one assumes that few buyers remain. This leaves more potential sellersthan buyers, despite the bullish sentiment. This suggests that priceswill trend down, and is an example of contrarian trading.

[edit] Industry

The industry is globally represented by the International Federation of Technical Analysts (IFTA), which is a Federation of regional and national organizations and the Market Technicians Association (MTA). In the United States, the industry is represented by both the Market Technicians Association (MTA) and the American Association of Professional Technical Analysts (AAPTA). The United States is also represented by the Technical Security Analysts Association of San Francisco (TSAASF). In the United Kingdom, the industry is represented by the Society of Technical Analysts (STA). In Canada the industry is represented by the Canadian Society of Technical Analysts. Some other national professional technical analysis organizations are noted in the External Links section below.

Professional technical analysis societies have worked on creating abody of knowledge that describes the field of Technical Analysis. Abody of knowledge is central to the field as a way of defining how andwhy technical analysis may work. It can then be used by academia, aswell as regulatory bodies, in developing proper research and standardsfor the field. The Market Technicians Association (MTA) has published a body of knowledge, which is the structure for the MTA's Chartered Market Technician (CMT) exam.

[edit] Use

Traders generally share the view 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 automatedtrading strategies. These often rely on underlying technical analysisprinciples (see algorithmic trading article for an overview).

[edit] Systematic trading

[edit] Neural networks

Since the early 1990s when the first practically usable types emerged, artificial neural networks (ANNs) have rapidly grown in popularity. They are artificial intelligenceadaptive software systems that have been inspired by how biologicalneural networks work. They are used because they can learn to detectcomplex patterns in data. In mathematical terms, they are universal function approximators,^[22][23]meaning that given the right data and configured correctly, they cancapture and model any input-output relationships. This not only removesthe need for human interpretation of charts or the series of rules forgenerating entry/exit signals, but also provides a bridge to fundamental analysis, as the variables used in fundamental analysis can be used as input.

As ANNs are essentially non-linear statistical models, theiraccuracy and prediction capabilities can be both mathematically andempirically tested. In various studies, authors have claimed thatneural networks used for generating trading signals given varioustechnical and fundamental inputs have significantly outperformedbuy-hold strategies as well as traditional linear technical analysismethods when combined with rule-based expert systems.^[24][25][26]

While the advanced mathematical nature of such adaptive systems haskept neural networks for financial analysis mostly within academicresearch circles, in recent years more user friendly neural network softwarehas made the technology more accessible to traders. However,large-scale application is problematic because of the problem ofmatching the correct neural topology to the market being studied.

[edit] Rule-based trading

Rule-based trading is an approach intended to create trading plansusing strict and clear-cut rules. Unlike some other technical methodsand the approach of fundamental analysis, it defines a set of rulesthat determine all trades, leaving minimal discretion. The theorybehind this approach is that by following a distinct set of tradingrules you will reduce the number of poor decisions, which are oftenemotion based.

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

[edit] Combination with other market forecast methods

John Murphy states that the principal sources of information available to technicians are price, volume and open interest.^[27] Other data, such as indicators and sentiment analysis, are considered secondary.

However, many technical analysts reach outside pure technicalanalysis, combining other market forecast methods with their technicalwork. One advocate for this approach is John Bollinger, who coined theterm rational analysis in the middle 1980s for the intersection of technical analysis and fundamental analysis.[1] Another such approach, fusion analysis, [2] overlays fundamental analysis with technical, in an attempt to improve portfolio manager performance.

Technical analysis is also often combined with quantitative analysis and economics. For example, neural networks may be used to help identify intermarket relationships.[3] A few market forecasters combine financial astrologywith technical analysis. Chris Carolan's article "Autumn Panics andCalendar Phenomenon", which won the Market Technicians Association DowAward for best technical analysis paper in 1998, demonstrates howtechnical analysis and lunar cycles can be combined.[4] Calendar phenomena, such as the January effectin the stock market, are generally believed to be caused by tax andaccounting related transactions, and are not related to the subject of financial astrology.

Investor and newsletter polls, and magazine cover sentiment indicators, are also used by technical analysts.[5]

[edit] Charting terms and indicators

[edit] Types of charts

• OHLC "Bar Charts" — Open-High-Low-Close charts, also known as bar charts, plot the span between the high and low prices of a trading period as a vertical line segment at the trading time, and the open and close prices with horizontal tick marks on the range line, usually a tick to the left for the open price and a tick to the right for the closing price.
• Candlestick chart — Of Japanese origin and similar to OHLC, candlesticks widen and fill the interval between the open and close prices to emphasize the open/close relationship. In the West, often black or red candle bodies represent a close lower than the open, while white, green or blue candles represent a close higher than the open price.
• Line chart — Connects the closing price values with line segments.
• Point and figure chart — a chart type employing numerical filters with only passing references to time, and which ignores time entirely in its construction.

[edit] Concepts

• Resistance — a price level which acts as a ceiling above prices
• Support — a price level which acts as a floor below prices
• Breakout — the concept whereby prices forcefully penetrate an area of prior support or resistance, usually, but not always, accompanied by an increase in volume.
• Trending — the phenomenon by which price movement tends to persist in one direction for an extended period of time
• Average true range — averaged daily trading range, adjusted for price gaps
• Chart pattern — distinctive pattern created by the movement of security prices on a chart
• Dead cat bounce — the phenomenon whereby a spectacular decline in the price of a stock is immediately followed by a moderate and temporary rise before resuming its downward movement
• Elliott wave principle and the golden ratio to calculate successive price movements and retracements
• Fibonacci ratios — used as a guide to determine support and resistance
• Momentum — the rate of price change
• Point and figure analysis — A priced-based analytical approach employing numerical filters which may incorporate time references, though ignores time entirely in its construction.

[edit] Overlays

Overlays are generally superimposed over the main price chart.

• Resistance — an area that brings on increased selling
• Support — an area that brings on increased buying
• Trend line — a sloping line of support or resistance
• Channel — a pair of parallel trend lines
• Moving average — an average that lags behind the price action but filters out short term movements
• Bollinger bands — a range of price volatility
• Parabolic SAR — Wilder's trailing stop based on prices tending to stay within a parabolic curve during a strong trend
• Pivot point — derived by calculating the numerical average of a particular currency's or stock's high, low and closing prices
• Ichimoku kinko hyo — a moving average-based system that factors in time and the average point between a candle's high and low

[edit] Price-based indicators

These indicators are generally shown below or above the main price chart.

• Advance decline line — a popular indicator of market breadth
• Average Directional Index — a widely used indicator of trend strength
• Commodity Channel Index — identifies cyclical trends
• MACD — moving average convergence/divergence
• Relative Strength Index (RSI) — oscillator showing price strength
• Stochastic oscillator — close position within recent trading range
• Trix — an oscillator showing the slope of a triple-smoothed exponential moving average
• Momentum — the rate of price change

[edit] Volume-based indicators

• Accumulation/distribution index — based on the close within the day's range
• Money Flow — the amount of stock traded on days the price went up
• On-balance volume — the momentum of buying and selling stocks
• PAC charts — two-dimensional method for charting volume by price level

[edit] Empirical evidence

Whether technical analysis actually works is a matter ofcontroversy. Methods vary greatly, and different technical analysts cansometimes make contradictory predictions from the same data. Manyinvestors claim that they experience positive returns, but academicappraisals often find that it has little predictive power.^[28] Modern studies may be more positive: of 95 modern studies, 56 concluded that technical analysis had positive results, although data-snooping bias and other problems make the analysis difficult.^[6] Nonlinear prediction using neural networks occasionally produces statistically significant prediction results.^[29] A Federal Reserve working paper^[14] regarding support and resistancelevels in short-term foreign exchange rates "offers strong evidencethat the levels help to predict intraday trend interruptions," althoughthe "predictive power" of those levels was "found to vary across theexchange rates and firms examined".

Technical trading strategies were found to be effective in the Chinese marketplace by a recent study that states,

"Finally, we find significant positive returns on buy tradesgenerated by the contrarian version of the moving average crossoverrule, the channel breakout rule, and the Bollinger band trading rule,after accounting for transaction costs of 0.50 percent."^[30]

Critics of technical analysis include well-known fundamental analysts. For example, Peter Lynch once commented, "Charts are great for predicting the past."^{[citation needed]} Warren Buffetthas said, "I realized technical analysis didn't work when I turned thecharts upside down and didn't get a different answer" and "If pasthistory was all there was to the game, the richest people would belibrarians."^{[citation needed]}

An influential 1992 study by Brock et al. which appeared to findsupport for technical trading rules was tested for data snooping andother problems in 1999;^[31] the sample covered by Brock et al. was robust to data snooping.

Subsequently, a comprehensive study of the question by Amsterdameconomist Gerwin Griffioen concludes that: "for the U.S., Japanese andmost Western European stock market indices the recursive out-of-sampleforecasting procedure does not show to be profitable, afterimplementing little transaction costs. Moreover, for sufficiently hightransaction costs it is found, by estimating CAPMs,that technical trading shows no statistically significantrisk-corrected out-of-sample forecasting power for almost all of thestock market indices."^[9]Transaction costs are particularly applicable to "momentum strategies";a comprehensive 1996 review of the data and studies concluded that evensmall transaction costs would lead to an inability to capture anyexcess from such strategies.^[32]

In a paper published in the Journal of Finance,Dr. Andrew W. Lo, director MIT Laboratory for Financial Engineering,working with Harry Mamaysky and Jiang Wang found that "Technicalanalysis, also known as "charting," has been a part of financialpractice for many decades, but this discipline has not received thesame level of academic scrutiny and acceptance as more traditionalapproaches such as fundamental analysis.One of the main obstacles is the highly subjective nature of technicalanalysis—the presence of geometric shapes in historical price charts isoften in the eyes of the beholder. In this paper, we propose asystematic and automatic approach to technical pattern recognitionusing nonparametric kernel regression,and apply this method to a large number of U.S. stocks from 1962 to1996 to evaluate the effectiveness of technical analysis. By comparingthe unconditional empirical distribution of daily stock returns to theconditional distribution—conditioned on specific technical indicatorssuch as head-and-shoulders or double-bottoms—we find that over the31-year sample period, several technical indicators do provideincremental information and may have some practical value." ^[33]In that same paper Dr. Lo wrote that "several academic studies suggestthat ... technical analysis may well be an effective means forextracting useful information from market prices."^[34] Some techniques such as Drummond Geometryattempt to overcome the past data bias by projecting support andresistance levels from differing time frames into the near-term futureand combining that with reversion to the mean techniques.^[35]

[edit] Efficient market hypothesis

The efficient market hypothesis(EMH) contradicts the basic tenets of technical analysis by statingthat 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 Financein 1970, and said "In short, the evidence in support of the efficientmarkets model is extensive, and (somewhat uniquely in economics)contradictory evidence is sparse."^[36]EMH advocates say that if prices quickly reflect all relevantinformation, no method (including technical analysis) can "beat themarket." Developments which influence prices occur randomly and are unknowable in advance.

Technicians say that EMH ignores the way markets work, in that manyinvestors base their expectations on past earnings or track record, forexample. Because future stock prices can be strongly influenced byinvestor expectations, technicians claim it only follows that pastprices influence future prices.^[37] They also point to research in the field of behavioral finance,specifically that people are not the rational participants EMH makesthem out to be. Technicians have long said that irrational humanbehavior influences stock prices, and that this behavior leads topredictable outcomes.^[38] 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, irrationalpreferences, and the dynamics of group behavior, behavioral financeoffers succinct explanations of excess market volatility as well as theexcess returns earned by stale information strategies.... cognitiveerrors may also explain the existence of market inefficiencies thatspawn the systematic price movements that allow objective TA [technicalanalysis] methods to work.^[37]

EMH advocates reply that while individual market participants do notalways act rationally (or have complete information), their aggregatedecisions balance each other, resulting in a rational outcome(optimists who buy stock and bid the price higher are countered bypessimists who sell their stock, which keeps the price in equilibrium).^[39]Likewise, complete information is reflected in the price because allmarket participants bring their own individual, but incomplete,knowledge together in the market.^[39]

[edit] Random walk hypothesis

The random walk hypothesismay be derived from the weak-form efficient markets hypothesis, whichis based on the assumption that market participants take full accountof any information contained in past price movements (but notnecessarily other public information). In his book A Random Walk Down Wall Street, Princeton economist Burton Malkielsaid that technical forecasting tools such as pattern analysis mustultimately be self-defeating: "The problem is that once such aregularity is known to market participants, people will act in such away that prevents it from happening in the future."^[40]In the late 1980's, professors Andrew Lo and Craig McKinlay published apaper positively proving the random walk does not exist and never has.Several years passed before the acadmics finally and reluctantlyadmitted they were right. In a 1999 response to Malkiel, Lo andMcKinlay collected empirical papers that questioned the hypothesis'applicability^[41]that suggested a non-random and possibly predictive component to stockprice movement, though they were careful to point out that rejectingrandom walk does not necessarily invalidate EMH, an entirely separateconcept from RWH.

Technicians say the EMH and random walk theories both ignore therealities of markets, in that participants are not completely rationaland that current price moves are not independent of previous moves.^[21][42]Critics reply that one can find virtually any chart pattern after thefact, but that this does not prove that such patterns are predictable.Technicians maintain that both theories would also invalidate numerousother trading strategies such as index arbitrage, statistical arbitrage and many other trading systems.^[37]

[edit] See also

• Algorithmic trading
• Business cycle
• Market analysis
• Market timing
• Pivot point
• Quantitative analyst
• Stock market bottom
• Stock market cycles
• Technical analysis software
• Technical indicator
• Trader (finance)
• 7 Tools Of The Trade Retrieved 06-Feb-2010
• Technical Analysis Retrieved 06-Feb-2010

[edit] Notes

1. ^ See e.g. Kirkpatrick and Dahlquist Technical Analysis: The Complete Resource for Financial Market Technicians (Financial Times Press, 2006), page 3.
2. ^ Joseph de la Vega, Confusión de Confusiones, 1688
3. ^ Nison, Steve (1991). Japanese Candlestick Charting Techniques. pp. 15–18. ISBN 0139316507. 
4. ^ Nison, Steve (1994). Beyond Candlesticks: New Japanese Charting Techniques Revealed, John Wiley and Sons, p. 14. ISBN 0-471-00720-X
5. ^ ^{a b c} John J. Murphy, Technical Analysis of the Financial Markets (New York Institute of Finance, 1999), pages 1-5,24-31.
6. ^ ^{a b} Irwin, Scott H. and Park, Cheol-Ho. (2007). "What Do We Know About the Profitability of Technical Analysis?" Journal of Economic Surveys, Vol. 21, No. 4, pp. 786-826. Available at SSRN. DOI: 10.1111/j.1467-6419.2007.00519.x.
7. ^ Paulos, J.A. (2003). A Mathematician Plays the Stock Market. Basic Books. 
8. ^ Fama, Eugene (May 1970). "Efficient Capital Markets: A Review of Theory and Empirical Work," The Journal of Finance, v. 25 (2), pp. 383-417.
9. ^ ^{a b} Griffioen, Technical Analysis in Financial Markets
10. ^ "Getting Started in Technical Analysis" 1999 Jack D. Schwager Page 2
11. ^ Taylor, Mark P.; Allen, Helen (1992). "The Use of Technical Analysis in the Foreign Exchange Market". Journal of International Money and Finance 11 (3): 304–314. doi:10.1016/0261-5606(92)90048-3. 
12. ^ 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.
13. ^ Brock, William; Lakonishok, Josef; Lebaron, Blake (1992). "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns". The Journal of Finance 47 (5): 1731–1764. doi:10.2307/2328994. http://jstor.org/stable/2328994. 
14. ^ ^{a b} Osler, Karen (July 2000). "Support for Resistance: Technical Analysis and Intraday Exchange Rates," FRBNY Economic Policy Review (abstract and paper here).
15. ^ Neely, Christopher J., and Paul A. Weller (2001). "Technical analysis and Central Bank Intervention," Journal of International Money and Finance, 20 (7), 949–70 (abstract and paper here)
16. ^ Taylor, M.P.; Allen, H. (1992). "The use of technical analysis in the foreign exchange market". Journal of International Money and Finance 11 (3): 304–314. doi:10.1016/0261-5606(92)90048-3. http://ideas.repec.org/a/eee/jimfin/v11y1992i3p304-314.html. Retrieved 2008-03-29. 
17. ^ Frankel, J.A.; Froot, K.A. (1990). "Chartists, Fundamentalists, and Trading in the Foreign Exchange Market". The American Economic Review 80 (2): 181–185. http://links.jstor.org/sici?sici=0002-8282(199005)80%3A2%3C181%3ACFATIT%3E2.0.CO%3B2-F. Retrieved 2008-03-29. 
18. ^ Neely, C.J (1998). "Technical Analysis and the Profitability of US Foreign Exchange Intervention". Federal Reserve Bank of St. Louis Review 80 (4): 3–17. http://ideas.repec.org/a/fip/fedlrv/y1998ijulp3-17nv.80no.4.html. Retrieved 2008-03-29. 
19. ^ Lento, Camillo (2008). "A Combined Signal Approach to Technical Analysis on the S&P 500". Journal of Business & Economics Research 6 (8): 41–51. 
20. ^ David M. Cutler, James M. Poterba, Lawrence H. Summers, "What Moves Stock Prices?", NBER Working Paper #2538 (March 1988), pp 13-14.
21. ^ ^{a b} 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. ISBN 0-13-134597-4.
22. ^ K. Funahashi, On the approximate realization of continuous mappings by neural networks, Neural Networks vol 2, 1989
23. ^ K. Hornik, Multilayer feed-forward networks are universal approximators, Neural Networks, vol 2, 1989
24. ^ R. Lawrence. Using Neural Networks to Forecast Stock Market Prices
25. ^ B.Egeli et al. Stock Market Prediction Using Artificial Neural Networks
26. ^ M. Zeki?. Neural Network Applications in Stock Market Predictions - A Methodology Analysis
27. ^ John J. Murphy, Technical Analysis of the Financial Markets (New York Institute of Finance, 1999).
28. ^ Browning, E.S. (July 31, 2007). "Reading market tea leaves". The Wall Street Journal Europe (Dow Jones): pp. 17–18. 
29. ^ Skabar, Cloete, Networks, Financial Trading and the Efficient Markets Hypothesis
30. ^ Nauzer J. Balsara, Gary Chen and Lin Zheng "The Chinese Stock Market: An Examination of the Random Walk Model and Technical Trading Rules" The Quarterly Journal of Business and Economics, Spring 2007
31. ^ Sullivan, R.; Timmermann, A.; White, H. (1999). "Data-Snooping, Technical Trading Rule Performance, and the Bootstrap". The Journal of Finance 54 (5): 1647–1691. doi:10.1111/0022-1082.00163. 
32. ^ Chan, L.K.C.; Jegadeesh, N.; Lakonishok, J. (1996). "Momentum Strategies". The Journal of Finance (The Journal of Finance, Vol. 51, No. 5) 51 (5): 1681–1713. doi:10.2307/2329534. http://links.jstor.org/sici?sici=0022-1082(199612)51:5%3C1681:MS%3E2.0.CO;2-D. Retrieved 2008-03-29. 
33. ^ Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation, with Harry Mamaysky and Jiang Wang, Journal of Finance 55(2000), 1705-1765.
34. ^ Lo, Andrew W.; Mamaysky, Harry; Wang, Jiang (2000). "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation". Journal of Finance 55: 1705–1765. doi:10.1111/0022-1082.00265. http://www.nber.org/papers/w7613. 
35. ^ David Keller, "Breakthroughs in Technical Analysis; New Thinking from the World's Top Minds," New York, Bloomberg Press, 2007, ISBN 978-1-57660-242-3 pp.1-19
36. ^ 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.
37. ^ ^{a b c} Aronson, David R. (2006). Evidence-Based Technical Analysis, Hoboken, New Jersey: John Wiley and Sons, pages 357, 355-356, 342. ISBN 978-0-470-00874-4.
38. ^ 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.
39. ^ ^{a b} 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.
40. ^ Burton Malkiel, A Random Walk Down Wall Street, W. W. Norton & Company (April 2003) p. 168.
41. ^ Lo, Andrew and MacKinlay, Craig, A Non-Random Walk Down Wall Street, Princeton University Press (1999)
42. ^ Poser, Steven W. (2003). Applying Elliott Wave Theory Profitably, John Wiley and Sons, p. 71. ISBN 0-471-42007-7.

[edit] Books

• The Disciplined Trader, Mark Douglas, New York Institute of Finance, 1990, ISBN 0-13-215757-8
• Getting Started in Technical Analysis, Jack D. Schwager, Wiley, 1999, ISBN 0-471-29542-6
• New Concepts in Technical Trading Systems, J. Welles Wilder, Trend Research, 1978, ISBN 0-89459-027-8
• Reminiscences of a Stock Operator, Edwin Lefèvre, John Wiley & Sons Inc, 1994, ISBN 0-471-05970-6
• Street Smarts, Connors/Raschke, 1995, ISBN 0-9650461-0-9
• Technical Analysis: The Complete Resource for Financial Market Technicians, Kirkpatrick/Dahlquist, 2007, ISBN 0-13-153113-1
• Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points, Martin J. Pring, McGraw Hill, 2002, ISBN 0-07-138193-7
• Technical Analysis of Stock Trends, 9th Edition (Hardcover), Robert D. Edwards, John Magee, W.H.C. Bassetti (Editor), American Management Association, 2007, ISBN 0-8493-3772-0
• Technical Analysis of the Financial Markets, John J. Murphy, New York Institute of Finance, 1999, ISBN 0-7352-0066-1
• The Profit Magic of Stock Transaction Timing, J.M. Hurst, Prentice-Hall, 1972, ISBN 0-13-726018-0

[edit] External links

Find more about Technical analysis on Wikipedia's sister projects: Definitions from Wiktionary
Textbooks from Wikibooks
Quotations from Wikiquote
Source texts from Wikisource
Images and media from Commons
News stories from Wikinews
Learning resources from Wikiversity

International and national organizations

• International Federation of Technical Analysts
• Market Technicians Association
• New Zealand: Society of Technical Analysts of New Zealand
• Singapore: Technical Analysts Society (Singapore)

v • d • e Technical analysis Concepts Support and resistance · Trend line · Breakout · Market trend · Dead cat bounce · Elliott wave principle · Fibonacci ratios · Gap · Pivot point · Dow Theory Type of Charts Japanese Candlestick · OHLC Bar Chart · Line chart · Point and figure chart Patterns Chart Head and shoulders · Cup and handle · Double top and double bottom · Double top · Triple top and triple bottom · Broadening top · Price channels · Wedge pattern · Triangle · Flag and Pennant Candlestick Simple Doji · Hammer · Hanging man  · Inverted hammer · Shooting star  · Marubozu · Spinning top Complex Three white soldiers · Three Black Crows · Morning star Indicators Trend Average Directional Index (ADX) · Ichimoku Kinkô Hyô · Moving Average Convergence/Divergence (MACD) · Mass Index · Moving Average (MA) · Parabolic SAR (SAR) · Trix · Vortex Indicator (VI) Momentum Relative Strength Index (RSI) · Stochastic Oscillator · Williams %R (%R) Volume Accumulation/Distribution Index · Money Flow Index (MFI) · On-balance volume (OBV) · Volume Price Trend (VPT) · Force Index (FI) · Negative Volume Index (NVI) · Ease of movement Volatility Average True Range (ATR) · Bollinger Bands (BB) · Donchian channel · Standard deviation (σ) Other Advance/Decline Line (ADL) · Commodity Channel Index (CCI) · Coppock curve · Keltner channel · McClellan Oscillator · Ulcer Index · Ultimate Oscillator v • d • e Stock market Types of stocks Stock · Common stock · Preferred stock · Tracking stock · Outstanding stock · Treasury stock · Authorised stock · Restricted stock · Concentrated stock · Golden share Participants Investor · Stock trader/investor · Market maker · Floor trader · Floor broker · Broker-dealer · Proprietary trader Exchanges Stock exchange · List of stock exchanges · List of market opening times · Over-the-counter · Electronic communication network Stock valuation Gordon model · Dividend yield · Earnings per share · Book value · Earnings yield · Beta · Alpha · CAPM · Arbitrage pricing theory · T-Model Financial ratios P/CF ratio · P/E · PEG · P/S ratio · P/B ratio · D/E ratio · Dividend payout ratio · Dividend cover · SGR · ROIC · ROCE · ROE · ROA · EV/EBITDA · RAROC · Sharpe ratio · Treynor ratio · Cap rate Trading theories
and strategies Efficient-market hypothesis · Random walk hypothesis · Fundamental analysis · Technical analysis · Modern portfolio theory · Post-modern portfolio theory · Mosaic theory · Pairs trade · Algorithmic trading · Market timing · Swing trading · Buy and hold · Day trading Related terms Dividend · Stock split · Reverse stock split · Growth stock · Speculation · Trade · IPO · Market trend · Short selling · Momentum · DuPont Model · Dark liquidity · Market depth · Margin · Rally · Volatility · Free float · Uptick rule · Stock dilution · Market capitalization · Voting interest · Block trade · Open outcry · Slippage · Haircut · Mean reversion · Flight-to-quality · Short squeeze · Market manipulation Retrieved from "http://en.wikipedia.org/wiki/Technical_analysis"Categories: Technical analysis | Stock market