Concepts

Average True Range

Average True Range (ATR) is a technical analysis indicator developed by J. Welles Wilder, based on trading ranges smoothed by an N-day exponential moving average.

The range of a day's trading is simply high − low. The true range extends it to yesterday's closing price if it was outside of today's range:

true range = max(high,close_prev) − min(low,close_prev)

The average true range is then an N-day exponential moving average of the true range values. Wilder recommended a 14-period smoothing. Note this is by his reckoning of EMA periods (see the EMA article on that), meaning an α=2/(1+14)=0.1333.

The idea of ranges is that they show the commitment or enthusiasm of traders. Large or increasing ranges suggest traders prepared to continue to bid up or sell down a stock through the course of the day. Decreasing range suggests waning interest.

Chart pattern

A chart pattern is a pattern that is formed within a chart when prices are graphed. In stock and commodity markets trading, chart pattern studies play a large role during technical analysis. When data is plotted there is usually a pattern which naturally occurs and repeats over a period of time. Chart patterns are used as either reversal or continuation signals.

Some people claim that by recognizing chart patterns they are able to predict future stock prices and profit by this prediction; other people respond by quoting "past performance is no guarantee of future results" and argue that chart patterns are merely illusions created by people's subconscious. Certain theories of economics hold that if there were a way to predict future stock prices and profit by it then when enough people used these techniques they would become ineffective and cease to be profitable. On the other hand, if you can predict what other people will predict the market to do then that would be valuable information.

Coppock curve

The Coppock curve or Coppock indicator is a technical analysis indicator for long-term stock market investors created by E.S.C. Coppock, first published in Barron's magazine in October 15, 1962.

The indicator is designed for use on a monthly time scale. It's the sum of a 14-month rate of change and 11-month rate of change, smoothed by a 10-period weighted moving average.

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Coppock, the founder of Trendex Research in San Antonio, Texas, was an economist. He had been asked by the Episcopal church to identify buying opportunities for long-term investors. He thought market downturns were like bereavements and required a period of mourning. He asked the church bishops how long that normally took for people, their answer was 11 to 14 months and so he used those periods in his calculation.

A buy signal is generated when the indicator is below zero and turns upwards from a trough. No sell signals are generated (that not being its design). The indicator is trend-following, and based on averages, so by its nature it doesn't pick a market bottom, but rather shows when a rally has become established.

Coppock designed the indicator (originally called the "Trendex Model") for the S&P 500 index, and it's been applied to similar stock indexes like the Dow Jones Industrial Average. It's not regarded as well-suited to commodity markets, since bottoms there are more rounded than the spike lows found in stocks.

Dead cat bounce

A dead cat bounce is a firugative term used by traders in the finance industry to describe a pattern wherein a spectacular decline in the price of a stock is immediately followed by a moderate and temporary rise before resuming its downward movement, with the connotation that the rise was not an indication of improving circumstances in the fundamentals of the stock. It is derived from the notion that "even a dead cat will bounce if it falls from a great height".

The phrase has been used on the trading floors for many years. However the earliest recorded use of the phrase dates from 1985 when the Singaporean and Malaysian stock markets bounced back after a hard fall during the recession of that year. Journalist Christoper Sherwel of the Financial times reported a stock broker as saying the market rise was a "dead cat bounce". It has also been used in reference to political polling numbers.

The reasons for such a bounce can be technical, as investors may have standing orders to buy shorted stocks if they fall below a certain level or to cover certain option positions. Once those limits are reached, the buy orders are activated and the sudden rise in demand causes the price of the stock to rise as well. The bounce may also be the result of speculation. Since bounces often occur, traders buy into what they hope is the bottom of the market, expecting a bounce and thereby reaping a quick profit. Thus, the very act of anticipating a bounce can create and magnify it.

A market rise after a sharp fall can only really be seen to be a "dead cat bounce" with the benefit of hindsight. If the stock starts to fall again in the following days and weeks, then it was a true dead cat bounce. If the market starts to climb again after the first short bounce, then the continued rise in price action would be considered a trend reversal and not a dead cat bounce. Since this distinction only becomes obvious in hindsight, the evaluation may vary depending upon the initial and final points of reference.

Elliott wave principle

The Elliott wave principle is a form of technical analysis that attempts to forecast trends in the financial markets and other collective activities. It is named after Ralph Nelson Elliott (1871–1948), an accountant who developed the concept in the 1930s: he proposed that market prices unfold in specific patterns, which practitioners today call Elliott waves. Elliott published his views of market behavior in the book The Wave Principle (1938), in a series of articles in Financial World magazine in 1939, and most fully in his final major work, Nature’s Laws – The Secret of the Universe (1946). Elliott argued that because humans are themselves rhythmical, their activities and decisions could be predicted in rhythms, too. Critics argue that the Elliott wave principle is pseudoscientific and contradicts the efficient market hypothesis.

Hikkake Pattern

The Hikkake Pattern (or Hikkake), is a technical analysis pattern used for determining market turning-points and continuations. It is a simple pattern that can be observed in market price data, using traditional bar chart, or Japanese Candlestick Chart.

The phrase "Hikkake" is a Japanese verb which means to "trick" or "ensnare." The pattern is comprised of a measurable period of rest and volatility contraction in the market, followed by a relatively brief price move that encourages unsuspecting traders and investors to adopt a false assumption regarding the likely future direction of price. Some technical analysts may also refer to the hikkake pattern as an "inside day false breakout," however this term has never been formally adopted. The pattern, once formed, yields its own set of trading parameters for the time and price of market entry, the dollar risk amount (i.e., where to place protective stops), and the expected profit target. The pattern is not meant as a stand alone "system" for market speculation, but rather as an ancillary technique to traditional technical and fundamental market analysis methods.

Due to its popularity among institutional traders, the hikkake pattern has been adopted for use by INTSTREAM, the Nordic power trading software company, in their E2 Energy Market Analysis Platform.

Growing interest in the hikkake pattern among traders and investors has also spawned attention from various book authors. See: "Technical Analysis: The Complete Resource for Financial Market Technicians" by Charles D. Kirkpatrick and Julie R. Dahlquist, and "Long/Short Market Dynamics: Trading Strategies for Today's Markets" by Clive M. Corcoran.

The hikkake pattern was first discovered and introduced to the financial community through a series of published articles written by technical analyst Daniel L. Chesler, CMT .

Momentum (technical analysis)

Momentum and rate of change (ROC) are simple technical analysis indicators showing the difference between today's closing price and the close N days ago. Momentum is the absolute difference:

Rate of change scales by the old close, so as to represent the increase as a fraction,

"Momentum" in general refers to prices continuing to trend. The momentum and ROC indicators show trend by remaining positive while an uptrend is sustained, or negative while a downtrend is sustained.

A crossing up through zero may be used as a signal to buy, or a crossing down through zero as a signal to sell. How high (or how low when negative) the indicators get shows how strong the trend is.

The way momentum shows an absolute change means it shows for instance a $3 rise over 20 days, whereas ROC might show that as 0.25 for a 25% rise over the same period. One can choose between looking at a move in dollar terms, relative point terms, or proportional terms. The zero crossings are the same in each, of course, but the highs or lows showing strength are on the respective different bases.

Point and figure chart

Point and figure is a charting technique used in technical analysis, a form of heterodox economics used to attempt to predict financial market prices. Point and figure charting is unique in that it does not plot price against time as all other techniques do. Instead in plots price against changes in direction by plotting a column of Xs as the price rises and a column of Os as the price falls.

Types of Charts

Open-high-low-close chart

An open-high-low-close chart (also OHLC chart, or simply bar chart) is a type of chart typically used to illustrate movements in the price of a financial instruments over time. Each vertical line on the chart shows the price range (the highest and lowest prices) over one unit of time, e.g. one day or one hour. Tick mark project from each side of the line indicating the opening price (e.g. for a daily bar chart this would be the starting price for that day) on the left, and the closing price for that time period on the right. The bars may be shown in different hues depending on whether prices rose or fell in that period.

The Japanese candlestick chart is another way of displaying market price data, with the opening and closing prices defining a rectangle within the range for each time unit. Both charts show the exact same data, i.e. the opening, high, low, and closing prices during a particular time frame. Some traders find the candlestick chart easier to read.

Candlestick chart

A candlestick chart is a style of bar-chart used primarily to describe price movements of a security (finance), derivative, or currency over time.

It is a combination of a line-chart and a bar-chart, in that each bar represents the range of price movement over a given time interval. It is most often used in technical analysis of equity and currency price patterns. They appear superficially similar to error bars, but are unrelated.

Line chart

A line chart or line graph is a type of graph created by connecting a series of data points together with a line.

Charting terms and indicators

Types of Charts

• OHCL - Open High Low Close charts plot the high and low of the price movement vertically and the open and close horizontally. Used to graph range and outliers.
• Cnadlestick Chart- Similar to OHLC, but open and close are filled. Often Black or Red candles represent a close lower than the open. While White, Green or Blue candles represent a close higher than the open.
• Line chart- Connects each closing interval together on a line

Concepts

• Average true range- averaged daily trading range, adjusted for price gaps
• Chart pattern
  
• Coppock curve - Edwin coppock developed the Coppock Indicator to identify the commencement of bull markets
  
• 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
• Hikkake pattern- pattern for identifying reversals and continuations
• Momentum- the rate of price change
• Point and figure charts - charts based on price without time

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
• Breakout - when a price passes through and stays above an area of support or resistance
• Trend line - a sloping line of support or resistance
• Channel - a pair of parallel trend lines
• Moving average - lags behind the price action but filters out short term movements
• Bollinger bands - a range of price volatility
• Pivot point- derived by calculating the numerical average of a particular currency's or stock's high, low and closing prices

Price-based indicators

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

• Accumulation/distribution index—based on the close within the day's range
• 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
• Parabolic SAR- Wilder's trailing stop based on prices tending to stay within a parabolic curve during a strong trend
• 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, developed in the 1980s by Jack Hutson

Volume based indicators

• 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

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." In a 1999 response to Malkiel, Andrew Lo and Craig McKinlay collected empirical papers that questioned the hypothesis' applicability that suggested a non-random and possibly predictive component to stock price movement, though they were careful to point out that rejecting random walk does not necessarily invalidate EMH.

Technicians say the EMH and random walk theories both ignore the realities of markets, in that participants are not completely rational and that current price moves are not independent of previous moves. 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.

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." 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. The vast majority of academic papers find that technical trading rules, after consideration for trading costs, are not profitable.

Technicians say that EMH ignores the way markets work, in that many investors base their expectations on past earnings or track record, for example. Because future stock prices can be strongly influenced by investor expectations, technicians claim it only follows that past prices influence future prices. 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. 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.

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). Likewise, complete information is reflected in the price because all market participants bring their own individual, but incomplete, knowledge together in the market.

Empirical evidence

Whether technical analysis actually works is a matter of controversy. Methods vary greatly, and different technical analysts can sometimes make contradictory predictions from the same data. Many investors claim that they experience positive returns, but academic appraisals often find that it has little predictive power. Modern studies may be more positive, however –- of 95 modern studies, 56 concluded that technical analysis had positive results, although data snooping and other problems make the analysis difficult. Nonlinear prediction using neural networks occasionally produces statistically significant prediction results. A Federal Reserve working paper 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."

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 trades generated by the contrarian version of the moving average crossover rule, the channel breakout rule, and the Bollinger band trading rule, after accounting for transaction costs of 0.50 percent." Nauzer J. Balsara, Gary Chen and Lin Zheng The Chinese Stock Market: An Examination of the Random Walk Model and Technical Trading Rule

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."

An influential 1992 study by Brock et al. which appeared to find support for technical trading rules was tested for data snooping and other problems in 1999; the sample covered by Brock et al. was robust to data snooping.

Subsequently, 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." Transaction costs are particularly applicable to "momentum strategies"; a comprehensive 1996 review of the data and studies concluded that even small transaction costs would lead to an inability to capture any excess from such strategies.

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 "Technical analysis, also known as "charting," has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis---the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution---conditioned on specific technical indicators such as head-and-shoulders or double-bottoms---we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value." In that same paper Dr. Lo wrote that "several academic studies suggest that...technical analysis may well be an effective means for extracting useful information from market prices.

Combination with other market forecast methods

John Murphy 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 advocate for this approach is John Bollinger, who coined the term rational analysis in the middle 1980s for the intersection of technical analysis and fundamental analysis. Another such approach, fusion analysis, 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. 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. It is worth noting, however, that some of the calendar related phenomena, such as the January effect in the stock market, have been associated with tax and accounting related reasons.

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