Monday, April 9, 2012

Technical Analysis of Stock Trends

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Sceeto and True Reckoning are some of the most advanced indicators ever developed although There are many indicators out there like Trend line — a sloping line described by at least two peaks or two troughs Channel — a pair of parallel trend lines Moving average — the last n-bars of price divided by "n" -- where "n" is the number of bars specified by the length of the average. A moving average can be thought of as a kind of dynamic trend-line.Bollinger bands — a range of price volatilityParabolic 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.Price-based indicators
These indicators are generally shown below or above the main price chart.
Average Directional Index — a widely used indicator of trend strength
Commodity Channel Index — identifies cyclical trends
MACD — moving average convergence/divergence
Momentum — the rate of price change
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
%C — denotes current market environment as range expansion or contraction plus
highlights ta extremes when the condition should be changing
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 Technical analysis
Courtesy of Wikipedia From Wikipedia, the free encyclopedia
Financial markets
In finance, technical analysis is security analysis discipline for forecasting the direction of

prices through the study of past market data, primarily price and volume.[1] Behavioral

economics and quantitative analysis build on and incorporate many of the same tools of

technical analysis [2] [3][4] [5], which, being an aspect of active management, stands in

contradiction to much of modern portfolio theory. The efficacy of both technical and

fundamental analysis is disputed by efficient-market hypothesis which states that stock

market prices are essentially unpredictable.[6]
[edit] History

The principles of technical analysis are derived from hundreds of years of financial markets

data.[7] Some aspects of technical analysis began to appear in Joseph de la Vega's

accounts of the Dutch markets in the 17th century. In Asia, technical analysis is said to be a

method developed by Homma Munehisa during early 18th century which evolved into the

use of candlestick techniques, and is today a technical analysis charting tool.[8][9] In the

1920s and 1930s Richard W. Schabacker published several books which continued the work

of Charles Dow and William Peter Hamilton in their books Stock Market Theory and Practice

and Technical Market Analysis. In 1948 Robert D. Edwards and John Magee published

Technical Analysis of Stock Trends which is widely considered to be one of the seminal

works of the discipline. It is exclusively concerned with trend analysis and chart patterns

and remains in use to the present. As is obvious, early technical analysis was almost

exclusively the analysis of charts, because the processing power of computers was not

available for statistical analysis. Charles Dow reportedly originated a form of point and figure

chart analysis.

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 at the end of the

19th century. Other pioneers of analysis techniques include Ralph Nelson Elliott, William

Delbert Gann and Richard Wyckoff who developed their respective techniques in the early

20th century. More technical tools and theories have been developed and enhanced in

recent decades, with an increasing emphasis on computer-assisted techniques using

specially designed computer software.
[edit] General description
    This section needs additional citations for verification. Please help improve this

article by adding citations to reliable sources. Unsourced material may be challenged and

removed. (January 2011)

While fundamental analysts examine earnings, dividends, new products, research and the

like, technical analysts examine what investors fear or think about those developments and

whether or not investors have the wherewithal to back up their opinions; these two

concepts are called psych (psychology) and supply/demand. Technicians employ many

techniques, one of which is the use of charts. Using charts, technical analysts seek to

identify price patterns and market trends in financial markets and attempt to exploit those

patterns.[10] Technicians use various methods and tools, the study of price charts is but


Technicians using charts search for archetypal price chart patterns, such as the well-known

head and shoulders or double top/bottom reversal patterns, study technical 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


Technical analysts also widely use market indicators of many sorts, some of which are

mathematical transformations of price, often including up and down volume,

advance/decline data and other inputs. These indicators are used to help assess whether an

asset is trending, and if it is, the probability of its direction and of continuation. Technicians

also look for relationships between price/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 indicators such as Put/Call ratios, bull/bear ratios, short interest, Implied

Volatility, etc.

There are many techniques in 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 from more than one technique. Some

technical analysts use subjective judgment to decide which pattern(s) a particular

instrument reflects at a given time and what the interpretation of that pattern should be.

Others employ a strictly mechanical or systematic approach to pattern identification and


Technical analysis is frequently contrasted with fundamental analysis, the study of

economic factors that influence the way investors price financial markets. Technical

analysis holds that prices already reflect all such trends before investors are aware of them.

Uncovering those trends is what technical indicators are designed to do, imperfect as they

may be. Fundamental indicators are subject to the same limitations, naturally. Some traders

use technical or fundamental analysis exclusively, while others use both types to make

trading decisions.
[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, business cycles, stock market cycles or,

classically, through recognition of chart patterns.

Technical analysis stands in contrast to the fundamental analysis approach to security and

stock analysis. Technical analysis analyzes price, volume and other market information,

whereas fundamental analysis looks at the facts of the company, market, currency or

commodity. Most large brokerage, trading group, or financial institutions will typically have

both a technical analysis and fundamental analysis team.

Technical analysis is widely used among traders and financial professionals and is very

often used by active day traders, market makers and pit traders. In the 1960s and 1970s it

was widely dismissed by academics. In a recent review, Irwin and Park[11] reported that 56

of 95 modern studies found that it produces positive results but noted that many of the

positive results were rendered dubious 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.[12] 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.[13][14] Users hold that even if technical analysis cannot predict the future, it

helps to identify trading opportunities.[15]

In the foreign exchange markets, its use may be more widespread than fundamental

analysis.[16][17] This does not mean technical analysis is more applicable to foreign

markets, but that technical analysis is more recognized as to its efficacy there than

elsewhere. While some isolated studies have indicated that technical trading rules might

lead to consistent returns in the period prior to 1987,[18][19][20][21] most academic work

has focused on the nature of the anomalous position of the foreign exchange market.[22] It

is speculated that this anomaly is due to central bank intervention, which obviously

technical analysis is not designed to predict.[23] Recent research suggests that combining

various trading signals into a Combined Signal Approach may be able to increase

profitability and reduce dependence on any single rule.[24]
[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 support and vice versa.

A fundamental principle of technical analysis is that a market's price reflects all relevant

information, so their analysis looks at the history of a security's trading pattern rather than

external drivers such as economic, fundamental and news events. Price action also tends to

repeat itself because investors collectively tend toward patterned behavior – hence

technicians' focus on identifiable trends and conditions.[citation needed]
[edit] Market action discounts everything

Based on the premise that all relevant information is already reflected by prices, technical

analysts believe it is important to understand what investors think of that information,

known and perceived.
[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 definition of a price trend was originally put forward by Dow


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.[25] In other words, each time the stock moved 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 does not 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.
[edit] History tends to repeat itself

Technical analysts believe that investors collectively repeat the behavior of the investors

that preceded them. To a technician, the emotions in the market may be irrational, but they

exist. Because investor behavior repeats itself so often, technicians believe that

recognizable (and predictable) price patterns will develop on a chart.[10]

Technical analysis is not limited to charting, but it always considers price trends.[1] 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[26] 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.[27]

Recently, Kim Man Lui, Lun Hu, and Keith C.C. Chan have suggested that there is statistical

evidence of association relationships between some of the index composite stocks

whereas there is no evidence for such a relationship between some index composite others.

They show that the price behavior of these Hang Seng index composite stocks is easier to

understand than that of the index.[28]
[edit] Industry

The industry is globally represented by the International Federation of Technical Analysts

(IFTA), which is a Federation of regional and national organizations. 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.[29] In

Australia, the industry is represented by the Australian Professional Technical Analysts

(APTA) Inc [30] and the Australian Technical Analysts Association (ATAA).

Professional technical analysis societies have worked on creating a body of knowledge that

describes the field of Technical Analysis. A body of knowledge is central to the field as a

way of defining how and why technical analysis may work. It can then be used by academia,

as well as regulatory bodies, in developing proper research and standards for the field.[31]

The Market Technicians Association (MTA) has published a body of knowledge, which is the

structure for the MTA's Chartered Market Technician (CMT) exam.[32]
[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 intelligence adaptive

software systems that have been inspired by how biological neural networks work. They are

used because they can learn to detect complex patterns in data. In mathematical terms,

they are universal function approximators,[33][34] 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.

As ANNs are essentially non-linear statistical models, their accuracy and prediction

capabilities can be both mathematically and empirically tested. In various studies, authors

have claimed that neural networks used for generating trading signals given various

technical and fundamental inputs have significantly outperformed buy-hold strategies as

well as traditional linear technical analysis methods when combined with rule-based expert


While the advanced mathematical nature of such adaptive systems has 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. However, large-scale application is problematic because of the problem of matching

the correct neural topology to the market being studied.
[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.[10] 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.[38] Another such approach, fusion analysis,

[39] 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.[40] 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.[41] Calendar phenomena, such as the

January effect in the stock market, are generally believed to be caused by tax and

accounting 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.[42]
[edit] 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.[43] Of 95 modern studies, 56

concluded that technical analysis had positive results, although data-snooping bias and

other problems make the analysis difficult.[11] Nonlinear prediction using neural networks

occasionally produces statistically significant prediction results.[44] A Federal Reserve

working paper[19] 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."[45]

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;[46] 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."[14] 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.[47]

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.[48]

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."[49] Some techniques such as Drummond Geometry attempt to overcome the past

data bias by projecting support and resistance levels from differing time frames into the

near-term future and combining that with reversion to the mean techniques.[50]
[edit] 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."[51]

Technicians say[who?] 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.[52] 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.[53] 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).[54] Likewise,

complete information is reflected in the price because all market participants bring their

own individual, but incomplete, knowledge together in the market.[54]
[edit] 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."[55] Malkiel has

stated that while momentum may explain some stock price movements, there is not enough

momentum to make excess profits. Malkiel has compared technical analysis to "astrology".


In the late 1980s, professors Andrew Lo and Craig McKinlay published a paper which cast

doubt on the random walk hypothesis. In a 1999 response to Malkiel, Lo and McKinlay

collected empirical papers that questioned the hypothesis' applicability[57] 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,

which is an entirely separate concept from RWH. In a 2000 paper, Andrew Lo back-analyzed

data from U.S. from 1962 to 1996 and found that "several technical indicators do provide

incremental information and may have some practical value".[49] Burton Malkiel dismissed

the irregularities mentioned by Lo and McKinlay as being too small to profit from.[56]

Technicians say[who?] that 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.[25][58] Some signal processing researchers negate the

random walk hypothesis that stock market prices resemble Wiener processes, because the

statistical moments of such processes and real stock data vary significantly with respect

window size and similarity measure. [59] They argue that feature transformations used for

the description of audio and biosignals can also be used to predict stock market prices

successfully which would contradict the random walk hypothesis.

The random walk index (RWI) is a technical indicator that attempts to determine if a stock’s

price movement is random or nature or a result of a statistically significant trend. The

random walk index attempts to determine when the market is in a strong uptrend or

downtrend by measuring price ranges over N and how it differs from what would be

expected by a random walk (randomly going up or down). The greater the range suggests a

stronger trend.[60]
[edit] Charting terms and indicators
[edit] Concepts

    Resistance — a price level that may prompt a net increase of selling activity
    Support — a price level that may prompt a net increase of buying activity
    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

    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

    Cycles — time targets for potential change in price action (price only moves up, down, or

    Market Condition — the state of price movement as being in a state of range expansion or

a range contraction.

[edit] Types of charts

    Open-high-low-close chart — OHLC 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] Overlays

Overlays are generally superimposed over the main price chart.

    Resistance — a price level that may act as a ceiling above price
    Support — a price level that may act as a floor below price
    Trend line — a sloping line described by at least two peaks or two troughs
    Channel — a pair of parallel trend lines
    Moving average — the last n-bars of price divided by "n" -- where "n" is the number of bars

specified by the length of the average. A moving average can be thought of as a kind of

dynamic trend-line.
    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.

    Average Directional Index — a widely used indicator of trend strength
    Commodity Channel Index — identifies cyclical trends
    MACD — moving average convergence/divergence
    Momentum — the rate of price change
    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
    %C — denotes current market environment as range expansion or contraction plus

highlights ta extremes when the condition should be changing.

[edit] Breadth Indicators

These indicators are based on statistics derived from the broad market

    Advance Decline Line — a popular indicator of market breadth
    McClellan Oscillator - a popular closed-form indicator of breadth
    McClellan Summation Index - a popular open-form indicator of breadth

[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

 See also other articles from Wikipedia   Public market


Bond market

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Stock market

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Foreign exchange

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Other markets

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Practical trading

    Clearing house
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Finance series

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