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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]
Contents
[show]
[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
one.
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
patterns.
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
interpretation.
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
Theory.[10]
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
systems.[35][36][37]
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.
[52]
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".
[56]
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
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.
Cycles — time targets for potential change in price action (price only moves up, down, or
sideways)
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
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