## Monday, April 9, 2012

### Stock Market

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Stock market

Financial markets
Reserve-Bank-of-India.jpg

Public market

Exchange
Securities

Bond market

Fixed income
Corporate bond
Government bond
Municipal bond
Bond valuation
High-yield debt

Stock market

Stock
Preferred stock
Common stock
Registered share
Voting share
Stock exchange

Derivatives market

Securitization
Hybrid security
Credit derivative
Futures exchange

Over-the-counter

Spot market
Forwards
Swaps
Options

Foreign exchange

Exchange rate
Currency

Other markets

Money market
Reinsurance market
Commodity market
Real estate market

Participants
Clearing house
Financial regulation

Finance series

Banks and banking
Corporate finance
Personal finance
Public finance

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A stock market or equity market is a public entity (a loose network of economic transactions, not a physical facility or discrete entity) for the trading of company stock (shares) and derivatives at an agreed price; these are securities listed on a stock exchange as well as those only traded privately.

The size of the world stock market was estimated at about $36.6 trillion at the beginning of October 2008.[1] The total world derivatives market has been estimated at about$791 trillion face or nominal value,[2] 11 times the size of the entire world economy.[3] The value of the derivatives market, because it is stated in terms of notional values, cannot be directly compared to a stock or a fixed income security, which traditionally refers to an actual value. Moreover, the vast majority of derivatives 'cancel' each other out (i.e., a derivative 'bet' on an event occurring is offset by a comparable derivative 'bet' on the event not occurring). Many such relatively illiquid securities are valued as marked to model, rather than an actual market price.

The stocks are listed and traded on stock exchanges which are entities of a corporation or mutual organization specialized in the business of bringing buyers and sellers of the organizations to a listing of stocks and securities together. The largest stock market in the United States, by market capitalization, is the New York Stock Exchange (NYSE). In Canada, the largest stock market is the Toronto Stock Exchange. Major European examples of stock exchanges include the Amsterdam Stock Exchange, London Stock Exchange, Paris Bourse, and the Deutsche Börse (Frankfurt Stock Exchange). In Africa, examples include Nigerian Stock Exchange, JSE Limited, etc. Asian examples include the Singapore Exchange, the Tokyo Stock Exchange, the Hong Kong Stock Exchange, the Shanghai Stock Exchange, and the Bombay Stock Exchange. In Latin America, there are such exchanges as the BM&F Bovespa and the BMV.

Market participants include individual retail investors, institutional investors such as mutual funds, banks, insurance companies and hedge funds, and also publicly traded corporations trading in their own shares. Some studies have suggested that institutional investors and corporations trading in their own shares generally receive higher risk-adjusted returns than retail investors.[4]
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The London Stock Exchange
The New York Stock Exchange

Participants in the stock market range from small individual stock investors to large hedge fund traders, who can be based anywhere. Their orders usually end up with a professional at a stock exchange, who executes the order of buying or selling.

Some exchanges are physical locations where transactions are carried out on a trading floor, by a method known as open outcry. This type of auction is used in stock exchanges and commodity exchanges where traders may enter "verbal" bids and offers simultaneously. The other type of stock exchange is a virtual kind, composed of a network of computers where trades are made electronically via traders.

Actual trades are based on an auction market model where a potential buyer bids a specific price for a stock and a potential seller asks a specific price for the stock. (Buying or selling at market means you will accept any ask price or bid price for the stock, respectively.) When the bid and ask prices match, a sale takes place, on a first-come-first-served basis if there are multiple bidders or askers at a given price.

The purpose of a stock exchange is to facilitate the exchange of securities between buyers and sellers, thus providing a marketplace (virtual or real). The exchanges provide real-time trading information on the listed securities, facilitating price discovery.

The New York Stock Exchange is a physical exchange, also referred to as a listed exchange – only stocks listed with the exchange may be traded. Orders enter by way of exchange members and flow down to a floor broker, who goes to the floor trading post specialist for that stock to trade the order. The specialist's job is to match buy and sell orders using open outcry. If a spread exists, no trade immediately takes place—in this case the specialist should use his/her own resources (money or stock) to close the difference after his/her judged time. Once a trade has been made the details are reported on the "tape" and sent back to the brokerage firm, which then notifies the investor who placed the order. Although there is a significant amount of human contact in this process, computers play an important role, especially for so-called "program trading".

The NASDAQ is a virtual listed exchange, where all of the trading is done over a computer network. The process is similar to the New York Stock Exchange. However, buyers and sellers are electronically matched. One or more NASDAQ market makers will always provide a bid and ask price at which they will always purchase or sell 'their' stock.[5]

The Paris Bourse, now part of Euronext, is an order-driven, electronic stock exchange. It was automated in the late 1980s. Prior to the 1980s, it consisted of an open outcry exchange. Stockbrokers met on the trading floor or the Palais Brongniart. In 1986, the CATS trading system was introduced, and the order matching process was fully automated.

From time to time, active trading (especially in large blocks of securities) have moved away from the 'active' exchanges. Securities firms, led by UBS AG, Goldman Sachs Group Inc. and Credit Suisse Group, already steer 12 percent of U.S. security trades away from the exchanges to their internal systems. That share probably will increase to 18 percent by 2010 as more investment banks bypass the NYSE and NASDAQ and pair buyers and sellers of securities themselves, according to data compiled by Boston-based Aite Group LLC, a brokerage-industry consultant.[6]

Now that computers have eliminated the need for trading floors like the Big Board's, the balance of power in equity markets is shifting. By bringing more orders in-house, where clients can move big blocks of stock anonymously, brokers pay the exchanges less in fees and capture a bigger share of the $11 billion a year that institutional investors pay in trading commissions.  Market participants Market participants include individual retail investors, institutional investors such as mutual funds, banks, insurance companies and hedge funds, and also publicly traded corporations trading in their own shares. Some studies have suggested that institutional investors and corporations trading in their own shares generally receive higher risk-adjusted returns than retail investors.[7] A few decades ago, worldwide, buyers and sellers were individual investors, such as wealthy businessmen, usually with long family histories to particular corporations. Over time, markets have become more "institutionalized"; buyers and sellers are largely institutions (e.g., pension funds, insurance companies, mutual funds, index funds, exchange-traded funds, hedge funds, investor groups, banks and various other financial institutions). The rise of the institutional investor has brought with it some improvements in market operations. Thus, the government was responsible for "fixed" (and exorbitant) fees being markedly reduced for the 'small' investor, but only after the large institutions had managed to break the brokers' solid front on fees. other than the shares of beneficiary from the account (They then went to 'negotiated' fees, but only for large institutions.[citation needed]) However, corporate governance (at least in the West) has been very much adversely affected by the rise of (largely 'absentee') institutional 'owners'.[citation needed]  History Established in 1875, the Bombay Stock Exchange is Asia's first stock exchange In 12th century France the courratiers de change were concerned with managing and regulating the debts of agricultural communities on behalf of the banks. Because these men also traded with debts, they could be called the first brokers. A common misbelief is that in late 13th century Bruges commodity traders gathered inside the house of a man called Van der Beurze, and in 1309 they became the "Brugse Beurse", institutionalizing what had been, until then, an informal meeting, but actually, the family Van der Beurze had a building in Antwerp where those gatherings occurred;[8] the Van der Beurze had Antwerp, as most of the merchants of that period, as their primary place for trading. The idea quickly spread around Flanders and neighboring counties and "Beurzen" soon opened in Ghent and Amsterdam. In the middle of the 13th century, Venetian bankers began to trade in government securities. In 1351 the Venetian government outlawed spreading rumors intended to lower the price of government funds. Bankers in Pisa, Verona, Genoa and Florence also began trading in government securities during the 14th century. This was only possible because these were independent city states not ruled by a duke but a council of influential citizens. Italian companies were also the first to issue shares. Companies in England and the Low Countries followed in the 16th century. The Dutch East India Company (founded in 1602) was the first joint-stock company to get a fixed capital stock and as a result, continuous trade in company stock emerged on the Amsterdam Exchange. Soon thereafter, a lively trade in various derivatives, among which options and repos, emerged on the Amsterdam market. Dutch traders also pioneered short selling - a practice which was banned by the Dutch authorities as early as 1610.[9] There are now stock markets in virtually every developed and most developing economies, with the world's largest markets being in the United States, United Kingdom, Japan, India, China, Canada, Germany (Frankfurt Stock Exchange), France, South Korea and the Netherlands.[10]  Importance of stock market  Function and purpose The main trading room of the Tokyo Stock Exchange,where trading is currently completed through computers. The stock market is one of the most important sources for companies to raise money. This allows businesses to be publicly traded, or raise additional financial capital for expansion by selling shares of ownership of the company in a public market. The liquidity that an exchange affords investors the ability to quickly and easily sell securities. This is an attractive feature of investing in stocks, compared to other less liquid investments such as real estate.[citation needed] Some companies actively increase liquidity by trading in their own shares.[11][12] History has shown that the price of shares and other assets is an important part of the dynamics of economic activity, and can influence or be an indicator of social mood. An economy where the stock market is on the rise is considered to be an up-and-coming economy. In fact, the stock market is often considered the primary indicator of a country's economic strength and development.[citation needed] Rising share prices, for instance, tend to be associated with increased business investment and vice versa. Share prices also affect the wealth of households and their consumption. Therefore, central banks tend to keep an eye on the control and behavior of the stock market and, in general, on the smooth operation of financial system functions. Financial stability is the raison d'être of central banks.[citation needed] Exchanges also act as the clearinghouse for each transaction, meaning that they collect and deliver the shares, and guarantee payment to the seller of a security. This eliminates the risk to an individual buyer or seller that the counterparty could default on the transaction.[citation needed] The smooth functioning of all these activities facilitates economic growth in that lower costs and enterprise risks promote the production of goods and services as well as possibly employment. In this way the financial system is assumed to contribute to increased prosperity.[citation needed]  Relation of the stock market to the modern financial system The financial system in most western countries has undergone a remarkable transformation. One feature of this development is disintermediation. A portion of the funds involved in saving and financing, flows directly to the financial markets instead of being routed via the traditional bank lending and deposit operations. The general public interest in investing in the stock market, either directly or through mutual funds, has been an important component of this process. Statistics show that in recent decades shares have made up an increasingly large proportion of households' financial assets in many countries. In the 1970s, in Sweden, deposit accounts and other very liquid assets with little risk made up almost 60 percent of households' financial wealth, compared to less than 20 percent in the 2000s. The major part of this adjustment is that financial portfolios have gone directly to shares but a good deal now takes the form of various kinds of institutional investment for groups of individuals, e.g., pension funds, mutual funds, hedge funds, insurance investment of premiums, etc. The trend towards forms of saving with a higher risk has been accentuated by new rules for most funds and insurance, permitting a higher proportion of shares to bonds. Similar tendencies are to be found in other industrialized countries. In all developed economic systems, such as the European Union, the United States, Japan and other developed nations, the trend has been the same: saving has moved away from traditional (government insured) bank deposits to more risky securities of one sort or another  United States S&P stock market returns (assumes 2% annual dividend) Years to December 30 2011 Average Annual Return % Average Compounded Annual Return % 1 2.0 2.0 3 14.3 8.1 5 2.3 4.4 10 5.1 3.9 15 7.7 3.6 20 9.6 4.6 30 11.7 6.2 40 10.2 7.0 50 9.3 7.3 60 10.5 7.5 [13]  The behavior of the stock market NASDAQ in Times Square, New York City From experience we know that investors may 'temporarily' move financial prices away from their long term aggregate price 'trends'. (Positive or up trends are referred to as bull markets; negative or down trends are referred to as bear markets.) Over-reactions may occur—so that excessive optimism (euphoria) may drive prices unduly high or excessive pessimism may drive prices unduly low. Economists continue to debate whether financial markets are 'generally' efficient. According to one interpretation of the efficient-market hypothesis (EMH), only changes in fundamental factors, such as the outlook for margins, profits or dividends, ought to affect share prices beyond the short term, where random 'noise' in the system may prevail. (But this largely theoretic academic viewpoint—known as 'hard' EMH—also predicts that little or no trading should take place, contrary to fact, since prices are already at or near equilibrium, having priced in all public knowledge.) The 'hard' efficient-market hypothesis is sorely tested by such events as the stock market crash in 1987, when the Dow Jones index plummeted 22.6 percent—the largest-ever one-day fall in the United States.[14] This event demonstrated that share prices can fall dramatically even though, to this day, it is impossible to fix a generally agreed upon definite cause: a thorough search failed to detect any 'reasonable' development that might have accounted for the crash. (But note that such events are predicted to occur strictly by chance, although very rarely.) It seems also to be the case more generally that many price movements (beyond that which are predicted to occur 'randomly') are not occasioned by new information; a study of the fifty largest one-day share price movements in the United States in the post-war period seems to confirm this.[14] However, a 'soft' EMH has emerged which does not require that prices remain at or near equilibrium, but only that market participants not be able to systematically profit from any momentary market 'inefficiencies'. Moreover, while EMH predicts that all price movement (in the absence of change in fundamental information) is random (i.e., non-trending), many studies have shown a marked tendency for the stock market to trend over time periods of weeks or longer. Various explanations for such large and apparently non-random price movements have been promulgated. For instance, some research has shown that changes in estimated risk, and the use of certain strategies, such as stop-loss limits and Value at Risk limits, theoretically could cause financial markets to overreact. But the best explanation seems to be that the distribution of stock market prices is non-Gaussian (in which case EMH, in any of its current forms, would not be strictly applicable).[15][16] Other research has shown that psychological factors may result in exaggerated (statistically anomalous) stock price movements (contrary to EMH which assumes such behaviors 'cancel out'). Psychological research has demonstrated that people are predisposed to 'seeing' patterns, and often will perceive a pattern in what is, in fact, just noise. (Something like seeing familiar shapes in clouds or ink blots.) In the present context this means that a succession of good news items about a company may lead investors to overreact positively (unjustifiably driving the price up). A period of good returns also boosts the investor's self-confidence, reducing his (psychological) risk threshold.[17] Another phenomenon—also from psychology—that works against an objective assessment is group thinking. As social animals, it is not easy to stick to an opinion that differs markedly from that of a majority of the group. An example with which one may be familiar is the reluctance to enter a restaurant that is empty; people generally prefer to have their opinion validated by those of others in the group. In one paper the authors draw an analogy with gambling.[18] In normal times the market behaves like a game of roulette; the probabilities are known and largely independent of the investment decisions of the different players. In times of market stress, however, the game becomes more like poker (herding behavior takes over). The players now must give heavy weight to the psychology of other investors and how they are likely to react psychologically. The stock market, as with any other business, is quite unforgiving of amateurs. Inexperienced investors rarely get the assistance and support they need. In the period running up to the 1987 crash, less than 1 percent of the analyst's recommendations had been to sell (and even during the 2000–2002 bear market, the average did not rise above 5 %). In the run up to 2000, the media amplified the general euphoria, with reports of rapidly rising share prices and the notion that large sums of money could be quickly earned in the so-called new economy stock market. (And later amplified the gloom which descended during the 2000–2002 bear market, so that by summer of 2002, predictions of a DOW average below 5000 were quite common.)  Irrational behavior Sometimes, the market seems to react irrationally to economic or financial news, even if that news is likely to have no real effect on the fundamental value of securities itself. But, this may be more apparent than real, since often such news has been anticipated, and a counterreaction may occur if the news is better (or worse) than expected. Therefore, the stock market may be swayed in either direction by press releases, rumors, euphoria and mass panic; but generally only briefly, as more experienced investors (especially the hedge funds) quickly rally to take advantage of even the slightest, momentary hysteria. Over the short-term, stocks and other securities can be battered or buoyed by any number of fast market-changing events, making the stock market behavior difficult to predict. Emotions can drive prices up and down, people are generally not as rational as they think, and the reasons for buying and selling are generally obscure. Behaviorists argue that investors often behave 'irrationally' when making investment decisions thereby incorrectly pricing securities, which causes market inefficiencies, which, in turn, are opportunities to make money.[19] However, the whole notion of EMH is that these non-rational reactions to information cancel out, leaving the prices of stocks rationally determined. The Dow Jones Industrial Average biggest gain in one day was 936.42 points or 11 percent, this occurred on October 13, 2008.[20]  Crashes Main article: Stock market crash Further information: List of stock market crashes Globe icon. The examples and perspective in this section may not represent a worldwide view of the subject. Please improve this article and discuss the issue on the talk page. (March 2009) Robert Shiller's plot of the S&P Composite Real Price Index, Earnings, Dividends, and Interest Rates, from Irrational Exuberance, 2d ed.[21] In the preface to this edition, Shiller warns, "The stock market has not come down to historical levels: the price-earnings ratio as I define it in this book is still, at this writing [2005], in the mid-20s, far higher than the historical average... People still place too much confidence in the markets and have too strong a belief that paying attention to the gyrations in their investments will someday make them rich, and so they do not make conservative preparations for possible bad outcomes." Price-Earnings ratios as a predictor of twenty-year returns based upon the plot by Robert Shiller (Figure 10.1,[21] source). The horizontal axis shows the real price-earnings ratio of the S&P Composite Stock Price Index as computed in Irrational Exuberance (inflation adjusted price divided by the prior ten-year mean of inflation-adjusted earnings). The vertical axis shows the geometric average real annual return on investing in the S&P Composite Stock Price Index, reinvesting dividends, and selling twenty years later. Data from different twenty year periods is color-coded as shown in the key. See also ten-year returns. Shiller states that this plot "confirms that long-term investors—investors who commit their money to an investment for ten full years—did do well when prices were low relative to earnings at the beginning of the ten years. Long-term investors would be well advised, individually, to lower their exposure to the stock market when it is high, as it has been recently, and get into the market when it is low."[21] A stock market crash is often defined as a sharp dip in share prices of equities listed on the stock exchanges. In parallel with various economic factors, a reason for stock market crashes is also due to panic and investing public's loss of confidence. Often, stock market crashes end speculative economic bubbles. There have been famous stock market crashes that have ended in the loss of billions of dollars and wealth destruction on a massive scale. An increasing number of people are involved in the stock market, especially since the social security and retirement plans are being increasingly privatized and linked to stocks and bonds and other elements of the market. There have been a number of famous stock market crashes like the Wall Street Crash of 1929, the stock market crash of 1973–4, the Black Monday of 1987, the Dot-com bubble of 2000, and the Stock Market Crash of 2008. One of the most famous stock market crashes started October 24, 1929 on Black Thursday. The Dow Jones Industrial lost 50 % during this stock market crash. It was the beginning of the Great Depression. Another famous crash took place on October 19, 1987 – Black Monday. The crash began in Hong Kong and quickly spread around the world. By the end of October, stock markets in Hong Kong had fallen 45.5 %%, Australia 41.8 %%, Spain 31 %%, the United Kingdom 26.4 %%, the United States 22.68 %%, and Canada 22.5 %%. Black Monday itself was the largest one-day percentage decline in stock market history – the Dow Jones fell by 22.6 %% in a day. The names “Black Monday” and “Black Tuesday” are also used for October 28–29, 1929, which followed Terrible Thursday—the starting day of the stock market crash in 1929. The crash in 1987 raised some puzzles-–main news and events did not predict the catastrophe and visible reasons for the collapse were not identified. This event raised questions about many important assumptions of modern economics, namely, the theory of rational human conduct, the theory of market equilibrium and the hypothesis of market efficiency. For some time after the crash, trading in stock exchanges worldwide was halted, since the exchange computers did not perform well owing to enormous quantity of trades being received at one time. This halt in trading allowed the Federal Reserve system and central banks of other countries to take measures to control the spreading of worldwide financial crisis. In the United States the SEC introduced several new measures of control into the stock market in an attempt to prevent a re-occurrence of the events of Black Monday. Since the early 1990's, many of the largest exchanges have adopted electronic 'matching engines' to bring together buyers and sellers, replacing the open outcry system. Electronic trading now accounts for the majority of trading in many developed countries. Computer systems were upgraded in the stock exchanges to handle larger trading volumes in a more accurate and controlled manner. The SEC modified the margin requirements in an attempt to lower the volatility of common stocks, stock options and the futures market. The New York Stock Exchange and the Chicago Mercantile Exchange introduced the concept of a circuit breaker. The circuit breaker halts trading if the Dow declines a prescribed number of points for a prescribed amount of time. In February 2012, the Investment Industry Regulatory Organization of Canada (IIROC) introduced single-stock circuit breakers.[22] New York Stock Exchange (NYSE) circuit breakers[23] % drop time of drop close trading for 10 before 2 pm one hour halt 10 2 pm – 2:30 pm half-hour halt 10 after 2:30 pm market stays open 20 before 1 pm halt for two hours 20 1 pm – 2 pm halt for one hour 20 after 2 pm close for the day 30 any time during day close for the day  Stock market index Main article: Stock market index The movements of the prices in a market or section of a market are captured in price indices called stock market indices, of which there are many, e.g., the S&P, the FTSE and the Euronext indices. Such indices are usually market capitalization weighted, with the weights reflecting the contribution of the stock to the index. The constituents of the index are reviewed frequently to include/exclude stocks in order to reflect the changing business environment.  Derivative instruments Main article: Derivative (finance) Financial innovation has brought many new financial instruments whose pay-offs or values depend on the prices of stocks. Some examples are exchange-traded funds (ETFs), stock index and stock options, equity swaps, single-stock futures, and stock index futures. These last two may be traded on futures exchanges (which are distinct from stock exchanges—their history traces back to commodities futures exchanges), or traded over-the-counter. As all of these products are only derived from stocks, they are sometimes considered to be traded in a (hypothetical) derivatives market, rather than the (hypothetical) stock market.  Leveraged strategies Stock that a trader does not actually own may be traded using short selling; margin buying may be used to purchase stock with borrowed funds; or, derivatives may be used to control large blocks of stocks for a much smaller amount of money than would be required by outright purchase or sales.  Short selling Main article: Short selling In short selling, the trader borrows stock (usually from his brokerage which holds its clients' shares or its own shares on account to lend to short sellers) then sells it on the market, hoping for the price to fall. The trader eventually buys back the stock, making money if the price fell in the meantime and losing money if it rose. Exiting a short position by buying back the stock is called "covering a short position." This strategy may also be used by unscrupulous traders in illiquid or thinly traded markets to artificially lower the price of a stock. Hence most markets either prevent short selling or place restrictions on when and how a short sale can occur. The practice of naked shorting is illegal in most (but not all) stock markets.  Margin buying Main article: margin buying In margin buying, the trader borrows money (at interest) to buy a stock and hopes for it to rise. Most industrialized countries have regulations that require that if the borrowing is based on collateral from other stocks the trader owns outright, it can be a maximum of a certain percentage of those other stocks' value. In the United States, the margin requirements have been 50 % for many years (that is, if you want to make a$1000 investment, you need to put up $500, and there is often a maintenance margin below the$500).

A margin call is made if the total value of the investor's account cannot support the loss of the trade. (Upon a decline in the value of the margined securities additional funds may be required to maintain the account's equity, and with or without notice the margined security or any others within the account may be sold by the brokerage to protect its loan position. The investor is responsible for any shortfall following such forced sales.)

Regulation of margin requirements (by the Federal Reserve) was implemented after the Crash of 1929. Before that, speculators typically only needed to put up as little as 10 percent (or even less) of the total investment represented by the stocks purchased. Other rules may include the prohibition of free-riding: putting in an order to buy stocks without paying initially (there is normally a three-day grace period for delivery of the stock), but then selling them (before the three-days are up) and using part of the proceeds to make the original payment (assuming that the value of the stocks has not declined in the interim).
 New issuance
Main article: Thomson Reuters league tables

Global issuance of equity and equity-related instruments totaled $505 billion in 2004, a 29.8 % increase over the$389 billion raised in 2003. Initial public offerings (IPOs) by US issuers increased 221 % with 233 offerings that raised $45 billion, and IPOs in Europe, Middle East and Africa (EMEA) increased by 333 %, from$ 9 billion to $39 billion.  Investment strategies Main article: Stock valuation This section's tone or style may not reflect the formal tone used on Wikipedia. Specific concerns may be found on the talk page. See Wikipedia's guide to writing better articles for suggestions. (November 2011) One of the many things people always want to know about the stock market is, "How do I make money investing?" There are many different approaches; two basic methods are classified by either fundamental analysis or technical analysis. Fundamental analysis refers to analyzing companies by their financial statements found in SEC Filings, business trends, general economic conditions, etc. Technical analysis studies price actions in markets through the use of charts and quantitative techniques to attempt to forecast price trends regardless of the company's financial prospects. One example of a technical strategy is the Trend following method, used by John W. Henry and Ed Seykota, which uses price patterns, utilizes strict money management and is also rooted in risk control and diversification. Additionally, many choose to invest via the index method. In this method, one holds a weighted or unweighted portfolio consisting of the entire stock market or some segment of the stock market (such as the S&P 500 or Wilshire 5000). The principal aim of this strategy is to maximize diversification, minimize taxes from too frequent trading, and ride the general trend of the stock market (which, in the U.S., has averaged nearly 10 % per year, compounded annually, since World War II).  Taxation Main article: Capital gains tax According to much national or state legislation, a large array of fiscal obligations are taxed for capital gains. Taxes are charged by the state over the transactions, dividends and capital gains on the stock market, in particular in the stock exchanges. However, these fiscal obligations may vary from jurisdictions to jurisdictions because, among other reasons, it could be assumed that taxation is already incorporated into the stock price through the different taxes companies pay to the state, or that tax free stock market operations are useful to boost economic growt Program trading From Wikipedia, the free encyclopedia Jump to: navigation, search Program trading is a generic term used to describe a type of trading in securities, usually consisting of baskets of fifteen stocks or more that are executed by a computer program simultaneously based on predetermined conditions.[1] They are often used to arbitrage temporary price discrepancies between related financial instruments. More specifically, program trading in the US is described as a type of trading in securities, usually consisting of stocks traded on the New York Stock Exchange with a combined value of at least$1 million, and their corresponding options traded on the Chicago Board Options Exchange and/or the American Stock Exchange; and the Standard & Poor's 500 Index futures contract traded on the Chicago Mercantile Exchange. The trading of these items is based purely on their price in relation to each other on a predetermined basis; and not on any fundamental analysis reason such as an individual company's earnings, dividends, or growth prospects; or, on any overall economic reasons such as interest rate movements, currency fluctuations, or governmental or political actions.

According to the New York Stock Exchange, in 2006 program trading accounts for about 30% and as high as 46.4% of the trading volume on that exchange every day.[2] These historical percentages show the dominance of Program Trading listed on the NYSE.
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 History

Three factors help to explain the explosion in program trading. First, technological advances spawned the growth of electronic communication networks. These electronic exchanges, like Instinet and Archipelago Exchange, allow thousands of buy and sell orders to be matched very rapidly, without human intervention.

Second, the Securities and Exchange Commission mandated in 2001 that the major stock exchanges price stocks in dollars and cents instead of fractions. A stock previously priced at 7 1/8 is now listed at $7.13. Pricing stocks in penny increments instead of 1/16 increments results in 100 price points within a dollar instead of the previous eight price points. That means all the willing buyers and sellers are dispersed over many more prices, making it more difficult for them to meet on price. Third, and perhaps most significant, the proliferation of hedge funds with all their sophisticated trading strategies is driving program-trading volume.[3] As technology advanced and access to electronic exchanges became easier and faster, program trading developed into the much broader algorithmic trading and high-frequency trading strategies employed by the investment banks and hedge funds.  Program Trading Firms Program Trading is a strategy normally used by large institutional traders such as Goldman Sachs, Morgan Stanley, Credit Suisse First Boston, UBS Securities, Barclays Capital, ISI and SG America's Securities. During the second quarter of 2009, Goldman Sachs recorded record trading profits, with much of those gains ascribed to program trading, according to heavy press coverage.[4] Barrons shows a detailed breakdown of the NYSE-published program trading figures each week, identifying index and non-index arbitrage.[5]  Index Arbitrage Index Arbitrage is another form of Program Trading. The major institutional traders using Index Arbitrage are Royal Bank of Canada and the Deutsche Bank. Index Arbitrage ranges from 2% - 10% of the active Program Trading volume daily. On some occasions the Royal Bank of Canada and Deutsche Bank will push Index Arbitrage to move as high as 20% but that is rare, as the market size of the non-Index Arbitrage Program Trading firms, primarily Goldman Sachs and Morgan Stanley, tend to dominate.[6]  Premium Buy and Sell Execution Levels The "premium" (PREM) or "spread" is the difference between the most active S&P 500 Stock Index Futures Contract fair value minus the actual S&P 500 Stock Index (cash). The decision to execute a program is based on this difference, which usually ranges between$5.00 to $-5.00, and slowly decays or rises as the S&P 500 Futures Contract approaches expiration. When the PREM difference rises to a certain execution level, "buy" programs kick in. Large institutional traders then buy the stocks in the S&P 500 Stock Index on the New York Stock Exchange and sell the S&P 500 Stock Index Futures Contract against those positions on the Chicago Mercantile Exchange. When the PREM difference drops to a certain execution level, "sell" programs kick in and those large institutional traders do the exact opposite.[7] It is possible to compute the fair value of a futures contract. The calculation is based on the work of Professor Hans Stoll from Vanderbilt University, one of the foremost authorities on the subject. The formula to calculate fair value is:[8] Fair Value FV = S [1 + (I - D)] The equation represents the value of the S&P 500 Index (S), plus the risk-free interest rate, or the margin rate to borrow to pay for the purchased shares (I), minus the dividend received from the stocks (D).  See also Algorithmic trading also known as automated trading, algo trading, black-box trading or robo trading High-frequency trading Alternative Trading Systems Electronic trading platform Outline of finance Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. See Terms of use for details. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization. ### Technical Analysis of Stock Trends If you trade the S&P 500 Emini Futures, or trade the Nasdaq, Dow Jones, Rusell mini futures, or if you trade Forex and Crude Oil you need to check out www.sceeto.com for one of the worlds most advanced indicators. A no obligation Free Trial is availible. www.sceeto.com Most Indicators And technical Analysis lag. Check out http://www.sceeto.com...it does not lag. Get a free trial at www.sceeto.com and use the discount code "save35" to get up to 35% off your subscription. .In today's electronic markets, day traders are going up against massive trading desks that maintain large staffs of computer professionals to help them manage their orders and to help provide them with insightful information....sceeto will alert you in real-time when substantive program trading is taking place and that will have a material effect on price movements....sceeto monitors each and every trade in today's electronic markets so When the big banks and trading desks buy or sell, ...sceeto can alert you instantly. ...sceeto allows you to mimic some of the massive brains and software that the pros use which enables you to trade with bots and not against them. Be on the side of more winning trades. 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 But at the end of the day in our opinion there is no indicator that can come close to the sheer processing power of sceeto and true reckoning and in real time. Know what the market is going to do before it does it. 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]  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.  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.  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]  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]  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.  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.  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]  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]  Systematic trading  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.  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]  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]  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]  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]  Charting terms and indicators  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.  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.  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  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.  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  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 Exchange Securities Bond market Fixed income Corporate bond Government bond Municipal bond Bond valuation High-yield debt Stock market Stock Preferred stock Common stock Registered share Voting share Stock exchange Derivatives market Securitization Hybrid security Credit derivative Futures exchange Over-the-counter Spot market Forwards Swaps Options Foreign exchange Exchange rate Currency Other markets Money market Reinsurance market Commodity market Real estate market Practical trading Participants Clearing house Financial regulation Finance series Banks and banking Corporate finance Personal finance Public finance Market analysis Market timing Price action trading Chartered Market Technician Behavioral finance Mathematical finance Multimedia Information Retrieval Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. See Terms of use for details. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization. ### Win More Trades With Sceeto If you trade the S&P 500 Emini Futures, or trade the Nasdaq, Dow Jones, Rusell mini futures, or if you trade Forex and Crude Oil you need to check out www.sceeto.com for one of the worlds most advanced indicators. A no obligation Free Trial is availible. www.sceeto.com Binary options are a great way to trade futures like the emini futures, the ym, the russell, s&p 500, dow jones, crude oil and more. Binary option Courtesy of Wikipedia From Wikipedia, the free encyclopedia (Redirected from Binary options) In finance, a binary option is a type of option where the payoff is either some fixed amount of some asset or nothing at all. The two main types of binary options are the cash-or-nothing binary option and the asset-or-nothing binary option. The cash-or-nothing binary option pays some fixed amount of cash if the option expires in-the-money while the asset-or-nothing pays the value of the underlying security. Thus, the options are binary in nature because there are only two possible outcomes. They are also called all-or-nothing options, digital options (more common in forex/interest rate markets), and Fixed Return Options (FROs) (on the American Stock Exchange). Binary options are usually European-style options. For example, a purchase is made of a binary cash-or-nothing call option on XYZ Corp's stock struck at$100 with a binary payoff of $1000. Then, if at the future maturity date, the stock is trading at or above$100, $1000 is received. If its stock is trading below$100, nothing is

In the popular Black-Scholes model, the value of a digital option can be expressed in terms

of the cumulative normal distribution function.
Contents

Binary options contracts have long been available Over-the-counter (OTC), i.e. sold directly

by the issuer to the buyer. They were generally considered "exotic" instruments and there

was no liquid market for trading these instruments between their issuance and expiration.

They were often seen embedded in more complex option contracts.

Since mid-2008 binary options web-sites called binary option trading platforms have been

offering a simplified version of exchange-traded binary options. It is estimated that around

50 such platforms (including white label products) have been in operation as of January

2011, offering options on some 70 underlying assets.

In 2007, the Options Clearing Corporation proposed a rule change to allow binary options,[1]

and the Securities and Exchange Commission approved listing cash-or-nothing binary

options in 2008.[2] In May 2008, the American Stock Exchange (Amex) launched exchange-

traded European cash-or-nothing binary options, and the Chicago Board Options Exchange

(CBOE) followed in June 2008. The standardization of binary options allows them to be

Amex offers binary options on some ETFs and a few highly liquid equities such as Citigroup

and Google.[3] Amex calls binary options "Fixed Return Options"; calls are named "Finish

High" and puts are named "Finish Low". To reduce the threat of market manipulation of

single stocks, Amex FROs use a "settlement index" defined as a volume-weighted average

of trades on the expiration day.[4] The American Stock Exchange and Donato A. Montanaro

submitted a patent application for exchange-listed binary options using a volume-weighted

settlement index in 2005.[5]

CBOE offers binary options on the S&P 500 (SPX) and the CBOE Volatility Index (VIX).[6] The

tickers for these are BSZ[7] and BVZ,[8] respectively. CBOE only offers calls, as binary put

options are trivial to create synthetically from binary call options. BSZ strikes are at 5-point

intervals and BVZ strikes are at 1-point intervals. The actual underlying to BSZ and BVZ are

based on the opening prices of index basket members.

Both Amex and CBOE listed options have values between $0 and$1, with a multiplier of 100,

and tick size of $0.01, and are cash settled.[6][9] In 2009 Nadex, the North American Derivatives Exchange, launched and now offers a suite of binary options vehicles.[10]. Nadex binary options are available on a range Stock Index Futures, Spot Forex, Commodity Futures, and Economic Events.  Example of a Binary Options Trade A trader who thinks that the EUR/USD strike price will close at or above 1.2500 at 3:00 p.m. can buy a call option on that outcome. A trader who thinks that the EUR/USD strike price will close at or below 1.2500 at 3:00 p.m. can buy a put option or sell the contract. At 2:00 p.m. the EUR/USD spot price is 1.2490. the trader believes this will increase, so he buys 10 call options for EUR/USD at or above 1.2500 at 3:00 p.m. at a cost of$40 each.

The risk involved in this trade is known. The trader’s gross profit/loss follows the ‘all or

nothing’ principle. He can lose all the money he invested, which in this case is $40 x 10 =$400, or make a gross profit of $100 x 10 =$1000. If the EUR/USD strike price will close at or

above 1.2500 at 3:00 p.m. the trader's net profit will be the payoff at expiry minus the cost of

the option: $1000 -$400 = $600. The trader can also choose to liquidate (buy or sell to close) his position prior to expiration, at which point the option value is not guaranteed to be$100. The larger the gap between

the spot price and the strike price, the value of the option decreases, as the option is less

likely to expire in the money.

In this example, at 3:00 p.m. the spot has risen to 1.2505. The option has expired in the

money and the gross payoff is $1000. The trader's net profit is$600.
 Black-Scholes Valuation

In the Black-Scholes model, the price of the option can be found by the formulas below.[11]

In these, S is the initial stock price, K denotes the strike price, T is the time to maturity, q is

the dividend rate, r is the risk-free interest rate and \sigma is the volatility. \Phi denotes the

cumulative distribution function of the normal distribution,

\Phi(x) = \frac{1}{\sqrt{2 \pi}} \int_{-\infty}^x e^{-\frac{1}{2} z^2} dz.

and,

d_1 = \frac{\ln\frac{S}{K} + (r-q+\sigma^{2}/2)T}{\sigma\sqrt{T}},\,d_2 = d_1-\sigma\sqrt{T}. \,

 Cash-or-nothing call

This pays out one unit of cash if the spot is above the strike at maturity. Its value now is

given by,

C = e^{-rT}\Phi(d_2). \,

 Cash-or-nothing put

This pays out one unit of cash if the spot is below the strike at maturity. Its value now is

given by,

P = e^{-rT}\Phi(-d_2). \,

 Asset-or-nothing call

This pays out one unit of asset if the spot is above the strike at maturity. Its value now is

given by,

C = Se^{-qT}\Phi(d_1). \,

 Asset-or-nothing put

This pays out one unit of asset if the spot is below the strike at maturity. Its value now is

given by,

P = Se^{-qT}\Phi(-d_1). \,

 Foreign exchange
Further information: Foreign exchange derivative

If we denote by S the FOR/DOM exchange rate (i.e. 1 unit of foreign currency is worth S units

of domestic currency) we can observe that paying out 1 unit of the domestic currency if the

spot at maturity is above or below the strike is exactly like a cash-or nothing call and put

respectively. Similarly, paying out 1 unit of the foreign currency if the spot at maturity is

above or below the strike is exactly like an asset-or nothing call and put respectively. Hence

if we now take r_{FOR} , the foreign interest rate, r_{DOM} , the domestic interest rate, and

the rest as above, we get the following results.

In case of a digital call (this is a call FOR/put DOM) paying out one unit of the domestic

currency we get as present value,

C = e^{-r_{DOM} T}\Phi(d_2) \,

In case of a digital put (this is a put FOR/call DOM) paying out one unit of the domestic

currency we get as present value,

P = e^{-r_{DOM}T}\Phi(-d_2) \,

While in case of a digital call (this is a call FOR/put DOM) paying out one unit of the foreign

currency we get as present value,

C = Se^{-r_{FOR} T}\Phi(d_1) \,

and in case of a digital put (this is a put FOR/call DOM) paying out one unit of the foreign

currency we get as present value,

P = Se^{-r_{FOR}T}\Phi(-d_1) \,

 Skew

In the standard Black-Scholes model, one can interpret the premium of the binary option in

the risk-neutral world as the expected value = probability of being in-the-money * unit,

discounted to the present value.

To take volatility skew into account, a more sophisticated analysis based on call spreads

can be used.

A binary call option is, at long expirations, similar to a tight call spread using two vanilla

options. One can model the value of a binary cash-or-nothing option, C, at strike K, as an

infinitessimally tight spread, where C_v is a vanilla European call:[page needed],[12][13]

C = \lim_{\epsilon \to 0} \frac{C_v(K-\epsilon) - C_v(K)}{\epsilon}

Thus, the value of a binary call is the negative of the derivative of the price of a vanilla call

with respect to strike price:

C = -\frac{dC_v}{dK}

When one takes volatility skew into account, \sigma is a function of K:

C = -\frac{dC_v(K,\sigma(K))}{dK} = -\frac{\partial C_v}{\partial K} - \frac{\partial C_v}{\partial

\sigma} \frac{\partial \sigma}{\partial K}

The first term is equal to the premium of the binary option ignoring skew:

-\frac{\partial C_v}{\partial K} = -\frac{\partial (S\Phi(d_1) - Ke^{-rT}\Phi(d_2))}{\partial K} = e^{-

rT}\Phi(d_2) = C_{noskew}

\frac{\partial C_v}{\partial \sigma} is the Vega of the vanilla call; \frac{\partial \sigma}{\partial K}

is sometimes called the "skew slope" or just "skew". Skew is typically negative, so the value

of a binary call is higher when taking skew into account.

C = C_{noskew} - Vega_v * Skew

 Relationship to vanilla options' Greeks

Since a binary call is a mathematical derivative of a vanilla call with respect to strike, the

price of a binary call has the same shape as the delta of a vanilla call, and the delta of a

binary call has the same shape as the gamma of a vanilla call.[14]
 Interpretation of prices

In a prediction market, binary options are used to find out a population's best estimate of an

event occurring - for example, a price of 0.65 on a binary option triggered by the Democratic

candidate winning the next US Presidential election can be interpreted as an estimate of

65% likelihood of him winning.

In financial markets, expected returns on a stock or other instrument are already priced into

the stock. However, a binary options market provides other information. Just as the regular

options market reveals the market's estimate of variance (volatility), i.e. the second

moment, a binary options market reveals the market's estimate of skew, i.e. the third

moment.

In theory, a portfolio of binary options can also be used to synthetically recreate (or valuate)

any other option (analogous to integration), although in practical terms this is not possible

due to the lack of depth of the market for these relatively thinly traded securities.

In theory a portfolio of options can synthetically recreate any other financial instrument,

including conventional options.[14]
 Structured Binary Options Strategies

It may come as a surprise to many interested in the options space that put options were not

introduced on the CBOE until 1977, nine years after call options were. The binary options

market at present is in the same 'no-mans-land' where there is a vibrant FX binary options

market with sophisticated binary options strategies, while at the other extreme there are a

plethora of platforms offering one-hour bets dressing themselves up as 'investments'.
But the binary options market too has its range of straddles, strangles, call spreads,

butterflies, condors etc.. which as yet have not been explored by the mainstream

exchanges. Tunnels, aka rangebets, aka corridors are reasonably well-known and are

priced in the manner of a conventional call spread although the tunnel is primarily a

volatility trade. Others such as the Duke of York, Tug of War, Accumulators provide a rich

seam of varied instruments providing distinct and unique P&L profiles.
As indicated above, binary options are generally perceived as European-style options that

cannot be exercised before expiry. The American-style binary options are out there but are

usually referred to as one-touch options. A comprehensive list of binary options strategies

would include European and American binary options, 'knock-in' binary options, 'knock-out'

binary options and two-asset binary options.