User talk:Mahantaratan

Computational Finance
Computational finance is a branch of applied computer science that deals with problems of practical interest in finance.[1] Some slightly different definitions are the study of data and algorithms currently used in finance[2] and the mathematics of computer programs that realize financial models or systems.[3]

Computational finance emphasizes practical numerical methods rather than mathematical proofs and focuses on techniques that apply directly to economic analyses.[4] It is an interdisciplinary field between mathematical finance and numerical methods.[5] Two major areas are efficient and accurate computation of fair values of financial securities and the modeling of stochastic price series

Algorithmic Trading
Algorithmic trading, also called automated trading, black-box trading, or algo trading, is the use of electronic platforms for entering trading orders with an algorithm deciding on aspects of the order such as the timing, price, or quantity of the order, or in many cases initiating the order without human intervention.

Algorithmic trading is widely used by pension funds, mutual funds, and other buy side (investor driven) institutional traders, to divide large trades into several smaller trades to manage market impact, and risk.[1][2] Sell side traders, such as market makers and some hedge funds, provide liquidity to the market, generating and executing orders automatically.

A special class of algorithmic trading is "high-frequency trading" (HFT), in which computers make elaborate decisions to initiate orders based on information that is received electronically, before human traders are capable of processing the information they observe. This has resulted in a dramatic change of the market microstructure, particularly in the way liquidity is provided.[3]

Algorithmic trading may be used in any investment strategy, including market making, inter-market spreading, arbitrage, or pure speculation (including trend following). The investment decision and implementation may be augmented at any stage with algorithmic support or may operate completely automatically.

A third of all European Union and United States stock trades in 2006 were driven by automatic programs, or algorithms, according to Boston-based financial services industry research and consulting firm Aite Group.[4] As of 2009, HFT firms account for 73% of all US equity trading volume.[5]

In 2006 at the London Stock Exchange, over 40% of all orders were entered by algo traders, with 60% predicted for 2007. American markets and European markets generally have a higher proportion of algo trades than other markets, and estimates for 2008 range as high as an 80% proportion in some markets. Foreign exchange markets also have active algo trading (about 25% of orders in 2006).[6] Futures and options markets are considered fairly easy to integrated into algorithmic trading,[7] with about 20% of options volume expected to be computer-generated by 2010.[dated info][8] Bond markets are moving toward more access to algorithmic traders.[9]

One of the main issues regarding HFT is the difficulty in determining just how profitable it is. A report released in August 2009 by the TABB Group, a financial services industry research firm, estimated that the 300 securities firms and hedge funds that specialize in this type of trading took in roughly US$21 billion in profits in 2008.[10]

Algorithmic and HFT have been the subject of much public debate since the U.S. Securities and Exchange Commission and the Commodity Futures Trading Commission said they contributed to some of the volatility during the 2010 Flash Crash,[11][12][13][14][15][16][17][18] when the Dow Jones Industrial Average suffered its second largest intraday point swing ever to that date, though prices quickly recovered. (See List of largest daily changes in the Dow Jones Industrial Average.) A July, 2011 report by the International Organization of Securities Commissions (IOSCO), an international body of securities regulators, concluded that while "algorithms and HFT technology have been used by market participants to manage their trading and risk, their usage was also clearly a contributing factor in the flash crash event of May 6, 2010."

High Frequency Trading
High-frequency trading (HFT) is the use of sophisticated technological tools and computer algorithms to trade securities on a rapid basis.[1][2][3]

HFT usually uses proprietary trading strategies that are carried out by computers. Unlike regular investing, an investment position in HFT may be held for only seconds, or fractions of a second (though sometimes it may extend to longer), with the computer trading in and out of positions thousands of tens of thousands of times a day.[4] At the end of a day of HFT there is no open position in the market. Firms engaged in HFT rely heavily on the processing speed of their trades, and on their access to the market. Many high-frequency traders provide liquidity and price discovery to the markets through market-making and arbitrage trading; and high-frequency traders also take liquidity to manage risk or lock in profits.[5]

High-frequency traders compete on a basis of speed with other high-frequency traders, not long-term investors (who typically look for opportunities over a period of weeks, months, or years), and compete for very small, consistent profits.[6][7] As a result, high-frequency trading has been shown to have a potential Sharpe ratio (measure of reward per unit of risk) thousands of times higher than the traditional buy-and-hold strategies.[8]

Aiming to capture just a fraction of a penny per share or currency unit on every trade, high-frequency traders move in and out of such short-term positions several times each day. Fractions of a penny accumulate fast to produce significantly positive results at the end of every day.[2] High-frequency trading firms do not employ significant leverage, do not accumulate positions, and typically liquidate their entire portfolios on a daily basis.[7]

By 2010 high-frequency trading accounted for over 70% of equity trades in the US and was rapidly growing in popularity in Europe and Asia.[citation needed]

High-frequency trading may cause new types of serious risks to the financial system.[1][9] Algorithmic and high-frequency trading were both found to have contributed to volatility in the May 6, 2010 Flash Crash, when high-frequency liquidity providers were in fact found to have withdrawn from the market.[10][11][12][13][14][15][16][17] A July, 2011 report by the International Organization of Securities Commissions (IOSCO), an international body of securities regulators, concluded that while "algorithms and HFT technology have been used by market participants to manage their trading and risk, their usage was also clearly a contributing factor in the flash crash event of May 6, 2010."[1][18] An October 2012 study by the Chicago Federal Reserve found that "every exchange interviewed had experienced one or more errant algorithms" and recommended "limits on the number of orders that can be sent to an exchange within a specified period of time."[9]