20 de mayo de 2007

Algorithmic trading

Algorithmic trading

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In electronic financial markets, algorithmic trading, also known as algo, automated, black-box, or robo trading, is the use of computer programs for entering trading orders with the computer algorithm deciding on certain aspects of the order such as the timing, price, or even the final quantity of the order. It is widely used by pension funds, mutual funds, and other institutional traders to divide up a large trade into several smaller trades in order to avoid market impact costs and reduce other transaction costs.[1]. It is also used by hedge funds and similar traders to make the decision to initiate orders based on information that is received electronically, before human traders are even aware of the information.

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 US stock trades in 2006 were driven by automatic programs, or algorithms, according to Boston-based consulting firm Aite Group LLC. By 2010, that figure will reach 50 percent, according to Aite.[2]

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 equity 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).[3] Futures and options markets are considered to be fairly easily integrated into algorithmic trading[4], and bond markets are moving toward more access to algorithmic traders.[5]



[edit] History

Computerization of the order flow in financial markets began as early as the 1970s with some landmarks being the introduction of the New York Stock Exchange’s “designated order turnaround” system (DOT, and later SuperDOT) which routed orders electronically to the proper trading post to be executed manually, and the "opening automated reporting system" (OARS) which aided the specialist in determining the market clearing opening price.

Program trading is defined by the New York Stock Exchange as an order to buy or sell 15 or more stocks valued at over $1 million total. In practice this means that all program trades are entered with the aid of a computer. In the 1980s program trading became widely used in trading between equity and futures markets.

In stock index arbitrage a trader would buy (sell) a stock index futures contract such as the S&P 500 Index futures and sell (buy) a portfolio of up to 500 stocks at the NYSE matched against the futures trade. The program trade at the NYSE would be pre-programmed into a computer to enter the order automatically into the NYSE’s electronic order routing system at a time when the futures price and the stock index were far enough apart to make a profit.

At about the same time portfolio insurance was designed to create a synthetic put option on a stock portfolio by dynamically trading stock index futures according to a computer model based on the Black-Scholes option pricing model.

Both strategies, often simply lumped together as “program trading,” were blamed by many people (for example by the Brady report) for exacerbating or even starting the 1987 stock market crash.

Financial markets with fully electronic execution and similar electronic communication networks developed in the late 1980s and 1990s. In the U.S., decimalization, which change the minimum tick size from 1/16th of a dollar ($0.0625) to $0.01 per share, may have encouraged algorithmic trading as it changed the market microstructure by decreasing the market-maker’s trading advantage and reduced market liquidity.

The reduced liquidity was reflected by smaller trade sizes, which led to institutional traders splitting up orders according to computer algorithms in order to execute their orders at a better average price. These average price benchmarks are measured (and calculated by computer) by the time weighted (i.e unweighted) average price TWAP or more usually by the volume weighted average price VWAP.

As more electronic markets opened, other algorithmic trading strategies became possible including arbitrage and statistical arbitrage. These strategies are more easily implemented by computer because machines can react more rapidly to temporary mispricing and examine prices from several markets simultaneously.

The Man Group’s program AHL was an early trading algorithm. According to the New York Times, the Man Group claims that it has had an annualized return of 17.9 percent since December 1990. Part of its program includes trend following.[6]

[edit] Strategies

Many different algorithms have been developed to implement different trading strategies. These algorithms or techniques are commonly given names such as "iceberging," "Guerrilla," "benchmarking," "Sniper" and "Snif-fer." [7]

[edit] Transaction cost reduction

Large orders are broken down into several smaller orders and entered into the market over time. This basic strategy is called "iceberging." The success of this strategy may be measured by the average purchase price against the VWAP for the market over that time period. One algorithm designed to find hidden orders or icebergs is called "Guerrilla."

[edit] Arbitrage

A classical arbitrage strategy might involve three or four securities such as covered interest rate parity in the foreign exchange market which gives a relation between the prices of a domestic bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the currency. If the market prices are sufficiently different from those implied in the model to cover transactions cost then four transactions can be made to guarantee a risk-free profit. Algorithmic trading allows similar arbitrages using models of greater complexity involving much more than 4 securities.

[edit] Market making

Market making involves placing a limit order to sell (or offer) above the current market price or a buy limit order (or bid) below the current price in order to benefit from the bid-ask spread.

[edit] More complicated strategies

A "benchmarking" algorithm is used by traders attempting to mimic an index's return. An algorithm designed to discover which markets are most volatile or unstable is called "Snif-fer."

Any sort of pattern recognition or predictive model can be used to initiate algo trading. Neural networks and genetic programming have been used to create these models. Strong competition to build more sophisticated models has developed including well known computer scientists such as Ray Kurzweil.

“Now it’s an arms race,” said Andrew Lo, director of the Massachusetts Institute of Technology’s Laboratory for Financial Engineering. “Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits.”[8]

[edit] Issues and developments

More sophisticated models and intelligent programs have created the question of whether the models will break down.

“The downside with these systems is their black box-ness,” Mr. Williams said. “Traders have intuitive senses of how the world works. But with these systems you pour in a bunch of numbers, and something comes out the other end, and it’s not always intuitive or clear why the black box latched onto certain data or relationships.”[9]

Regulators in Great Britain are watching the development of algo trading.

“The Financial Services Authority has been keeping a watchful eye on the development of black box trading. In its annual report the regulator remarked on the great benefits of efficiency that new technology is bringing to the market. But it also pointed out that ‘greater reliance on sophisticated technology and modelling brings with it a greater risk that systems failure can result in business interruption’.”[10]

Other issues include the technical problem of latency or the delay in getting quotes to traders,[11] security and front running, and the possibility of a complete system breakdown leading to a market crash.[12]

Financial market news is now being formatted by firms such as Reuters, Dow Jones, Bloomberg, and Thomson Financial, to be read and traded on via algorithms.

“Computers are now being used to generate news stories about company earnings results or economic statistics as they are released. And this almost instantaneous information forms a direct feed into other computers which trade on the news.”[13]

The algorithms do not simply trade on simple news stories but also interpret more difficult to understand news.

“There is a real interest in moving the process of interpreting news from the humans to the machines” says Kiristi Suutani, global business manager of algorithmic trading at Reuters. “More of our customers are finding ways to use news content to make money.”[14]

[edit] Effects

Though its development may have been prompted by decreasing trade sizes caused by decimalization, algorithmic trading has reduced trade sizes further. More fully automated markets such as NASDAQ have gained market share from less automated markets such as the NYSE. Spending on computers and software in the financial industry increased to $26.4 billion in 2005.[15] Jobs once done by human traders are being switched to computers. The speeds of computer connections, measured in milliseconds, have become very important. [16] [17] Brokers have found it more difficult to monitor the risk of their clients' positions, especially for clients such as hedge funds.

Economies of scale in electronic trading have contributed to lowering commissions and trade processing fees, and contributed to international mergers and consolidation of financial exchanges.

[edit] See also

[edit] References

  1. ^ Moving markets Shifts in trading patterns are making technology ever more important, The Economist, Feb 2, 2006
  2. ^ The Ultimate Money Machine, Iran Daily May 7, 2007
  3. ^ A London Hedge Fund That Opts for Engineers, Not M.B.A.’s by Heather Timmons, August 18, 2006
  4. ^ Looking for options Derivatives drive the battle of the exchanges, April 15, 2007, Economist.com
  5. ^ MTS to mull bond access, The Wall Street Journal Europe, April 18, 2007, p. 21
  6. ^ A London Hedge Fund That Opts for Engineers, Not M.B.A.’s by Heather Timmons, August 18, 2006
  7. ^ Trading with the help of 'guerrillas' and 'snipers,' Financial Times, March 19, 2007
  8. ^ Artificial intelligence applied heavily to picking stocks by Charles Duhigg, November 23, 2006
  9. ^ Artificial intelligence applied heavily to picking stocks by Charles Duhigg, November 23, 2006
  10. ^ Black box traders are on the march The Telegraph, August 26, 2006
  11. ^ Enter algorithmic trading systems race or lose returns, report warns, Financial Times, October 2, 2006
  12. ^ Cracking The Street's New Math, Algorithmic trades are sweeping the stock market.
  13. ^ "If you're reading this, it's too late: a machine got here first," The Financial Times, April 16, 2007, p.1
  14. ^ "If you're reading this, it's too late: a machine got here first," The Financial Times, April 16, 2007, p.1
  15. ^ Moving markets Shifts in trading patterns are making technology ever more important, The Economist, Feb 2, 2006
  16. ^ Dodgy tickers, The Economist, March 8, 2007
  17. ^ Pleasures and Pains of Cutting-Edge Technology Mar 19, 2007

[edit] External links

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