Algorithmic trading
From The Art and Popular Culture Encyclopedia
|
Related e |
|
Featured: |
Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume.
Contents |
History
Early developments
Computerization of the order flow in financial markets began in the early 1970s, when the New York Stock Exchange introduced the "designated order turnaround" system (DOT). SuperDOT was introduced in 1984 as an upgraded version of DOT. Both systems allowed for the routing of orders electronically to the proper trading post. The "opening automated reporting system" (OARS) aided the specialist in determining the market clearing opening price (SOR; Smart Order Routing).
With the rise of fully electronic markets came the introduction of program trading, which is defined by the New York Stock Exchange as an order to buy or sell 15 or more stocks valued at over US$1 million total. In practice, program trades were pre-programmed to automatically enter or exit trades based on various factors. In the 1980s, program trading became widely used in trading between the S&P 500 equity and futures markets in a strategy known as index arbitrage.
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. Yet the impact of computer driven trading on stock market crashes is unclear and widely discussed in the academic community.
Refinement and growth
The financial landscape was changed again with the emergence of electronic communication networks (ECNs) in the 1990s, which allowed for trading of stock and currencies outside of traditional exchanges. In the U.S., decimalization changed the minimum tick size from 1/16 of a dollar (US$0.0625) to US$0.01 per share in 2001, and may have encouraged algorithmic trading as it changed the market microstructure by permitting smaller differences between the bid and offer prices, decreasing the market-makers' trading advantage, thus increasing market liquidity.
This increased market liquidity led to institutional traders splitting up orders according to computer algorithms so they could execute orders at a better average price. These average price benchmarks are measured and calculated by computers by applying the time-weighted average price or more usually by the volume-weighted average price.
- It is over. The trading that existed down the centuries has died. We have an electronic market today. It is the present. It is the future.
| source = Robert Greifeld, NASDAQ CEO, April 2011
A further encouragement for the adoption of algorithmic trading in the financial markets came in 2001 when a team of IBM researchers published a paper at the International Joint Conference on Artificial Intelligence where they showed that in experimental laboratory versions of the electronic auctions used in the financial markets, two algorithmic strategies (IBM's own MGD, and Hewlett-Packard's ZIP) could consistently out-perform human traders. MGD was a modified version of the "GD" algorithm invented by Steven Gjerstad & John Dickhaut in 1996/7; the ZIP algorithm had been invented at HP by Dave Cliff (professor) in 1996. In their paper, the IBM team wrote that the financial impact of their results showing MGD and ZIP outperforming human traders "...might be measured in billions of dollars annually"; the IBM paper generated international media coverage.
In 2005, the Regulation National Market System was put in place by the SEC to strengthen the equity market. This changed the way firms traded with rules such as the Trade Through Rule, which mandates that market orders must be posted and executed electronically at the best available price, thus preventing brokerages from profiting from the price differences when matching buy and sell orders.
As more electronic markets opened, other algorithmic trading strategies were introduced. These strategies are more easily implemented by computers, because machines can react more rapidly to temporary mispricing and examine prices from several markets simultaneously. Chameleon (developed by BNP Paribas), Stealth (developed by the Deutsche Bank), Sniper and Guerilla (developed by Credit Suisse), arbitrage, statistical arbitrage, trend following, and mean reversion are examples of algorithmic trading strategies.
See also
- 2010 Flash Crash
- Alternative trading system
- Artificial intelligence
- Best execution
- Complex event processing
- Electronic trading platform
- Mirror trading
- Technical analysis
