Black Box Hedge Fund Strategy

Black box trading uses complicated computer programs.

Black box trading, or algorithmic trading, is a trading system that uses mathematical models to determine the best time to buy and sell stocks. The term "black box trading" is used because the trader does not know the data involved; he just does what a computer algorithm tells him to do. This type of trading is most commonly used by hedge funds and other large institutional investors in order to obtain the best possible price when buying and selling stocks.

How It Works

Algorithmic trading works by using computer programs to determine the best time to buy and sell large numbers of shares. When large amounts of stock are bought and sold, the stock price may go up or down, affecting the profit that can be earned by a hedge fund. For example, when a hedge fund buys a large number of shares, the stock price immediately goes up, making it more expensive for the fund to buy more shares in that stock. To prevent this outcome, the shares are divided up into blocks. A computer algorithm is used to compare stock prices and movements and determine the best time to buy each block of shares in order to get the best price without causing the stock price to rise.

History

Black box trading only became possible after the stock exchanges became automated. For example, all NASDAQ buy orders were made by telephone until 1983, when the stock exchange converted to computer-assisted order systems. Within a few years, major hedge fund managers, such as Morgan Stanley, began to use computers to track trades and determine the best time to buy and sell. By the end of the 1980s, most hedge fund trading had become automated. At the same time, the algorithms used to determine the best time to trade became more and more complex. The difference of even a fraction of a second could translate into millions of dollars, so hedge funds began to compete to develop faster and more accurate algorithms.

High-Frequency Trading

Black box trading has become faster and faster over time and is often referred to as high-frequency trading because it deals in trading huge amounts of shares at once. By 2011, more than 60 percent of all trades in the United States used computer algorithms to determine when to trade. Many of these black box trades involve funds buying hundreds of thousands of shares and then selling them a second or less than a second later, earning a fraction of a penny on each share. When this is done many times each day, the fractions add up to real profit. Black box trading earned investors $12.9 billion between 2009 and 2011, according to market specialist Tabb Group. Traders with the money to develop complex algorithms and to buy ultra-fast computers can execute millions of orders a second. They buy and sell based on trading trends that small investors, using slower computer programs, cannot spot. In black box trading, traders with the fastest programs make the most money.

Problems

On May 6, 2010, stocks on the New York Stock Exchange plunged by more than 700 points over a few seconds, before recovering a few minutes later. The “flash crash” was blamed on a snafu in black box high-frequency trading. Following this event, regulators in Europe and America began to criticize black box trading for causing volatility in the stock market and for focusing on quick profits rather than building stable investments. Some analysts also argued that the algorithms used in black box trading are now so complex and automated that humans no longer have enough control to prevent more crashes.

References

• The High Frequency Trading Review: High Frequency Trading & Algorithmic Trading; Tyrone; 2010
• "Wired": Algorithms Take Control of Wall Street; Felix Salmon and Jon Stokes; 2010
• "The Guardian": Black Box Trading is the Real Hazard to Markets, Says Lord Myners; Simon Bowers; 2011
• Nerds on Wall Street: Algorithm Wars
• "New York Times": High-Frequency Trading; 2011