High-Frequency Algorithmic Trading

High-frequency trading (HFT) or algorithmic trading is well-established in today's markets—and it's here to stay. But HFT isn't especially well understood, and it's often a source of controversy.

One primary reason? There's not yet a widely accepted definition. Though the strategy has been around for decades, its use has picked up steam as rapid advancements in computing power and data analytics are applied to what people have been doing for centuries: buying and selling.

Here are some frequently asked questions about HFT and algorithmic trading.

Broadly defined, high-frequency trading (a.k.a "black box" trading) refers to automated, electronic systems that often use complex algorithms (strings of coded instructions for computers) to buy and sell much faster and at much greater scale than any human could do (though, ultimately, people oversee these systems).

Such systems are often designed to make just a tiny profit on each transaction, but through sheer speed and volume, they can generate large returns for their firms.

According to Nasdaq^®, there are two types of systems: execution trading, when an order is completed via a computerized algorithm designed to get the best possible price, and a second type that seeks small trading opportunities.

How "fast" is fast? Blink, and you'll miss it. Today's increasingly powerful computers can execute thousands, if not millions, of transactions in seconds, and HFT is often measured in milliseconds (thousandths of a second) or microseconds (millionths of a second).

For perspective, a blink of your eye takes about 400 milliseconds, or four-tenths of a second. With that in mind, imagine the speed at which HFT can allow a firm to make trades, in what has been referred to as unfathomably miniscule amounts of time.

Bad press, for one. A certain algorithm is "like a tiger that lurks in the woods and waits for the prey, then jumps on it," according to Michael Lewis' 2014 book, Flash Boys, which brought some of the ills of HFT to the forefront.

The image of algorithmic traders as predators fleecing the average investor still lingers. Certain market events—such as the flash crash of May 2010 or the U.S. market's sharp swings in December 2018—can raise questions about whether algorithms exacerbate volatility.

Indeed, regulators like the U.S. Securities and Exchange Commission (SEC) have in recent years fined some high-frequency traders for price manipulation or other fraudulent trading.

High-frequency traders are said to contribute vital liquidity to markets, helping narrow bid/ask spreads and bringing buyers and sellers together efficiently. Ultimately, this can potentially help bring down costs for investors.

Some traders believe algorithm trading firms serve a valuable purpose by "making markets" in thousands of stocks and other assets, providing liquidity far beyond what's available on established stock exchanges.

Many brokers route orders from retail investor clients to large trading firms, which then match buyers with sellers, known as order execution. Some of those firms could be considered high-frequency traders, bearing in mind the speed at which they operate and the number of trades they handle.

Fair and strategically positioned high-frequency trading could potentially make markets more efficient and knit liquidity together in a beneficial way for all participants. On the flip side, some HFT operators might be considered bad or predatory. As with many forms of advanced technology, high-frequency trading could be open to abuse.