High-Frequency Algorithmic Trading

Let's explore some basic questions about what high-frequency trading (HFT) is and how this trading technology impacts investors.

You've probably heard of high-frequency trading (HFT) or algorithmic trading. But do you know what it is or what it "looks" like?

HFT is well-established in today's markets—and it's here to stay. Yet, 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.

Let's answer some basic questions about HFT and algorithmic trading for investors.

What is high-frequency 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 high-frequency algorithmic trading?

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 with which HFT can allow a firm to make trades, what has been referred to as unfathomably miniscule amounts of time.

Why are there negative perceptions of algorithmic trading?

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, often raise questions about whether algos 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.

How is high-frequency trading beneficial to the markets?

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 algo 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 amount of trades they handle.

Good 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 might be considered bad or predatory. As in many forms of advanced technology, high-frequency trading could be open to abuse.

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The information here is for general informational purposes only and should not be considered an individualized recommendation or personalized investment advice. The type of securities and investment strategies mentioned may not be suitable for everyone. Each investor needs to review a security transaction for his or her own particular situation.

Investing involves risk, including the loss of principal.

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