How Algorithmic Trading Affects You

 

Algos = Computer-Driven Market Activity 

High-Speed Insider Trading

How Algo Trading Affects You

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Hardly a week goes by without a story or two about the growth of algorithmic strategies, High Frequency Trading (HFT), “dark pools” or other purportedly nefarious market schemes. There is a wide range of specific program types, but they’re all based on the idea of using super high-speed computerized transactions.

Although the various strategies go by many names, terms like “algorithmic trading” or “algorithmic traders” are used pretty broadly to refer to the overall genre of computer-driven market activity. Practitioners are often called “algos” for short.

To get some idea of public interest, consider that a Google search on “algo trading” returns 4,850,000 hits. And using Google Trends, searches for the word “algo” and searches for “HFT” have both tripled over the last eight years.

Some of the news accounts sound pretty scary, often reflecting dystopian notions that science and technology will ultimately turn against us and finally ruin what little we have left of our stock market Eden.

Some have said that the “Flash Crash” of May 6, 2010 resulted from high-frequency algo trading. And perhaps it was. But remember, the Flash Crash was reversed within minutes. Although horrifying for a day trader, for most of us, it was yesterday’s news by the time we even heard of it.

Also embedded in many news accounts is the notion that the fat-cat algos are taking advantage of the rest of us; that ordinary investors are losing out to investment banks and others with privileged access. (Some HFT computers are located right next to the market computers, so their orders are executed within milliseconds, while your order or mine must travel the slow route).

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The issue of high-speed insider advantage made headlines just last week when CNBC and The Wall Street Journal reported that Thomson Reuters released key economic reports to key clients seconds ahead of other clients, and minutes ahead of the general public—an “early peek,” for a fee. The story noted an example from March 15, when the high-speed traders got news of a disappointing consumer sentiment report two seconds before regular clients. In those two seconds, short sales in New York jumped about 15-fold as HFT institutions acted on their momentary information advantage.

I don’t like that kind of system, and I’m supposing you don’t either. But it’s not illegal, and (in my opinion) it’s not even unethical. It’s just unfair. Lots of things are unfair, but not necessarily wrong. (Do you have a fantastic pitching arm? Well then, maybe you have a big future in the major leagues. But not me. I never will make the big leagues. I just wasn’t born with it. Unfair, but inescapable … and perfectly ethical.)

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Anyway, we can’t change Thomson Reuters. And we don’t really have to.

The big untold story of all this algo nonsense is that those firms that trade by the minute are not really competing with you and me. They’re competing with each other for that penny or two a share. Nothing I buy, and nothing you buy, will be a winner or loser for a penny a share. The average holding period in the Cabot ETF Investing System is about 40 trading days (two months). In that time, an extra penny or two per share will come and go many times, and will be immaterial in the end.

It’s probably true that the HFT and algorithmic programs contribute to occasional volatility. But the key word there is “occasional.”

Most of the time they’re providing liquidity and continuity. With HFT comprising 50%-to-70% of total market trading volume, it’s obvious that much of the activity is one HFT going head-to-head with another HFT. If from time to time they all try to pile on at once, that’s not terribly different from what flesh-and-blood investors sometimes do—just bigger and faster.

And these battles often provide an opportunity for others willing to look at Flash Crash prices and say, “That’s crazy…I’ll take it!”

Sincerely,

Robin Carpenter
Editor of Cabot ETF Investing Systems

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