The problem with high frequency trading

By Felix Salmon
October 6, 2012
an essay about high frequency trading. I hope it's fun to listen to, but if you want to read it, here you go.

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Last night, on BBC Radio 3, I was featured reading an essay about high frequency trading. I hope it’s fun to listen to, but if you want to read it, here you go.

One of the many consequences of global warming is that it’s now, for the first time, possible to drill under the sea bed of the Arctic ocean. The oil companies are all there, of course, running geological tests and bickering with each other about the potential environmental consequences of an oil spill. But they’re not the only people drilling. Because there’s something even more valuable than oil just waiting to be found under the Arctic.

What is worth so much money that three different consortiums would spend billions of pounds to retrofit icebreakers and send them into some of the coldest and most dangerous waters in the world? The answer, of course, is information.

A couple of days ago, I called a friend in Tokyo, and we had a lovely chat. If he puts something up on Twitter, I can see it immediately. And on the web there are thousands of webcams showing me what’s going on in Japan this very second. It doesn’t look like there’s any great information bottleneck there: anything important which happens in Japan can be, and is, transmitted to the rest of the world in a fraction of a second.

But if you’re a City trader, a fraction of a second is a veritable eternity. Let’s say you want to know the price of a stock on the Tokyo Stock exchange, or the exact number of yen being traded for one dollar. Just like the light from the sun is eight minutes old by the time it reaches us, all that financial information is about 188 milliseconds old by the time it reaches London. That’s zero point one eight eight seconds. And it takes that much time because it has to travel on fiber-optic cables which take a long and circuitous route: they either have to cross the Atlantic, and then the US, and then the Pacific, or else they have to go across Europe, through the Middle East, across the Indian Ocean, and then up through the South China Sea between China and the Philippines.

But! If you can lay an undersea cable across the Arctic, you can save yourself about 5,000 miles, not to mention the risk of routing your information past a lot of political flash points. And when you’re sitting in your office in London and you get that dollar/yen exchange rate from Tokyo, it’s fresh from the oven, comparatively speaking: only 0.168 seconds old. If everybody else is using the old cables and you’re using the new ones, then you have somewhere between 20 milliseconds and 60 milliseconds when you know something they don’t.

Those are periods of time so short that humans can barely notice them. This essay, for instance, is about 900,000 milliseconds long, and it takes me hundreds milliseconds just to say the word “cable”. Which is a word with more than one meaning. To you, it probably means some kind of wire. But to City traders, it means 1.6254, or something very close to that number. Because in the City, “cable” means the pound/dollar exchange rate. And it’s named that after a transatlantic cable which was used to telegraph the exchange-rate information from London to New York as far back as 1858.

So what we’re talking about here is nothing new, in terms of kind. Nathan Rothschild built a significant chunk of his fortune by using a system of couriers who told him the result of the Battle of Waterloo a full day before anybody else in London knew it. And my own employer, the Reuters news agency, was founded on sending financial information between Brussels and Aachen using carrier pigeons.

What’s new is that billions of pounds can be made by having access to information not a day in advance, or an hour, or even a second, but even just a millisecond or two. Stock exchanges aren’t physical places where human beings bargain with each other any more: they’re racks of computers in places like Mahwah, New Jersey, where the cables are carefully measured to be exactly the same length so that no one has an infinitesimal advantage thanks to the amount of time it takes information to travel an extra few millimeters down a wire.

Obviously, only computer algorithms can make money from an information advantage which is measured in milliseconds. It’s computers which are making the decisions to buy and sell: if they had to wait for a human to sign off on those things, they’d never make any money at all. That’s a little bit scary, and not only because of the classic science-fiction stories where computers become so sophisticated that they gain consciousness and start waging battles against the humans who built them.

The more obvious problem with exchanges run by computers is that computers don’t have any common sense. We saw this on the 6th of May, 2010 — the day of the so-called “flash crash”, when in a matter of a couple of minutes the US stock market plunged hundreds of points for no particular reason, and some stocks traded at a price of just one cent. It was sheer luck that the crash happened just before 3pm, rather than just before 4pm, and that as a result there was time for the market to recover before the closing bell. If there hadn’t been, then Asian markets would have sold off as well, and then European markets, and hundreds of billions of pounds of value would have been destroyed, just because of a trading glitch which started on something called the e-mini contract in Chicago.

Most of the trading on US stock exchanges is done by something called algobots, these days. These are algorithms: they’re computers which are programmed to put in orders, take out orders, trade in big size, trade in small size – all according to very sophisticated rules, called algorithms. And one of the ironies about the flash crash is that it was actually caused in large part by algobots not trading. The US has over a dozen different stock exchanges, places where stocks are bought and sold. Most of us have only ever heard of the listing exchanges, the New York Stock Exchange and the Nasdaq. But there are many more you probably haven’t heard of, with names like Arca and BATS, as well as sinister-sounding things called Dark Pools. What happened in the flash crash is that when the trading got completely crazy, the algobots just switched themselves off. This was something they weren’t used to, they didn’t know how to react, and so they just went away. And there was suddenly no liquidity in the market. No one was offering to trade. And with no one offering to trade, the prices just plunged, all the way down to one cent. Because there were no bids in the market any more.

The algobots can be very useful, on a day-to-day basis. If a normal person like me buys a few shares in some company or other, that trade doesn’t even happen on any stock exchange at all. It just happens directly with a broker, an algobot, who’s happy to take the other side of my trade because small individual investors like me are normally pretty stupid, and tend to buy high and sell low.

In any case, if any given stock exchange is an incredibly complicated thing, the fragmentation of the stock exchanges has created a much more complex system yet. Most big banks and stockbrokers — and the algobots they control — have access to all of the different exchanges, and they trade wherever they think they can get the best prices. Since the best prices tend to be found wherever the most traders are trading, you end up with something a bit like six-year-olds playing football: everybody’s running towards the ball at the same time. And the result is these huge waves of activity, where traders move en masse, from one stock exchange to the next, in very unpredictable ways. If you layer that unpredictability on top of the complexity inherent in any system of multiple stock exchanges, you end up with something which will almost certainly break in a pretty catastrophic manner at some point. We don’t know how, and we don’t know when, but there’s an ironclad rule of any system: the more complex it is, the less predictable it is, and the more likely it is to fail catastrophically in some unforeseeable manner.

If Twitter fails, that’s fine. A bunch of people get annoyed, and then they want to express how cross they are on Twitter, and then they remember that they can’t, and that makes them even more annoyed. But little actual harm is done. If the stock market fails, on the other hand, or the bond market, or the foreign-exchange market, or the oil market, that’s really, really bad news. Billions or even possibly trillions of pounds could evaporate.

And that’s the biggest reason why it’s time to start cracking down on high-frequency trading. Virtually every major financial center in the world is trying to work out what to do and how to do it: these decisions aren’t easy, partly because any crackdown on the algobots is likely to have its own significant up-front costs.

After all, high-frequency trading has been genuinely wonderful for small investors like you and me. We might not be particularly clever, but when we put in an order to buy this or sell that, the order gets filled immediately. We pay almost nothing in trading costs — just a few pounds, normally. And we get the very best price in the market: something called NBBO, for “national best bid/offer”. If you look at all the prices being quoted on all of the stock exchanges in the country, we get the lowest price of all if we’re buying, and the highest price of all if we’re selling.

That wasn’t true ten years ago. During the dot-com boom, especially, small investors generally had no idea how much they were going to end up paying for a stock they wanted to buy, and all too often their trades could take minutes or even hours to get filled. Today, all individual investors get filled in a fraction of a second: we’ve never had it so good. So if anybody tells you that high-frequency trading is bad for the little guy, and that it means there isn’t a level playing field any more, they don’t know what they’re talking about. Yes, high-frequency traders do make money from small investors, but they do so honestly, just by assuming that whatever those small investors do, the opposite thing is likely to make money. As a result, there’s always someone willing to take the opposite side of the trade whenever you want to buy or sell a stock.

This is a real improvement, which means that the rise of high-frequency trading had genuinely beneficial effects between, say, 2002 and 2007. In those years, the computers helped markets to become ever more efficient and liquid — and they were just in time, too. When the financial crisis came along in 2008, bond markets seized up, but the world’s stock markets actually came through with flying colors. They did what markets are supposed to do: they went down when people were selling, and they kept on falling until they were so cheap that people started buying again. If you wanted to sell, you could always sell, and if you wanted to buy, you could always buy. We take these things for granted, but creating a system which stays that liquid, all the way through such a big crisis, is a real achievement, and the algobots deserve a lot of credit there. After all, imagine what would have happened if you had to phone up your broker at Lehman Brothers in order to sell your shares.

But if you look at what’s happened over the past five years, since 2007, the benefits of high-frequency trading have pretty much plateaued. And the downsides are becoming more and more obvious. There was the flash crash, of course, and then there was the implosion of Knight Capital, one of the biggest and most respected high-frequency trading shops, which released a faulty algorithm one morning and was almost bankrupt an hour later, after losing somewhere in the region of $10 million per minute. If that could happen to Knight, it could happen to anybody. Then there was the botched flotation of one of the stock exchanges, BATS. Once again, its algorithms turned out to be not up to the task. And this was in an expected, rather than an unexpected, situation.

There are more subtle signs, too, which are if anything even more worrying. For instance, look at stock-market volume — the amount of money which changes hands every day. That’s going nowhere: if anything, it’s going down, even as high-frequency traders get bigger and bigger. That says two things.

The first is that real-money investors, the people who the market needs the most, are being scared away by the algobots, because even if the bots are good for the little guy, they’re really bad for big, institutional investors. For big investors, the stock market is more of a rigged game now than it has been in a long time – and they’re taking their ball and they’re going home.

The second reason that volumes are dropping is that the algobots are getting so sophisticated at sparring with each other that they’re not even trading with each other any more. They’re called high-frequency traders, but maybe that’s a misnomer: a better name might be high-frequency spambots. Because what they’re doing, most of the time, is putting buy or sell orders out there on the stock market, only to take those orders back a fraction of a second later, and replace them with new ones. The result is millions of orders, but almost no trades.

I’ll give you one example from a stock with the ticker symbol EFZ. It doesn’t matter what that ticker represents: the computers certainly don’t care. On September 11, between 6:51 and 7:08 in the morning, the US stock markets saw more than 280,000 quotes to trade EFZ. And how many times did it actually trade? Zero.

I can even demonstrate what that kind of thing sounds like. If you map all those offers to buy or sell onto a piano, and play them back, you get something which sounds like this. Remember, each note is an offer to buy or to sell; there’s no actual trading going on.

 

There’s no value being created here. If the economics of high-frequency trading means that fiber-optic cables get laid under the Arctic ocean, that’s good for everybody. But if it all just devolves into meaningless noise, then something has gone very, very wrong. Especially since the more noise and complexity you have, the bigger the danger that everything could just implode for some unforeseeable reason. Any one of these notes has the potential to be the butterfly wing-flap which results in global disaster. If they’re not doing anybody any good, it makes sense that regulators should crack down on them.

 

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