Felix Salmon

In February 2009, Deutsche Bank announced that its Credit Trading desk had managed to lose €3.4 billion in the fourth quarter of 2008, with €1 billion of those losses directly attributable to the bank’s prop desk.

The losses in the Credit Proprietary Trading business were mainly driven by losses on long positions in the U.S. Automotive sector and by falling corporate and convertible bond prices and basis widening versus the Credit Default Swaps (CDS) established to hedge them.

In English, Deutsche Bank had put on a basis trade: it owned credit instruments, like bonds, and it also owned credit default swaps designed to hedge against those loans. And then the trade blew up.

The Deutsche trader responsible for the monster losses was Boaz Weinstein, who eventually left the bank to start his own hedge fund, Saba Capital. His first job, obviously, was to make sure he didn’t blow up a second time. But his second job, it seems, was to use his experience at Deutsche to be able to notice when someone else was about to blow up on a massive basis trade. In this case, JP Morgan.

Go back to early February, long before the articles about the “London Whale” came out in Bloomberg and the WSJ, and you’ll find Weinstein revealing his biggest trade at the Harbor Investment Conference:

The derivatives trader and legendary hedge fund manager said his trade idea is to buy Investment Grade Series 9 10-Year Index CDS (maturing on 12/20/2017).

“They are very attractive,” he explained adding that they can be bought at a “very good discount.”

At the time, Weinstein didn’t know — or necessarily even suspect — that his big trade would involve a zero-sum bet with one of the biggest hedge funds in the world, JP Morgan’s Chief Investment Office. But over time, as he bought more and more protection but the price stubbornly refused to rise, he began to learn just how big the other size of the trade was. Whale big.

Tracy Alloway and Sam Jones have pieced together the best account yet of what exactly JP Morgan was up to. Yet again, it was a basis trade, although this one was horribly complex even by basis-trade standards. Essentially, that CDX.NA.IG.9 position was a second-order hedge, designed to offset volatility in JP Morgan’s first-order hedge, which was designed to offset credit risk in the rest of the bank’s portfolio.

The first-order hedge itself doesn’t make a great deal of sense — Iksil seems to have bought “tranches” of CDS indices, which would pay off if some (but not all) credits suddenly got into trouble. For a bank which had broad economic exposure to European meltdown and/or a US double dip, that seems like a pretty narrow hedge.

But if the first-order hedge is weird, the second-order hedge is downright scary. Do you remember the notorious Howie Hubler trade at Morgan Stanley, where he made a smart bet against dangerous subprime securities, but then put on a much larger “hedge” which ended up costing him $9 billion? Iksil’s trade seems a bit like that:

Because of the mechanics of the trade, in order to achieve a “market neutral” position, whereby JPMorgan hedged the bet against volatility as best it could and offset the cost of its short positions, the bank had to sell far more units of cheap protection on the IG.9 as a whole than it bought on short, more expensive tranches.

Inevitably things started to go wrong. There are two things you can do when something starts to go wrong in the markets. You can unwind your position at a loss. Or you can try to fix it. Iksil, and Drew, chose the latter:

The two legs of JPMorgan’s trade did not move according to the relationship the bank had expected, meaning the position became imperfectly hedged. Like many credit models before it, JPMorgan appeared to misjudge correlation – one of the hardest market phenomena to accurately capture in mathematics.

In order to try and stay risk neutral, the dynamic hedge required even more long protection to be sold. The bank continued to write swaps on the IG.9, causing a pricing distortion that was spotted by more and more hedge funds seeking profit.

The rest, pretty much, is history.

Iksil, we’re told, is going to leave JP Morgan, while taking his own sweet time doing so: “although a spokeswoman for the bank said Mr. Iksil is still employed, he is no longer trading on behalf on the bank and is expected to be gone by the end of the year”. I’m sure he’ll use the intervening months to feel out his chances of being able to raise a few billion dollars for a hedge fund of his own, and weigh them up against simply joining a fund like Saba. Iksil’s now learned a $2 billion lesson — and as Boaz Weinstein can attest, once learned, those lessons can be surprisingly valuable.

We’re still not much the wiser on exactly how the London Whale managed to lose $2 billion this quarter, but I think Matt Levine has the smartest take. (This is why the blogosphere is so great: it’s full of people who used to do this kind of thing for a living, rather than just people who write about people who do this for a living.)

The key thing to note here is that while the monster hit to the P&L is what got all the headlines, the real problem here lay with JP Morgan’s risk models. A hint of far out of whack they are is given in the difference between the bank’s earnings release, which showed $67 million of value-at-risk in the Whale’s division in the first quarter, and the new SEC filing, which showed that number as actually being $129 million. Here’s Levine:

This was attributed to modeling changes made over the last year, and someone asked on the call “why did you change the VaR model?,” but I’m not convinced that’s exactly the right question. This, I suspect, is not an issue of a thing called a “VaR model” that sits in a central location and spits out numbers for regulators and 10-Qs; rather, this looks like the CIO’s trading desk modelling the actual P&L and risks of the trade wildly wrong. That seems to me like the simplest way to lose a billion dollars without noticing it.

I’d put this another way. JP Morgan’s Bruno Iksil, it seems, managed to find an incredibly profitable way of hedging the bank’s positions. Like any other economically rational actor, when he saw a lot of dollar bills lying on the sidewalk, he decided to pick them up. But in Iksil’s highly-complex world, a dollar bill isn’t really a dollar bill. Instead, it’s the output of a model. And if a trader can’t trust his model, he’s flying blind.

The problem is that pretty much by definition, it’s impossible to model model risk. We now know that Iksil’s model was deeply flawed. And indeed the minute that the rest of the world found out about his positions, they didn’t really pass the smell test: it’s very hard to see how writing an enormous amount of protection on an off-the-run CDX index would hedge anything much.

This is where grown-ups like Jamie Dimon are meant to step in. If they see billions of dollars in super-senior mortgage exposure, or in off-the-run CDX exposure, they’re meant to say “I know that your highfalutin’ models say that these exposures are risk free, but I don’t understand how this isn’t risky, so go unwind this trade”. Dimon has historically been very good at that — very good at refusing to simply trust that superstar traders earning eight-figure bonuses are doing nothing that might blow up in their faces. In this case, however, for some reason, he had blind faith in Iksil — and in Iksil’s models, which proved to be very faulty.

A modern trading desk is a bit like a high-tech airplane: nearly all of the time, you’re better off trusting your instruments than trusting your gut. But at the same time, if your instruments are broken, then trusting them can lead you to fly straight into the ocean.

This is why Basel I turned out to be much more robust than Basel II. Your sophisticated platform needs to be built on a foundation of dumb rules: simple limits on how big any one position can get, on how much exposure you can have to any one counterparty, or in general on any trade which is based on the hypothesis that your desk is smarter than anybody else on Wall Street.

Those kind of rules won’t prevent all blow-ups, of course, but they’ll help. They would have prevented this one, and they would have put an end to Jon Corzine’s disastrous MF Global trades, as well.

The problem is that traders hate dumb rules, because they cap the amount of money they can make. And traders have enormous power at investment banks these days, because they make the lion’s share of the profits. That’s why it’s important that the CEO of an investment bank not be a trader. And certainly it’s crucial that the CEO shouldn’t have his own trading account and buy and sell from his Blackberry during meetings, as Corzine did. That’s just a recipe for disaster.

Kara Swisher says that Twitter might start selling access to its “firehose” — the full stream of all public tweets from its tens of millions of users — to Google and Microsoft. Such companies, she says, might be willing to pay “several million dollars” for such a product.

Which raises the obvious question: if the Twitter firehose is worth millions to a search engine, how much would it be worth to algo traders and data miners? And how much of a premium would they be willing to pay to get that information a few milliseconds before anybody else? Indeed, would they be willing to pay Twitter a huge amount of money just for the privilege of hosting its servers in a the same location as their own proprietary stock-trading black boxes?

There’s been a lot of speculation of late about how on earth Twitter could be worth $1 billion. Maybe this is it! After all, the stock market, like Twitter, is basically a reflection of real-time sentiment. If you could somehow mine Twitter to isolate changes in sentiment, that could be worth billions.

Equity Private has a stinging and hilarious response to my post about the Zero Hedgies, which accused them of being day-traders:

It is not completely clear what “day trader” means in this context (it is possible this refers to individuals who manage their own portfolios rather than dump their assets into long-only 401k funds like smart, patriotic investors are supposed to) but I have reason to believe that they are generally considered “losers” and otherwise unsuitable degenerates. This appears to be connected to the dot-com crash… I have, therefore, undertaken an anecdotal analysis of our audience to pinpoint other weak points and pools that may contain high concentrations of short-sellers, precious metal devotees, former index fund investors, “retail investors” (I have used 5 definitions of this term to be expansive) gypsies, jews, homosexuals and former members of the band “Air Supply.” Obviously, if word leaked out of these associations our readership would plummet and we may face regulatory sanction or even prison.

She’s right: commenters are not necessarily representative of a site’s overall readership, and I’m sure that there are many smart and important finance and regulatory types who read ZH. A large part of the blog’s function is disintermediation: it comprises market participants talking directly to each other without being filtered by professional (or even amateur) journalists.

But if you follow this particular thread too far, you end up with an even more unsettling conclusion than my original one. Far from reflecting the conspiracy-minded and often-disjointed ramblings of harmful-only-to-themselves retail day-traders, could ZH actually be holding up a mirror to what the market’s really like, once you strip away the artificial polish of the PR departments and the urbane investment-banking types? Are finance types, in general, much more Howard Lindzon than Robert Rubin?

That’s what the wisdom-of-crowds hypothesis says: that if you take a million idiots, all trading with and against each other, yes you’ll get occasional mass delusions, and bubbles and busts, and ad-hoc groupings of vaguely like-minded individuals, like ZH. But somehow, in aggregate, those million idiots will be more right, more often, than any regulatory panjandrum or media pooh-bah. And anybody who’s spent any time with bankers and buy-siders will tell you that there’s precious little correlation between intelligence, on the one hand, and success in the markets, on the other. Yes, there are smart people who make a lot of money, but for every one of those I can show you two equally-smart people who have lost just as much.

It’s kinda ironic that there’s so much bad blood between ZH and CNBC, because in many ways both of them are very good at unveiling the market’s id. Look beneath the pat explanations of the daily market reports (“markets rose on optimism that oil prices might” etc etc), and what you see is a roiling, chaotic mess. It’s something which is almost completely invisible to readers of the Wall Street Journal, while occasional visitors to the Yahoo message boards can be forgiven for thinking that the only people who frequent such places can’t possibly be representative of the market as a whole. But with the advent of ZH and CNBC, it’s getting harder to escape the conclusion that this really is what the market is like, and the sanitized version found in middlebrow publications and mutual-fund reports is at heart a fiction.

I’m somewhere in the middle. On an intraday timescale, I think that noise traders really do set prices and move markets. If you look at markets over a period of months, however, their movements are much more a function of macroeconomic activity, global capital flows, and long-term decisions made by large institutional investors. These things are almost invisible on a day-to-day basis: they’re like gravity, which can happily be ignored at the quantum level, but which is the only thing that matters when you scale up a lot.

So ZH is probably a pretty accurate representation of many people who pay close attention to what the markets are doing on a minute-to-minute or even day-to-day basis. Which is as good a reason as any not to do that.

The existence of arbitrage, and arbitrageurs, is a necessary precondition for having a reasonably efficient market. Arbitrage allows the law of one price to become roughly true, and in turn belief in the law of one price is the central faith of any arbitrageur, who will pick up on price discrepancies safe in the knowledge that sooner rather than later the law will turn those trades into profits.

The problem is that the law of one price is not some kind of physical law with replicable effects, and sometimes it disappears entirely. So this kind of thing is actually true of all arbitrage:

DLC arbitrage is characterized by substantial idiosyncratic return volatility and a high incidence of large negative returns.

DLC (dual-listed company) arbitrage — where you look at the share price of the same company in different countries, and bet that they’ll converge — is one of the purest forms of arbitrage there is. But it’s not surprise to learn that it comes with “a high incidence of large negative returns”: any arbitrage strategy is ultimately a game of picking up nickels in front of a steamroller. Unless you have unlimited liquidity and never need to worry about margin calls, the market is likely to move against you just until you give up, at which time it will snap back to where you would have made a huge profit. Just ask the guys at LTCM, or the stat-arb hedgies who blew up in 2007.

This is why only the biggest and most liquid companies tend to try their hands at arbitrage: it’s very much a don’t-try-this-at-home strategy. Even if you’re convinced that the trade is risk-free, it really isn’t.

Aaron Lucchetti has a 2,000-word front-page WSJ story today which appears online under a “management” heading and with the headline “Wall Street’s B-List Firms Trade on Bigger Rivals’ Woes”. Really, however, it’s all about the obscene displays of wealth being perpetrated by Steven Schonfeld, the new poster boy for conspicuous consumption.

Schonfeld’s new $90 million house, on Whitney Lane in Old Westbury, Long Island, not only has “a poolside cabana designed to look like the Cove Atlantis resort in the Bahamas” but also sports a 9-hole golf course which is off-limits to anybody if Schonfeld isn’t at home:

“It’s not a private golf course,” he explains. “It’s a personal golf course.”

And then there’s this:

At one dinner with traders, he said that anyone who looked at the menu for more than 90 seconds was in the wrong business…

At high-end restaurants, Mr. Schonfeld has been known to order one of everything on the menu, with his party leaving much of the food uneaten.

Schonfeld got into a spot of bother (including $1.1 million in fines from the NYSE) when he tried to be a broker, so now he’s basically just a trader, hiring laid-off employees from big Wall Street firms and seeding them with his own money. Or, as Forbes put it in 2005,

Schonfeld oversees a harem of semi-independent traders who use his equipment, his software and his capital, sharing profits with him and paying him trading commissions.

It’s possible that he’s exaggerating the amount of money he’s spent on his house, and is giving obnoxious interviews to the WSJ, in order to stand out from a crowded field of small trading shops. Or else he’s maybe just a leveraged day-trader who made lots of money from the volatility of 2008 and now wants to flash his cash. Either way, I suspect that Schonfeld is going to be spending much more time on his private personal golf course than at the Old Westbury Country Club down the street. This kind of attitude tends not to sit well with one’s upscale neighbors.

Steve Forbes interviewed Duncan Niederauer, the CEO of the NYSE, and asked him about high-frequency trading. Unsurprisingly, Niederauer came out in favor of high-frequency traders:

They’re actually probably the most consistent source of liquidity provision in the market today. I actually don’t think they added to the volatility in the crisis. If anything, the studies that we’ve done would suggest it’s a pretty consistent provision of liquidity that would dampen volatility.

The NYSE has done studies on this? Does anybody know where I might be able to find them?

Michael Wellman has an intriguing idea for solving all the issues surrounding high-frequency trading at a stroke: switch from a market with continuous clearing to a market which clears once per second.

Orders accumulate over the interval, with no information about the order book available to any trading party. At the end of the period, the market clears at a uniform price, traders are notified, and the clearing price becomes public knowledge. Unmatched orders may expire or be retained at the discretion of the submitting traders.

Even with a period as short as one second, the call market totally eliminates any advantage of HFT systems. It does not eliminate the opportunities for algorithmic trading in general–just those that come from sub-second response time. No party has privileged information about order flow, and no party benefits by getting a shorter wire to the “trading floor”.

According to Wellman, there would be other advantages to discrete-time trading too, including lower volatility.

Would this plan essentially give everybody in the market the advantages of being in a dark pool which only exists for one second? On its face, I think it’s a good idea. What would the downside be?

I have a bit more clarity on the $20 billion figure for total profits from high-frequency trading: it comes from the TABB Group. In a recent publication, TABB’s Robert Iati writes:

TABB Group estimates that annual aggregate profits of low latency arbitrage strategies exceed $21 billion, spread out among the few hundred firms that deploy them. While we know all the large investment banks such as Goldman Sachs are committed to prop trading profitability, the hundreds of smaller, private high frequency prop shops extend much greater influence in the marketplace by providing liquidity that keeps activity flowing.   

The Bloomberg article, meanwhile, explains the figure thusly:

The firms compete for a slice of $21.8 billion in annual profits from equities and derivatives market making and arbitrage, according to Tabb. Among the largest are hedge funds Citadel Investment Group LLC, D.E. Shaw & Co. and Renaissance Technologies Corp., as well as the automated brokerages Getco LLC, Hudson River Trading LLC and Wolverine Trading LLC.

When John Hempton, then, says that “quantifications of this as a $20 billion issue are insane”, I think there are two questions: firstly, what is “this”, and secondly, how profitable is it, in aggregate.

It would be most convenient if the HFT algorithms were split nicely into a “trading” bucket and a “quant arbitrage” bucket, so that Hempton could complain mildly about the “trading” algos while saying at the same time that they’re not all that big of a problem, while ignoring the stat-arb shops and other high-frequency, low-latency traders. But in reality there’s very little difference: the traders all have strategies, and the stat-arb strategies are all implemented so as to maximize trading profits.

To put it another way, I don’t think people are making billions of dollars in profit just by being fast. But there are definitely people making billions of dollars in profits through strategies for which being fast is a necessary precondition.

Which leads us to the second question: if you tot up all the profits from high-frequency, low-latency traders, including big shops like Citadel, Renaissance, and Goldman, can you get to $20 billion? My gut feeling is that you can, and that the TABB estimate is not obviously unreasonable.

I also got a note from Jon Stokes yesterday which is worth disseminating more widely:

It’s quite remarkable to me that many of the econ and finance folks who insist that “HFT is the same thing we always did, just way faster” don’t seem to realize that frequency and amplitude matter a whole lot, and that for any given phenomenon when you suddenly increase those two factors by an order of magnitude you typically end up with something very different than what you started with. This is true for isolated phenomena, and it’s doubly true for complex systems, where you have to deal with systemic effects like feedback loops and synchronization/resonance.

What I’ve noticed anecdotally is that engineers and IT pros are more concerned about HFT than people who just handle money for a living. These guys have a keen sense for just how fragile and unpredictable these systems-of-systems are even under the best of conditions, and how when things go wrong they do so spectacularly and at very inconvenient moments (they get paid a lot of money to rush into the office to put out fires at 4am).

There’s an analogy here with e-voting, which I did quite a bit of work on. In the e-voting fiasco, you had people who were specialists in elections but who had little IT experience greenlighting what they thought was an elections systems rollout, but in actuality they had signed on for a large IT deployment and they had no idea what they were getting into. To them, it was just voting, but with computers, y’know? They found out the hard way that networked computer systems are a force multiplier not just for human capabilities, but for human limitations, as well.

This is why I’m sympathetic to Paul Wilmott’s view of all this: there’s simply too much complexity here for comfort, and too many things which can go wrong. When the stat-arb shops imploded in the summer of 2007, the systemic consequences were mild-to-nonexistent, and that does provide a certain amount of reassurance. But we can’t be sure that if and when such a thing happens again, the consequences won’t be much worse.

There’s an interesting debate in the comments to my post on high frequency trading about the widely-cited $20 billion figure for the profits attributable to HFT. In Jon Stokes’s Ars Technica article on the subject, he writes this:

At least two different groups, the TABB Group and FIXProtocol, estimate that high-frequency trading generated around $20 billion in profits for the financial sector last year. Goldman Sachs accounts for some 20 percent of global high-frequency trading activity, and the bank recently had a blow-out quarter in which its HFT-heavy trading operation racked up a record number of days where profits topped $100 million.

If Goldman Sachs alone can make $100 million a day from HFT, then $20 billion globally seems reasonable. But that’s a very big if, and I’d love to see how TABB Group and FIXProtocol arrived at their figures. (It would also be nice if HFT was clearly defined, which it isn’t, although I think most people agree that it’s a superset of flash trading.)

Elsewhere, Paul Wilmott (con) and Tyler Cowen (pro) join the debate. I’m more convinced by Wilmott than Cowen, although both make good points: Wilmott says that the complex algorithms driving HFT are prone to spectacular failure, while Cowen notes that “the correct judgment of efficiency occurs at the system-wide level, not at the level of the individual trading strategy”.

To that point, I’d be inclined to think that the massive volatility we’ve seen in the stock market of late is an indication that it’s not getting any more efficient, and therefore that it’s entirely plausible that HFT is hurting efficiency. Zero Hedge (now with its own domain name) puts the case in its strongest form:

Long-term buy and hold investors have already departed the market, as they have realized the traditional methods of approaching stock valuation such as fundamental and technical analysis have gone out of the window and been replaced by such arcane concepts as quant factors.

I think that’s overstating things, but even if it’s only true at the margin, it’s still a negative development.

At heart, the debate comes down to liquidity: is HFT a good thing or a bad thing, from a liquidity perspective? Cowen thinks it’s a good thing:

High-frequency trading brings more liquidity into the market. Call it “low quality liquidity” if you wish, but it still looks like net liquidity to me.

I don’t think that case is proven, although again the term “liquidity” is vague enough that it’s important to be able to define terms here. I think the important sense of liquidity is not narrow bid-offer spreads, but rather the ease of doing big deals at the market price, and/or the ability to buy or sell stock without moving the market. In that sense, HFT hurts, rather than helps: every time anybody tries to buy anything, the predatory algos try to pick them off. If that makespeople more reluctant to trade (“if you don’t like it, you can trade yourself at much lower frequencies”, says Cowen) then that ultimately hurts price discovery and transparency.

My bottom line is that HFT is a black box which very few people understand, and that one thing we’ve learned over the course of the crisis is that if there’s a financial innovation which doesn’t make a lot of sense and which is hard to understand, there’s a good chance there’s systemic risk there. Is it possible that HFT is entirely benign and just provides liquidity to the market? Yes. But that seems improbable to me.

The high frequency trading (HFT) debate continues today, fueled by a rather credulous Bloomberg article (elegantly fisked by Ryan Chittum) but, more substantively, moved along by Jon Stokes, who has a good article on the subject at Ars Techica. I asked him, via email, where he stood on all these questions; I think his answers are very good. Essentially, HFT turns out to be one of those “financial innovations” which lots of people like in theory but which only seem to benefit financial-market professionals in practice. I, for one, don’t think that there’s $20 billion worth of net societal benefit to it. Anyway, here’s the Q&A with Jon:

FS: Did you see today’s Bloomberg article?

JS: Yeah, and like the NYT article there is a slight conflation of flash orders with HFT. I don’t even mention flash orders in my article… I thought they were the least interesting aspect of HFT, at least to me as a computer guy. But clearly the idea of anyone getting market info ahead of anyone else touches nerves (at least among those who are getting the info second… the folks who are getting it first love it).

FS: Can you translate this, from the Bloomberg article, into English?

At Bats, the third-largest U.S. stock exchange, about half of its customers use flash orders, Chief Executive Officer Joe Ratterman said in an interview yesterday. The system is open to everyone and allows brokers to submit prices that are more competitive because the delay gives them a way to anticipate moves in the market, he said.

JS: This is actually a very good explanation of flash orders and the controversy around them.

As for the above, I can tell you what he’s trying to get across to the reporter, in the context of the currently bubbling controversy over flash orders and whether or not the NYSE will allow them in order to remain competitive with other exchanges and with dark pools: “this is no big deal, and doesn’t create a two-tiered market because any broker can use flash orders on our exchange to get a better price (than the people who aren’t using flash orders).”

Actually, my plain English rephrasing sort of crudely encapsulates the entire HFT debate. I.e., the argument of the “move along, nothing to see here” crowd on almost any HFT-related issue is something like:

“HFT does not create a multi-tiered market because anyone with enough money can move up to the highest tier by simply buying more speed and lower latency. So something with multiple tiers, where you can move to a higher tier if you can afford it, is not /really/ ‘multi-tiered’ because the tiers are open to everyone based on ability to pay. See how that works? No? Then go away, commie.”

FS: Can you tell me whether you think that, at the margin HFT improves liquidity? I’ve heard the opposite argued: that because HFT orders tend to be small and fleeting, they actually act against liquidity. That’s one reason why dark pools had to be invented — they’re the only way of trading in size without moving the market.

JS: At this point, you have to speak in more specifics about what you mean by “HFT.” Are predatory algos improving liquidity? I can’t see how one could claim they are for any reasonably useful definition of “liquidity”.

Are stat arbs and AMMs improving liquidity? Yeah, they are when they’re actually in the market. But they have no obligation to provide liquidity, so when things get choppy (i.e. when you need liquidity the most) they can just bail and take all that liquidity with them.

Are flash orders improving liquidity? I have to think way more about it to answer that.

FS: If HFT makes $20B a year, whose money is that?

JS: On one level, the answer to this question is easy for most of what goes on under the heading of “HFT”: the money is coming from whoever is buying stocks that are marked up a penny or so because they were down a rung on the speed/latency ladder. This could be pension funds, retail investors, or anyone else in the world. So it’s coming from the market participants.

(Of course, on some level, the guy who bought Cisco in March of 2000 is “down a few rungs on the speed/latency ladder” from the guy who bought it in August of ’99. But I think it’s important to draw a line here between what HFT is doing and what went on in a lower-frequency age, the same way that we all recognize a line between “speculation” and “investing” that’s drawn based on the time period that you hold an asset. Much of the debate in HFT is over the drawing of these kinds of lines.)

But to justify this $20B/year “fee” you have to make the case that the market system as a whole is getting something of value to all the payers in return. So supporters will say that it’s the price of liquidity and innovation, and, besides, they’ll argue, everyone who has been participating in the markets for decades has been paying these hidden liquidity taxes (and I’d rather call them taxes than fees) to specialists and any other market maker. But when you see this tax ballooning at Internet speed–much the same way that finance has ballooned as a portion of GDP–you have to take a step back and ask, “what is the real, fundamental benefit that we’re all paying for here when we collectively direct money into this?”