Streetwise Professor

Fellow Houstonian Cameron Smith (of HFT shop Quantlab) and HFT gadfly Haim Bodek have an oped in the FT that makes recommendations on how to fix the US equity markets. (That’s a key right there. There’s HFT in futures, but it doesn’t generate near the heartburn as it does in equities.)

The recommendations are a mixture of good, bad, and ugly. The good are recommendations to fix RegNMS, specifically by allowing locked markets, and moving away from one-size-fits-all tick sizes.  The bad/ugly are their recommendations on dark pools and especially on exchange policies regarding data access and pricing.

All of this is too much for one post, so I will defer discussions of RegNMS reform and dark pools. I will focus here on the data issues.

Here’s what they say about data access and pricing:

Make market data free
Free market data would eliminate the disparity between professionals and investors. It would also cut the $400m of revenues divided among exchanges – which essentially subsidises the creation of otherwise useless markets. At the same time, we must ensure that data disseminated by the public consolidator is synchronised with the private exchange data feeds so that all the data are received by investors at the same time, eliminating the perception of unfairness. A technology company should be dedicated to this task.

This is bad/ugly because overlooks the basic microeconomics of entry and investment into HFT. Let’s think through the implications of this recommendation.

The fundamental error is in the first sentence: making data free would not eliminate the disparity between professionals and investors. Nor would making it possible for all participants to access the data simultaneously by synchronizing the data feeds.  To understand where Smith and Bodek err, it is necessary to think through the equilibrium effects of their recommendation.

There would still be disparities because access to data is a necessary but not sufficient condition to eliminate them. HFT firms take the private data feed they get from exchanges, and also make additional investments in hardware and software in order to use that data to drive their trading strategies. Without these complementary investments, the data is useless in implementing HFT-type strategies. Given the cost of private data feeds, there is investment in hardware and software and other supporting resources to implement HFT. In a reasonably competitive market, entry and investment in these other resources will proceed to the point where for the marginal HFT firm, risk adjusted profits cover its cost of capital. We’ve seen that process in action: HFT profits were high in 2008-2009, but have subsequently fallen substantially as entry and investment into this business has occurred.  This is the way that competitive markets work.

Note that not everybody decides to make the investments in the resources necessary to implement HFT. Even many big institutional investors eschew doing so. Certainly individual investors do. This is because the returns on the investment in hardware and software (where returns depend on the costs of data) do not cover the related capital costs. This is why disparities exist. The disparities in speed and strategies are the result of maximizing choices made by myriad market participants, and these maximizing decisions reflect the costs of engaging in various market activities.

Understanding this, let’s consider the economic effects of mandating free access to data and synchronizing access. To a first approximation, data charges are a fixed cost. Therefore, making data free would reduce the fixed costs of becoming an HFT firm. Reducing fixed costs will induce entry into HFT: costs are just covered by the marginal firm when data must be paid for, meaning that when data is free all existing firms at existing scale will earn profits above the cost of capital.  This economic profit induces entry. Entry means there will be more HFT activity when data is free. (If lowering data charges also reduce the marginal costs of HFT, existing HFT firms will expand, reinforcing this effect.)

Again, entry will occur to the point where the profits of the marginal HFT firm cover the cost of capital.  Moreover, many market participants will choose not to make the additional investments required to engage in HFT. There will still be disparities. Some firms will be faster than others (i.e., the firms that make the investments necessary to engage in HFT will be faster than “investors” who don’t make the investments in hardware and software and people necessary to engage in HFT.) Moreover, there will be more HFT activity, for the simple reason that the cost of engaging in HFT has gone down.

In other words: if you want to want to reduce disparities and discourage entry into HFT, don’t make data free, tax it. Smith and Bodek’s policy recommendation will have the exact opposite effect from what they intend.

There are other things to consider here. Data revenues represent a substantial source of income for exchanges. Forcing them to forego these revenues will affect their economics. It is conceivable that the loss in revenue will induce some exchanges to exit, reducing competition which would tend to result in an increase in fees paid by investors. Even if exit doesn’t happen, the loss of revenue may affect exchange decisions on other margins: they may choose, for instance, to invest less in systems or technology. I just raise this as a possibility: the effects of the loss of data revenues on these other decision margins are likely to be complex and subtle, and I don’t pretend to understand them, and to do so would require considerable research and thought. (Moreover, given my agnosticism about the welfare effects of financial trading generally, the effects of adjustments on these other margins on welfare are even more complex and mysterious.)

This analysis brings out a general point. You need to think through the equilibrium implications of policy changes, taking into account how market participants will respond on all margins. Making data free reduces the costs of engaging in HFT. This induces entry into HFT, and leads to more of it, not less.

In other words, in analyzing HFT and market structure generally, not just microstructure is important. Microeconomics 101 is too.

The debate over Lewis’s Flash Boys is generating more informed commentary than the book itself. One thing that is emerging in the debate is the identity of the main contending parties: HFT vs. the Buy Side, mainly big institutional traders.

One of the criticisms of HFT is that it engages in various strategies to attempt to ferret out institutional order flows, which upsets the buy side. But the issue is not nearly so clearcut as the buy side would have you believe.

The main issue is that not all institutional orders are alike. In particular, there is considerable variation in the informativeness of institutional order flow. Some (e.g., index fund order flow) is unlikely to be informed. Other order flow is more informed: some may even be informed by inside information.

Informed order flow is toxic for market makers. They lose on average when trading against it. So they try to determine what order flow is informed, and what order flow isn’t.

Informed order flow must hide in order to profit on its information. Informed order flow uses various strategies based on order types, order submission strategies, choice of trading venues, etc., to attempt to become indistinguishable from uninformed order flow. Uninformed order flow tries to devise in strategies to signal that it is indeed uninformed, but that encourages the informed traders to alter their strategies to mimic the uninformed.

To the extent that market makers-be they humans or machines-can get signals about the informativeness of order flow, and  in particular about undisclosed flow that may be hitting the market soon, they can adjust their quotes accordingly and mitigate adverse selection problems. The ability to adjust quotes quickly in response to information about pending informed orders allows them to quote narrower markets. By pinging dark pools or engage in other strategies that allow them to make inferences about latent informed order flow, HFT can enhance liquidity.

Informed traders of course are furious at this. They hate being sniffed out and seeing prices change before their latent orders are executed. They excoriate “junk liquidity”-quotes that disappear before they can execute. Because the mitigation of adverse selection reduces the profits they generate from their information.

It can be frustrating for uninformed institutional investors too, because to the extent that HFT can’t distinguish perfectly between uninformed and informed order flow,  the uninformed will often see prices move against them before they trade too.  This creates a commercial opportunity for new trading venues, dark pools, mainly, to devise ways to do a better way of screening out informed order flow.

But even if uninformed order flow often finds quotes running away from them, their trading costs will be lower on average the better that market makers, including HFT, are able to detect more accurately impending informed orders. Pooling equilibria hurt the uninformed: separating equilibria help them. The opposite is true of informed traders. Market makers that can evaluate more accurately the informativeness of order flow induce more separation and less pooling.

Ultimately, then, the driver of this dynamic is the informed traders. They may well be the true predators, and the uninformed (or lesser informed) and the market makers are their prey. The prey attempt to take measures to protect themselves, and ironically are often condemned for it: informed traders’ anger at market makers that anticipate their orders is no different that the anger of a cat that sees the mouse flee before it can pounce. The criticisms of both dark pools and HFT (and particularly HFT strategies that attempt to uncover information about trading interest and impending order flow) are prominent examples.

The welfare impacts of all this are unknown, and likely unknowable. To the extent that HFT or dark pools reduce the returns to informed trading, there will be less investment in the collection of private information. Prices will be less informative, but trading will be less costly and risk allocation improved. The latter effects are beneficial, but hard to quantify. The benefits of more informative prices are impossible to quantify, and the social benefits of more informed prices may be larger, perhaps substantially so, than the private benefits, meaning that excessive resources are devoted to gathering private information.

More informative prices can improve the allocation of capital. But not all improvements in price efficiency improve the allocation of capital by anything near the cost of acquiring the information that results in these improvements, or the costs imposed on uninformed traders due to adverse selection. For instance, developing information that permits a better forecast of a company’s next earnings report may have very little effect on the investment decisions of that company, or any other company. The company has the information already, and other companies for which this information may be valuable (e.g., firms in the same industry, competitors) are going to get it well within their normal decision making cycle.  In this case, incurring costs to acquire the information is a pure waste. No decision is improved, risk allocation is impaired (because those trading for risk allocation reasons bear higher costs), and resources are consumed.

In other words, it is impossible to know how the social benefits of private information about securities values relate to the private benefits. It is quite possible (and in my view, likely) that the private benefits exceed the social benefits. If so, traders who are able to uncover and anticipate informed trading and take measures that reduce the private returns to informed trading are enhancing welfare, even if prices are less informative as a result.

I cannot see any way of evaluating the welfare effects of financial trading, and in particular informed trading. The social benefits (how do more informative prices improve the allocation of real resources) are impossible to quantify: they are often difficult even to identify, except in the most general way (“capital allocation is improved”). Unlike the trade for most goods and services, there is no reason to believe that social and private benefits align. My intuition-and it is no more than that-is that the bulk of informed trading is rent seeking, and a tax on the risk allocation functions of financial markets.

It is therefore at least strongly arguable that the development of trading technologies that reduce the returns to informed trading are a good thing. To the extent that one of the charges against HFT-that it is better able to detect and anticipate (I will not say front-run) informed order flow-is true, that is a feature, not a bug.

I don’t know and I am pretty sure nobody knows or even can know the answers to these questions. Which means that strongly moralistic treatments of HFT or any other financial market technology or structure that affects the returns to informed trading is theology, not economics/finance. Agnosticism is a defensible position. Certitude is not.

Michael Lewis’s new book on HFT, Flash Boys, has been released, and has unleashed a huge controversy. Or put more accurately, it has added fuel to a controversy that has been burning for some time.

I have bought the book, but haven’t had time to read it. But I read a variety of accounts of what is in the book, so I can make a few comments based on that.

First, as many have pointed out, although this has been framed as evil computer geniuses taking money from small investors, this isn’t at all the case. If anyone benefits from the tightening of spreads, especially for small trade sizes, it is small investors. Many of them (most, in fact) trade at the bid-ask midpoint via internalization programs with their brokers or through payment-for-order-flow arrangements. (Those raise other issues for another day, but have been around for years and don’t relate directly to HFT.)

Instead, the battle is mainly part of the struggle between large institutional investors and HFT. Large traders want to conceal their trading intentions to avoid price impact. Other traders from time immemorial have attempted to determine those trading intentions, and profit by trading before and against the institutional traders.  Nowadays, some HFT traders attempt to sniff out institutional orders, and profit from that information.  Information about order flow is the lifeblood of those who make markets.

This relates to the second issue. This has been characterized as “front running.” This terminology is problematic in this context. Front running is usually used to describe a broker in an agency relationship with a customer trading in advance of the customer’s order, or disclosing the order to another trader who then trades on that information. This is a violation of the agency relationship between the client and the broker.

In contrast, HFT firms use a variety of means-pinging dark pools, accessing trading and quoting information that is more extensive and obtained more quickly than via the public data feeds-to detect the presence of institutional orders. They are not in an agency relationship with the institution, and have no legal obligation to it.

And this is nothing new. Traders on the floor were always trying to figure out when big orders were coming, and who was submitting them. Sometimes they obtained this information when they shouldn’t have, because a broker violated his obligation. But usually it was from watching what brokers were trading, knowing what brokers served what customers, looking at how anxious the broker appeared, etc.  To throw the floor of the track, big traders would use many brokers. Indeed, one argument for dual trading was that it made it harder for the floor to know the origin of an order if the executing broker dual traded, and might be active because he was trading on his own account rather than for a customer.

This relates too to the third issue: reports that the FBI is investigating for possible criminal violations. Seriously? I remember how the FBI covered itself in glory during the sting on the floors in Chicago in ’89. Not really. The press reports say that the the FBI is investigating whether HFT trades on “non-public information.”  Well, “non-public information” is not necessarily “inside information” which is illegal to trade on:  inside information typically relates to that obtained from someone with a fiduciary duty to shareholders. Indeed, ferreting out non-public information contributes to price discovery: raising the risk of prosecution for trading on information obtained through research or other means, but which is not obtained from someone with a fiduciary relationship to a company, is a dangerous slippery slope that could severely interfere with the operation of the market.

Moreover, it’s not so clear that order flow information is “non-public”.  No, not everyone has it: HFT has to expend resources to get it, but anybody could in theory do that. Anybody can make the investment necessary to ping a dark pool. Anybody can pay to get a faster data feed that allows them to get information that everyone has access to more quickly. Anybody can pay to get quicker access to the data, either through co-location, or the purchase of a private data feed. There is no theft or misappropriation involved. If firms trade on the basis of such information that can be obtained for a price that not everyone is willing to pay, and that is deemed illegal, how would trading on the basis of what’s on a Bloomberg terminal be any different?

Fourth, one reason for the development of dark pools, and the rules that dark pools establish, are to protect order flow information, or to make it less profitable to trade on that information. The heroes of Lewis’s book, the IEX team, specifically designed their system (which is now a dark pool, but which will transition to an ECN and then an exchange in the future) to protect institutional traders against opportunistic HFT. (Note: not all HFT is opportunistic, even if some is.)

That’s great. An example of how technological and institutional innovation can address an economic problem. I would emphasize again that this is not a new issue: just a new institutional response. Once upon a time institutional investors relied on block trading in the upstairs market to prevent information leakage and mitigate price impact. Now they use dark pools. And dark pools are competing to find technologies and rules and protocols that help institutional investors do the same thing.

I also find it very, very ironic that a dark pool is now the big hero in a trading morality tale. Just weeks ago, dark pools were criticized heavily in a Congressional hearing.  They are routinely demonized, especially by the exchanges. The Europeans have slapped very restrictive rules on them in an attempt to constrain the share of trading done in the dark. Which almost certainly will increase institutional trading costs: if institutions could trade more cheaply in the light, they would do so. It will also almost certainly make them more vulnerable to predatory HFT because they will be deprived of the (imperfect) protections that dark pools provide.

Fifth, and perhaps most importantly from a policy perspective, as I’ve written often, much of the problem with HFT in equities is directly the result of the fragmented market structure, which in turn is directly the result of RegNMS. For instance, latency arbitrage based on the slowness of the SIP results from the fact that there is a SIP, and there is a SIP because it is necessary to connect the multiple execution venues. The ability to use trades or quotes on one market to make inferences about institutional trades that might be directed to other markets is also a consequence of fragmentation. As I’ve discussed before, much of the proliferation of order types that Lewis (and others) argue advantage HFT is directly attributable to fragmentation, and rules relating to locked and crossed markets that are also a consequence of RegNMS-driven fragmentation.

Though HFT has spurred some controversy in futures markets, these controversies are quite different, and much less intense. This is due to the fact that many of the problematic features of HFT in equities are the direct consequence of RegNMS and the SEC’s decision (and Congress’s before that) to encourage competition between multiple execution venues.

And as I’ve also said repeatedly, these problems inhere in the nature of financial trading. You have to pick your poison. The old way of doing business, in which order flow was not socialized as in the aftermath of RegNMS, resulted in the domination of a single major execution venue (e.g., the NYSE). And for those with a limited historical memory, please know that these execution venues were owned by their members who adopted rules-rigged the game if you will-that benefited them. They profited accordingly.

Other news from today brings this point home. Goldman is about to sell its NYSE specialist unit, the former Spear, Leeds, which it bought for $6.5 billion (with a B) only 14 years ago.  It is selling it for $30 million (with an M).  That’s a 99.5 decline in market value, folks. Why was the price so high back in 2000? Because under the rules of the time, a monopoly specialist franchise on a near monopoly exchange generated substantial economic rents. Rents that came out of the pockets of investors, including small investors.  Electronic trading, and the socialization of order flow and the resultant competition between execution venues, ruthlessly destroyed those rents.

So it’s not like the markets have moved from a pre-electronic golden age into a technological dystopia where investors are the prey of computerized super-raptors. And although sorting out cause and effect is complicated, the decline in trading costs strongly suggests that the new system, for all its flaws, has been a boon for investors. Until regulators or legislators find the Goldilocks “just right” set of regulations that facilitates competition without the pernicious effects of fragmentation (and in many ways, “fragmentation” is just a synonym for “competition”), we have to choose one or the other. My view is that messy competition is usually preferable to tidy monopoly.

The catch phrase from Lewis’s book is that the markets are rigged. As I tweeted after the 60 Minutes segment on the book, by his definition of rigging, all markets have always been rigged. A group of specialized intermediaries has always exercised substantial influence over the rules and practices of the markets, and has earned rents at the expense of investors. And I daresay it would be foolish to believe this will ever change. My view is that the competition that prevails in current markets has dissipated a lot of those rents (although some of that dissipation has been inefficient, due to arms race effects).

In sum, there doesn’t appear to be a lot new in Lewis’s book. Moreover, the morality tale doesn’t capture the true complexity of the markets generally, or HFT specifically. It has certainly resulted in the release of a lot of heat, but I don’t see a lot of light. Which is kind of fitting for a book in which a dark pool is the hero.

One of the annoying things about the debate over HFT, particularly related to the quality of liquidity supplied by HFT traders, is the lack of any historical context.  HFT firms are criticized for pulling liquidity suddenly, particularly when volatility ticks up, thereby exacerbating price moves and the impact of order flow on prices.

The thing is, that market makers/liquidity suppliers have been doing this since time immemorial. Liquidity suppliers have always chosen flight over fight.

I’ve written about that in the past (focusing, for instance, on Black Monday).  This recent blog post provides some excellent historical color that illustrates the point quite well.

Having traded the markets since 1985, I would like to explain what market liquidity actually meant in the pre-electronic days and what it means in the world of electronic trading.

During the 1987 October crash, Black Monday, I was a junior trader in government bonds and futures. Black Monday was not a crash which was over in seconds or minutes; no, the world stopped turning actually for days. The mini crash of 1989 had a similar pattern. Chernobyl 1986, Gorbachev crisis in 1991 and UK election day in 1992, were all similar liquidity gap events seen by market traders.

The beauty of those days (the late 80s) for a trader, was that markets were very often in a ‘fast market’ condition. This meant that one was able to trade at any price and all rules were thrown overboard until the board officials were able to control the pit again. Without computers, we simply could not deal with the enormous amount of activity in the markets at those times and simply stopped (or delayed) sending price information out, not just for seconds, but minutes and even (during the week of Black Monday) days. Clearers had backlogs to clients for days.

What investors, customers, institutions looked at in those days were screen prices on systems like Reuters or Bloomberg: feeds fed by the voice of the official. If the official did not yell a price in the mic, the data group would not enter a price, and the world would never see this price. Under fast markets no prices were entered and the screen would just read ‘fast’ (if you were lucky). Future and option prices on screen were seen as tradeable prices and in slow markets they may have been pretty close to the truth. In fast markets they were inaccurate and it was not possible to trade the price indicated on the Reuters screen. Yet the world still thought this was the correct price.

Being in the pit, one cannot keep buying or selling an instrument, so traders would hedge their positions. Hundreds of times I have been in situations where there was simply no bid or offer in the instrument to hedge in. The signal from a broker out of the futures pit simply stated, ‘no bid, no bid’, yet all the screens where showing a bid or an offer. Was this liquidity? The world clearly thought it was, but we all knew there was panic on the floor and you were lucky to trade one lot.

Yes, I know Mr. Spanbroek is talking his book.  (The EPTA represents HFT firms.)  But what he writes about the good old days on the floor are accurate.  They were good days mainly for the guys on the floor, who would supply liquidity when it was profitable, but would head for the exits when the order flow was toxic.  Ditto traders upstairs in the OTC markets.  This is a characteristic of liquidity supply and liquidity suppliers pretty much everywhere and always.

This subject always brings to mind a legendary-and I mean legendary-trader, who told me in the early-00s he was all in favor of the markets going electronic because he was “sick of getting raped by the floor.”  About 2 years ago he told me that he couldn’t compete with HFT.  I guess there was a Goldilocks “just right” era in between, say 2002-2007 or so, but the criticisms of liquidity suppliers is a hardy perennial.  And there is some justice to the charges.  The point is that it is not new, and it is not unique to HFT.  It is inherent in the economics of liquidity supply.

And there is no easy policy solution.  A policy intended to fix one problem will just create others.  Again, the problems inhere in the economics of liquidity supply.

I laughed out loud, and very hard, watching this video (from RT, no less, though via Tabb Group) about HFT.  It is an interview with David Greenberg, who spent 25 years trading CL on the floor of the NYMEX.

What made me laugh?  The part (starting around the 1:30 mark) where Greenberg notes that when public news comes out, HFT traders pull their quotes “in nanoseconds.”  But back on the floor, there would be brokers who would be quoting customer limit orders, and “before his clerk could grab him on his neck, I could go ‘SOLD!'”

Let me translate.  Floor traders had a speed advantage over customers off the floor who were trading by limit order.  When market-moving news came out, floor traders like Greenberg could exploit their speed advantage, and trade against stale customer limit orders that were entered before the information was released. In so doing, the locals, due to their time-and-space advantage, took money from customers.

Yeah, it didn’t happen in a nanosecond, but that doesn’t mean jack.  What matters is that even on the floor, the faster took money from the slower: seconds, nanoseconds, whatever.  This was true in 1990, 1890, and hell, in 1790 or 1690 in the Osaka rice market.  Locals on the floor had a speed advantage over those off the floor, which they exploited ruthlessly .  And this inevitably resulted in wider markets, as off-floor traders quoted wider spreads precisely because of their vulnerability to being picked off.

Not to go all Einstein on you (as if), but speed is relative.  The stale quote problem is not a function of the absolute speed with which some traders can react, but the relative speed.  Humanoid locals in the pit were pitifully slow, compared to robot traders-especially customers who had to play telephone (literally) to yank their orders.  But that doesn’t mean that predatory trading-taking advantage of the slowest gnu in the herd-was less severe on the floor than in an electronic market.  What drives the losses from stale quotes is relative speed.  And I would argue that relative speed differences were even greater in the floor days than now.

And what ticks off old-timers like Greenberg is that the easy pickings aren’t there for the taking anymore.  HFT traders, through their tremendous speed, and their ability to scrape numerous sources of electronic information, can pull their quotes in an instant when market-moving news comes out.  They thereby protect themselves from being picked off.

Which is precisely why they can quote very tight markets.  And which is precisely why guys like Greenberg hate them.  They have ruined a total racket.  How dare they?!?!

Greenberg also talks about how in the floor days prices would move in stages to a new level after information was released.  Now the movement is much faster.  That’s bad how, exactly?

One could argue that if HFT completely withdraw their quotes, that the market will overshoot.  Prices move a lot because HFT traders totally pull their quotes.   Once the information has been digested, they quote again, and presumably the new quote level is between the pre-information release price and the quote/price immediately following the pulling of HFT quotes.

This has a testable prediction: price reversals should be larger in electronic markets than floor markets.  Or to refine the test: price reversals in the aftermath of big price moves (in response to the release of particularly salient information) should be bigger in electronic than floor markets.  To refine the test: the magnitude of price reversals should vary directly with the prevalence of HFT.

This all brings to mind two conversations, held more than 10 years apart, with a legendary trader.  A guy who made 100s of millions of dollars.  In about 2000, this trader told me he was ecstatic about the advent of electronic trading because he was “tired of getting raped by the floor.”  (Exact quote.)  In 2012, he was lamenting the advent of electronic trading because he couldn’t keep up with HFT.

Keep this in mind when you hear laments about HFT, especially from market veterans.  It’s all about whose gnu is gored, or about how the gnus have become so fast that it’s hard to gore them anymore.

A paper on HFT by Baron, Brogaard and Kirilenko is attracting a lot of attention.  They provide shocking evidence: HFT traders make money.  Who knew?

What is garnering the most attention is their analysis of profit by counterparty.  For instance, Bart Chilton was shocked to find that passive HFT traders earned a $5 per contract profit on average when trading with small traders.

The numbers are interesting and all very nice, but the interpretation is far more equivocal than the authors-and HFT critics like Chilton-would have you believe.

One issue is “how big is big?” or “big compared to what?”  Is the per contract profit supposed to be zero?

The paper reports eye-popping Sharp ratios, which suggests that HFT earns abnormal profits.  But great caution is required.  This Sharp ratio only takes into account the authors’ estimate of the capital required to support the HFT positions: given the rapid mean reversion of positions (especially for “passive” HFTs), this capital can be quite modest.  But HFT also requires a substantial investment in systems, software and human capital.  And data! A huge cost.  Arguably the trading capital is the smallest portion of the capital required to setup an HFT and keep it running.  Leaving those investments out of a Sharp ratio calculation renders the number utterly meaningless.  The fact that these are numbers from a single market and HFT traders usually trade in many markets also makes it impossible to know what the profitability of HFT firms is.

Indeed, given that there is free entry into HFT, why wouldn’t we expect the marginal entrant to just earn the market rate of return on all capital employed?  If a study that takes all the relevant costs and investments into account finds that the marginal HFT trader earns abnormal returns, the appropriate response would be to identify  the source of the entry barrier that gives rise to this rent, and attack that.

Another issue of interpretation relates to defining the but-for.  That is, what is the alternative to which HFT is being compared?  An electronic market without HFT?  Floor markets?  Consider the latter.  There is no doubt that floor traders-locals-reaped profits in the same way as the HFT traders in the Baron et al analysis.  Some were “passive”-quoting bids and asks, and supplying liquidity and trading against order flow.  Others were “active”-speculating on price movements, and always alive to picking off stale limit customer limit orders.   Some did both.  Given that the per contract profits reported in the Baron et al paper are fractions of a tick, it’s almost certain that floor traders earned profits as large or larger than they report for HFT, particularly if you leave out important cost/capital components (e.g., the opportunity cost of a seat).

When it comes to floor trading, we know that the marginal trader was earning a rent.  Know as in metaphysically certain.  How do we know that: seat prices were positive, and frequently very large. (A paper I published in 1999 shows that Q-ratios for exchanges calculated using seat prices were astronomical.  This provides clear evidence of rents.)  The seat price is the capitalized value of the abnormal profits earned by the marginal floor trader.  And we know the entry barrier that gave rise to these abnormal profits: exchange limitations on the number of memberships.

So that would be an interesting comparison.  The (appropriately calculated) profit of the marginal HFT firm vs. the marginal profit of floor traders (as measured using seat prices).

The paper also reports substantial skewness in the profitability in HFT.  But there was substantial skewness in profit on the floor too.  The Tom Baldwins and Harris Brumfelds made huge money, a lot of other traders did quite well, and a very large fraction-arguably a majority-were on breaking even and at risk of getting blown out.    And this isn’t a recent phenomenon.  In the 19th and early 20th centuries, titans like Old Hutch made substantially more money than the run of the mill pit trader.

Which brings me back to my constant refrain: what’s really all that different about HFT?  The functions and behaviors of market participants are pretty much static.  We have uninformed traders and informed traders.  We have liquidity suppliers and liquidity demanders.  We have opportunistic traders looking to take advantage of a time-and-space advantage, and the ability to respond sooner than others (thereby profiting at the expense of the slow).  The functions haven’t changed: just the technology for carrying out those functions has changed.

I’d wager that an analysis based on exchange street books from the floor days (which report price, volume, and trader type by CTI indicator) would produce very similar results to the HFT study’s, although the inability to observe the bid-ask on the floor makes it difficult or impossible to break down members trading on their own account (CTI1s) into “active” and “passive” categories in the same way Baron et al do.  I am highly confident that such a study would find that: CTI1s made per contract profits as large or larger than HFT traders; CTI1s profited primarily in trades with CTI4s (customers), and that there was variation in the per contract profit depending on whether the CTI4 was big or small; that floor trader Sharp ratios based only on the capital required to support their positions would be very large; and that there was substantial skewness in the profitability of CTI1s.

Perhaps the most interesting aspect of the study is the distinction between aggressive and passive HFT traders.  It is very hard to criticize passive HFT, because they are clearly providing liquidity (although no doubt Bart will find bad things to say).  The activities of aggressive HFT-HFT firms that are almost always hitting bids and lifting offers-could well be less salutary and perhaps inefficient.

Aggressive firms could be creating adverse selection.  One aggressive strategy could be to invest substantial real resources into scooping up huge amounts of public data, analyzing it faster than anybody else, and trading faster on it than anybody else when they recognize that quotes don’t reflect the information.  Some of this information could be obtained by scraping news and social media sites.  Some of it could be generated by statistical arbitrage: analyzing vast quantities of data to identify pricing relationships across multiple instruments; identifying times when prevailing quotes across these markets deviate from these relationships; and buying cheap and selling rich when such deviations are found.  To the extent quote submitters-including passive HFT firms-do not have this information, the aggressive firms will be better informed, profit at the expense of quote setters, and thereby cause liquidity suppliers to widen their quotes.

If that’s all there is to it, aggressive HFT would be inefficient.  Real resources are devoted to earning a rent, and this rent seeking distorts prices (notably the price of liquidity), thereby inducing others trading for risk-related reasons to trade too little.

But price discovery is the flip side of adverse selection.  Informed trading drives the price discovery process.  The question becomes what is the value of the price discovery provided by aggressive HFT.  Impossible to answer quantitatively.  I would surmise that the conventional wisdom is that the value is small.  Why do we need to have prices adjust by a fraction of a cent to reflect information a fraction of a second sooner?  What real decision (e.g., investment decision, planting decision) is going to change if a price embeds slightly more information a blink of an eye sooner?  I am similarly skeptical about the value of speeding up price discovery, and therefore am quite willing to accept that some aggressive HFT is opportunistic rent seeking, and hence inefficient.

But even granting that, what are you going to do about it?  Is there a way of restricting rent seeking HFT that does not also burden beneficial HFT, for instance by imposing costs on passive HFTs that supply liquidity?

It’s not obvious that there are discriminating ways of pricing trading services in ways that reduce opportunistic trading without also reducing liquidity supply and trading for risk shifting reasons.  Tinkering with maker-taker fee structures, or tinkering with price schedules more generally seems to be the most sensible way to do it.  The question becomes: who should do the tinkering?  Exchanges presumably have the strongest incentives to drive out the opportunistic wolves that feed on the sheep, and the best information to do it.  I therefore don’t see a strong case for external regulation intended to reduce rent seeking HFT.  Let the exchanges handle it.

It is obvious that some of the regulations that have been proposed would make things far worse, rather than better.  Time-in-force rules are probably the best-or worst-example of that.  Requiring quote submitters-including passive HFT firms-to keep quotes in force for some minimum time period makes them incredibly vulnerable to the opportunistic HFT firms.  The aggressive HFT firms feed off of quotes that aren’t adjusted to reflect the information they produce by stat arb or whatever.  Time-in-force rules make it harder to adjust quotes, giving more targets of opportunity to aggressive HFT traders.  Prediction: TIF rules will increase aggressive HFT trading; reduce passive HFT trading; lead to aggressive HFT representing a higher proportion of HFT overall; and lead to wider spreads.

And ironically, these effects will increase the profits HFT firms earn at the expense of non-HFT traders. So, Bart: if you think the profits that HFT earns off the little guys are unconscionable now, be careful what you ask for.  Some of the restrictions on the “cheetah traders” that you advocate will make those profits even more unconscionable.

Put differently: Time-in-force rules don’t constrain aggressive traders who are taking quotes instead of making them.  They only constrain those making quotes and thereby make them vulnerable to the takers.  This makes sense how, exactly?

In almost any human endeavor, there is opportunism and rent seeking and inefficient behavior that goes on side-by-side with beneficial, wealth increasing conduct.  We don’t have the information or sufficiently discriminating and cheap deterrents to eliminate bad conduct altogether.

When it comes to any financial trading-old school floor trading or HFT-dominated electronic trading-rent seeking trading will occur.  When evaluating HFT generally, and restrictions on HFT that do not discriminate between the virtuous and vicious forms of the activity, you have to take the good with the bad and see which predominates.  The vast bulk of the existing empirical evidence shows that HFT is associated with better market quality in terms of spreads and depth.  So even though some HFT is almost certainly predatory, the effects of the predatory trading are more than offset by the efficient, wealth-increasing kinds.   Indiscriminate regulations that constrain both types of HFT are therefore highly objectionable.  Not a good idea to shoot a prize bull in the head because it has a parasite that available drugs won’t kill.  But that’s what those who get all hot and bothered about aggressive-and arguably parasitical-HFT threaten to do with indiscriminate regulations that would impede all HFT.