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Streetwise Professor

July 21, 2014

Doing Due Diligence in the Dark

Filed under: Exchanges,HFT,Regulation — The Professor @ 8:39 pm

Scott Patterson, WSJ reporter and the author of Dark Pools, has a piece in today’s journal about the Barclays LX story. He finds, lo and behold, that several users of the pool had determined that they were getting poor executions:

Trading firms and employees raised concerns about high-speed traders at Barclays PLC’s dark pool months before the New York attorney general alleged in June that the firm lied to clients about the extent of predatory trading activity on the electronic trading venue, according to people familiar with the firms.

Some big trading outfits noticed their orders weren’t getting the best treatment on the dark pool, said people familiar with the trading. The firms began to grow concerned that the poor results resulted from high-frequency trading, the people said.

In response, at least two firms—RBC Capital Markets and T. Rowe Price Group Inc —boosted the minimum number of shares they would trade on the dark pool, letting them dodge high-speed traders, who often trade in small chunks of 100 or 200 shares, the people said.

This relates directly to a point that I made in my post on the Barclays story. Trading is an experience good. Dark pool customers can evaluate the quality of their executions. If a pool is not screening out opportunistic traders, execution costs will be high relative to other venues who do a better job of screening, and users who monitor their execution costs will detect this. Regardless of what a dark pool operator says about what it is doing, the proof of the pudding is in the trading, as it were.

The Patterson article shows that at least some buy side firms do the necessary analysis, and can detect a pool that does not exclude toxic flows.

This long FT piece relies extensively on quotes from Hisander Misra, one of the founders of Chi-X, to argue that many fund managers have been ignorant of the quality of executions they get on dark pools. The article talked to two anonymous fund managers who say they don’t know how dark pools work.

The stated implication here is that regulation is needed to protect the buy side from unscrupulous pool operators.

A couple of comments. First, not knowing how a pool works doesn’t really matter. Measures of execution quality are what matter, and these can be measured. I don’t know all of the technical details of the operation of my car or the computer I am using, but I can evaluate their performances, and that’s what matters.

Second, this is really a cost-benefit issue. Monitoring of performance is costly. But so is regulation and litigation. Given that market participants have the biggest stake in measuring pool performance properly, and can develop more sophisticated metrics, there are strong arguments in favor of relying on monitoring.  Regulators can, perhaps, see whether a dark pool does what it advertises it will do, but this is often irrelevant because it does not necessarily correspond closely to pool execution costs, which is what really matters.

Interestingly, one of the things that got a major dark pool (Liquidnet) in trouble was that it shared information about the identities of existing clients with prospective clients. This presents interesting issues. Sharing such information could economize on monitoring costs. If a a big firm (like a T. Rowe) trades in a pool, this can signal to other potential users that the pool does a good job of screening out the opportunistic. This allows them to free ride off the monitoring efforts of the big firm, which economizes on monitoring costs.

Another illustration of how things are never simple and straightforward when analyzing market structure.

One last point. Some of the commentary I’ve read recently uses the prevalence of HFT volume in a dark pool as a proxy for how much opportunistic trading goes on in the pool. This is a very dangerous shortcut, because as I (and others) have written repeatedly, there is all different kinds of HFT. Some adds to liquidity, some consumes it, and some may be outright toxic/predatory. Market-making HFT can enhance dark pool liquidity, which is probably why dark pools encourage HFT participation. Indeed, it is hard to understand how a pool could benefit from encouraging the participation of predatory HFT, especially if it lets such firms trade for free. This drives away the paying customers, particularly when the paying customers evaluate the quality of their executions.

Evaluating execution quality and cost could be considered a form of institutional trader due diligence. Firms that do so can protect themselves-and their investor-clients-from opportunistic counterparties. Even though the executions are done in the dark, it is possible to shine a light on the results. The WSJ piece shows that many firms do just that. The question of whether additional regulation is needed boils down to the question of whether the cost and efficacy of these self-help efforts is superior to that of regulation.

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July 15, 2014

Oil Futures Trading In Troubled Waters

Filed under: Commodities,Derivatives,Economics,Energy,Exchanges,HFT,Regulation — The Professor @ 7:16 pm

A recent working paper by Pradeep Yadav, Michel Robe and Vikas Raman tackles a very interesting issue: do electronic market makers (EMMs, typically HFT firms) supply liquidity differently than locals on the floor during its heyday? The paper has attracted a good deal of attention, including this article in Bloomberg.

The most important finding is that EMMs in crude oil futures do tend to reduce liquidity supply during high volatility/stressed periods, whereas crude futures floor locals did not. They explain this by invoking an argument I did 20 years ago in my research comparing the liquidity of floor-based LIFFE to the electronic DTB: the anonymity of electronic markets makes market makers there more vulnerable to adverse selection. From this, the authors conclude that an obligation to supply liquidity may be desirable.

These empirical conclusions seem supported by the data, although as I describe below the scant description of the methodology and some reservations based on my knowledge of the data make me somewhat circumspect in my evaluation.

But my biggest problem with the paper is that it seems to miss the forest for the trees. The really interesting question is whether electronic markets are more liquid than floor markets, and whether the relative liquidity in electronic and floor markets varies between stressed and non-stressed markets. The paper provides some intriguing results that speak to that question, but then the authors ignore it altogether.

Specifically, Table 1 has data on spreads in from the electronic NYMEX crude oil market in 2011, and from the floor NYMEX crude oil market in 2006. The mean and median spreads in the electronic market: .01 percent. Given a roughly $100 price, this corresponds to one tick ($.01) in the crude oil market. The mean and median spreads in the floor market: .35 percent and .25 percent, respectively.

Think about that for a minute. Conservatively, spreads were 25 times higher in the floor market. Even adjusting for the fact that prices in 2011 were almost double than in 2006, we’re talking a 12-fold difference in absolute (rather than percentage) spreads. That is just huge.

So even if EMMs are more likely to run away during stressed market conditions, the electronic market wins hands down in the liquidity race on average. Hell, it’s not even a race. Indeed, the difference is so large I have a hard time believing it, which raises questions about the data and methodologies.

This raises another issue with the paper. The paper compares at the liquidity supply mechanism in electronic and floor markets. Specifically, it examines the behavior of market makers in the two different types of markets. What we are really interested is the outcome of these mechanisms. Therefore, given the rich data set, the authors should compare measures of liquidity in stressed and non-stressed periods, and make comparisons between the electronic and floor markets. What’s more, they should examine a variety of different liquidity measures. There are multiple measures of spreads, some of which specifically measure adverse selection costs. It would be very illuminating to see those measures across trading mechanisms and market environments. Moreover, depth and price impact are also relevant. Let’s see those comparisons too.

It is quite possible that the ratio of liquidity measures in good and bad times is worse in electronic trading than on the floor, but in any given environment, the electronic market is more liquid. That’s what we really want to know about, but the paper is utterly silent on this. I find that puzzling and rather aggravating, actually.

Insofar as the policy recommendation is concerned, as I’ve been writing since at least 2010, the fact that market makers withdraw supply during periods of market stress does not necessarily imply that imposing obligations to make markets even during stressed periods is efficiency enhancing. Such obligations force market makers to incur losses when the constraints bind. Since entry into market making is relatively free, and the market is likely to be competitive (the paper states that there are 52 active EMMS in the sample), raising costs in some state of the world, and reducing returns to market making in these states, will lead to the exit of market making capacity. This will reduce liquidity during unstressed periods, and could even lead to less liquidity supply in stressed periods: fewer firms offering more liquidity than they would otherwise choose due to an obligation may supply less liquidity in aggregate than a larger number of firms that can each reduce liquidity supply during stressed periods (because they are not obligated to supply a minimum amount of liquidity).

In other words, there is no free lunch. Even assuming that EMMs are more likely to reduce supply during stressed periods than locals, it does not follow that a market making obligation is desirable in electronic environments. The putatively higher cost of supplying liquidity in an electronic environment is a feature of that environment. Requiring EMMs to bear that cost means that they have to recoup it at other times. Higher cost is higher cost, and the piper must be paid. The finding of the paper may be necessary to justify a market maker obligation, but it is clearly not sufficient.

There are some other issues that the authors really need to address. The descriptions of the methodologies in the paper are far too scanty. I don’t believe that I could replicate their analysis based on the description in the paper. As an example, they say “Bid-Ask Spreads are calculated as in the prior literature.” Well, there are many papers, and many ways of calculating spreads. Hell, there are multiple measures of spreads. A more detailed statement of the actual calculation is required in order to know exactly what was done, and to replicate it or to explore alternatives.

Comparisons between electronic and open outcry markets are challenging because the nature of the data are very different. We can observe the order book at every instant of time in an electronic market. We can also sequence everything-quotes, cancellations and trades-with exactitude. (In futures markets, anyways. Due to the lack of clock synchronization across trading venues, this is a problem in a fragmented market like US equities.) These factors mean that it is possible to see whether EMMs take liquidity or supply it: since we can observe the quote, we know that if an EMM sells (buys) at the offer (bid) it is supplying liquidity, but if it buys (sells) at the offer (bid) it is consuming liquidity.

Things are not nearly so neat in floor trading data. I have worked quite a bit with exchange Street Books. They convey much less information than the order book and the record of executed trades in electronic markets like Globex. Street Books do not report the prevailing bids and offers, so I don’t see how it is possible to determine definitively whether a local is supplying or consuming liquidity in a particular trade. The mere fact that a local (CTI1) is trading with a customer (CTI4) does not mean the local is supplying liquidity: he could be hitting the bid/lifting the offer of a customer limit order, but since we can’t see order type, we don’t know. Moreover, even to the extent that there are some bids and offers in the time and sales record, they tend to be incomplete (especially during fast markets) and time sequencing is highly problematic. I just don’t see how it is possible to do an apples-to-apples comparison of liquidity supply (and particularly the passivity/aggressiveness of market makers) between floor and electronic markets just due to the differences in data. Nonetheless, the paper purports to do that. Another reason to see more detailed descriptions of methodology and data.

One red flag that indicates that the floor data may have some problems. The reported maximum bid-ask spread in the floor sample is 26.48 percent!!! 26.48 percent? Really? The 75th percentile spread is .47 percent. Given a $60 price, that’s almost 30 ticks. Color me skeptical. Another reason why a much more detailed description of methodologies is essential.

Another technical issue is endogeneity. Liquidity affects volatility, but the paper uses volatility as one of its measures of stressed markets in its study of how stress affects liquidity. This creates an endogeneity (circularity, if you will) problem. It would be preferable to use some instrument for stressed market conditions. Instruments are always hard to come up with, and I don’t have one off the top of my head, but Yanev et al should give some serious thought to identifying/creating such an instrument.

Moreover, the main claim of the paper is that EMMs’ liquidity supply is more sensitive to the toxicity of order flow than locals’ liquidity supply. The authors use order imbalance (CTI4 buys minus CTI4 sells, or the absolute value thereof more precisely), which is one measure of toxicity, but there are others. I would prefer a measure of customer (CTI4) alpha. Toxic (i.e., informed) order flow predicts future price movements, and hence when customer orders realize high alphas, it is likely that customers are more informed than usual and earn positive alphas. It would therefore be interesting to see the sensitivities of liquidity supply in the different trading environments to order flow toxicity as measured by CTI4 alphas.

I will note yet again that market maker actions to cut liquidity supply when adverse selection problems are severe is not necessarily a bad thing. Informed trading can be a form of rent seeking, and if EMMs are better able to detect informed trading and withdraw liquidity when informed trading is rampant, this form of rent seeking may be mitigated. Thus, greater sensitivity to toxicity could be a feature, not a bug.

All that said, I consider this paper a laudable effort that asks serious questions, and attempts to answer them in a rigorous way. The results are interesting and plausible, but the sketchy descriptions of the methodologies gives me reservations about these results. But by far the biggest issue is that of the forest and trees. What is really interesting is whether electronic markets are more or less liquid in different market environments than floor markets. Even if liquidity supply is flightier in electronic markets, they can still outperform floor based markets in both unstressed and stressed environments. The huge disparity in spreads reported in the paper suggests a vast difference in liquidity on average, which suggests a vast difference in liquidity in all different market environments, stressed and unstressed. What we really care about is liquidity outcomes, as measured by spreads, depth, price impact, etc. This is the really interesting issue, but one that the paper does not explore.

But that’s the beauty of academic research, right? Milking the same data for multiple papers. So I suggest that Pradeep, Michel and Vikas keep sitting on that milking stool and keep squeezing that . . . data ;-) Or provide the data to the rest of us out their and let us give it a tug.

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July 11, 2014

25 Years Ago Today Ferruzzi Created the Streetwise Professor

Filed under: Clearing,Commodities,Derivatives,Economics,Exchanges,HFT,History,Regulation — The Professor @ 9:03 am

Today is the 25th anniversary of the most important event in my professional life. On 11 July, 1989, the Chicago Board of Trade issued an Emergency Order requiring all firms with positions in July 1989 soybean futures in excess of the speculative limit to reduce those positions to the limit over five business days in a pro rata fashion (i.e., 20 percent per day, or faster). Only one firm was impacted by the order, Italian conglomerate Ferruzzi, SA.

Ferruzzi was in the midst of an attempt to corner the market, as it had done in May, 1989. The EO resulted in a sharp drop in soybean futures prices and a jump in the basis: for instance, by the time the contract went off the board on 20 July, the basis at NOLA had gone from zero to about 50 cents, by far the largest jump in that relationship in the historical record.

The EO set off a flurry of legal action. Ferruzzi tried to obtain an injunction against the CBT. Subsequently, farmers (some of whom had dumped truckloads of beans at the door of the CBT) sued the exchange. Moreover, a class action against Ferruzzi was also filed. These cases took years to wend their ways through the legal system. The farmer litigation (in the form of Sanner v. CBT) wasn’t decided (in favor of the CBT) until the fall of 2002. The case against Ferruzzi lasted somewhat less time, but still didn’t settle until 2006.

I was involved as an expert in both cases. Why?

Well, pretty much everything in my professional career post-1990 is connected to the Ferruzzi corner and CBT EO, in a knee-bone-connected-to-the-thigh-bone kind of way.

The CBT took a lot of heat for the EO. My senior colleague, the late Roger Kormendi, convinced the exchange to fund an independent analysis of its grain and oilseed markets to attempt to identify changes that could prevent a recurrence of the episode. Roger came into my office at Michigan, and told me about the funding. Knowing that I had worked in the futures markets before, asked me to participate in the study. I said that I had only worked in financial futures, but I could learn about commodities, so I signed on: it sounded interesting, my current research was at something of a standstill, and I am always up for learning something new. I ended up doing about 90 percent of the work and getting 20 percent of the money :-P but it was well worth it, because of the dividends it paid in the subsequent quarter century. (Putting it that way makes me feel old. But this all happened when I was a small child. Really!)

The report I (mainly) wrote for the CBT turned into a book, Grain Futures Contracts: An Economic Appraisal. (Available on Amazon! Cheap! Buy two! I see exactly $0.00 of your generous purchases.) Moreover, I saw the connection between manipulation and industrial organization economics (which was my specialization in grad school): market power is a key concept in both. So I wrote several papers on market power manipulation, which turned into a book . (Also available on Amazon! And on Kindle: for some strange reason, it was one of the first books published on Kindle.)

The issue of manipulation led me to try to understand how it could best be prevented or deterred. This led me to research self-regulation, because self-regulation was often advanced as the best way to tackle manipulation. This research (and the anthropological field work I did working on the CBT study) made me aware that exchange governance played a crucial role, and that exchange  governance was intimately related to the fact that exchanges are non-profit firms. So of course I had to understand why exchanges were non-profits (which seemed weird given that those who trade on them are about as profit-driven as you can get), and why they were governed in the byzantine, committee-dominated way they were. Moreover, many advocates of self-regulation argued that competition forced exchanges to adopt efficient rules. Observing that exchanges in fact tended to be monopolies, I decided I needed to understand the economics of competition between execution venues in exchange markets. This caused me to write my papers on market macrostructure, which is still an active area of investigation: I am writing a book on that subject. This in turn produced many of the conclusions that I have drawn about HFT, RegNMS, etc.

Moreover, given that I concluded that self-regulation was in fact a poor way to address manipulation (because I found exchanges had poor incentives to do so), I examined whether government regulation or private legal action could do better. This resulted in my work on the efficiency of ex post deterrence of manipulation. My conclusions about the efficiency of ex post deterrence rested on my findings that manipulated prices could be distinguished reliably from competitive prices. This required me to understand the determinants of competitive prices, which led to my research on the dynamics of storable commodity prices that culminated in my 2011 book. (Now available in paperback on Amazon! Kindle too.)

In other words, pretty much everything in my CV traces back to Ferruzzi. Even the clearing-related research, which also has roots in the 1987 Crash, is due to Ferruzzi: I wouldn’t have been researching any derivatives-related topics otherwise.

My consulting work, and in particular my expert witness work, stems from Ferruzzi. The lead counsel in the class action against Ferruzzi came across Grain Futures Contracts in the CBT bookstore (yes, they had such a thing back in the day), and thought that I could help him as an expert. After some hesitation (attorneys being very risk averse, and hence reluctant to hire someone without testimonial experience) he hired me. The testimony went well, and that was the launching pad for my expert work.

I also did work helping to redesign the corn and soybean contracts at the CBT, and the canola contract in Winnipeg: these redesigned contracts (based on shipping receipts) are the ones traded today. Again, this work traces its lineage to Ferruzzi.

Hell, this was even my introduction to the conspiratorial craziness that often swirls around commodity markets. Check out this wild piece, which links Ferruzzi (“the Pope’s soybean company”) to Marc Rich, the Bushes, Hillary Clinton, Vince Foster, and several federal judges. You cannot make up this stuff. Well, you can, I guess, as a quick read will soon convince you.

I have other, even stranger connections to Hillary and Vince Foster which in a more indirect way also traces its way back to Ferruzzi. But that’s a story for another day.

There’s even a Russian connection. One of Ferruzzi’s BS cover stories for amassing a huge position was that it needed the beans to supply big export sales to the USSR. These sales were in fact fictitious.

Ferruzzi was a rather outlandish company that eventually collapsed in 1994. Like many Italian companies, it was leveraged out the wazoo. Moreover, it had become enmeshed in the Italian corruption/mob investigations of the early 1990s, and its chairman Raul Gardini, committed suicide in the midst of the scandal.

The traders who carried out the corners were located in stylish Paris, but they were real commodity cowboys of the old school. Learning about that was educational too.

To put things in a nutshell. Some crazy Italians, and English and American traders who worked for them, get the credit-or the blame-for creating the Streetwise Professor. Without them, God only knows what the hell I would have done for the last 25 years. But because of them, I raced down the rabbit hole of commodity markets. And man, have I seen some strange and interesting things on that trip. Hopefully I will see some more, and if I do, I’ll share them with you right here.

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June 25, 2014

The 40th Anniversary of Jaws, Barclays Edition: Did the LX Dark Pool Keep Out the Sharks or Invite Them In?

Filed under: Economics,Exchanges,HFT,Politics,Regulation — The Professor @ 8:33 pm

Today’s big news is the suit filed by NY Attorney General Eric Schneiderman alleging that Barclays defrauded the customers of its LX dark pool.

In the current hothouse environment of US equity market structure, this will inevitably unleash a torrent of criticism of dark pools. When evaluating the ensuing rhetoric, it is important to distinguish between criticism of dark pools generally, and this one dark pool in particular. That is, there are two distinct questions that are likely to be all tangled up. Are dark pools bad? Or, are dark pools good (or at least not bad), but did Barclays  not do what dark pools are supposed to do while claiming that it did?

What dark pools are supposed to do is protect traders (mainly institutional traders who can be considered uninformed) from predatory traders. Predatory traders can be those with better information, or those with a speed advantage (which often confers an information advantage, through arbitrage or order anticipation). Whether dark pools in general are good or bad depends on the effects of the segmentation of the market. By “cream skimming” the (relatively) uninformed order flow, dark pools make the exchanges less liquid. Order flow on the exchanges tends to be more “toxic” (i.e., informed), and these information asymmetries widen spreads and reduce depth, which raises trading costs for the uninformed traders who cannot avail themselves of the dark pool and who trade on the lit market instead. This means that the trading costs of some uninformed traders (those who can use the dark pools) goes down and the trading costs of some uninformed traders (those who can’t use dark pools) goes up. The distributive effect is one thing that makes dark pools controversial: the losers don’t like them. The net effect is impossible to determine in general, and depends on the competitiveness of the exchange market among other things: even if dark pools reduce liquidity on the exchange, they can provide a source of competition that generates benefits if the exchange markets are imperfectly competitive.

What’s more, dark pools reduce the returns to informed trading.  The efficiency effects of this are also ambiguous, because some informed trading enhances efficiency (by improving the informativeness of prices, and thereby leading to better investment decisions), but other informed trading is rent seeking.

In other words, it’s complicated. There is no “yes” or “no” answer to the first question. This is precisely why market structure debates are so intense and enduring.

The second question is what is at issue in the Barclays case. The NYAG alleges that Barclays promised to protect its customers from predatory HFT sharks, but failed to do so. Indeed, according to the complaint, Barclays actively tried to attract sharks to its pool. (This is one of the problematic aspects of the complaint, as I will show). So, the complaint really doesn’t take a view on whether dark pools that indeed protect customers from sharks are good or bad. It just claims that if dark pools claim to provide shark repellent, but don’t, they have defrauded their customers.

Barclays clearly did make bold claims that it was making strenuous efforts to protect its customers from predatory traders, including predatory HFT. This FAQ sets out its various anti-gaming procedures. In particular, LX performed “Liquidity Profiling” that evaluated the users of the dark pool on various dimensions. One dimension was aggressiveness: did they make quotes or execute against them? Another dimension was profitability. Traders that earn consistent profits over one second intervals are more likely to be informed, and costly for others without information to trade with. Based on this information, Barclays ranked traders on a 0 to 5 scale, with 0 being profitable, aggressive, predatory sharks, and 5 representing passive, gentle blue whales.

Furthermore, Barclays claimed that it allowed its customers to limit their trading to counterparties with certain liquidity profiles, and to certain types of counterparties. For instance, a user could choose not to be matched with a trader with an aggressive profile. Similarly, a customer could choose not to trade against an electronic liquidity provider. In addition, Barclays said that it would exclude traders who consistently brought toxic order flow to the market. That is, Barclays claimed that it was constantly on alert for sharks, and kept the sharks away from the minnows and dolphins and gentle whales.

The NYAG alleges this was a tissue of lies. There are several allegations.

The first is that in its marketing materials, Barclays misrepresented the composition of the order flow in the pool. Specifically,  a graph that  depicted Barclays’ “Liquidity Landscape” purported to show that very little of the trading in the pool was aggressive/predatory. The NYAG alleges that this chart is “false” because it did not include “one of the largest and most toxic participants  [Tradebot] in Barclays’ dark pool.” Further, the NYAG alleges that Barclays deceptively under-reported the amount of predatory HFT trading activity in the pool.

The second basic allegation is that Barclays did not exclude the sharks, and that by failing to update trader profiles, the ability to avoid trading with a firm with a 0 or 1 liquidity profile ranking was useless. Some firms that should have been labeled 0′s were labeled 4′s or 5′s, leaving those that tried to limit their counterparties to the 4′s or 5′s vulnerable to being preyed on by the 0′s. Further, the AG alleges that Barclays promised to exclude the 0′s, but didn’t.

(The complaint also makes allegations about Barclays order routing procedures for its customers, but that’s something of a separate issue, so I won’t discuss that here).

Fraud and misrepresentation are objectionable, and should be punished for purposes of deterrence. They are objectionable because they result in the production of goods and services that are worth less than the cost of producing them. Thus, if Barclays did engage in fraud and misrepresentation, punishment is in order.

One should always be cautious about making judgments on guilt based on a complaint, which by definition is a one-sided representation of the facts. This is particularly true where the complaint relies on selective quotes from emails, and the statements of ex-employees. This is why we have an adversarial process to determine guilt, to permit a thorough vetting of the evidence presented by the plaintiff, and to allow the defendant to present exculpatory evidence (including contextualizing the emails, presenting material that contradicts what is in the proffered emails, and evidence about the motives and reliability of the ex-employees).

Given all this, based on the complaint there is a colorable case, but not a slam dunk.

There is also the question of whether the alleged misrepresentations had a material impact on investors’ decisions regarding whether to trade on LX or not: any fraud would have led to a social harm only to the extent too many investors used LX, or traded too much on it. Here there is reason to doubt whether the misrepresentations mattered all that much.

Trading is an “experience good.” That is, one gets information about the quality of the good by consuming it. Someone may be induced to consume a shoddy good once by deceptive marketing, but if consuming it reveals that it is shoddy, the customer won’t be back. If the product is viable only if it gets repeat customers, deception and fraud are typically unviable strategies. You might convince me to try manure on a cone by telling me it’s ice cream, but once I’ve tried it, I won’t buy it again. If your business profits only if it gets repeat customers, this strategy won’t succeed.

Execution services provided by a dark pool are an experience good that relies on repeat purchases. The dark pool provides an experience good because it is intended to reduce execution costs, and market participants can evaluate/quantify these costs, either by themselves, or by employing consultants that specialize in estimating these costs. Moreover, most traders who trade on dark pools don’t trade on a single pool. They trade on several (and on lit venues too) and can compare execution costs on various venues. If Barclays had indeed failed to protect its customers against the sharks, those customers would have figured that out when they evaluated their executions on LX and found out that their execution costs were high compared to their expectations, and to other venues.  Moreover, dark pool customers trade day after day after day. A dark pool generates succeeds by reducing execution costs, and if it doesn’t it won’t generate persistently large and growing volumes.

Barclays LX generated large and growing volumes. It became the second largest dark pool. I am skeptical that it could have done so had it really been a sham that promised superior execution by protecting customers from sharks when in fact it was doing nothing to keep them out. This suggests that the material effect of the fraud might have been small even had it occurred. This is germane for determining the damages arising from the fraud.

It should also be noted that the complaint alleges that not only did Barclays not do what it promised to keep sharks out, it actively recruited sharks. This theory is highly problematic. According to the complaint, Barclays attracted predatory HFT firms by allowing them to trade essentially for free.

But how does that work, exactly? Yes, the HFT firms generate a lot of volume, but a price of zero times a volume of a zillion generates revenues of zero. You don’t make any money that way. What’s more, the presence of these sharks would have raised the trading costs of the fee-paying minnows, dolphins, and whales, who would have had every incentive to find safer waters, thereby depriving Barclays of any revenues from them. Thus, I am highly skeptical that the AG’s story regarding Barclays’ strategy makes any economic sense. It requires that the non-HFT paying customers must have been enormously stupid, and unaware that they were being served up as bait. Indeed, that they were so stupid that they paid for the privilege of being bait.

It would make sense for Barclays to offer inducements to HFT firms that supply liquidity, because that would reduce the trading costs of the other customers, attracting their volume and making them willing to pay higher fees to trade in the pool.

All we have to go on now is the complaint, and some basic economics. Based on this information, my initial conclusion is that it is plausible that Barclays did misrepresent/overstate the advantages of LX, but that this resulted in modest harm to investors, and that even if the customers of LX got less than they had expected, they did better than they would have trading on another venue.

But this is just an initial impression. The adversarial process generates information that (hopefully) allows more discriminating and precise judgments. I would focus on three types of evidence. First, a forensic evaluation of the LX trading system: did the Liquidity Profile mechanism really allow users to limit their exposure to toxic/predatory order flow? Second, an appraisal of the operation of the system: did it accurately categorize traders, or did Barclays, as alleged in the complaint, systematically mis-categorize predatory traders as benign, thereby exposing traders who wanted to avoid the sharks to their tender mercies? Third, a quantification of the performance of the system in delivering lower execution costs. If LX was indeed doing what a dark pool should do, users should have paid lower execution costs than they would have on other venues. If LX was in fact a massive fraud that attracted customers with promises of protection from predatory traders, but then set the sharks on them, these customers would have in fact incurred higher execution costs than they could have obtained on other venues. At root, the AG alleges that LX promised to lower execution costs, but failed to do so because it did not protect customers from predatory traders: the proof of that pudding is in the eating.

The adversarial judicial process makes it likely that such evidence will be produced, and evaluated by the trier of fact. The process is costly, and often messy, but given the stakes I am sure that these analyses will be performed and that justice will be done, if perhaps roughly.

My bigger concern is  in the adversarial political process. Particularly in the aftermath of Flash Boys, all equity market structure market issues are extremely contentious. Dark pools are a particularly fraught issue. The exchanges (NYSE/ICE and NASDAQ) resent the loss of order flow to dark pools, and want to kneecap them. Many in Congress are sympathetic to their pleas. As I noted at the outset, although the efficiency effects of dark pools are uncertain, their distributive effects are not: dark pools create winners (those who can trade on them, mainly) and losers (those who can’t trade on them, and rent seeking informed traders who lose the opportunity to exploit those who trade on dark pools). Distributive issues are inherently political, and given the sums at stake these political battles are well-funded.

There is thus the potential that the specifics of the Barclays case are interpreted to tar dark pools generally, resulting in a legislative and regulatory over-reaction that kills the good dark pools as well as the bad ones. The facts that AGs are by nature grand-standers generally, and that Schneiderman in particular is a crusader on the make, make such an outcome even more likely.

Given this, I will endeavor to provide an economics-based, balanced analysis of developments going forward. As I have written so often, equity market issues are seldom black and white. Given the nature of equity trading, specifically the central role played by information in it, it is hard to analyze the efficiency effects of various structures and policies. We are in a second best world, and comparisons are complex and messy in that world. In such a world, it is quite possible that both Barclays and the AG are wrong. We’ll see, and I’ll call it as I see it.

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April 19, 2014

HFT, Dark Pools, Third Markets, and the Second Best

Filed under: Economics,HFT,Regulation — The Professor @ 12:05 pm

In his Atlanta Fed paper, Stiglitz uses second best considerations in his argument against HFT. My basic response is that second best considerations cut both ways.

Put simply, second best considerations mean that if one optimality condition is violated, then it may be efficiency enhancing to violate another optimality condition: or, one “market failure” can mitigate another. A simple example would be that it might be better for a polluting industry to be monopolistic or oligopolistic instead of competitive.  The monopolist’s reduction of output offsets the incentive to produce too much that occurs when there is an externality.

In the context of HFT, my second best argument is that since informed trading can be rent seeking, things that might otherwise be inefficient, such as anticipating orders or engaging in “arms races” to enhance trading speed, can be efficiency enhancing.

This is not a new theme with me. In fact, it’s quite old. I wrote a paper in 1998 titled “Third Markets and the Second Best” that applied this argument to off-exchange trading, and the free riding off of price discovery on exchanges. I discussed this further in my 2002 JLEO paper, “Securities Market Macrostructure: Property Rights and the Efficiency of Securities Trading“.

In these papers, I showed that off exchange trading venues-third markets-that free ride off of the prices produced by exchanges and limit trading to the verifiably uninformed can be efficiency enhancing even if this exacerbates adverse selection problems on the exchange because this free riding mitigates two problems: the market power of dominant exchanges (where the market power arises from the liquidity network effect) and rent seeking informed trading (i.e., the expenditure of real resources to obtain information in order to extract profits by trading with the less-informed who buy and sell for portfolio balance or risk management reasons).

Similar arguments can be applied to dark pools today. Indeed, many dark pools (and internalization) perform a similar function to third markets back in the day: they are venues that use various means to screen out informed traders, in order to reduce execution costs for the verifiably less-informed. This loss of uninformed order flow on “lit” exchanges tends to increase adverse selection costs there, but the same competition and rent seeking informed trading second best considerations arise here, meaning that the costs of lower liquidity on exchanges may be more than offset by other benefits.

And many of the very same considerations apply to HFT. Thus, contra Stiglitz, second best considerations do not unambiguously favor the adoption of restrictions on HFT.

Indeed, the thing that is most striking about the trading of financial instruments is that there are so many potential violations of optimality conditions that the entire analysis of market structure becomes an exercise in the theory of the second best.

Which can be a problem. For as George Stigler said, “Well, there are second best considerations” is a conversation stopper. But the conversation about market structure isn’t going to stop anytime soon, so we have to grasp the nettle of the second best if that conversation is going to shed more light than heat. It is good that Stiglitz makes the second best issue explicity. If only he had applied this reasoning more consistently, and recognized that informed trading can be a deviation from optimality which can be addressed by things that seem in isolation to be inefficient.

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April 18, 2014

Stiglitz on HFT

Filed under: Derivatives,Economics,Exchanges,HFT,Regulation — The Professor @ 11:39 am

Joe Stiglitz presented a paper on HFT at the Atlanta Fed conference earlier this week that has received a lot of attention. The paper is worth reading, but I actually recommend Felix Salmon’s synopsis, which breaks out the issues nicely.

I agree with Stiglitz in part, and disagree in part. The agreement is that Stiglitz hits many of the themes of my recent posts on HFT, notably that when there is private information, financial markets are unlikely to reach first best outcomes, and that making welfare comparisons is very difficult: I would say nigh-on to impossible, actually. Stiglitz also recognizes that HFT affects the incentives to collect information, which is another theme that I’ve emphasized.

Where I disagree is that Stiglitz (like DeLong) concludes from these insights that HFT is wasteful and should be restricted. This conclusion does not follow at all, and can be traced to some implicit assumptions about the nature of informed trading by non-HFT traders.

Stiglitz says “HFT discourages the acquisition of information which would make the market more informative in a relevant sense.” And by “relevant sense” he means fundamental information about the real economy. He laments that HFT “can be thought of as stealing the information rents that otherwise would have gone to those who had invested in information.” Further, he criticizes that much of what HFT does is merely accelerate the revelation of this information, and this acceleration is so small that it cannot improve any decision on any margin, and hence the resources used by HFT are wasted.

But this implicitly assumes that the information produced by non-HFT traders, the collection of which is reduced by the “stealing of information rents”, is in fact fundamental information that would improve decisions. But as I’ve noted repeatedly, many of the informed traders who HFT firms sniff out are producing information that does not improve any economic decision on any margin. Getting better information about an impending earnings report can be very profitable, but revelation of this information doesn’t improve decision making.

By assuming that non-HFT informed traders are producing information that invariably improves decisions, Stiglitz misunderstands what a great deal of informed trading is about, and thereby ignores a benefit of HFT order anticipation-based trading, and crucially, of HFT quote adjustments that cause markets to run away from big traders and thereby limits their ability to profit on their information.

One way to think about it is that there is cash flow relevant information, and decision relevant information. Pretty much all decision relevant information is cash flow relevant, but not all cash flow information is decision relevant. One major example is what Stiglitz emphasizes: the slight acceleration of revelation of information. But I claim that a lot of the information produced by institutional traders is of exactly this type. Stiglitz (and DeLong) ignore this, which leads them to biased appraisals of the efficiency of HFT.

That is, once one recognizes that some informed trading is rent seeking, and socially wasteful, “stealing of information rents” by HFT can be a feature, not a bug.

Stiglitz also ignores that even if HFT reduces the amount of decision relevant information produced and incorporated into prices, reducing this source of private information still reduces the adverse selection costs incurred by uninformed investors trading for portfolio rebalancing or hedging reasons. This reduction in adverse selection costs tends to improve the allocation of risk. This benefit must be weighed against any cost arising from the reduction in the production of decision relevant information.

In brief, Stiglitz and I agree that HFT reduces the incentive to collect information. Where we differ is that Stiglitz believes this is an unmitigated bad, whereas I strongly believe that this is totally wrong, because Stiglitz’s characterization of informed trading is very unrealistic. My point is that non-HFT informed trading can be parasitic, but Stiglitz does not recognize this or account for it in his analysis.

Stiglitz also complains that HFT liquidity is junk liquidity. In particular, prices move before large orders can be executed.

This is a variant on the criticism that HFT reduces information rents. Moreover, Stiglitz fails to make comparisons between realistic alternatives. The ability to adjust quotes faster reduces adverse selection costs, and allows HFT to quote tighter markets. Restricting HFT in some way will lead to wider spreads and lower quoted depth. Either way, big orders will have a price impact.

Stiglitz also claims that HFT reduces other, better forms of liquidity. Salmon actually explains this point more clearly:

HFT does not improve the important type of liquidity.

If you’re a small retail investor, you have access to more stock market liquidity than ever. Whatever stock you want to buy or sell, you can do so immediately, at the best market price. But that’s not the kind of liquidity which is most valuable, societally speaking. That kind of liquidity is what you see when market makers step in with relatively patient balance sheets, willing to take a position off somebody else’s book and wait until they can find a counterparty to whom they can willingly offset it. Those market makers may or may not have been important in the past, but they’re certainly few and far between today.

HFT also reduces natural liquidity.

Let’s say I do a lot of homework on a stock, and I determine that it’s a good buy at $35 per share. So I put in a large order at $35 per share. If the stock ever drops to that price, I’ll be willing to buy there. I’m providing natural liquidity to the market at the $35 level. In the age of HFT, however, it’s silly to just post a big order and keep it there, since it’s likely that your entire order will be filled — within a blink of an eye, much faster than you can react — if and only if some information comes out which would be likely to change your fair-value calculation. As a result, you only place your order for a tiny fraction of a second yourself. And in turn, the market becomes less liquid.

These points are pretty dubious. The kinds of market makers that HFT displaces (locals on futures exchanges, specialists, day traders) were hardly characterized by “relatively patient balance sheets.” Their holding periods were also quite short. Indeed, one of the filters academics use to identify HFT traders is firms that end the day flat: this exactly what most locals and specialists strove to do. And most traders that “do a lot of homework on a stock” were not doing so to supply liquidity through limit orders that they did not adjust frequently. Those who do a lot of homework are usually liquidity takers, not liquidity suppliers.

In sum, although Stiglitz’s analytical framework and broad conclusions are correct, his specific conclusions about HFT are not. They are not correct primarily because he has a very unrealistic view of the nature of informed trading. Once one recognizes that much informed trading is a form of rent seeking-the point that Hirshleifer made over 40 years ago-most of Stiglitz’s objections to HFT dissolve. Put differently, Stiglitz is right to believe that the financial sector may be too big, in part because there can be excessively strong incentives to collect information and trade on it, but he fails to take this point to its logical conclusion when evaluating HFT.

I do find it rather odd that strongly left-leaning economists like Stiglitz and DeLong who are broadly skeptical of financial markets focus their criticism on one new feature of those markets-HFT-without considering the implications of their broader critiques of the financial sector. At root, their criticism is that much financial market activity is rent seeking. If you believe that, you have to consider how HFT affects these rent seeking activities. Once you do that, it is impossible to sustain the critiques of HFT, because even if there are rent seeking aspects to HFT, it also can reduce other forms of rent seeking.

 

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April 12, 2014

A Serious Question For Brad DeLong

Filed under: Economics,Exchanges,HFT,Politics,Regulation — The Professor @ 4:39 pm

This is totally serious. 100 percent snark free. The answer (and more importantly, the explanation) will help make explicit assumptions and logic, and thereby advance the discussion.

So here it is:

Do you oppose or support laws prohibiting trading by corporate insiders on material, non-public information? (Alternative formulation: Do you support the expenditure of resources to enforce laws prohibiting trading by corporate insiders on material, non-public information?) Explain your reasoning.

The explanation is more important than the answer.

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Yes, Brad, It’s Just You (And Others Who Oversimplify and Ignore Salient Facts)

Filed under: Derivatives,Economics,Exchanges,HFT,Politics,Regulation,Uncategorized — The Professor @ 2:48 pm

Brad DeLong takes issue with my Predator/Prey HFT post. He criticizes me for not taking a stand on HFT, and for not concluding that HFT should be banned because it is a parasitic. Color me unpersuaded. De Long’s analysis is seriously incomplete, and some of his conclusions are incorrect.

At root, this is a dispute about the social benefits of informed trading. De Long takes the view that there is too little informed trading:

In a “rational” financial market without noise traders in which liquidity, rebalancing, and control/incentive traders can tag their trades, it is impossible to make money via (4). Counterparties to (4) will ask the American question: If this is a good trade for you, how can it be a good trade for me? The answer: it cannot be. And so the economy underestimates in fundamental information, and markets will be inefficient–prices will be away from fundamentals, and so bad real economic decisions will be made based on prices that are not in fact the appropriate Lagrangian-multiplier shadow values–because of free riding on the information contained in informed order flow and visible market prices. [Note to Brad: I quote completely, without extensive ellipses. Pixels are free.]

Free riding on the information in prices leading to underinvestment in information is indeed a potential problem. And I am quite familiar with this issue, thank you very much. I used similar logic in my ’94 JLE paper on self-regulation by exchanges to argue that exchanges may exert too little effort to deter manipulation because they didn’t internalize the benefits of reducing the price distortions caused by corners. My ’92 JLS paper applied this reasoning to an evaluation of exchange rules regarding the disclosure of information about the quantity and quality of grain in store. It’s a legitimate argument.

But it’s not the only argument relating to the incentives to collect information, and the social benefits and costs and private benefits and costs of trading on that information. My post focused on something that De Long ignores altogether, and certainly did not respond to: the possibility that privately informed trading can be rent seeking activity that dissipates resources.

This is not a new idea either. Jack Hirshleifer wrote a famous paper about it over 40 years ago. Hirsleifer emphasizes that trading on information has distributive effects, and that people have an incentive to invest real resources in order to distribute wealth in their direction. The term rent seeking wasn’t even coined then (Ann Kreuger first used it in 1974) but that is exactly what Hirshleifer described.

The example I have in my post is related to such rent seeking behavior. Collecting information that allows a superior forecast of corporate earnings shortly before an announcement can permit profitable trading, but (as in one of Hirshleifer’s examples) does not affect decisions on any margin. The cost of collecting this information is therefore a social waste.

De Long says that the idea that there is too little informed trading “does not seem to me to scan.” If it doesn’t it is because he has ignored important strands of the literature dating back to the early-1970s.

Both the free riding effects and the rent seeking effects of informed trading certainly exist in the real world. Too little of some information is collected, and too much of other types is collected. And that was basically my point: due to the nature of information, true costs and benefits aren’t internalized, and as a result, evaluating the welfare effects of informed trading and things that affect the amount of informed trading is impossible.

One of the things that affects the incentives to engage in informed trading is market microstructure, and in particular the strategies followed by market makers and how those strategies depend on technology, market rules, and regulation. Since many HFT are engaging in market making, HFT affects the incentives surrounding informed trading. My post focused on how HFT reduced adverse selection costs-losses to informed traders-by ferreting out informed order flow. This reduces the losses to informed traders, which is the same as saying it reduces the gains to informed traders. Thus there is less informed trading of all varieties: good, bad, and ugly.

Again the effects of this are equivocal, precisely because the effects of informed trading are equivocal. To the extent that rent seeking informed trading is reduced, any reduction in adverse selection cost is an unmitigated gain. However, even if collection of some decision improving information is eliminated, reducing adverse selection costs has some offsetting benefits. De Long even mentions the sources of the benefits, but doesn’t trace through the logic to the appropriate conclusion.

Specifically, De Long notes that by trading people can improve the allocation of risk and mitigate agency costs. These trades are not undertaken to profit on information, and they are generally welfare-enhancing. By creating adverse selection, informed trading-even trading that improves price informativeness in ways that leads to better real investment decisions-raises the cost of these welfare-improving risk shifting trades. Just as adverse selection in insurance markets leads to under provision of insurance (relative to the first best), adverse selection in equity or derivatives markets leads to a sub optimally small amount of hedging, diversification, etc.

So again, things are complicated. Reducing adverse selection costs through more efficient market making may involve a trade-off between improved risk sharing and better decisions involving investment, etc., because prices are more informative. Contrary to De Long, who denies the existence of such a trade off.

And this was the entire point of my post. That evaluating the welfare effects of market making innovations that mitigate adverse selection is extremely difficult. This shouldn’t be news to a good economist: it has long been known that asymmetric information bedevils welfare analysis in myriad ways.

De Long can reach his anti-HFT conclusion only by concluding that the net social benefits of privately informed trading are positive, and by ignoring the fact that any kind of privately informed trading serves as a tax on beneficial risk sharing transactions. To play turnabout (which is fair!): there is “insufficient proof” for the first proposition. And he is flatly wrong to ignore the second consideration. Indeed, it is rather shocking that he does so.*

Although De Long concludes an HFT ban would be welfare-improving, his arguments are not logically limited to HFT alone. They basically apply to any market making activity. Market makers employ real resources to do things to mitigate adverse selection costs. This reduces the amount of informed trading. In De Long’s world, this is an unmitigated bad.

So, if he is logical De Long should also want to ban all exchanges in which intermediaries make markets. He should also want to ban OTC market making. Locals were bad. Specialists were bad. Dealers were bad. Off with their heads!

Which raises the question: why has every set of institutions for trading financial instruments that has existed everywhere and always had specialized intermediaries who make markets? The burden of proof would seem to be on De Long to demonstrate that such a ubiquitous practice has been able to survive despite its allegedly obvious inefficiencies.

This relates to a point I’ve made time and again. HFT is NOT unique. It is just the manifestation, in a particular technological environment, of economic forces that have expressed/manifested themselves in different ways under different technologies. Everything that HFT firms do-market making, arbitrage activities, and even some predatory actions (e.g., momentum ignition)-have direct analogs in every financial trading system known to mankind. HFT market makers basically put into code what resides in the grey matter of locals on the floor. Arbitrage is arbitrage. Gunning the stops is gunning the stops, regardless of whether it is done on the floor or on a computer.

One implication of this is that even if HFT is banned, it is inevitable-inevitable-that some alternative way of performing the same functions would arise. And this alternative would pose all of the same conundrums and complexities and ambiguities as HFT.

In sum, Brad De Long reaches strong conclusions because he vastly oversimplifies. He ignores that some informed trading is rent seeking, and that there can be a trade-off between more informative prices (and higher adverse selection costs) and risk sharing.

The complexities and trade-offs are exactly why debates over speculation and market structure have been so fierce, and so protracted. There are no easy answers. This isn’t like a debate over tariffs, where answers are much more clean-cut. Welfare analyses are always devilish hard when there is asymmetric information.

Although a free-market guy, I acknowledge such difficulties, even though that means that implies that I know the outcome is not first best. Brad De Long, not a free market guy, well, not so much. So yes, Brad, it is just you-and other people who oversimplify and ignore salient considerations that are present in any set of mechanisms for trading financial instruments, regardless of the technology.

* De Long incorrectly asserts that informed trading cannot occur in the absence of “noise trading,” where from the context De Long defines noise traders as randomizing idiots: “In a ‘rational’ financial market without noise traders in which liquidity, rebalancing, and control/incentive traders can tag their trades, it is impossible to make money via [informed trading].” Noise trading (e.g., in a Kyle model) is a modeling artifice that treats “liquidity, rebalancing and control/incentive” trades-trades that are not information-driven-in a reduced form fashion.  Randomizing idiots don’t trade on information. But neither do rational portfolio diversifiers subject to endowment shocks.

It is possible-and has been done many, many times-to produce a structural model with, say, rebalancing traders subject to random endowment shocks who trade even though they lose systematically to informed traders. (De Long qualifies his statement by referring to traders who can “tag their trades.” No idea what this means. Regardless, completely rational individuals who benefit from trading because it improves their risk exposure (e.g., by permitting diversification) will trade even though they are subject to adverse selection.) They will trade less, however, which is the crucial point, and which is a cost of informed trading, regardless of whether that informed trading improves other decisions, or is purely rent-seeking.

 

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April 9, 2014

Smith and Bodek on Equity Market Reforms: Good, Bad, and Ugly

Filed under: Economics,HFT,Regulation — The Professor @ 1:44 pm

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.

 

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April 5, 2014

Pinging: Who is the Predator, and Who Is the Prey?

Filed under: Economics,Exchanges,HFT,Politics,Regulation — The Professor @ 11:59 am

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.

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