Audio Interview Peter Kovac Part 2 – Disruption, IEX, Dark Pools, Blowups

Editors Note: I highly recommend consuming this interview in audio form, by clicking the start box above. So much more interesting!

Please see interview Part one with Peter Kovac here, on HFT market making front-running and profitability

Part 2 of the interview HERE:

Michael:          Hi, my name is Michael, and I used to be a hedge fund manager.

Peter:              My name is Peter Kovac, and I’m the author of Flash Boys: Not So Fast.

Michael:          Flash Boys: Not So Fast came out recently[1] on Amazon and is available. One version I will paraphrase parts of your book and maybe synthesize, which is there is a system of legacy broker-dealers, the Goldmans, Morgans, and in Katsuyama’s case Royal Bank of Canada that is in the business of earning reasonably large spreads as market makers between where they can buy and where they can sell, when interacting with customers.

And then there’s this disruptive group of nerdy quants in flip-flops like you, who are getting in there, narrowing the spreads, and sort of stealing their lunch little by little. And in the course of doing that, in your telling, they’re profitable and also squeezing the profit margins of market making. So the real tension here is between legacy broker dealers and these new techy guys in flip-flops, – who are profitable, but really just at the expense of the legacy broker-dealers, the old Wall Street folks. Is that the right paraphrase of your version of what’s happening here?

Peter:              I think it’s close. I was surprised when I heard Flash Boys was coming out, I thought that Lewis would love the story of a band of outsiders who had no experience on Wall Street. They came to Wall Street and made everything faster, efficient, more transparent. And as a result lowered the cost for everyone.

Michael:          The high-frequency guys would be the Oakland As of Wall Street[2], right?

Peter:              Exactly.

Michael:          They’re cheaper, faster, quant-ey, numbers driven. They’re the plucky underdogs that everybody likes.

Peter:              Exactly. I was quite surprised when I read the book and found out they had become the villains. That the underdog was in fact the old school on Wall Street. It is a bit more complicated than just the high-frequency traders versus a Katsuyama. There’s more at play here. Katsuyama isn’t exactly a market maker. He’s trying to work these orders into the market. He’s interacting with the market makers.

But I think his beef is overall with high-frequency traders as an explanation for why things are going awry. And why are things going awry for him? For people in that boat, there’s a lot of pressures that came to bear as the market started to change in the last decade.

high frequency trading volume

When you start to have Regulation NMS, a lot more exchanges popping up, a completely different mindset in terms of rules, and you have the electronification of the markets really taking hold, all those factors make it very confusing. It’s a lot of upheaval, a lot of change.

On top of that, and the real competition for someone like a big bank equity trader like Katsuyama, is that people are using computers to develop algorithms that would replace him. This is what he does in Flash Boys. He creates an algorithm called Thor that basically replaces his job function. He becomes a sales person for Thor instead of handling the orders himself.

But if you’re in his shoes in 2007 and you see all of the rules changing, these new electronic exchanges, computers becoming a much bigger factor, and then you see these algorithms for trading that are going to replace your job, it’s a really scary world.

The thing is it’s very hard to blame these algorithms like Thor. It’s much easier to blame some class of people, particularly if those people aren’t very well known and don’t have a very big voice. So you can blame the high-frequency traders for these changes, and you can say around the same time we started hearing about high-frequency traders we started hearing about computers in the market. Started hearing about more markets. And Reg NMS came out around the same time we were hearing about these high-frequency traders. They were there beforehand, but that’s when he’s hearing about them.

All these changes, I think it’s natural for them to try to find someone to blame. These are the people that they blame. But really, a lot of these traditional traders were displaced by a number of other factors.

There are also other traders who have survived and thrived. The complexity of the markets does not reduce their value in any way. In fact, one could argue it enhances their value. But you have to be someone who has embraced that complexity and you can tell your clients I know how to navigate this market, and there is still a role for a human trader who knows how to work this kind of an order.

Ironically at our own firm, if we had a large order of our own we needed to do for some sort of special hedge, we would have a human do it. We wouldn’t just program it into a computer. And I believe there’s a value for humans in the market.

Introducing IEX

Michael:          So one of the major parts of Flash Boys, as well as in your telling, is the introduction of IEX, which is Katsuyama’s exchange. Is there something new about that exchange or is this just a solution in search of a problem? Meaning, if there’s no front running, and IEX is supposedly there to solve front running, is IEX doing something good? Is there something real there, or is it just a marketing technique?

iex

Peter:              I think I kind of agree with your characterization: “a solution in search of a problem.” Yes, and Lewis kind of sets up the entire book saying IEX is designed to prevent high-frequency traders from this front running scam. This will prove it because no high-frequency traders will want to trade on IEX. Now we’ve blocked them all from doing their front-running scam.

Which makes it all the more remarkable that on day one, the largest purchase on IEX is a high frequency trading firm. In fact, today a large portion of liquidity on IEX comes from high-frequency trading firms. I think that’s part of your answer right there.

However, as a solution in search of a problem, they actually have created a problem. If you ask most people, say nine months ago, are you concerned about high frequency trading, they’d say high frequency what. But after Flash Boys and IEX came out, suddenly this has become a problem people have to deal with. There were a lot of asset managers on Wall Street who then had to tell their clients whether or not they felt high-frequency trading was a problem for them, where never before did they have to explain that.

So now that there is this crisis of confidence that they themselves have created, they offer themselves as a solution to it. From that perspective, if they do give people confidence, then that does add value. I know it sounds strange.

Michael:          A little convoluted.

Peter:              Right. They created their own crisis, but if they solve that crisis by giving people confidence in the markets again, then that’s a benefit.

Positives of IEX – Transparency, and Order Types

Peter:              Another positive about IEX is that they really trumpeted the role of transparency in dark pools. That goes a long way because one of the biggest concerns about dark pools is you don’t know what’s going on in them. It’s wonderful they have been able to bring that element back to the debate.

They also came out and said we want to have a limited number of order types. And basically the order types are what governs how your intentions are managed by a market center. They’ve gotten incredibly complex across all these market centers and the fear is that not only do people not understand them when they’re using them, and they could shoot themselves in the foot, but then also as you have this increasing complexity it makes the exchanges more likely to do something incorrectly.

And so IEX started off with a premise that they wanted to have a very few basic order types. I think that’s very laudable, to really try to simplify the process. Since then they’ve added a couple more order types because their customers have asked for it, but the principle still stands that the simplicity is best in terms of having a smoothly functioning market.

Michael:          As an outsider I found the explanation of order types hard to follow. They were all new to me. The reasoning behind order types mixed with ‑‑ in the case of Lewis’s book – the order types mixed in with payments for order flow are pretty hard to understand.

In the absence of understanding we want to believe a conspiracy theory. That’s one of the hardest parts of understanding, both his book and then your book also in response to it, because I’m totally unfamiliar with the order types.

Peter:              I struggle with how to explain those order types because they are complicated.

Michael:          To get into why somebody would want a certain order probably requires many pages of explanation.

Peter:              It does. It’s very difficult. You kind of nailed it when you said some people will just jump to conclusions. If it’s that complicated it can’t be good. I agree that many of these complicated order types aren’t good. What I don’t agree with is someone saying if it’s that complicated it can’t be good, and someone must have implemented this because of some evil conspiracy. It’s that last part that doesn’t follow from the first part.

It can be just complicated and not good because someone wasn’t thinking it through. That’s the case for most of these order types. That someone wasn’t thinking it through or they created one order type, and because of that it created another problem. And they needed another order type to solve that.

Michael:          The analogy from my Wall Street experience is CDOs which are very complicated. But the people operating and creating very complex debt structures understand the waterfalls. It’s not not understandable. They’re specialists and they understand what they’re doing, but when you have a journalist describing what went wrong with CDOs, they decided it’s the complexity itself that’s wrong with it. Which is not exactly true.

But from an outsider reading that you go “Wow, so the complexity is the evil part.” No, actually it’s the leverage that’s the evil part. Or it’s the stuff that made the CDO that’s the evil part, not necessarily the complexity of it. That’s my analogy for understanding what you’re saying which is: these order types have a place, and there may be legitimate things about it. But it certainly appears as an outsider that there can’t be anything good with something that complex.

Peter:              I would also say from an outsider in the CDO world, that the complexity becomes a problem when people employ that ‑‑ in this case employ a CDO without understanding what it does. Without someone explaining it to them correctly, so when they believe they have the correct explanation but they don’t.

I think with these order types, one thing that’s interesting is that the order types actually are explained correctly. All the order types are submitted to the SEC. There’s a filing that explains it. So they may be complicated but there is a clear description of what they do. If someone doesn’t understand them and they decide to use them, the burden was on them to actually look at it. The materials were out there. That said, it would be a lot simpler if you didn’t have them.

Dark Pools

Michael:          I would like to know about dark pools because they’re completely opaque to me, so to speak. Why do institutional investors use dark pools? Maybe by background to the listeners, dark pools as far as I understand are usually set up by a hedge fund or broker-dealer, in which block traders get to anonymously trade and it’s “off exchange” I guess, is the key to a dark pool, so that the trades are not reported. Why would somebody want this?

dark_pools
This is a Google Image of a ‘dark pool,’ obviously

Peter:              Correct. In a dark pool none of the orders are actually broadcast. There’s no market data. You never see the quotes. You never know actually what the current market is in a dark pool.

That and the fact that ‑‑ there’s some regulatory oversight, but it’s much less than an exchange, so you’re not necessarily guaranteed to know how they match orders. They can tell you that, but they also might not tell you exactly what algorithm they used to match different orders. You can’t really review at the end of the day and say should my order have been executed or not; did I get the price I wanted or not.

Michael:          You can’t compare it to the exchange.

Peter:              No. So they don’t provide a lot of that data. That transparency isn’t there. The rationale for dark pools is that the public markets have a lot of people who are trading and they’re out to get an edge. Why don’t you come into this dark pool, our private group here. And hopefully you’ll be able to get the price you want.

In a way, it’s kind of remarkable that only on Wall Street could you have someone making the pitch saying you don’t want to do everything out in public where there’s full transparency, you can see exactly every single step that’s happening. Instead, why don’t you come back here in this dark alley with this bank? You can trust us. Just forget about all this stuff from the financial crisis. Trust us.

Michael:          So you would say, and probably most people would say that within the dark pool there’s quite a lot less transparency, no price disclosure, and less regulation. So what’s not to love, I guess?

Peter:              If you’re traditional Wall Street, what’s not to love.

Michael:          So why do people want to use a dark pool? I don’t get that.

Peter:              There probably a number of valid reasons. Part of it is the marketing pitch basically saying you don’t want to be in the public markets. And some people buy that because they like this exclusivity or they’ve been convinced that there is something in the public market that they should fear.

One selling point that is often mentioned is that in a dark pool you may be able to arrange larger trades. And so the idea is that in the public markets you have all these small trades, wherein the dark pool maybe you could say I only want to transact with other people doing these large blocks of trades.

That is true in a very small number of dark pools. The SEC did a study a while back and looked at 44 different dark pools. Out of those they found that only five of them had average order sizes of more than 1,000 shares. So when they looked at these dark pools they said 60% of all the orders that were entered into the dark pools were for 100 shares.

Michael:          Averages are difficult. We could be fooled by averages. You could have a lot of small 100-share orders, and also a significant number of large-share orders.

Peter:              That’s true. But they were saying average order sizes of 1,000 shares. I think that kind of indicates where they’re coming in on those dark pools. What I am saying for those five dark pools they do seem to be more biased or configured towards supporting these large transactions. To me, that seems like that could be a credible reason for having your dark pool.

The other dark pools seem a lot like any other market, only they’re less transparent and a little more exclusive. They may have their own tweak, or hook, here and there. IEX has its own little hook. But from a macro view you wonder “Do we really need to have 40 dark pools? Are they really providing value to our markets or does it take the public out of the public markets?”

Michael:          In my experience, all of bond trading was over-the-counter, meaning there was no exchange and no price disclosure. But if I took a customer out to a really nice dinner, or we went to the Kncks game, he’s definitely doing a trade with me the next morning as a thank you. That’s our job as bond salesmen, to take the customer out and you will get ‑ at least in the short run ‑ you’ll get a thank you trade, or a couple of thank you trades, or a big order to work or something. Is that’s what driving dark pools, also? If I’m a Goldman equity salesman, do I take Putnam out to Cirque de Soleil and then I’m getting his trades through my dark pool?

Peter:              That sounds plausible but it’s not something I can speak to. It’s not my side of the business.

Michael:          It all seems like – having not been in that world – the downside of the dark pools would outweigh the upsides. What do I know? That remains mysterious to me.

Peter:              I can tell you we were always reluctant ourselves to trade in dark pools. I don’t know what that says to you but the high-frequency firm, that was not our preference to trade in dark pools. We were very reluctant to enter into dark pools, for all the reasons I mentioned.

Maybe we were being overly cautious, but to us it just seemed like there were a lot of risks going to a place where we couldn’t see what was going on.

Michael:          In Lewis’s telling, within the dark pools it’s rife with high-frequency traders who are sniffing out big block trades that they can essentially front run through small trigger trades, where you lay out a little trap, and that trap ‑‑ which you’ve said is basically impossible, you can’t judge the size or magnitude of these orders through these trigger trades. But that seems to be a big part of Lewis’s contention which is this is a great place for high-frequency traders to deviously figure out who’s trading, in what size, and how they’re doing it, and therefore anticipate and get in front of it.

Peter:              Right, and the trigger trade idea is even more comic because in the book they’ve had this hypothesis about these trigger trades. They call them bait, 100 shares. Then finally when they launch IEX they see these 100-share orders coming in there. And they say aha, we found evidence of this bait. It turns out it’s a bank that’s sending those orders in there, not a high-frequency trader.

The remarkable thing to me is the next paragraph in the book doesn’t say “And then they realized that perhaps their theory was incorrect.” Instead, they come up with other theories about the bank must be doing that intentionally to harm IEX or something like that, or to make IEX look bad.

The SEC said that 60% of all the orders going into a dark pool were for exactly 100 shares. So if 60% of all the orders you’re receiving are supposed to trigger some front-runner to go off and buy 100,000 shares, it’s going to create chaos. It’s more than half of the orders coming in there would be this bait that they’re talking about. It’s ridiculous.

Michael:          You can’t imagine a scenario under which firms that are situated like your firm could ever make money doing that?

Peter:              If that were the case that these 100-share orders really did trigger a front runner to go out and buy 100,000 shares, you would see a pattern in the market and it would be very clear. 100 share order, 100,000 shares purchased. You don’t see that. You would actually see 10,000 times more volume in the markets. You don’t see that. It’s pretty obvious when you look at the data.

The Impending Blowup?

Michael:          One other big thing that is my biggest fear about high-frequency trading or algorithmic trading which is that I have a theory which seems to be borne out by history, which is that financial innovation always runs faster than financial regulation. And that most of the blowups we see in the markets, which eventually affect the economy are some instance of really smart innovative people doing things that regulators, and common-sense, and risk-control people can’t anticipate.

The blow up will be a function of the new market structures or new financial technology. And I see algorithmic trading as the last 20 years has taken over, and yet I suspect we don’t really know what happens when it gets out of control. That’s my not particularly informed view of the Flash Crash,  and that’s my big worry about this industry. I’m worried about complete catastrophe, in milliseconds or seconds, without humans being able to stop it. And the ’87 crash was some version of this where people had bought computer-driven portfolio insurance and the thing crashed almost on not something particularly fundamental. Do you worry about this also or is this just me and my paranoid, don’t really understand computers, saw too much of the Terminator movies? Am I a whack-job when it comes to this fear?

terminator
The Rise of the Machines

Peter:              Not at all. I think you’re right that financial innovation does often come ahead of regulation.

I think you are spot on. That is a concern we should all have. It’s healthy to have that concern. That should always be a concern that’s driving all of our views of market structure. The SEC should have that concern, and they do have that concern. Market participants should have that concern. It should be something we’re always looking at.

The equities markets are very interesting because we actually do implement a lot of changes into the equity markets and they tend to have stuck more than other areas of the financial industry.

As you pointed out in your blog, the few reforms we made in the housing market are already getting rolled back. But since the equity problems we had, like the Flash crash in 2010, there have been a lot of changes. They keep layering on additional changes.

Not only did they implement more circuit breakers, not only did they say we need to focus more on improving our technology; we need to improve communication among the exchanges, make sure they’re more consistent in their policies; they focused on the broker-dealers too.

They said you have to have risk controls in place. Now the SEC has proposed yet another set of regulations that mandate the type of testing that you have to have for these systems. There’s a lot going on to try to make sure that these problems don’t occur, or if they do occur they’re going to be limited in scope. And I think that’s one of the very interesting things about the equity market that there are problems that occur. But those problems are all very limited in scope.

It’s really interesting to see how robust the market is right now. When you do have a problem on a particular exchange, the markets ‑‑ you have all the other exchanges. All the other exchanges can provide liquidity. And so the market actually is much more robust than a lot of these other markets where you have only a single-liquidity or only a single place where you can do your transactions.

Michael:          You have this built-in multi exchange, built-in backup exchange at all times versus my fear which is everybody makes the same risk-control errors or misses the same innovative technology problem at the same time. So when that problem – whatever it is – happens, everybody is caught off guard. You’re saying multiple changes, each slightly different and stands on their own. We’re not going to face a breakdown of every exchange all at the same time, or every market maker at the same time.

Peter:              Correct, the diversity of exchanges, the diversity of market makers ensures you’re not going to have everything break down at the same time. But if you do, the interesting thing is that there are also a lot of controls built into the market now to pause trading. We’ve learned from the mistakes of the past. We said if that does occur are you going to pause trading. And people will reevaluate and they’ll see what happens.

It’s unfortunate we had to learn the way we did, but we’ve internalized a lot of those lessons and so if a particular firm does screw up you’re going to have the market pausing, figuring out how we’re going to deal with this. That firm will go out of the market, and the market will recover.

Michael:          It’s a pretty optimistic view. I remain frightened but I agree ‑‑ the most optimistic view that I have is company firms like Knight Trading, which famously kind of went out of business shortly after some bad programming and errant trades that went wrong. They get e
liminated and the survivors are the ones that have risk controls, and do know how to slow down or turn off their algorithms at the right moments.

Some trust, because I’m not close to it like you are, that the various circuit breakers or regulatory changes even in the past few years are going to prevent the next big one. But I’m so far from understanding that or having the updates, that I just sit, and ponder, and get scared. But I take some comfort in if you’re close to it and you’ve met with these regulators, and you follow the industry, and you think it’s pretty darn good. And there’s a learning process going on, that we’re responding positively to shocks and mini crashes and that kind of thing.

Peter:              I think there is a learning process. There is some optimism involved. You can never predict the future 100%. But the very encouraging thing is that it’s not just that we’ve learned from things that happened in the past, but that we’ve learned in multiple ways. There are multiple fixes in place due to the errors that we’ve seen. When we see a new error, people are very vocal in the markets, and they demand a fix for it. That fix comes. It might not come as quickly as they want but it comes. And we have a lot of layers of protection in the market.

Michael:          A reason for optimism.

[1] Actually about a year ago, I’m just slow to post podcast interviews sometimes.

[2] Everybody should of course read Michael Lewis’ Moneyball, which we are referencing here, and which is awesome.

Please see related posts:

Book Review of Flash Boys by Michael Lewis

Book Review of Flash Boys Not So Fast, by Peter Kovac

Book Review of Inside The Black Box, by Rishi Narang

Are HFTs a force for good?

What D&D Alignment are HFTs?

and audio interviews:

Peter Kovac, Part 1 – HFT Profitability, Front-Running, Market Making

(and upcoming) Peter Kovac Part 3 –  Cheating and Morality

 

 

Post read (100) times.

Audio Interview – Peter Kovac on High Frequency Trading – Market Making, Profits, Front-Running

I recommend listening to the audio version of this interview…It’s the best part!

 

Michael:          Hi, my name is Michael, and I used to be a hedge fund manager.

Peter:              My name is Peter Kovac, and I’m the author of Flash Boys: Not So Fast.

Michael:          Flash Boys: Not So Fast came out recently on Amazon[1] and is of course available, and is a response to Michael Lewis’ book named Flash Boys in which I’d characterize you as disturbed enough by his version of high-frequency trading or algorithmic trading to try to correct the record, which you have done, and I have reviewed. I just thought it would be interesting if we could talk through some of the issues.

I’m assuming for the purposes of this that people are familiar with the broad outline of Flash Boys. The hero of Flash Boys is this guy Brad Katsuyama and he doesn’t end up as a hero in your retelling. I describe him – kind of paraphrasing your words – as he’s either a dupe for not really understanding how equities trade in 2012, or he’s mostly a salesman. Is this a correct characterization of your view of him, or am I being overly aggressive?

Peter:              Michael Lewis kind of channeled the entire story of Flash Boys through him. And so I’m really responding to the way that Lewis has portrayed Katsuyama. In Lewis’s portrayal, it does kind of seem like he’s a dupe. He seems like the guy who is the head of a major equities desk,  yet apparently is unaware what the fee structure is across the market and he has to Google it. He makes very large trades that have huge price impact on the market, and then is unable to explain them. So it really seems to me like it is Michael Lewis who set him up this way and I’m just responding to how he’s been portrayed in the book.
Michael:          Okay, I’m not an equities guy, so I learned stuff from both Michael Lewis’s book about how the equity market does or might work, and I learned stuff from your book about how the equity market does or might work. I’m assuming lots of people listening to this know even less than me. But what’s the traditional role of a guy like Katsuyama or a legacy Wall Street equity trader? Can you describe that a bit before we get to what is “algorithmic” or how HFTs or high-frequency traders get involved?

Price Impact

Peter:              Sure. Let me describe specifically the role that Lewis has put Katsuyama into here, which is to take a large order a client has, and the client says this order is too large for me to handle myself. I don’t have the specialized expertise. I don’t know how to put that order into the market, and not create a huge price impact. Price impact is a really fancy way of saying what you learned in Econ 101.

In Econ 101 you learned that if you have a big change in supply or demand it’s going to affect the price. And so if you have a really big order you want to place in the market, obviously if you’re going to be selling a lot of shares, the market is going to drop in price as you’re selling those shares. If you’re buying a lot of shares, the demand is increasing and the market is going to go up.

If you aren’t doing this every day of your life, there’s a chance you’re going to screw it up and not do it as well as it could be done. The function of a trader like Katsuyama in Flash Boys is to take some institution or individual’s large order, and as Lewis says, work on it for hours in the market to try to put a little bit of it out now, a little out later, trying to minimize the impact on the supply-and-demand dynamics so that way you can get a price that’s fairly reasonable.

Michael:          Okay, so we would call that maybe block trading by Wall Street. They have a big block of shares they need to sell into the market without moving the market. And he found or at least as Lewis describes he could no longer do that in 2011 or 2012 when he’s reporting on this story, because prices react extremely quickly, in ways that suggest there’s some kind of conspiracy or the markets are not as they a ppear when he tries to trade. Is that an accurate description of Lewis’s version?

Peter:              I think so. I think he makes a couple of trades and he sees the market reacting to his trades. Happy to go into those trades. He kind of explains why it’s very reasonable the way the market reacted. But he sees the market react to those trades, and then suddenly instead of being the master of price impact, the master of trying to work these orders over many hours, he says I’m the victim of this price impact. He looks to blame someone.

Michael:          Can you tell me in your words what role high-frequency trading firms – because that is your background – interact with somebody like Katsuyama or the rest of the market?

Market Makers

Peter:              I can speak to how my firm would have interacted with him. My firm was an electronic market-making firm. What that means is at any given moment we stood ready to buy and to sell any particular security. Whenever you say I want to go out and buy shares of IBM, you may have wondered “It’s funny that when I go out to buy some shares of IBM there’s someone out there who wants to sell them to me at the exact same time. That’s very convenient.:

The reason there is, is there’s a function in the market called a market maker. That person’s or firm’s job is to provide those prices on a continuous basis. And the way market makers make money is if they’re saying we’re going to buy shares of IBM at $164 and we’re going to sell them at $164.01. Then if the market doesn’t move all day long, and equal numbers go and buy and sell, then I’ll make a penny every round trip. That’s how they make their money.

ibm

Someone like Katsuyama who then comes into the market and says today I have to buy three million shares of IBM will place an order in the market to buy and we would have orders in the market that are willing to sell. So when his orders come into the market to buy, they would interact with our sell orders. We would never know we were interacting with him. It’s completely anonymous. We would never see his order. We would never know how much he wanted. We would simply get a report back telling my firm you placed an order saying you were willing to sell 10,000 shares. And someone has bought 10,000 shares from you.

We might say great, you know what? We’re going to place another order to sell 10,000 shares because that’s what we do. We put in another order to sell 10,000 shares and then maybe he comes back again and buys 10,000 shares from us. He would be doing that throughout the course of the day, from us and many other firms who are market makers. By the end of the day he has completed his transaction, and hopefully we were able to buy those shares back at a slightly lower price. And we didn’t lose our shirt on this whole transaction.

Michael:          I’m familiar because I sat on a trading desk of a bond desk, which is not the same as stocks, but the way the bond desk works is just as you’re describing. We’re trying to buy it at one price and sell it, it being the bond or the stock or the security, slightly lower than where we sell it. And we don’t have a fundamental view on whether that share or that bond is going up or down. We’re just trying to make a tiny difference between where we buy it and sell it. That’s what I understand is a market maker.

I think that you’re describing that what you do is basically the same thing. Although in Lewis’s telling of it you’re doing something fundamentally different. Maybe that’s kind of the heart of the difference between Lewis’s version of high frequency trading and your version. Am I getting that right, that what you’re describing is exactly like a faster and narrower spread basis, but it’s exactly like what I witnessed and participated in, as a bond trading market-maker. Is there anything fundamentally different about what you guys are doing?

Peter:              Not particularly. One of the differences is that in the US equities market, unlike the bond market, there are even more constraints on the price that anyone can transact at in the market. For example, in the bond market different brokers might give you different prices. Where in the equity market, you as a customer, by law, are entitled to get the best price in the market. So it makes it extremely competitive and it also protects the consumer.

One distinction I would make with what Michael Lewis is saying is that he doesn’t actually distinguish among the many different types of trading strategies, so broadly looking at it, he never actually defines high frequency trading. He kind of casts a really broad net and if you look at that broad net, you’re basically saying high frequency trading is more of a technology or technique. It’s almost like saying e-commerce.

It sounded like it was a very specific label in 1996, but now pretty much every single company, even your brick-and-mortar boutique down the street has an ecommerce profile. Same thing with high frequency trading, where guess what, a lot of people are trading with computers. A lot of people are trading rather frequently.

There’s a whole variety of different strategies and approaches that are in the high-frequency trading world. Lewis kind of blurs the lines and smears them together and as a result he comes out with a message saying high frequency trading is bad.

But at other points in his book he says actually high frequency trading is good, but just a couple different aspects of it. So what I’m referring to here is market making. It’s what I know best. And I don’t think any credible person in the market would ever say that market making is a bad business. Althought, Lewis does make some allusions to market making having some nefarious aspect, but I’m not really sure what his point is there. Mainly, he’s targeting the high-frequency trading industry with his front running allegation.

Michael:          What you’re saying is one version of high-frequency trading is market making, trying to make the tiny spread between where you buy it and sell it, and essentially the service is providing liquidity to buyers and sellers, and the business model is to buy slightly lower than you sell.

But I think you’re also saying there’s an entire world of other strategies which involve buying and selling securities quickly, that aren’t providing liquidity in that same way.

Because some of the conspiracy ‑‑ the credible part of the conspiracy theory that Lewis is talking about rests on the idea that hardly any of us who are not in the world of high frequency trading can even grasp what the strategies are. Can you give an example of some strategies without giving up the secret sauce of your own firm, but give us something concrete that we can think about?

Peter:              Would you like a market-making strategy or something beyond that?

Michael:          Market making I’m going to describe as buy it here, sell it here plus a tiny bit.

Peter:              I’m most familiar with market-making strategies but I can kind of speculate on something else. Let’s say you had a strategy where you say whenever I see FedEx is increasing in value by 1% over the course of a day, then I’m going to buy UPS. That’s kind of a pairs type strategy where you’re trading two related companies and you’re saying based on one of these companies moving, another company is going to move.

fedex_ups_pairs_trading
An example of a pairs trade

It’s not necessarily a market-making strategies but it’s one where you’re saying I think that this other component of the market should move because its leading indicator is moving as well.

Michael:          The theory being FedEx and UPS are essentially in the same business. What’s good for FedEx is probably good for UPS. And FedEx is moving without UPS responding, so the logical thing is UPS should be moving in that same direction.

Peter:              Correct.

Michael:          The frequency with which you would need to purchase UPS, we’re doing this on a millisecond basis or a minute basis or over the course of an hour?

Peter:              That’s a great question. Let’s say you have this theory that FedEx should always be worth about two times UPS exactly. As soon as you see that it’s worth 2.01 times you’re going to buy UPS because UPS needs to increase in a bit of value to be on par with FedEx. In that example you’ll be watching and every time you see FedEx pick up a little bit more you say now I need to buy UPS. Or if FedEx kicks down you say FedEx is worth 1.99 [times] so now let me sell some UPS and kind of put that back into balance in terms of my portfolio. You wouldn’t wind up doing a lot of trades. But in the end ‑‑ this is a classic strategy that Wall Street has been running since the last century.

Michael:          But in the case of algorithmic trading, it’s just sped up, and it could be done in the space of less than a second, in milliseconds?

Peter:              Exactly and it’s for much smaller quantities. Instead of someone saying I’m going to  wait until they diverge by 5%, and then I’m going to make a big bet on this, which is the way you would have seen it play out on Wall Street, say 30 years ago. Now you have someone saying I’m going to make a lot of smaller bets, when there’s a smaller divergence.

The advantage from a trading perspective is that a lot of these smaller bets you’re less likely to lose a lot of money if your bet goes south. For the market, you could argue that it’s keeping these prices a little bit more closely aligned because you’re doing more frequent adjustments rather than someone doing a large adjustment on a periodic basis.

Profitability

Michael:          Which brings up one of the examples I really liked about your book, which is a response to the claim from high-frequency trading critics, the fact that these firms are not unprofitable enough. Or that is to say that on almost every single trading day they’re reporting profit, which in the normal world you go “that’s impossible!” Nobody is that good without there being a trick. Show me the magic trick or the cheating. That’s the allegation.

large_numbers
Law of Large Numbers

What I really liked about one of your responses to that was here’s why it’s not cheating: We’re doing 1,000 trades, and even if we only have a 51% chance of making some money, when you apply the law of probability over 1,000 different trades, when you make money 51% of the time and lose money 49% of the time, you’re going to end up profitable pretty much every single time. I’m probably butchering your language around that, but maybe you can express that better than  me. That was one of the strongest arguments in favor of consistent profitability, was the law of large numbers and probabilities applied to small trades done many times.

Peter:              Thank you. You characterized that perfectly. Interestingly enough, just last week, there was a professor from the University of California Santa Cruz who did a research paper that was highlighted in the Wall Street Journal, where he went through one of the firms that Lewis had singled out for this winning record. He went through all their filings and said it’s actually incredible that they lost money on that day, given the law of large numbers.

As you said, the law of large numbers does explain this and it seems counterintuitive at first, but the way I like to explain it to people is if I think about baseball. Over the past 22 years the Yankees have won just 59% of the time. It’s a bit better than even but not much better. If you win slightly more often than you lose and you do it consistently for all of 162 games in the season, you’re likely to come out ahead. They’ve only had one losing season in the past 22 years. That’s kind of remarkable. It’s just 162 times with a tiny bias toward winning, and it comes out to a winning season every time.

The opposite is also true. During the same time the Pittsburgh Pirates won only 45% of the time. They had 20 losing seasons out of the past 22, applied over 162 games.

Now, if you’re trading not 162 times a day but 10,000 times a day, 100,000 times a day, it becomes more and more inevitable that you’re either going to be guaranteed to make money or guaranteed to lose money. The losing money is also another interesting side of the discussion because any firm that is around right now, who is doing this, and is doing it successfully, by definition is making money.

If they had a slight bias to losing money on their trades, they’re already gone. And that’s happened to a number of firms. Some of the people you’ve heard of because they were well known at one point and then they started to lose just slightly more often than they won, and they’re gone. The firms you hear about now of course are going to be the ones with consistent results.

Lastly, there’s another way to think about this, which is that if you are more of a service provider, as opposed to a speculative risk taker, then it also makes sense. If you’re a market maker you’re getting paid for the service of making a market. You’re not speculating on the markets and so the example I gave is if your entrée into the art world is selling greeting cards, you’re selling cards. You’re going to make a penny or two per greeting card. But you’re not going to really lose money. It’s greeting cards, not a high-margin business.

If you are an art investor, you may spend a couple million dollars on a piece of art. It may turn out that that piece of art in ten years is the hottest thing on earth. And now it’s worth 20 or 30-million dollars and you made a huge killing. Or it may turn out that it’s not worth anything at all. It’s a different business model.

When you’re comparing the results of an electronic market maker who’s doing the service repeatedly, 100,000 times a day, million times a day, whatever it may be, versus someone who is making multi-million-dollar bets on obscure derivatives, you’re going to come up with different results.

Michael:          Makes sense. I’d like to return to your example of the Yankees. As a Red Sox fan that hurts. I’d like to say A-Rod was a cheater. And don’t mention Big Papi or Manny Ramirez and their PED scandals.

Peter:              Definitely no Bucky Dent.

Michael:          And please don’t mention Bucky Dent.

bucky_dent_aaron_boone
Bucky Dent and Aaron Boone. F- those guys. Somehow they must have cheated.

Front Running

Michael:          On the issue of cheating, Lewis’s main allegation is that a main part of profitability of high-frequency traders is you’re front running and front running is not super easy to define, but I’ll take a stab at it. You can correct me. It’s in the role of market maker a customer comes in to trade and you use the information you gain from their sale or purchase to anticipate that that security is going to respond to that flow. You can either buy ahead of them and sell it to the customer at a higher price or sell ahead of them and purchase from the customer at a lower price. In any case, using the information of the customer flow to make profitable trades. I don’t know if that’s the only definition but that’s my words for front running.

Lewis says this is the main business of high-frequency traders. They’re getting information in a millisecond that’s coming into the exchanges. They’re responding to it quicker than anybody else can. Getting in front of the customer flow, and making guaranteed profits. Tell me why this is wrong.

Peter:              First, I think you explain what front running is pretty accurately. Sadly, it did happen in the past and it can still happen today. But in a very different way, and that’s when a broker has a customer’s information on their order. And that particular broker who received the order trades ahead of the customer because they have that information.

What used to happen was the broker would get that information. They would look at the price and quantity on the customer’s order. And then they would go out and trade in the market for their own account, buying or selling ahead of the customer. And then they would turn around and then from their own inventory sell those shares back to the customer at a higher price.

The key things that they relied upon there was the ability to see the price and quantity of the customer’s order, and to be able to give the customer a different price than what the market price was. Those are the key things that they needed.

Lewis doesn’t try to explain how those elements could possibly be present in the current market. And the reason he doesn’t try to explain it is it’s probably because it’s impossible to do so. So you can’t explain how someone is determining the price and quantity of the shares you desire. In today’s market, the orders are anonymous. So if you submit an order to your broker, and that broker then submits it to an exchange, no one in the rest of the market ever knows the quantity of your order or the price of your order.

Even if your order trades against the market maker, that market maker still does not know what the price and quantity on your order were. All they receive is a report that says you transacted this many shares. That’s it. They don’t know what the price of your order was. They don’t know the quantity on your order. That information is never available to them.

As a result, they never have the information that will be a prerequisite to any front-running scam. Beyond that, the issue of manipulating the price to give the customer a different price in the market is also not possible. When Reg NMS came into effect, we have this requirement that the customer must get the best price that is displayed in the markets.

reg_nms_chronology
Evolution of Reg NMS and HFT

The only way someone can change that price is by buying every single share in the market. So just to be very specific here; if you place an order to buy Microsoft at $49, and Lewis is alleging your front runner is going out there and is going to buy all the shares at $49 ahead of you, and then sell it back to you a penny higher.

They would have to go into the market and on every single exchange buy every single share offered. They may wind up buying 30,000 shares, a million shares, in order to allegedly front run your order of 500 shares. That’s the only way to possibly move the share price. Obviously, that doesn’t make any sense. It’s ridiculous.

Further, if they did move the share price, guess what? There’s already another million shares behind that at the new price point, so they would be the ones selling them back to you. Someone else is already first in line to sell them back to you. It’s impossible for a would-be front-runner to be able to manipulate the price.

It used to be possible when the brokers had more discretion on the pricing. But in today’s market it’s simply not possible. And Lewis never took the time to understand how the market works, what these rules are about price protection in the market, which makes his allegations completely impossible.

I guess the last thing I would say is that he kind of justifies the whole thing by saying you can’t really prove or disprove this because the data doesn’t exist. It couldn’t exist to prove or disprove. And that’s completely false. The data is out there.

We would have a homework assignment for our trainees to look at a particular trade that a strategy did in the market, and explain exactly how the market reacted; what happened after it; what trades occurred after it. All the data is there.

You can get it from a Bloomberg terminal, you can get it from your own systems. This is the industry in the world that is the most awash in data, and it’s completely ridiculous to say that one cannot find any data to substantiate his claims. The only explanation that makes sense for that is that there isn’t any data to substantiate his claims.

Michael:          How about this; why do large buy-side firms believe the thesis? That if you’re Putnam or Fidelity and doing large-block trading, do they believe that high-frequency trader are front running them or do they not believe that? Or are they not sure?

Peter:              I think there’s no single answer because I think there’s a variety of opinions. That’s something that is very interesting and Lewis kind of glosses over that. For example, Vanguard came out and said all of these changes from electronic markets are good. We’ve seen that our price to complete a trade has decreased by half a percentage point. It’s incredible for them to say this is how much more efficient the markets are nowadays.

vanguard

The SEC has estimated that for institutional investors, the people you’re talking about, their cost has fallen by about 40% on their trades since 2003. That’s the cost of actually transacting in the market, not the processing after the fact. Literally, the pricing they’re getting in the market versus what they desire has improved by that much. I think on the whole the industry realizes the benefits of the current market structure, and of electronic market makers.

You do have people who are complaining and I think that’s unfortunate, but it’s also understandable. People do have a tendency to blame someone else when things go wrong. It’s very convenient when you take a little risk on your trade, and it goes against you ‑ it’s much more convenient to blame someone else than it is to take responsibility for it. It’s kind of the mantra on Wall Street.

flash_boys_not_so_fast

Michael:          Yeah, if things go badly it’s the fault of the market. If things go well, it’s because of my brilliance for sure. That’s the only way to get paid.

[1] Actually, the book came out almost a year ago at this point, but I have been slow in uploading this interview! My apologies all around.

Please see related posts:

Book Review of Flash Boys by Michael Lewis

Book Review of Flash Boys Not So Fast, by Peter Kovac

Book Review of Inside The Black Box, by Rishi Narang

Are HFTs a force for good?

What D&D Alignment are HFTs?

and upcoming audio interviews:

with Peter Kovac, Part 2 – Dark Pools, IEX, Disruption, Blowups

and Peter Kovac Part 3 –  Cheating and Morality

 

Post read (781) times.

High Speed Trading – And DandD Alignments

half_elf_bard
Apparantly this is me in D&D, except I think minus the pony tail

I’m trying to figure out where electronic and high frequency trading firms fit with respect to Alignment, and how I feel about that.

Alignment

I mean “Alignment” the way a Dungeons and Dragons (D&D) player means it (full disclosure: I’m a Lawful Good Half-Elf Bard in real life. I mean, in the game. I mean, outside of my writing. Whatever, you know what I mean.) All D&D characters are either Good, Neutral, or Evil, and act either Lawfully, Chaotically (law-breaking), or Neutrally (in between).

So, on our quest to understand electronic trading, it is helpful to know which alignments electronic traders fall under, including high frequency traders (HFTs)?

But First: A Definition

Instead of making human judgments about when and how much to trade stocks (or bonds, or currencies, or commodities or derivatives) electronic traders program computers to make those decisions, usually based on some set of conditions that indicate a momentarily profitable opportunity. Electronic, or ‘algorithmic’ trading, is about 25 to 30 years old. High frequency trading is a subset of electronic trading, except done at a humanly unimaginable pace – like 1,000 to 10,000 buy or sell orders per second. High frequency trading is about 10 to 15 years old. In recent years electronic trading accounts for between 50-75 percent of all stock market trading. Like SkyNet or Hal 9000, this naturally makes the humans nervous. But are they good or evil or neutral? [1]

Google_as_skynet
They slyly dropped “Don’t Be Evil” from their corporate motto

Are HFTs Chaotic Evil?

If you read Michael Lewis’ 2014 book Flash Boys the most widely read story about the high frequency trading industry to date – you would develop the strong impression that these firms hew to chaotic evil on the D&D alignment compass.

In Lewis’ story, HFTs operate as predatory sharks attracting unwitting investors inside broker-sponsored ‘dark pools,’ all the better to extract trading profits through quick-strike trading against slow-footed prey. These evil creatures also use ‘spoofing’ subterfuge and aggressive ‘front running’ tactics. I expand on these tactics below.

‘Spoofing’ – in which electronic trading firms send large numbers of false orders to market exchanges, only to cancel them immediately, is a ploy (I admit I can’t explain in plain English exactly how this would work) to manipulate markets, and is clearly chaotic. It’s also illegal, and would lead to enforcement action against any firm doing this and getting caught.[2]

‘Front-running’ – in which an electronic trading firm uses prior knowledge about a customer order to buy or sell ahead of a customer for its own profit is also evil, as well as clearly illegal.[3]

And Flash Crashes

Many blame recent occurrences of “flash crashes” on algorithmic trading. Flash crashes are exactly the kind of mess that chaotic evil-doers would wish on markets.

Increasing the frequency or severity of flash crashes is the most likely way in which electronic trading causes chaotic evil effects. I don’t mean intentionally, but rather as an unintended consequence of numerous market players pursuing their own strategy. Something like: All market signals indicate to the algorithms the need to sell – all at the same time – which becomes a self-fulfilling downward spiral for prices. That type of unintended effect, however, predates the rise of HFTs. The 1987 Crash, for example, stemmed from the rise of ‘portfolio insurance’ that caused many institutions to suddenly need to sell securities, all at the same time, to limit losses. In the absence of real news, prices drop on such rush-for-the-exits stampedes.

On the issue of crashes and market glitches, there’s the not-too-infrequent case of human traders – not only computer traders – doing a bad job of ensuring orderly markets. This happened in August in a high-profile case of the floor trader on the NYSE who halved the value of publically traded KKR, a company whose markets he was responsible for trading, for about 15 minutes, for no apparent reason. The right standard for comparing human trader to computer trader is probably not “error-free,” but rather frequency and severity of mistakes and glitches like this. My point here is that human traders can probably screw up markets just as badly as programmed computers.

Or Lawful Good?

My friend Peter Kovac wrote a book last year – Flash Boys: Not So Fast, as a response to Michael Lewis’ book, in which he argues not only that Lewis got many details of the industry wrong, but perhaps the HFTs should be regarded instead as something like Lawful Good (my words, not Kovac’s.)

I’ll explore some of Kovac’s reasoning in follow-up posts, but for the moment I have in mind what I wish, and perhaps think to be true, regarding electronic traders.

Lawful Neutral

As a Dungeons and Dragons player (as well as a greedy capitalist,) I would hope for Lawful Neutral alignment among high-speed electronic traders. I mean, I don’t expect a trader to be saving the whales or reducing carbon emissions when he or she programs a computer algorithm to buy and sell securities at light speed. Their goals, as for-profit companies, are to make a profit. But I do expect them to always follow the law.

What Lawful Neutral means to me is that as long as they follow the rules – avoid conscious or even unintended evil-doing – then I’m ok with extraordinary profits accruing to electronic traders. That’s because I believe the profits of an algorithmic trading firm will mostly come at the expense of legacy Wall Street trading firms (the “old guard”) which are slower, or which operate at less efficient (meaning, wider) margins. I’ll write more about this next week as well.

 

A version of this post ran in the San Antonio Express News.

Please see related posts:

Book Review: Flash Boys by Michael Lewis

Book Review: Flash Boys Not So Fast by Peter Kovac

Book Review: Inside The Black Box, by Rishi Narang

 

 

[1] Assigning corporate alignments in D&D fashion is not necessarily new. Google’s previous corporate motto “Don’t Be Evil” is a seemingly simple standard, for example, from which to begin to evaluate high frequency trading. I’m not sure Google founders Brin and Page ever played D&D, but let’s just say it’s not unlikely that they know their way around a 20-sided die, right? Also, did anybody else notice Google dropped “Don’t Be Evil” as a corporate motto? Do you think it means what I think it means?

[2] My sense is that while this has happened in the past, it’s not normal market practice among electronic trading firms, any more than spamming is normal market practice among marketing companies. Sort of like: there are spammers, and there are marketers, but these are different types of firms with different business models

[3] Incidentally, front running as a business practice is probably as old as any stock brokerage business, it just happens that speedy trading could make it more easily perpetrated, and more easily hidden, at least for a time. Any firm shown to front-run as a business practice, however, would be fined and regulated out of the trading business.

Post read (705) times.

Would You Like To Understand High Frequency Trading?

My friend Pete Kovac got so peeved about Michael Lewis’ Flash Boys that he wrote a response, in the form of a book, called Flash Boys Not So Fast – An Insider’s Perspective On High Frequency Trading.

fight clubThe highly unusual part about this book is that high frequency trading up until now has basically been like Fight Club, in so far as the first rule of high frequency trading is that nobody talks about high frequency trading.

Well, here’s Pete actually talking (ok, writing) about it. He agrees with my objection to Flash Boys, which is that Lewis appears to have not gotten any access to actual real live high frequency traders, in the course of investigating his book. Which is kind of a problem.

Pete’s formal bio is as follows:

Kovac was COO of the electronic market making firm EWT from 2004 to 2011, managing regulatory compliance, risk management, finance, trading operations, and portions of the technology teams. During his tenure, EWT grew to one of the largest market making firms in the U.S., trading hundreds of millions of shares daily, an, together with its affiliates traded in over 50 markets worldwide. Kovac has been a frequent commenter to the SEC on regulatory issues.

And Pete’s informal description of his role:

“I am an industry insider, the kind of person who could have saved Lewis from making some really basic mistakes. I started programming trading strategies in 2003. After years in the trenches, I moved into management and ultimately became chief operating officer of my firm, EWT. I handled regulatory compliance, risk management, finance, trading operations, and a portion of the IT and software development teams – and I had to know every aspect of the stock market inside and out. By 2008, our company was one of the largest automated market-making firms in the U.S., trading hundreds of millions of shares of stock daily, and had expanded into many other asset classes domestically and internationally. I left it all three years ago when EWT was sold to Virtu Financial (in which, in the interest of full disclosure, I still retain a small stake).

Those eight years at EWT provided me with a front row seat to all the events described in Flash Boys, and much more. During that time, I shared my experience and perspective in discussions with regulators and lawmakers here and abroad, advocating for the continued improvement of the markets discussed in the book. Many of my comment letters on these topics are publicly available on the SEC website. Even though I no longer work in trading, I can still get answers from a diverse set of close sources when a truly new question arises.”

So – If that’s intrigued you – you can download the book here.

 

Please see related posts

An Excerpt of a Quant Trader’s Critique of Flash Boys

The Rise of The Machines

The Katsuyama Revolution Continues

Please see related book reviews:

Inside the Black Box, by Rishi Narang

Flash Boys, by Michael Lewis

 

Post read (1663) times.

Book Review: Flash Boys by Michael Lewis

The Rise of the Machines

Michael Lewis wrote Flash Boys to alert the non-finance world about the scourge of high frequency traders front-running investors and fracturing traditional capital markets.

Lewis does not distinguish between quantitative (or algorithmic) trading strategies and high frequency trading firms (HFTs), although it’s helpful to define these terms first.

Quantitative strategies – of which HFTs form a subset – are computer-driven trading models, in which the human input all occurs prior to a market’s opening bell. The human instructions come from computer programmers who tell the model to look for certain signals in the way securities trade to prompt a buy or sell order. HFTs are a type of quantitative strategy that rely on speed, in milliseconds, to successfully execute trades. A good primer generally on quant trading and HFTs from someone inside that world is Rishi Narang’s Inside The Black Box, which I reviewed recently.

Lewis points to at least four simultaneous innovations that have led to the profitable opportunity for high frequency trading firms over the past decade.

  • First, an investor-protection law from 2005 called Reg NMS demanded that investors receive the ‘best’ price visible on a stock exchange, even though sophisticated investors know that large investment purchases or sales may get a best overall price if done quietly ‘off-market’ without alerting the rest of the investment community. Following Reg NMS, HFTs can play games with the ‘visible’ market price by posting, say, 100 shares for purchase or sale, only to cancel that price as soon as a real order hits the market. In Lewis’ telling, the 100 share order from the HFTs becomes an electronic trip-wire to signal certain types of large investors are making a move, and allowing the HFTs to front-run that investor through superior trading speed.
  • Second, the fracturing of the equity markets into more than a dozen major electronic exchanges and 40 (or so) broker-dealer created ‘dark pools’ for anonymous electronic trading has created multiple opportunities for risk-less arbitrage between exchanges, for those HFTs who execute trades in milliseconds.
  • Third, the privatization of US stock exchanges like the Nasdaq and New York Stock Exchange led the exchanges to seek their own profit through fee arrangements with HFTs at the expense of investor-oriented protections, which would have limited the access of HFTs.
  • Fourth, technology – between lightning-fast software and speed-of-light fiber optic cable – created a haves and haves-not unfair playing field between investors in many markets.

Lewis’ narrative follows the evolution of his protagonist Brad Katsuyama who figures out just enough of the HFT game to become inspired to shut it down – first because it interferes with his job trading equities for the Royal Bank of Canada, and later because he’s a self-appointed evangelist for protecting real investors from the HFTs.

Bill Murray

Katsuyama and his plucky rag-tag group of Wall Street castoffs – and here Flash Boys most closely resembles the plot of every early Bill Murray movie like Meatballs and Stripes – set out to build a better exchange known as the Investors Exchange (IEX),[1] which through slow trading will box out the HFTs and their nasty algorithms.

 

The moralistic tone, and why it matters

Flash Boys differs from Lewis’ earlier finance books in the introduction of his moralistic tone – he seems genuinely outraged by the activities of high frequency trading firms. This moral outrage differs from the way that he was previously mostly amused by disgusting mortgage traders, stupid Icelandic Viking financiers, or Sub-prime CDO structurers.

In Liar’s Poker, Boomerang, and The Big Short Lewis distinguished himself from other financial journalists by adopting a knowing attitude toward Wall Street’s greedy ways. Whereas other financial journos portray a fairy tale world of virtuous small-time investors and evil greedy bullies, Lewis worked on Wall Street for a few years and knew better than to fall into that trap.

Lewis usually celebrates – at least up to a certain extent – those who outwit the competition to earn themselves a big payout.

Lewis’ bad guys in those earlier tales typically would receive a kind of satirical treatment for their excessive attitudes. Lewis found ways to laugh at his antagonists because he spent time enough with them to understand their strengths, weaknesses, and the right distinguishing characteristic to turn their unattractiveness into humor.

Lewis does not seem to have spent any time getting to know high frequency traders for Flash Boys, however, and here his moral tone – rather than knowing satire -exposes a weakness.

I’m not saying Lewis shouldn’t be upset about high frequency trading. He makes a compelling case that we should all take a much harder look at whether all of their activity acts like a massive, hidden, tax on capital markets. What I am saying is that the moral tone – which resembles the style of weaker financial journalists – exposes the fact that he hasn’t spent enough time getting to know actual high frequency traders.

flash_boys

If he had spent time with some, we would have gotten some funny anecdotes and satirical send-ups – That Russian programmer with the bad breath and an unhealthy obsession with Miley Cyrus! Ha! The South African technologist who keeps twenty cats in his office and eats only vegetables that start with the letter T! You can’t believe how funny these guys are! That kind of thing.

The humor is amusing in its own right of course, but the humor also tells us that Lewis was there, and got to know these people. Unique among journalists he has a track record of actually going out and finding the stories rather than create fairy tales based on preconceived moral views. The Good Guys = Brad Katsuyama & Team versus Bad Guys = Faceless & Nameless HFTs formula makes me suspect we only got a portion of the full story.

I’m thinking about Lewis’ apparent failure to talk to HFT folks because a friend of mine from the HFT industry thinks Lewis totally blew it when describing his world.

I do not know HFTs myself well enough to judge, but I know my friend has a moral compass and wants the HFT story portrayed accurately.

(And you should see his 20 cats! Just kidding.)

I’m hoping in coming weeks to learn enough to judge better the accuracy of Flash Boys. More importantly than judging the book, I’d like to know to what extend HFTs really threaten the system, as Lewis argues.

More questions than answers

For my own future reference, but also perhaps other readers, here’s my beginning list of further questions to explore and answer after reading Flash Boys.

rise_of_the_machines
We have got to stop SkyNet
  1. Lewis leaves practically unanswered what I think is the much greater problem of quantitative and high frequency trading: As computer algorithms constitutes 50-80% of all trading volume on US exchanges, what are we doing to shore up the system against massive technical fails like the Flash Crash of 2010, or like the Crash of ’87, for that matter? We haven’t seen The Big One yet but I’m pretty worried about it, and I hope regulators have a plan in place to prevent it. In other words, WE MUST PREVENT SKYNET! WHERE IS OUR JOHN CONNOR?
  2. If Katsuyama’s IEX is a better mousetrap and a solid protection against HFT front-running, as Lewis believes, how has it fared in the subsequent months since opening in October 2013? I’ll be curious to know if it has begun to siphon off volume from other exchanges and the broker-created dark pools. If investors are self-interested, they should want to participate in the IEX far more than the shark-infested dark pools.
  3. Lewis mentions only two HFT strategies that I can see, in simplest form: Strategy #1: Set up 100 share trip-wires inside these exchanges. When those get tripped, quickly front-run the direction of the market ahead of a big order. Strategy #2: Gain arbitrage opportunities by seeing an order in one exchange and then quickly executing in another exchange based on that order. Strategy #1 is borderline illegal so it strikes me as something that regulators could address. Strategy #2 is theoretically (marginally) ‘creating efficiencies,’ although not if the HFTs are, as they seem to be doing, seeing order flow to some exchanges faster than everyone else. IEX could put that strategy #2 out of business. But something tells me there are many dozens to hundreds more HFT strategies not described in this book. What are they?
  4. Whatever happened to the high-speed line built by Spread Networks from New Jersey to Chicago mentioned in the early chapters? And was it made obsolete by the microwave towers mentioned in the Epilogue, or is that part of the same network?
  5. My friend from the HFT firm mentioned this one to me: Lewis relays a very fishy anecdote about a hedge fund trader typing a buy order into his computer, only to watch the market suddenly shift away from him before he hits enter to execute the trade. This is, basically, impossible – unless the HFTs have hacked into the hedge fund guy’s computer – to see his trades before he even sends them to the exchange. Even I’m not that paranoid about Skynet yet. So, Lewis, what’s up with that anecdote?
  6. Can we, and should we, distinguish between quantitative trading – relying on computer algorithms rather than human input to execute trades – and HFTs in a meaningful way when it comes to regulation and treatment in a market exchange?

 

That’s my short list of questions. More to come later.

Please see related posts:

Book Review of Pete Kovac’s Flash Boys: Not So Fast

Book Review of Rishi Narang’s Inside The Black Box

Book Reviews of Michael Lewis’ previous books on finance:

Liar’s Poker

Boomerang

The Big Short

Crashes happen when quants take over the markets, in Rise of The Machines

 

 

[1] Fun fact: They didn’t use the full URL of the exchange name because, you know, investorsexchange.com could be interpreted a variety of ways.

 

 

Post read (7559) times.

Book Review: Inside the Black Box

I read Rishi Narang’s Inside the Black Box as a kind of primer on quantitative trading, in advance of reading Michael Lewis’ Flash Boys.

Need for a primer on quantitative trading

I need a primer before reading Flash Boys for several reasons.

I was an ‘over-the-counter’ institutional bond guy, rather than a participant in the exchange-traded stock markets that form the core of many quantitative trading strategies.  In addition, my Wall Street experience is a decade old at this point, (I left Goldman in 2004) and the development of dark pool trading, high-frequency trading (HFT), and alternate electronic exchange trading has proliferated since then.

Whereas the traditional stock exchanges with real-live human specialists (like the New York Stock Exchange – NYSE) was already in decline by the time I left Wall Street, the vast majority of stock-trading volumes now happen electronically on alternate exchanges and dark pools away from the traditional NYSE.

As I noted a few years ago, most stock-trading volume is entirely devoid of human decision-making, which is to say, computer-programmed algorithms determine what trades happen on a second-by-second, moment-by moment basis.

Finally, I have a few friends in the quantitative trading world and one of them reached out to me shortly after the publication of Lewis’ book and said there are laughably wrong statements and implications in Flash Boys. In the interest of having an intelligent dialogue with him, to figure out if Lewis really screwed up, I needed some background.

 

Different Audience for the Books

Narang’s audience for his book differs from Michael Lewis’ audience for Flash Boys. We rely on Lewis for funny anecdotes and characters, and enough financial sense to give the general public an accurate, entertaining, and non-technical narrative.

By contrast Narang’s book targets non-expert practitioners who need more quantitative trading knowledge such as: new hires into the quantitative trading world – or current and prospective investors/asset allocators to quant-trading funds. Narang currently is an investor/allocator to quantitative strategies, who previously founded, and ran, a quantitative trading firm directly.

Narang provides a simplified ‘how to understand’ or ‘how to evaluate’ quantitative trading firms, for readers without specialized trading and computer programming background.

When I say ‘simplified’ I don’t mean the book will be ‘simple’ for people outside finance. Narang’s language is clear and precise, even full of useful analogies for explaining concepts, but it’s academic in style. He systematically reviews, engineer-like, the common components and varieties of quantitative strategies.

I learned a ton, but folks without a need to know about quant trading will not rip through this like they would a Michael Lewis book on finance.

I had a number of questions about quant trading before I read Inside the Black Box and Narang sheds light on my questions.

rise_of_the_machines

What is quantitative trading and what should we know about it?

Quant trading, in its simplest definition, can be distinguished from discretionary trading by the role of human decision-making. In traditional, discretionary, investing we decide to sell or buy a financial instrument for any number of good or bad reasons. With quant trading, by contrast all of the human inputs occur ahead of time, that is to say, through the programming of a trading algorithm to look for and respond to the optimal opportunities to trade. The actual order to sell or buy occurs in response to the signals, rather than any in-the-moment human decision.

How big is quant trading compared to discretionary trading in the markets?

Narang cites a study from 2009 that indicated 60% of trading volume in US equities came from quantitative trading, as well as 45% of trading volume in European exchanges. So if 6 billion US shares changed hands per day, then Hal9000 algorithm-based trades accounted for 3.6 Billion of those, leaving only 2.4 Billion shares traded by human discretion. In addition, over 90% of commodity trading firms (CTAs) are quant-trading firms, rather than discretionary traders.[1]

So how do programmers and traders set up the trading algorithm to order trades ahead of time?

Naturally, the next question is about how this programming gets done, and answering this question forms the bulk of Inside the Black Box.

Narang comprehensively reviews and explains both the common elements of building a quant trading strategy, as well as the wide variety of approaches used by the entire spectrum of quant trading firms.

Typically, programming a successful trading algorithm depends on reviewing extraordinary amounts of historical data on observed trading responses of financial instruments, and then seeking patterns among the noise. If a quant trader can find predictable enough responses, then it becomes possible to test if trading on an expectation that the pattern continues could be profitable over time. If his algorithm recognizes the pattern well enough, he can, in effect, ‘see the future’ enough to trade predictably and profitably.

 

So, it’s pattern-seeking?

A few simple examples of patterns may be helpful here to give an introductory idea. Some stocks – often from the same industry – predictably track each other’s direction, such that a directional move from one typically will be mirrored by a directional move in the other. A similar pattern could hold for a single stock and a ‘basket’ of similar enough stocks.

Volume data on stocks – how much is being traded over any given time period – may predictably be followed by moves in the stock or financial instrument.

Some directional trends in financial instruments could indicate a predictable pattern of a continuation of that trend, or just as likely (and somewhat paradoxically) the likely reversal of a trend.

The common denominator in this type of trading, however, posit that past trading data indicates somehow what will happen in the near future.

To the extent that markets behave in patterned ways – and in fact most of the time financial markets do behave in recognizable historical patterns, or at least recognizable to quants and their computers – quant traders can make money with trading algorithms.  This doesn’t have to happen every time, with every trade or every day. In fact, quant strategies depend not so much on winning on every trade, but winning on enough trades to overcome the losses on other trades, as well as the costs, or ‘trading friction,’ of executing many trades simultaneously.

Some of the time, of course, market regimes may shift and past patterns become poor indicators of future moves. At that point, the human-element decision becomes whether to lower the trading exposure based on the algorithm, to shut down the algorithm altogether, or to reprogram the algorithm in response to updated market conditions and patterns.

My sense from reading Narang’s book, as well as from talking to friends in the business, is that quant traders must engage in constant vigilance around their algorithms:  Algorithms may cease to generate positive returns as competition eliminates the opportunity, or competitors’ speed eliminates the previous advantage, or patterns simply stop working.

Research and development to find new ‘signals’ inside the noise of past trading data requires constant work.

summer_glau
Summer Glau, my favorite terminator ever

Besides pattern-seeking and constant R & D, what are other elements common to the wide variety of quant traders?

DATA – The acquisition of historical data, the scrubbing of data to extract useful information, and the testing of hypotheses (will following that pattern actually make money?) using the historical data are common to all quants.

COSTS – Some strategies require thousands or even millions of trades, so costs associated with trading matter a lot.  Quants have to include in their model expected trading costs – not only commissions paid to brokers or exchange – but the ‘slippage’ that occurs due to trading. The scientific method analogy to this ‘slippage’ here would be the “Hawthorne Effect” or “Observer Effect,” meaning it’s difficult-to-impossible to collect ‘profit’ from the market without affecting the market opportunity itself.

When a quant finds a financial instrument momentarily ‘cheap’ according to some historical pattern, the act of purchasing the instrument to take advantage of the cheapness may cause the price to rise.

The larger the trade that the quant algorithm demands to take advantage of that momentary cheapness, the larger the ‘slippage’ due to the trade itself will actually eliminate the opportunity. Any historical model that fails to take into account this cost element will likely fail to work.

SCIENTIFIC METHOD – Quant-trading, more than discretionary trading, depends on a ‘scientific method’ approach to investing. While a discretionary trader may depend on fuzzy thinking and ‘gut feeling’ to guide his buy and sell signals, quant trading eliminates this part of trading in favor of a rigorous Hypothesis à Test à Hypothesis à Test continuous feedback loop, with consistent profitability (in trading-speak, the generation of “Alpha,” or excess return over risk) the primary indicator of whether the hypothesis has any validity in real markets. Like scientific hypotheses, the trading algorithm can never become ‘proven’ or ‘permanent law.’ In fact, many current predictors of market moves may be shown to be poor predictors in the future when markets change. A continuous testing of hypotheses about patterns through the scientific method helps distinguish quants from discretionary traders.

How different is High Frequency Trading (HFT) from other types of quant trading, and what should we know about the subset of quant trading known as HFT?

Narang devotes the final chapters of this book to explaining HFT. Narang suggests that the trading ideas of HFTs can often be much simpler than other quant trading – but depend on faster execution of the simpler idea.

Narang provides a relatively easy illustration of a typical, simple, HFT idea.

The S&P500 index consists of shares from the 500 largest market-capitalization US companies, in known relative weightings. Naturally whenever the price of an individual stock in the index changes, the value of the S&P 500 index as a whole changes slightly as well. An exchange traded fund (ETF) like the SPY, or an S&P500 futures contract (ES), both of which are meant to track the index precisely, should exactly mirror the price changes in the underlying stocks.

If, however, one of the underlying stocks goes up in value and the SPY value does not change precisely, instantaneously, to a new value based on the moves in the underlying stocks, a super-fast arbitrage can be had by buying the SPY and shorting the stock. These arbitrage opportunities are either rare or exist for very brief instances – all sorts of traders would look for precisely this simple risk-less opportunity – but trading speed makes exploiting a price difference like this possible.

Neo_dodging_bullets
Keanu: “Whoa”

The best way to explain this is that only a computer programmed to move as fast as Neo in that scene dodging Mr. Smith’s bullets, could hope to profit from the S&P/ SPY arbitrages. Nobody else has a chance.

The basic trading idea here is super-simple. On the other hand the technology involved in calculating the precise price discrepancies, in the right weightings, based on the right data feeds, and doing it faster than anyone else in the marketplace, is not easy.

The S&P 500 trade described above is probably version 1.0 of HFT trading, and most HFT firms seek out similar price opportunities with fewer competitors and less efficient instruments in myriad markets around the world.

HFTs may seek to use more complicated algorithms than this example, but at a certain point the computing time required to sift through the market signal may slow down the process, so that they begin to resemble other quant traders.

By keeping the trading idea dumb and simple, the HFTs retain the advantage that comes from technology. HFT is the Keanu Reeves of quant trading, dumb and dependent on Speed to keep the bus above 88 mph to prevent everything from exploding.

Conventional quants may have harder-to-explain patterns that they recognize and exploit, but probably do not depend on execution speed to the same extent as HFTs in order to be profitable.[2]

How likely, and with what severity, could we experience a destructive flash-crash catastrophe driven by the present and future take-over of markets by quant trading? In other words, should we the public (and regulators) be much more actively regulating SkyNet?

Narang addresses a version of this question in Chapter 10, “Risks Inherent to Quant Strategies,” although his goal is limited to showing – persuasively in my opinion – that large market blow-ups in the past cannot be blamed on quants.

The Long Term Capital Management (LTCM) blowup of September 1998, for example, may be described by some as a quant-fund disaster that fundamentally shook capital markets, and might have gotten worse without a Federal Reserve-Inspired ‘Bail-in’ of LTCM by Wall Street firms.

Narang points out, however, that while LTCM used sophisticated quantitative models in their trading, their strategy remained fundamentally discretionary – meaning humans made the calls on what went into and out of their portfolio.

The 2008 Mortgage-bond inspired crisis, in addition, cannot reasonably be linked to quant trading firms. While the creation and trading of highly-leveraged mortgage and default-swap CDOs involves sophisticated mathematics and modeling, the billions in losses owed more to a combination of leverage (debt), human error, poor risk-modeling, the connectivity of markets and counter-parts, and misunderstood incentives among disparate parts of the mortgage-bond origination chain than anything to do with quantitative trading – as explained well in Michael Lewis’ The Big Short.

Arguably, the most significant systemic market risk crash to date that could be blamed on quant-like trading is the October 1987 market crash, in which computer-based “portfolio-insurance” algorithms each triggered a cascade of automatic selling, independent of human judgment. The Crash of 1987 occurred prior to the rise of quant trading as we know it, so ‘blaming’ quant trading can really be done only by historic analogy. It’s the kind of crash we could imagine from algorithmic trading in which the size of computer trading orders, added to the overwhelming one-sidedness of the computer signal (a crowded trade) causes the devastation.

The actual quant trading blowups to date, as Narang explains, have caused localized losses without inflicting much observable or systemic market disruption.

During a few weeks in August 2007, for example, a majority of algorithmic traders simultaneously experienced sudden losses, as the trends suggested by historic patterns of trading relationships reversed. It turns out that the similarities in quantitative strategies at that time meant that as some quants lost money and unwound their portfolios they caused a cascade of losses in other quants’ positions.

In the meantime, the non-quantitative investment community – people who just own stocks or mutual funds, or non-quant hedge funds – barely noticed any particular change in market prices.

The market as a whole neither soared nor crashed. Only the historic patterns of short-term trading relationships broke down and then suddenly and severely reversed. As a silent market-crisis, only experienced by the quant community, August 2007 may be a harbinger of the kind of localized pain that non-quants can safely ignore. Or conversely, the August 2007 crisis indicates the growing power and ‘crowded trade’ nature of quant trading that regulators should worry about which could freeze up or destroy orderly markets in the future.

Other flash crashes, such as the May 2010 Flash Crash or other mini-crises such as happened in August 2012 that brought down Knight Trading, may fit the pattern of acceptable, localized, market blips.  Optimistically, programming errors like those are just a Darwinian way of punishing the inept and incentivizing the careful and level-headed to take advantage of others’ mistakes.

Or, they could be the early tremors in market-structure fault-lines created by quant trading that will eventually cause an irreversible earthquake in world markets. I don’t want to be alarmist on a topic where I’m an amateur, but I hope some regulators have taken on the ‘seismic geologist’ role of figuring out how to predict and prevent ‘The Big One’ caused by quant trading, before it happens.

I mean, we’ve all seen 2001 Space Odyssey, Tron, Terminator, and The Matrix, right, so we know this doesn’t bode well for algorithmic trading. Even Aliens and Planet of the Apes are basically warnings about quant trading, aren’t they? Wait, am I overthinking this?

Hal9000
Open the pod bay doors, please, Hal

Narang does not particularly address this much more troubling, forward-looking question, of whether and to what extent quants will significantly blow up markets in the future. His primary response in Chapter 16 to this worry or criticism points out that human errors have more frequently caused losses or mini-crashes in the markets than computer glitches. No doubt this is true, but that’s a bit like saying we shouldn’t worry about tornadoes because hurricanes have caused more damage up until now.

As we learned from the 2008 Crisis, markets have a way to experiencing the near-statistically impossible, stunningly unlikely, “6-sigma” event, or “1,000-year flood” just about every decade or two, causing massive wealth destruction in new and unpredictable ways.

Often we can see, in retrospect, that the root cause of a market crash has something to do with the rapid advance of a profitable financial technology far ahead of our ability to control all the risks of the financial technology or regulate the inequities or societal costs of the financial technology.

I’m not in a great position to evaluate the probability and consequences of a future algorithmic-trading driven market catastrophe, and perhaps Narang is not either, although he’s certainly better positioned than most.

I do remain worried about a future SkyNet-driven Crash.

 

Is quantitative trading in general, or HFT in particular (as the rapidest version of quantitative trading is known) inherently unfair, unethical, or taking advantage of other market participants?

I have not read Flash Boys yet, but I know enough about the plot line to see Michael Lewis’ book as a call to action about the inequities inherent in HFT, a subset of quantitative trading. Whether he’s right or not, regulators are already responding to their perception that HFTs are gaming the system in a way that’s harmful to markets overall.

Narang clearly feels that quantitative trading is an interesting and honorable way to make a living. In the final section of the book, he describes the methods and implications of High Frequency Trading, with noted contributions from his brother Manoj Narang, the CEO of a HFT firm Tradeworx and sometime commenter on the industry.

Given Narang’s experience and connection with the industry, we would not expect him to point a finger at quants or HFTs and declare them inherently unfair. What he does try to do instead is educate the non-expert in how quants may make money, and why HFTs in particular feel the need for speed.

To give one example of this need for speed, his brother’s firm Tradeworx researched the profit difference between placing purchase or sale orders first, versus placing them later in the queue for order filling. Inherent to equity trading, he explains, is the rule that when buy or sell orders get placed on an exchange at the same price, the first order placed gets filled first. The value of being first with certain types of orders, according to Tradeworx, turns out to be approximately 1.7 cents per share.

With other kinds of orders, they have found that immediate, speedy, fulfillment of the trade reduces the cost or ‘slippage’ due to trading. In a strategy dependent on a massive amount of trading to capture momentarily fleeting price reactions, the high speed fulfillment of orders matters tremendously.

How do HFTs get so fast?

In the same chapter, Narang mentions the advantage of locating HFT trading servers at, or near, market exchange servers, a major theme of Flash Boys.

Along a similar vein, Narang reports the lengths and costs endured by trading firms to reduce data transmission times between major trading centers such as New York, Chicago, and London. HFT trading firms will sign long-term, multi-million dollar contracts for fiber optic access in order to shave off five milliseconds (there are 1,000 milliseconds in every second, so five milliseconds is 5 thousandths of a second!) in data transmission times between New York and London, as an indication of the importance of being the first to post, or first to cancel, transaction orders.[3]

HFT firms, Narang points out, monitor data and order trades in tenths of a millisecond, creating myriad technical challenges, between hardware, software and network engineering.

Another speed bump in the way of the highest frequency trading, Narang notes, has been the regulatory rule adopted in July 2011 that US equity brokers must enact a ‘risk check’ for every trade, in order to ensure that the size and capacity of the customer and the trade are valid – meaning not too big, which might indicate a trade made in error.

The brokerage software required to do this risk check slows down every trade, which could dampen HFTs speed by 0.5 milliseconds. In response, some HFT created their own brokerages, in order to reduce the time-lag of using someone else’s risk check system.

 

Where do I differ from Narang’s perspective?

1. This is a bit nitpicky, but I have a philosophical issue with Narang’s definition of ‘value’ and the role of ‘timing’ in investing.  He argues in both Chapter 3 and Chapter 16 that all investors, all the way from HFTs on the one side of the spectrum to long-term investors such as Warren Buffett on the other side of the spectrum, depend on timing to beat the market. He means that investors have to execute their purchase (or sale) in advance of others’ execution, and it’s this earlier-than-others’ action that creates value.

In Narang’s presentation, the good investor buys first, others follow suit later, and the original investor can then sell at a higher price based on the fact that others have driven up the price of the instrument.

Here I think Narang’s missed the point of value-investing as advocated by Buffett: Value is created by purchasing future cash-flows at an attractive price, and value investors are mostly indifferent to other investors’ subsequent actions. If you conceive of stock ownership with a perpetual time horizon, as a value investor like Buffett seems to, and that value derives from those future cash flows purchased at a  good price, then timing falls out nearly completely as a consideration.

With a quant’s focus on short-term price changes rather than the cash-flow potential of financial instruments, I can see why Narang views timing as a key to all investing, but I think that focus leads to inaccurately describing other types of long-term investing.

2. I cannot tell from what I read and really this book doesn’t say – whether a constant stream of high frequency orders and cancelled orders constitutes a confusing ‘spamming’ of equity market exchanges, to the detriment of slower investors.  Rapid fire orders, the non-quant critics say, create the illusion of market liquidity where it doesn’t really exist, as it may disappear quickly when non-quants actually seek to trade.

I can understand the way in which HFTs seeking arbitrage opportunities between linked instruments (like the S&P500 and the SPY) create liquidity and efficiency to some extent, in a way that may benefit the smooth running of markets.

In addition, Narang claims that rapid order cancellation is a necessary component of HFT strategies[4] and he claims that no evidence supports the complaints from non-quants about damage done by this type of activity.

But do millisecond orders and cancelled orders jam up the orderly execution of others? Does algorithmic swarming of orders around non-quant investment flow hurt the non-quants in a fundamentally unhelpful or unfair way?

Clearly some non-quants traders and investors believe they are harmed by excessive order-making and order-cancellation, when it comes to trading. Are the non-quants being overly paranoid or jealous of HFT profits, or is there actual harm done? Basically I need a non-quant trader’s opinion here, and I don’t think Narang sufficiently addresses this type of externality of HFT activity.

3. I’ve already mentioned that his book does not dwell on the potential for future catastrophic losses, nor did I expect it to, as the future is unpredictable and Narang would be engaging in speculation about the unknown.  On the other hand, we’ve had enough mini-shocks in the market attributable to algorithmic trading that I hope someone, somewhere (ideally in a government regulatory office) is thinking deeply about ways to prevent The Big One from happening.

Please see related book review: Flash Boys, by Michael Lewis

as well as Flash Boys – Not So Fast by Pete Kovac

Please see related post:  The Rise of The Machines

 

[1] People, what I’m trying to say is: SkyNet already took over the commodities and equity markets. Good thing there’s some real live ex-bankers who still write finance blogs. Ha ha. Or maybe that’s just what I want you to think.

[2] Maybe in this analogy traditional quant trading is a more complex George Clooney-character? I don’t know, still working on it.

[3] I don’t generally begrudge hedge fund traders anything, but now I’m starting to think they never have to wait those annoying 10 seconds for Game of Thrones to load up on Amazon Prime, like I typically do. Damn, I hate those guys. If I was a well-compensated hedge fund trader right now I would happily pay 12 million dollars just to eliminate that delay.

[4] to avoid a problem he explains well, which is the ‘negative selection’ of passive orders on an exchange.

Post read (12269) times.