Pod Insights Odd Lots How the Speed of a Trade Got Down to Nearly the Speed of Light About this episode The average person can enter a stock trade on their computer, hit refresh, and the trade is done. As fast as that seems, there are professional traders moving even faster, executing thousands of trades per second. Over the years, the need for speed got so intense that competing firms would aim to get their own systems closer and closer to the exchange's computers, so as to minimize the length of the wires and get their trades in even faster. How did this happen? And how does this change the nature of trading itself? On this episode, we speak with Donald Mackenzie, a professor of sociology at the University of Edinburgh in Scotland. Professor Mackenzie has been studying the intersection of finance and tech for a long time, and in 2021 wrote the book, Trading at the Speed of Light. We discuss the history of finance technology and look at where the technological arms race is going next. Subscribe to the Odd Lots NewsletterJoin the conversation: discord.gg/oddlotsSee omnystudio.com/listener for privacy information.
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Tracy Alloway? There are some market stories where you want every detail. Joe and I have made quite a few podcasts on that basis. But sometimes you've only got 10 minutes and just want to know what's moving markets fast. That's the Barclays Brief Podcast. Every week, experts from Barclays Markets and Research get you up to speed on what's happening and what to watch next. Concise, focused, and brief. Search Barclays Brief wherever you get your podcasts.
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1:13 Joe Weisenthal? Hello and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.
1:19 Tracy Alloway? And I'm Tracy Alloway.
1:20 Joe Weisenthal? Tracy, one of the things that I think we like to do on this podcast is sort of de-abstract the things that we take for granted in the world.
1:30 Tracy Alloway? Right.
1:30 Joe Weisenthal? There are various processes. We always say this when there's like a blow up or something like that, where it's like, oh, if we're having to pay attention to X or Y, there must be something going on. But we don't always have to wait for blowups. But like, we live in this world where like, you click buttons and things happen and you have some intuition of what happens after the button is clicked. But you don't really have a great intuition of like, what happens between the button clicking and the thing happening.
1:56 Tracy Alloway? Absolutely. I actually don't know how the process of like, inputting an equities trade actually works, like where it kind of shows up. So that's a big question. I suspect a lot of people, even people who are in markets probably don't know like the entire sequence of events, partly because it's gotten more complicated over the years with like Reg NMS. Do you remember that?
2:16 Joe Weisenthal? Yeah.
2:16 Tracy Alloway? And stuff like that. The other thing I've been realizing about trading, obviously, the big trend here is high-frequency trading.
2:23 Joe Weisenthal? Yeah.
2:24 Tracy Alloway? Right? And it's just getting faster and faster and faster. When we first started writing about HFT, I guess in the sort of like mid 2000s, after the financial crisis.
2:36 Joe Weisenthal? Yeah.
2:36 Tracy Alloway? I remember thinking that it was all about the actual algorithm and finding like a really smart pattern in financial markets to exploit. But the more I learn about it and the more I read about it, I kind of realized it's not really that. It's about exploiting the market structure.
2:54 Joe Weisenthal? Yeah, yeah, totally. And there are so many, you know, we had that, I forget who we were talking to recently. Oh, is that guy from Hudson River Trading? And you know, there were the famous like wire wars where it's like, no, I want to be one inch closer to the main server, etc. It's like, God, this is like a good use of brain power. It's like, we're going to solve the market because we want to be-
3:12 Tracy Alloway? One more nanosecond faster.
3:14 Joe Weisenthal? Yeah, one nanosecond faster, etc. But you know, the other thing, you know, and sort of related to this, one of my longstanding questions is, you know, a jobs report will drop at 8:30 on a Friday, and the market immediately moves. And I'm like, how did that happen? Because it didn't happen because someone was staring, they had their like fingers above the buyer, the sell button, but also had, something had to be programmed such that that data could instantly be ingested and then some sort of like directional trade was made based on that. But I don't know how that happens. I don't know how that works.
3:45 Tracy Alloway? There's also, of course, the overall question of what all this electronic trading actually means for the market itself.
3:51 Joe Weisenthal? Yes.
3:51 Tracy Alloway? And people talk about things, you know, with the multi-strat funds, getting these feedback loops and maybe increasing volatility in the market and things like that. So we should discuss.
4:01 Joe Weisenthal? And now I just had one more thing. You said, what does it mean for the market itself? What does it mean for society itself that someone's effort is being placed on like getting a meter closer to the server room or whatever? And why is this a good use of time? And is this improved capital allocation? I'm really excited to say we do, in fact, have, I think, truly the perfect guest to talk about this. Someone who has a sort of an extraordinary body of life's work in a range of areas that is very distinct from almost any other academic or researcher that I can think of. We're going to be speaking with Donald Mackenzie. He's a professor of sociology at the University of Edinburgh in Scotland. I first came across his work. He wrote a fantastic book called An Engine, Not a Camera,
4:43 Joe Weisenthal? which is sort of about finance and the original birth of quantitative finance and the use of advanced act models and how these models didn't just reflect what was going on in the real world, but how the adoption of these models then created this feedback loop, the engine effect such that it actually started to drive markets themselves. More recently, he wrote a book called Trading at the Speed of Light, all about high-frequency trading. He's also written recently a book about digital advertising. And so truly a polymath in the world of thinking about this relationship between industry and the sort of technological substrates that drive them. Professor Mackenzie, thank you so much for coming on Outlaws.
5:23 Speaker 04 Well, thank you very much for inviting me to do that.
5:26 Joe Weisenthal? Absolutely. Why don't you start off by telling us the gestalt of your life work? What is your core underlying interest such that has produced books in these various realms?
5:37 Speaker 04 Yeah, I mean, fundamentally, I'm a sociologist of technology. So I'm interested in the technical systems that affect or could affect all of us. So over time, first major project in that area was on nuclear missile guidance technology.
6:00 Joe Weisenthal? Amazing.
6:00 Speaker 04 Then I moved on to safety critical computing technology, then the work on financial models that you've just mentioned. Then high frequency trading, then digital advertising, you know, because as well as driving us all insane by ads that we don't want to see appearing on our screens, that's also of course the big funding source for much of the everyday digital world. And then most recently of all, I've started working on AI and large language models. So you can see that you can see the picture, they're all highly technical areas, one way or the other, they all affect all of us.
6:42 Tracy Alloway? The other reason we wanted to talk to you is because you come at everything from this sociological perspective. And I absolutely love it when like anthropologists and sociologists go to Wall Street and write about it. Why did you take that approach, especially with your high frequency trading book?
7:00 Speaker 04 Yeah, well, I don't do kind of like quantitative social science, you know, I can leave that, for example, as far as markets are concerned, I leave that to economists. What I do, I like talking to people. I like going, looking at stuff to the extent that you can look at it. I like tracing how things have developed through time. My work's often got this kind of, you know, something of a historical dimension to it. But the most fun bit isn't writing the books. The most fun bit is talking to people. And that's the bit I've always enjoyed most.
7:37 Tracy Alloway? One interesting thing in this book, Joe, I don't know if you noticed, but Donald writes down like all his numbers of sources and who they are. So like, you know, people from the exchange, people from high frequency trading firms, what their seniority is, which is something I hadn't really seen before.
7:53 Joe Weisenthal? That is a really cool thing. By the way, as Donald says, the fun part is talking to people, not so much the writing of the book. As two people who talk to people every day and have never written a book, I feel already, now granted, we never actually went through the process of writing the book, because the talking part is so much more fun. I don't want to ever take a pause from the talking. So I already feel like to some extent, Donald is a kindred spirit. It's like, it's fun to talk to people, isn't it? Talk to us about how you find them, you know, it's like, okay, HFT is interesting to you. And like, you just want to have some conversations. What is the sociologist's toolkit here for knowing who to talk to?
8:32 Speaker 04 Yeah, well, it's always difficult and it's always very ad hoc. There's always a lot of luck involved in it. And with a financial market topic, I will typically start reading the Financial Times, finding names in the Financial Times, approaching those people and then maybe they pass me on to other people. But, you know, there's also, as I said, dumb luck involved, like crucial moment in the work I did in high-frequency trading was going to interview someone at the start of it and framed on his wall was the front cover of an issue of Forbes with the headline of the article,
9:16 Speaker 04 "Free Enterprise Comes to Wall Street." And I thought, oh, that sounds kinda interesting. And I checked that out and it was to do with a new electronic stock exchange called Island. And it turned out that Island, the story of Island was completely interwoven with the story of of high-frequency trading.
9:37 Tracy Alloway? Hmm. Before we get into exactly what high-frequency trading is and how it fits into, you know, placing orders for equities or futures or bonds, I have a cultural question, which is, whenever you go into an HFT firm's office, it always looks like a tech company.
9:55 Joe Weisenthal? Yeah.
9:55 Tracy Alloway? Right? It's very
9:56 Joe Weisenthal? With the chessboard and the matcha on tap, yeah.
9:59 Tracy Alloway? And very modern. Like, why do you think they've taken that approach? How did that aesthetic become the norm?
10:06 Speaker 04 Yeah, that's a really good indicator of cultural change. Because of course, previous to that, the sort of dominant image we might have of a financial market would be the trading floor of the New York Stock Exchange, you know, folks in colored jackets. They're typically televised when, even nowadays, when, you know, so there's been a big drop in the market or something, so the the cameras try to catch somebody who's looking kind of glum and worried. So we think of that as what finance is.
10:41 Joe Weisenthal? Or we think about like the Bud Fox in Wall Street of a bunch of guys and slicked-back hair, sort of, you know, on the phone.
10:49 Tracy Alloway? Boiler room.
10:49 Joe Weisenthal? Yeah, yelling at each other, looking at a green screen, right? Sorry, I didn't mean to interrupt that. But those are, I suspect, the two things people imagine.
10:57 Speaker 04 Yeah, yeah. And you know, there was a transition involved. By and large, the high-frequency trading firms hire people who know how to code, often, you know, with higher degrees in mathematical kinds of subjects. And even the people who refer to themselves as traders often have that kind of background. And, you know, I'm sure when the visitor isn't there, there'll be a fair bit of swearing at the screen when something goes wrong and that kind of stuff. But you're right, that the normal experience of those trading rooms,
11:36 Speaker 04 they're quiet, they're orderly, and you could indeed mistake them for Silicon Valley startup.
11:43 Joe Weisenthal? Yeah, and you see people in jeans. And I visit one, and like, I think I saw the CEO, and he was just like, wearing his like, college t-shirt, or something like that.
11:53 Speaker 04 Yeah, no, I've got a strong memory I've got, because, you know, in the previous work, they worked for an engine, not a camera, and some follow-on stuff. I would often go to investment banks. And in investment banks, you know, I kind of had to wear a suit and a nice shirt and a tie, and so on. So when I started interviewing in high-frequency trading, I turned up at one firm dressed like that, and the owner of the firm sort of snarled at me, "You're overdressed." Wow.
12:21 Joe Weisenthal? You mentioned Island. Why don't you tell us that story? You know, I want to get more into the tech, et cetera, but you're like, okay, this turned out to be an exchange. What was distinct? What is Island? I've heard of it, but it's again, one of these things that, I've heard of it, and then I moved on. What was distinct about this, and why is it so interwoven into the history of HFT? How is it different from other exchanges that have existed for hundreds of years?
12:45 Speaker 04 Yeah, yeah. You know, I'm going to oversimplify, of course, because there were predecessors to Island, that, you know, were a little bit like it and so on, but that would take us too long to go into. I mean, fundamentally, trading on Island was organized around an electronic order book, which was, is, a list of all the bids to buy, or offers to sell the shares in question. And that electronic order book is managed by something called a matching engine. And as the name implies, that looks for... A match, in other words, a bid to buy and an offer to sell at the same price. And when it finds that couple, it consummates the trade and the trade is done.
13:30 Speaker 04 So it's all done electronically. There's no direct human negotiation involved. You just enter your orders into the order book and the matching engine either executes them or fails to find a match. There were exchanges prior to Ireland that worked in that kind of way. But what was distinctive about Ireland is that its matching engine was blisteringly fast by the standards of the day, which were, you know, was essentially the late 1990s. So the closest analogue was a system called Instanet. And it might take a couple of seconds, the matching engine, to find the match and execute
14:15 Speaker 04 the trade. And of course, for a human being, sitting there, even if they're impatient, two seconds is not a very long time. Ireland improved on that a thousandfold. So it could execute trades in two milliseconds, two thousandths of a second. So that was the opening for high-frequency trading, that with exchange, I mean, strictly, Ireland was not an exchange, it was what was called an electronic communications network or ECN, but I'll call it an exchange for simplicity. If you've got an exchange like that and you've got an automated trading system, it's a marriage
14:59 Speaker 04 made in heaven. The two things, the exchange and the trading firm, fit each other very, very well. And amongst the consequences of that is that liquidity in Ireland, it traded NASDAQ stocks, and this is the time of the dot-com bubble, of course, where there's a lot of trading of NASDAQ tech stocks. Ireland brought a lot of liquidity to that market. So that's, you get a kind of feedback loop where you get automated trading.
15:35 Speaker 04 Bringing liquidity to exchanges that have the kind of technical features that make high-frequency trading attractive and feasible. So the established exchanges started to have to change how they did things because otherwise they were going to lose out to the new exchanges. And that's basically the feedback loop that's created today's electronic markets.
16:14 Joe Weisenthal? There are some market stories where you want every detail. Tracy and I have made quite a few podcasts on that basis, but sometimes you've only got 10 minutes and need to know what's moving markets and why. That's the Barclays Brief podcast. Every week, experts from Barclays Markets and Research get you up to speed on what matters and what to watch next, about the time it takes to grab a coffee. So search Barclays Brief wherever you get your podcasts.
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17:09 Tracy Alloway? Joe, whenever I hear terms like millisecond and nanosecond, I just, it's so hard to wrap my head around what that actually, it's faster than that.
17:16 Joe Weisenthal? Oh, yeah.
17:17 Tracy Alloway? For sure. Donald?
17:19 Speaker 04 I can actually help there. I'm going to hold my fingers. I'm holding them 30 centimeters apart, or since you're in the US, I'll say one foot apart. At the speed of light in a vacuum, it takes a nanosecond to get from one finger to the other finger. And that's an indication.
17:44 Speaker 04 Of how fast automated trading, specifically high-frequency trading, has become that when I started working on the topic in roughly 2011, people were still talking about milliseconds or thousands of a second. Two or three years later, it had become microseconds or millions of a second. And by the time I was finishing the research, nanoseconds were starting to account. Unbelievable. Light travelling that 30 centimetres, travelling that foot, that matter to high-frequency trading by roughly 2018, 2019, 2020, round about then.
18:24 Tracy Alloway? That's very helpful. I do have questions about the physical realities of how fast we can actually go with all this stuff. But before we go any further, can you talk about the process of, let's just focus on the equity market for now. Someone places an order to buy or sell a stock. What actually happens in the ecosystem between traders and market makers and the exchange that makes that happen? And what does it mean to actually make that happen and execute the trade?
18:53 Speaker 04 So what happens is your order via the broker, you're using, I mean, in the brokerage system is no longer a human being. Via the brokerage system gets placed in the exchange's order book. And then one of two things happens. The first is if the matching engine can find an existing order in the order book that matches the price of your order, it executes the trade. And the trade then happens, not quite instantly, but you know, very, very, very fast. And you're done. Yeah, that's it. It's over.
19:36 Speaker 04 If on the other hand, there is no match as the order book. Stands, your order rests in the order book and it stays there until either you cancel it or a matching order comes along and then it's executed at that point. So that's the basic process.
19:58 Joe Weisenthal? You know, so in a curse of me, like gains of speed in trading have been happening forever. Long before we were talking about, you know, anything electronic, I'm sure.
20:09 Speaker 04 Yeah.
20:09 Joe Weisenthal? You know, other technologies exist. Technological evolution is a long time thing. It is a pretty banal statement, I suppose. But what I'm curious about is the sense in which a change in degree becomes a change in kind, essentially.
20:23 Speaker 04 Yeah.
20:23 Joe Weisenthal? So that like, when you go from one second to a thousandth of a second to a nanosecond, how does that change, say, like, the types of strategies that can then be employed? Or the types of skills that might be required to be a successful trader in the nanosecond era versus the one second era? Like, talk to us about like that relationship.
20:45 Speaker 04 Yeah, yeah. Well, there's a wonderful book by the historian of technology, Jimena Canales, which is called A Tenth of a Second, A History. And the significance of the tenth of a second is that's the generally accepted lower threshold of the human perceptibility of time. You know, basically, we just can't mentally process time intervals that are less.
21:13 Joe Weisenthal? So Tracy, don't feel bad about not being able to build an intuition for a nanosecond.
21:18 Speaker 04 Less than a tenth of a second. You know, so what essentially happened is that we've moved from that kind of, you know, the tenth of a second or longer from that kind of epoch.
21:34 Speaker 04 Into an epoch where human beings, I mean, they can still be in overall control of the system, but they can't actually execute the actual trading decisions fast enough not to be outrun by an algorithm. So we moved from a kind of human-centered form of trading to a machine-centered form of trading. And the, you know, the actual threshold of the change is probably around that tenth of a second amount.
22:09 Tracy Alloway? I have another cultural and I guess market structure question, but one thing that I always thought was interesting about high-frequency trading was that the banks didn't really get into it, which, you know, there's one big reason why, which is the ban on prop trading after 2008. But even before then, they just never seemed to be able to compete with independent firms. Why did that happen?
22:33 Speaker 04 Yeah, it's a vast people that. And there's, I think, complex sorts of reasons. And let's let it be said, some banks have been more successful than others. Banks are not always bad at this, though most banks are bad at it. One way of thinking about it is that typically a bank will have an IT department that's separate from the other functions of the bank, like trading, like market making and so on. So if you, you know, if you're a trader, you got to persuade the IT people to give you a fast enough system, which involves
23:14 Speaker 04 them, you know, maybe writing some new software, buying some new kit. So you need to get higher level management sign off on it. And it all takes time. Whereas the high-frequency trading firms are typically pretty small, you know, 50 people is a decent size firm, 150 people is, you know, is a reasonably big high-frequency trading firm. Very often those firms are
23:44 Speaker 04 Owned and run by the, you know, the, the people who founded them. So there, there is a boss or bosses, but you know, other than that, it's a relatively flat organizational structure. If, for example, at least this was the case in the early days of high frequency trading, it's not quite as simple as this now, but in the early days of high frequency trading, you know, if some IT firm came out with a new, better, faster server and you were a trader in a firm like that and you know, okay, well, let's get this new server. You could just use your own personal credit card to buy the server, get it delivered to your office and then
24:25 Speaker 04 get your engineers to take it out to whatever data center that they were trading in and, and get it installed straight away. And you know, that could, maybe that would be a week or 10 days or something. Whereas in the bank, you'd be doing pretty well if you could achieve that within six months.
24:42 Speaker 01 Yeah.
24:43 Tracy Alloway? That's amazing.
24:44 Joe Weisenthal? Tracy, we don't know, we don't know anything about long waits for computers to arrive.
24:50 Tracy Alloway? No, we surely don't. Um, that's sarcasm, by the way. Actually, this reminds me of something that I wanted to ask, which is, we know there's competition between firms, high-frequency traders for the fastest connections to exchanges and things like that.
25:07 Speaker 04 Yeah.
25:08 Tracy Alloway? There's also competition, I imagine, within the firm itself, because setting aside the credit card anecdote, these can be expensive and there's also limited supply, you know, only so many servers can be co-located where they want to be.
25:22 Speaker 04 Yeah.
25:23 Tracy Alloway? In your research, how did you actually find executives at HFT places? How did they actually allocate the fastest connections to which team?
25:31 Speaker 04 Yeah. What I found was that high-frequency trading firms fell into two different camps, so to speak.
25:40 Speaker 04 In some of them, there were separate trading teams that didn't really communicate with each other and indeed by design didn't communicate with each other. In some cases, the the office was actually laid out in such a way that somebody in one team was not very likely accidentally to overhear something said by someone in another term. And in those in those firms, yes, I mean, they are essentially in competition. And I think that in that kind of firm, then the results of each trading desk, the P and L, the profit and loss, the little
26:20 Speaker 04 trading teams that are doing best would get the available bandwidth on the microwave links that are crucial to high frequency trading and and so on. And maybe they would get the fastest machines first and and so on, so forth. The other kind of trading firm was and is operated as a unified entity. In some cases, even without individual profit and loss in the individual P and L accounts for traders. And there's a lot of shared infrastructure in that kind of firm. And indeed, there's also shared infrastructure in the, you know, the segregated kind of a trading firm. Because if you're the boss of such a firm, there's obviously
27:04 Speaker 04 simple economies in not having completely separate IT systems for each each trading for each each trading team. But there's that kind of divide. Does the firm operate as a unified entity? Or does the firm operate as a sort of aggregate of competing trading teams?
27:25 Joe Weisenthal? So actually, let's just, you know, on the subject of who is the closest or who gets to have their server located where. Tell us a little bit more about the timeline. So, Island emerges in the late 90s. When did it start to dawn on people in the trading industry that, given this new physical reality, given the speed, we need to start thinking about who is going to have co-location? We need to start thinking about sort of like microwave radio line of sight. Where did that speed war? What was the genesis of it?
28:00 Speaker 04 Yeah, yeah. I mean, a kind of crucial date was two thousand and five, where Ireland, which had already been bought by Instanet, was acquired by NASDAQ. And the New York Stock Exchange acquired another of the pioneering electronic trading exchanges. This one was called Archipelago.
28:23 Joe Weisenthal? Is that just a coincidence, that Ireland and Archipelago? That's kind of like...
28:27 Speaker 04 Yeah, no, I'm sure it's not a coincidence, yeah. And they technologically reorganized themselves, the New York Stock Exchange and NASDAQ, technologically reorganized themselves around this new insurgent technological approach to trading, so to speak. And two thousand and five, because of the acquisitions in that year, is a kind of noteworthy year. But even before two thousand and five, people in trading firms started to become aware that you couldn't just do automated trading with the machine sitting in your office. For example, there's a Kansas City trading firm called TradeBot,
29:09 Speaker 04 whose owner, Dave Cummings, has written a really rather nice autobiography. And one of the things that Cummings came to realize is that trading in Ireland while having your machines in Kansas City was placing him at a disadvantage. So firms like that started to move their machines either directly into the Ireland computer room, or if they couldn't do that, into the offices of another firm in the building to shorten the distance. And that kind of thing was already in place by two thousand and five. Two things then happened.
29:50 Speaker 04 For a period, there was a kind of Wild West, so to speak, where there are lots of stories of high-frequency traders like drilling holes in walls so as to shorten the distance between their servers and the exchanges' matching engines.
30:10 Speaker 04 This by and large generally come to an end. What happens now in the in the data centers of all the major exchanges is there is a rule about equal cable length. So if you happen to have, if a trading firm happens to have its servers physically close to the exchange matching engines, the fiber optic cable that connects them is coiled. So that there's exactly the same cable length for each of the trading firms within that data center. The other thing that started to happen is that getting the signal
30:49 Speaker 04 from one exchange's data center to another exchange's data center started to become a technological speed race. Back to two thousand and five, by and large, it would just be sent by fiber optic cable, but the exact route was not under the control of the exchanges or of the trading farms. So there's a lot of sort of randomness. The crucial link here is actually between the Chicago Mercantile Exchange's data center. Then in downtown Chicago, it's now in in in the suburbs of Chicago.
31:29 Speaker 04 The link between that and the data centers that trade shares in northern New Jersey. And there was a kind of triple evolution there. The first evolution was that the particular trading firm, and since it's no longer directly in business, I can actually name it Getco, um, managed to, as it were, stitch together existing fiber optic cables to get the fastest route on the Chicago-New Jersey link. And that actually in the business was known as the gold line. Gold because of the money that you could make by having the fastest route.
32:11 Speaker 04 Then in twenty ten, if memory serves me right, a new firm, Spread Networks, actually dug an entire new cable from Chicago to northern New Jersey. Uh, you know, it could be the drilling, you know, sort of underneath car parks and, you know, just really trying to be. As close to what a geographer would call the geodesic. In other words, the fastest route on the surface of the earth from point A to point B. Then third phase, that was trumped by the arrival of microwave. Because light in fiber optic cable, I mean, the core of fiber optic cable is essentially a specialized form of glass.
32:53 Speaker 04 And that glass slows the light down to only two thirds of its speed in a vacuum. Whereas if you can shoot your electromagnetic signal through the atmosphere, it's not exactly at the speed of light in the in a vacuum, but it's very, very close to the speed of light in a vacuum. So that was the third phase when people moved from exclusive use of fiber optic cable to supplementing the fiber optic cable by microwave links between Chicago and northern New Jersey.
33:27 Joe Weisenthal? That was incredible.
33:44 Speaker 01 Your team is on a deadline, but moving all your enterprise data will take time your business simply doesn't have.
33:52 Speaker 01 IBM Technology lets AI work with your data where it already lives with an open hybrid cloud approach designed to build on your existing investments. It's another way IBM helps you put your data to work. Let's create smarter business. IBM. Visit ibm.com to learn more.
34:13 Tracy Alloway? I have another cultural slash market structure question, which is along with this intense competition to be faster than anyone else. There was a narrative.
34:26 Tracy Alloway? Around that time that this was a bad thing, right? And one of the interesting cultural things is you saw the high-frequency trading firms kind of divide themselves into good guys and bad guys. So there were some that were saying, well, we make liquidity, right? We're good for the market. And then there were others, I mean, they wouldn't describe themselves this way, but they were accused of being liquidity takers in the market. How should we think about that particular tension?
34:55 Speaker 04 Yeah, no, that's a very fundamental thing because you're quite right. There's a degree of differentiation between trading firms and indeed in the more segregated firms also within those firms, you have different desks, fallen others on different sides of that. I mean, the way to think about it is to remember that in all these exchanges, trading is organized around an electronic order book. As I said, a list of the bids, the buy and offers to sell the instrument in question.
35:31 Speaker 04 What a market making firm does is it places lots of orders into the order book at prices that can't immediately be executed, but those orders then populate the order book. So if somebody else comes along, an individual investor or maybe an asset management firm, somebody else comes along and needs to trade, they find an order book that's populated with lots of existing bids and offers that they can execute against.
36:09 Speaker 04 So the market making firm is providing liquidity and most people reckon that that's a good thing. The other kind of firm, the one where, as you say, there tends to be more controversy, they don't do that. They don't constantly populate the order book. Their systems constantly monitor the order book.
36:34 Speaker 04 And then when they detect what they think is a profitable opportunity, they execute against the orders that are already in the order book. And that is called liquidity taking because of course the execution removes the order from the order book. And in practice, a lot of this is actually going on between high frequency trading firms because most of the orders in the order book are placed by market making high frequency trading firms. And a lot of the executions against those are by the high frequency trading firms that specialize in taking liquidity.
37:20 Speaker 04 And this is the core of what gives the business its characteristic as an arms race in speed. So imagine for a moment that your algorithms are trading equities in one of the data centers in northern New Jersey and the relevant stock index future traded in Chicago changes in price or even there's a big shift in the order book for that stock index future. And let's say the price of the stock index future falls.
37:58 Speaker 04 That's very likely to lead within a tiny fraction of a second to falls in the price of the underlying shares being traded in New Jersey. And in that tiny little fraction of a second, in that intervening period, a lot of the market making firms orders in those order books become stale as people in the markets would put them. Making firms rush to try to cancel their stale orders and the taking firms race to execute against those stale orders. And that's a race that nowadays can literally
38:42 Speaker 04 be played out in nanoseconds.
38:44 Joe Weisenthal? That's interesting. I hadn't appreciated that dynamic at all, to be honest. You mentioned that these high-frequency trading firms, they're all sort of like, they're run and operated basically by the employees, the owner, or something like that. What is it that sort of distinguishes a high-frequency trading firm from, say, a hedge fund that would have limited partners and make distributions, et cetera? You know, you never hear about like, oh, I have an investment with Jane Street or something like that. They, I don't think that's what I think. What is it about the nature of the business such that essentially they trade their own capital?
39:19 Speaker 04 Yeah, I guess that's just, in a sense, it's one of those things that's historical evolution. I mean, in many cases, those high-frequency trading firms were initially set up by successful floor traders, particularly floor traders
39:35 Joe Weisenthal? Yeah.
39:35 Speaker 04 in the Chicago markets, the futures markets. And if you were successful in that, you could, you might not become a billionaire, but, you know, you could make decent amount of money, tens of millions of dollars, and that was enough to enable you to start an initially small automated trading firm. And you often didn't need any external capital to do it. You just had to buy the necessary technical kit and hire technically savvy people and so on. But you could start really quite small. You could start, I mean, a 10-person
40:17 Speaker 04 firm or something like that. Now, the business has got a lot more expensive since then because in good part of the speed race that we've just been talking about. But by and large, those firms made profits, the owners reinvested the profits in the firms. So the, you know, the capabilities of the firms grew to meet the growing demands on them.
40:44 Speaker 04 And then another thing that should perhaps be said is that those founders would have the majority of their net worth invested in the firm. And so their risk control was often, you know, pretty good. You know, risk management for those firms was not a sort of, you know, a separate bureaucratic department that the traders had to try to outwit, so to speak. The founder would quickly detect if you were trying to do something like that. Now, some automated trading firms are blown up nevertheless, but it's actually quite interesting how few of them have blown up.
41:25 Speaker 05 Yeah.
41:25 Speaker 04 Because of, you know, for example, because of classical software bugs. And that's, I think, because the relatively small structure, the hands-on involvement of the founders, et cetera, et cetera, has created a kind of technical culture that actually works pretty reasonably well, better than I would have expected it to work at the start of this research.
41:53 Tracy Alloway? So one thing I wanted to ask is this idea of physical limitations to how fast we can actually go. I'm pretty sure people always say that you can't go faster than the speed of light. There's probably some caveats there about like quantum entanglement and stuff like that, but surely we must be getting close to how fast things can actually go. What's your sense of how long the speed race can continue?
42:20 Speaker 04 Yeah, well, we can never get to zero, of course. I think Einstein is basically correct. You can't get faster than the, um, the speed of light in a, in a vacuum. And similarly, a computer system, there's always going to be a non-zero processing time of the system, but you can get ever, ever closer to that. So in mathematics speak, zero is an asymptotic limit. Um, that's to say, you can always get closer to it, but you're never actually going to get right there. And I think that's the nature of the business. You know, we're still.
43:05 Speaker 04 In the nanosecond regime, if I remember correctly, the next lowest time interval is the picosecond. Um, you know, we, I could imagine this continuing in a, in a domain of picoseconds. So that's the way I would see it, that there's a hard limit, but we're never actually going to get to the hard limit. We're still going to race to get as close as possible.
43:28 Joe Weisenthal? But your understanding as of the time of your work is that the race is not over, that for these firms, and I'm sure they have many different projects underplayed, including various things with AI, which we haven't gotten to, and I guess we won't, but the speed race is not, and will never be done?
43:45 Speaker 04 Yeah, I think that's correct. No, of course, there is an economic process
43:50 Joe Weisenthal? Yeah.
43:51 Speaker 04 at work here, which is to say that the investments that you make in speed have to be recoupable from
44:03 Joe Weisenthal? Right.
44:04 Speaker 04 the trading profits that you make from your trading. And I think Tracy, I think, said at the very beginning, what essentially is going on here is that structural features of financial markets are being exploited, like the relationship between the stock index future and the underlying equities. The amount of money to be made by exploiting those kinds of structural features is not trivial. It's been nicely measured by the Chicago economist Eric Suft and colleagues. It's not trivial, but it's perhaps single digit billions of
44:48 Speaker 04 dollars. So, you know, suddenly deciding you're going to invest $50 billion in the technology of speed would be a dumb
44:59 Joe Weisenthal? Right.
44:59 Speaker 04 thing to do because you wouldn't be able to recoup it. So there is that economic Factor that is, I'm pretty certain slowing, the speed race is still there, but it's, it's, you know, and things are getting faster, but the rate at which they're getting faster. Yeah. Certainly not accelerating. And I think that economic factor probably explains it. That makes sense.
45:24 Tracy Alloway? So we should talk about the impact of HFT on the overall market a little bit more. And one of the things that caught my eye in your book was you cite a previous study, I can't remember by who, but basically saying that the efficiency of financial markets has not improved between the 1880s and 2012, which is very counterintuitive.
45:46 Speaker 04 It seems like impossible to imagine, but what does that mean?
45:49 Tracy Alloway? Yeah.
45:49 Speaker 04 So that is work by Thomas Philippon, or his French, or pronounced in the American way, it's Thomas Philippon. What he means by efficiency there is really rather different from what I've been talking about. What he means by efficiency is what he calls the unit cost of financial intermediation, which essentially is, you know, basically, putting it crudely, how much it costs to do the kind of thing that investors want to do, that asset managers want to do, and so on.
46:27 Speaker 04 And, you know, it is a very striking finding that from the 1880s to, I think his most recent data goes up to 2015, that there was no really clear cut tendency for that cost to decline, despite all the advances in information and communication technologies over those many decades. And the explanation, to put it simplistically and crudely, is the capture of those efficiency gains in the form of high pay in the financial sector, you know, typically through the form of fees.
47:11 Speaker 04 So the fees that you pay for an index fund, say, for example, those have really gone down, but people have also been moving their money into private equity and the like hedge funds, which have much higher fees and so on. So those kind of effects seem to have sort of. Cancelled themselves out in the nineteen forties. A professional in finance was basically paid roughly the same as somebody with equivalent educational qualifications in a different line of business. Huh. And then from the nineteen seventies onwards, the gap has got bigger and bigger and bigger now.
47:55 Speaker 04 These days, of course, you can make a lot of money being a technologist in AI. I've heard. For example, but by and large, those sort of exceptions aside, finance is an extraordinarily well-paying professional, at least for those in the central roles in it. And that's essentially the explanation of of that finding by Philippon.
48:15 Tracy Alloway? Well, this is a good opportunity to ask about AI because I suppose it's inevitable as a sociologist who examines the tech industry or looks at how tech is impacting things. Your next project must be AI, right?
48:29 Speaker 04 Yes, it is.
48:30 Tracy Alloway? And what's what's the particular angle or what have you been discovering so far?
48:35 Speaker 04 Yeah, it's very early days. But the thing I'm most interested in so far is the question of scaling.
48:43 Tracy Alloway? Hmm.
48:43 Speaker 04 And AI, because of course it's no secret to anybody who reads reads a newspaper, subscribes to Bloomberg or or was whatever. I mean, the huge trillions of dollars are being thrown at AI infrastructure. And absolutely, there is a sort of logic there that's repeatedly stated that, you know, these systems are all built around neural networks. And the effectiveness of a neural network grows with the size of the network, the size of the training data, the number of parameters in the model and so on. And there are well-known scaling laws.
49:27 Speaker 04 But, and this is the thing that interests me, is the the but. There's a very nice little statement from Sam Altman in February of last year. That the intelligence of a system is roughly the log, the logarithm, in other words, of the resources devoted to training it, running it, to computation at inference time and so on. Now, of course, what Altman meant was, basically, give the industry more money and you'll get more intelligence. And that's, of course, indeed, the giving the more money to the industry is exactly what what's going on.
50:10 Speaker 04 But a logarithmic function, there's a bit of maths here. A logarithmic function, at least of the kind that Altman is referring to, is a diminishing returns function. You can draw its graph and it very clearly demonstrates diminishing returns. It'll be, you can always get better and better, but each increment costs you more in terms of the resources deployed. And we're dealing here where the horizontal axis in the graph, so to speak, is denominated in trillions of dollars of financial input
50:52 Speaker 04 or hundreds of megatons of carbon dioxide emitted by the electricity generation needed to power the data center. So the question becomes, on a diminishing returns curve, how far do you go? When do you decide we really got to stop? Can you decide we really got to stop? Do you have kind of quasi-magical belief, so to speak, that at some point, the diminishing returns, something qualitative will happen? That artificial general intelligence or superintelligence will suddenly appear?
51:35 Speaker 04 So that's the core of what I'm interested in right now. How far do you go along a diminishing returns curve?
51:43 Tracy Alloway? Joe, I just want to state for the record, if you give me more money, I get more intelligent. There's no diminishing returns, just for the record.
51:50 Joe Weisenthal? You know, um.
51:53 Joe Weisenthal? Noted, first of all. And it's interesting, you know, hearing this in the context and it suddenly makes so much sense how this fits into your work and this idea of like the arms race, right? Because yes, it's true, like, maybe there's only so much extra profit available for the firm. And maybe that pool of profit is shrinking. And maybe it gets more and more costly to sort of exploit the remaining profit that's available. But on the other hand, you can't fall behind. You can't let, and this is so there, it's true in HFT, and it's clearly true in AI, where, okay, like, it spends more and more money to improve the model. But you can't fall behind, even if the economics look worse
52:33 Joe Weisenthal? with each iteration. Anyway, Professor Mackenzie, lovely conversation. I really enjoyed that. We really learned a lot. Really appreciate you coming on Odd Lots. And yeah, thank you for joining us.
52:44 Speaker 04 Well, thank you both. Thank you both for inviting me, like I said, and you know, thanks for a really great conversation.
52:49 Joe Weisenthal? That's fantastic. Thank you so much.
52:50 Tracy Alloway? We'll have to have you back on when you publish your AI book.
52:53 Joe Weisenthal? Absolutely.
53:06 Joe Weisenthal? Tracy, I loved that conversation. Really interesting. I love like encountering people who are like, actually understand the tech, actually can articulate what the tech is doing. Especially, it's always impressive, someone with a sociology background, etc. to just sort of be like that comfortable. And I think that's like the through line of his work is like, he gets it.
53:28 Tracy Alloway? Right. So in the book, there's lots of like, field trips to data centers and looking at cables and things like that. But then also, as he stated, just talking to people and getting anecdotes. And there's funny stories about like, the battle of the Asterix and things like that. That's at the very end. But people should go and read it. The other thing that stood out to me from that conversation was Towards the end, when we discussed AI, you made the point that you get this similar dynamic between HFT and AI now where because everything is framed as existential, you just can't stop, right? You always have to keep going.
54:04 Joe Weisenthal? Totally. Look, I mean, I suppose the high-frequency trading is not sort of like existential in the broad sense, but it's existential at the firm level.
54:13 Tracy Alloway? That's what I was gonna say, yeah.
54:15 Joe Weisenthal? Yeah, at the firm level. So it's like, and I hadn't really thought about, okay, you like have this like pool of theoretical profit, which is the gap between where the futures are trading in Chicago and where these stocks are trading in New York. That's fixed, right? That's not gonna get that big. But again, like, someone gets faster at exploiting that. And I had never really heard quite until your question and his answer, this sort of maker taker dynamic of, okay, I have these orders and now I'm quickly rushing to cancel them and you're quickly rushing to fulfill them. And if you and I are both in the market, we can't slow. If you get faster, I must get faster. Cause you're gonna then snipe
54:57 Joe Weisenthal? me every time or vice versa, et cetera. But, you know, they still make a lot of money, it seems like. Unlike the AI firms, they make a ton of money.
55:05 Tracy Alloway? At the moment.
55:05 Joe Weisenthal? Yeah, exactly.
55:06 Tracy Alloway? That was a funny dynamic in HFT world, the like accusations of taker versus maker. And I always think of the, you know, the Spider-Man meme where they're all kind of pointing at each other.
55:16 Joe Weisenthal? They're all pointing at each other, yeah.
55:17 Tracy Alloway? It felt very much like that. But it was really great to catch up on HFT again. This was sort of a blast from the past because you used to hear about it more and now it's become so normalized that people just don't talk about it that much.
55:27 Joe Weisenthal? Well, you know, we heard about it a lot and especially post 2008.
55:31 Tracy Alloway? Yeah.
55:32 Joe Weisenthal? That was the ultimate finger pointing era, right?
55:34 Tracy Alloway? The Michael Lewis book.
55:36 Joe Weisenthal? Because you just have like, everyone had some, oh, it's the naked short sellers. It's the credit rating agency. It's the, you know, the law that forces banks to be equitable and who they distribute mortgages to, et cetera. Like, there was a million finger pointing, oh, maybe it's the HFT firms, maybe it's the short sellers, whatever. So I mean, part of the reason we don't hear about it as much is because there hasn't been a crisis, et cetera. But as he said, the race continues and there are various flavors.
56:04 Tracy Alloway? You can never get to zero, but you can always get closer.
56:07 Joe Weisenthal? You know, it's like, you know, I always think about some lines, they go like straight up, you know, it's like they ever like curve back around, etc. You get like negative space, like, can we do even better than line go up, like line go up and backwards? I guess Einstein would say no.
56:23 Tracy Alloway? If only we could have Einstein on as a guest to talk about trading.
56:27 Joe Weisenthal? To talk about high frequency trading.
56:29 Tracy Alloway? Shall we leave it there?
56:30 Joe Weisenthal? That would be a perfect guest. Let's leave it there.
56:31 Tracy Alloway? All right. This has been another episode of the Odd Lots Podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.
56:38 Joe Weisenthal? And I'm Joe Weisenthal. You can follow me at the Stalwart. Check out Donald Mackenzie's book, Trading at the Speed of Light. And of course, follow our producers, Carmen Rodriguez at Carmen Armin, Dashal Bennett at Dashbot, and Kail Brooks at Kail Brooks. And for more Odd Lots content, go to Bloomberg.com/oddlots, where we have a daily newsletter and all of our episodes. And you can chat about all these topics 24/7 in our Discord, discord.gg/oddlots.
57:04 Tracy Alloway? And if you enjoy Odd Lots, if you like it when we look back at the HFT boom and how it continues, I guess, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.
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