Odd Lots - What It Takes To Win At Quant Investing

Episode Date: October 8, 2020

Interest in quantitative investing strategies continues to grow; however, as the space gets more competitive, making money and winning gets harder and harder. Computation costs alone can be prohibitive. On the latest episode, we speak with Columbia Business School professor Ciamac Moallemi about how the world's best quant funds thrive.See omnystudio.com/listener for privacy information.

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Saturdays and Sundays starting at 7 a.m. Eastern. Make us part of your weekend routine on Bloomberg Television, radio, and wherever you get your podcasts. Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Wisenthall. And I'm Tracy Allaway. Tracy, you know what the funny thing is, is that even though it's been an incredible year in the stock market, I mean, just extraordinary by all accounts, as everyone knows, I feel like it's also probably been a frustrating one for a lot of investors.

Oh, yeah, for sure. I mean, first of all, markets didn't really do what a lot of people, I guess, would would say they should do rationally in the face of the biggest economic crisis in decades. But I feel like a lot of people just sort of missed various turning points in the market as well and are very, very frustrated. Absolutely. I mean, just super, super high levels of frustration. Also, even if you were along this market and sort of like generally bullish, the only way to have really won this year would be super concentration in tech stocks. And I feel like if you were under exposed to like a handful of text stocks, which we could count on about two hands, then you're almost guaranteed to be sort

of underperforming your benchmark this year, whatever it is. Yeah, I think that's absolutely true. And of course, we've been talking about for years and years and years that the big tech stocks fang, whatever you want to call it, are potentially overvalued. So it's doubly ironic that this year you would have underperformed had you not invested. in the stocks that people say might be the most overvalued. Right. And of course, that is a big frustration to investors who have been waiting a long time for other sort of factors to do well. So investors like to talk in the factors and the sort of the growth factor has done phenomenally well.

But historically, the value factor, so-called cheaper stocks, those have done well. And everyone keeps waiting for this turn or for other factors to emerge, whether it's value or low. beta or something else never seems to happen. And if anything this year did not prove to be a turning point in the market, but really just sort of an accelerant of it. Yeah, I think that's right. I'm actually looking at a chart from Bank of America, Merrill Lynch right now. And they point out that values relative performance to growth was the worst this year since the dot-com bubble. So something to remember. But we're not, this isn't this podcast. This podcast. isn't about value versus growth, is it?

No, it's not. But I think that the frustration that people probably have this year does lead to, you know, people looking for other approaches to investing. And of course, in times like this, people wonder if like maybe other sort of quantitative or algorithmic strategies more money should be poured into them as an alternative to this ride where you just sort of buy the big tech stocks and hope that you, you know, avoid the turning point. Well, I guess another way of putting it is a lot of the quant strategies are sort of momentum-based, right?

So if you can figure out where the money is flowing to, even if it's tech stocks, that might be a good way of investing in the current environment. If everything's about liquidity and following the flows, then quant investing or algorithmic trading, whatever you want to call it, might be a good way forward. Yeah. But, you know, backing up, it's like we talk about quant investing. And the word quant gets used all the time. And sometimes it's used to describe these super technical funds. And sometimes it gets used to just describe sort of anything that has some statistical analysis. And that term feels extremely vague to me. Yeah.

And potentially overused as well, right? Like everyone wants to seem like they are quantitative in some way or another. No one wants to say that they're investing purely on emotion and gut feeling and that kind of stuff. So Quant gets bandied about quite a bit. So today we are going to talk with an expert who knows a lot about quant investing studies that can help us define it. And also hopefully sort of explain to us what it takes to win in the space. Because again, everyone sort of wants to be in the space. Even, you know, traditional hedge funds over the years have allocated more and more money to Quant to hiring PhDs to building up their computer systems.

But what it really takes to win and can lots of players succeed is still kind of an open question. Yeah, I think that's exactly right. And as we're going to discuss, quant investing is probably one of the most expensive ventures that you can sort of embark on. Yes. Okay. So without further ado, let's bring in our guest. He is an expert in the field. He is Siamak Malemi. He is a professor of business. He's a professor at the Columbia Business School. done a lot of research in the area of quant investing. He's also a part-time partner at a fund himself. Thank you very much for joining us. Thanks for having me. I'm delighted to be here. When I say quant investing or when people say quant investing, what does that mean to you? Like, how would you just define that term so that it's a useful, so that it's a useful term? Well, people have different definitions. I personally define it as having two key characteristics.

The first characteristic is that the investment process is entirely systematic. So there's many different types of investment strategies that people implement that employ at some level quantitative methods. But I think the key to the quantitative methods that we're going to speak about today is that at the trade-by-trade level, there is no discretion, right? You set up an algorithm, a particular system, on a second-by-second trade-by-trade basis, everything is being automatically done. That isn't to say that there isn't like a portfolio manager involved. But the job of the portfolio manager is not so much deciding on trades and sizing them and so on, but more setting up the computer algorithms in advance and tweaking them and improving them over time. So that's really the first big component to be entirely systematic, i.e. non-discretionary.

The second component of the ones that I focus on is that they're really active investment strategies in the sense that you're buying now because you think the asset will be worth more later. It's mispriced in some level. Or alternatively, you're selling short now because you think the value later will be lower. There are other flavors of quantitative strategies that are somewhat more passive, things like exotic beta investing in factors and so on. Those are not so much, a little bit less my area. and I have my own views and then we can get into later, perhaps. But the key things I'm thinking about here,

you're using algorithms and data and machine learning and so on, you're taking an active view on what the current prices are relative to what the value might be later. So is quant investing proof that markets aren't efficient? I feel like this comes up a lot, but maybe it's worth asking this question early on. If the whole strategy is to automatically arbitrage price discrepancies in the short term,

versus the long term, does that mean that markets aren't doing their job? Well, I mean, I think if you want to sort of take the straw man that the markets are, you know, sort of a 100% efficient and prices are incorporating all potential information, I think that's clearly not true. And I think the long-term success and incredible performance of, you know, quant investors like Renaissance is sort of one piece of that. But that doesn't mean that markets are completely inefficient either. say Peterson, who's from NYU and AQR, he has that he has a nice phrase called inefficiently efficient, or I should say efficiently inefficient, meaning that there are inefficiencies,

but it's a competitive game, and there are lots of smart people with a lot of resources going after these inefficiencies, and when you identify them and trade on them, they disappear. They're arbed away. So, you know, these inefficiencies typically lie around the frontier of the transaction costs of what it costs to trade. So, yes, there are inefficient. but they're hard to find and they disappear over time. So one common concept that Quants talk about is alpha decay. Like you identify some signal or some inefficiency and generates a certain amount of P&L. And literally year over year you can see that decay away.

And that's because that inefficiency eventually is identified by other people. And as more and more people trade on it, you know, again, it disappears. So it's not that you set up an algorithm and it just sort of a principle, you know, know, sort of prints money, you know, some sort of gross violation of the efficient markets hypothesis. That's not how it works. The people who are successful at this are constantly investing and deploying enormous resources, hiring large numbers of PhDs, and progressively innovating in order to have new models

because the old stuff will simply stop working. So it sounds like, I mean, I guess you just said it, but it sounds like the key to winning, and we'll get more granular in a second, is that continuous process. It's not about identifying some flaw in the market or some inefficiency or some opportunity to make money. It's about having a team and a process to keep finding those over and over again. That's right. Again, because all the inefficiencies that I've ever seen are short lived. I'm Francie Lacqua, an award-winning journalist.

And I've got a new podcast, leaders with Francine Lacqua from Bloomberg Podcasts. I've interviewed everyone from heads of state to fashion icons. about the news of the moment. But I've always been curious, who are these people as leaders? I don't think there's one right way to be a leader. Make decisions. A poor decision is always better than no decision.

Listen to new episodes every other Monday. Follow leaders with Francine Lacroix wherever you get your podcasts. So can you maybe talk to us a little bit more than about how a quant strategy might be developed? So obviously you have the technological aspect. of it, the need for computers that are able to trade very, very quickly. You have the need for servers, many of them co-located close to the exchanges. But then you also have proprietary data sets sometimes, and then you have proprietary algorithms. So how does that all come together into one quant strategy and which one of those is sort of the most or the biggest investment for a

FONT firm. Got it. So I think there's definitely a technological investment may or may not involve things like co-location near the exchanges. So at least anecdotally, for example, Renaissance, which is a most successful quantitative firm, does not co-locate. Again, I don't know, but that's that's what I've heard. Co-location is quite important when you're trading and you require very low latency. And that's typically the high-frequency trading domain, which again intersects with quant in many ways, but if you're looking a little bit longer, if your horizons are a little bit longer, it becomes a little bit less important. Your broader point, I think, is correct. Technology is important. I think more important is kind of a research process. There's a number of kind of high-level

pieces to a successful quantitative strategy. It's not like there's just a black box and in-go's data, outgoes trades. There's a number of pieces in there that sort of split the problem into to kind of make it manageable. At the front end, going back to the heart of active investing, you've got to have a view on asset prices, right? So you're trading some universe of, I don't know, U.S. equity, something like that. You've got to have a view stock by stock. What's the price going to be in a day, two weeks, a month, so on and so forth, right? And so that front end is called signal generation or generating alphas, right? Using data and machine learning techniques to come up with anomalies that you identify and then you build models upon to sort of make a prediction

of what the price is going to be. So there's all sorts of types of data and algorithms that people use. Historically, much of quant investment has been building what are called quote-unquote technical models, wherein basically you're using historical price and trade data to forecast future price movements, right? So you might think of things like momentum or reversals or so on and so forth. That's, you know, leveraging, you know, kind of purely technical data from the markets. What we've seen emerge really over the past 10 years is there's also been a shift to sort of a quote unquote alternative data, right? So you might look at things like, you know, everybody's heard the famous story of satellite images of parking lots, right, to try and assess, you know,

what's the occupancy at Walmart this year? They're going to make their earnings. Quantitative investor will take that kind of data and leverage it to a model, which forecasts, okay, what's the return going to be for Walmart over the next week, the next month, the next two months, and so on. So at the front end, you have this identifying the data combined with the machine learning technology, which is going to build predictions. Now, oftentimes you're looking at, or I should say really always these days,

you're looking at having many, many anomalies. So you may have a technical model based on momentum and reversals. You may have bought a whole bunch of parking lot data. You have some model for the retail sector based on that. You have some credit card data, some social media data, maybe some news data. You have all of these. And so the second part of the process is to kind of combine these different types of signals or views into sort of one composite view.

Because at the end of the day, all you care about is net net. Is this asset price going to go up or go down? And that part is called alpha mixing or signal mixing, right? You have these separate models that you've built and you want to combine them to one kind of composite view. So that's kind of the front end, again, having a view on what prices are going to be over the relevant timeframes. Historically, that is where the vast majority of the energy was spent. The idea was that if you have good signals, if you have good predictions, you can make money.

If you don't have good signals, you're not going to make money, and the rest of it doesn't matter so much. So I believe if you don't have signals, you're not going to make money. That's certainly true. But these days, the market has gotten competitive enough, and there are enough kind of quant players that what you do with the signals also matters, how you try to monetize them. So here, kind of the next step is that you have a now, you're waking up, it's open to the market. It's 9.30 in the morning, right? You have a prediction for a universe of 3,000 U.S. equities. Now you have to kind of decide what's the target portfolio you want to form.

So that's kind of a portfolio construction phase, right? And so the kind of things you're thinking about are balancing sort of risk versus return. You know, you don't want to be long or short. Maybe you want to be market neutral. You don't want too much exposure in individual sectors, you know, so on and so forth, right? You're balancing that also with transaction costs and so on. And you kind of decide, like, you know, again, based on what my current, view is of the world, what's the target portfolio I want to hold? And this is something you periodically

revisit. It used to be sort of a quant sort of, you know, traded once a day and had a trade list at the beginning of the day and, you know, generated trades and revisited the next day. Now it's much more of a continuous procedure because, you know, as the market evolves and as you get more data and news comes out and so on, those underlying views which are driving the trades are changing. So that's the kind of the middle piece, figuring out what portfolio to hold. And then the final piece is actually sort of generating the trades. Sometimes quants do this themselves. I think more and more of fonts are doing this themselves. You can farm this also to basically every major bank or prime broker that services, wants, has an agency algorithms desk that will do this for you.

But here the idea is, okay, I've decided I need to buy $2 million of Google stock over the next 15 minutes. How can I do that? You know, should I use exchanges? Should I use dark pools, how should I spread that out over time? You know, should I use limit orders, market orders, this kind of thing. And again, historically, you know, people focused a little bit less on that. But now as the market has gotten more competitive, it's also being important. If you're not doing those latter two phases, the portfolio construction and the trade optimization well, you're leaving money on the table in a way that almost may not be profitable. I think one thing that's not obvious, or I should say it's quite different about quant trading versus other types of hedge fund trading.

If you look at a guy like, you know, I don't know, just to sort of pick someone random like Bill Ackman, right? When he goes in and buys a stock, he has like, you know, really kind of a strong conviction. He takes some massive positions. And he also, he probably expects to make 50% or, you know, something like that. Again, I don't do that type of trading. I don't know. But he expects to make tens of percent, right? A quant in any individual position, you probably measure your choice.

expected profit and basis points, right? And it's of this, and you know, you might expect to make three basis points and the transaction costs are two basis points, right? So you're really like carefully controlling your costs and managing execution and so on is extremely important. Like, you know, Bill Ackman, if he thinks he's going to make 20 percent on a particular trade, it doesn't matter if he's paying, you know, two basis points or 20 basis points or even 100 basis points, right? He's going to make so much more in his mind that's irrelevant, whereas for quants, you're really operating on a very thin margin. First of all, that was a sort of great explanation of the whole process, really nice overview.

But I want to go back to just the sort of search for the original signals or search for the initial inputs. And I'm thinking about large tech companies like Microsoft and Google and Facebook and how they have a lot of like researchers who are engaged in sort of pure tech research. And, you know, always out there filing patents. and there's probably a long sort of distance between anything that they discover and their own research budget, and then what ultimately might show up in a consumer product or a business product. And I'm curious if there is sort of an analogy in Quantland where you have people who really are sort of at the frontier without a sort of crystal clear idea of, okay, this is going to lead to something that will turn into a trade. but it's that process of sort of really exploring that frontier, which eventually leads to

concrete ideas that do lead to trades. And I'm curious if that's sort of like the analogy and how investors and how the portfolio managers think about where to explore and where those frontiers are and where to invest expensive sort of time, energy, and computing power in discovering these alpha-generating signals. So I think quantitative investors, operate quite differently than some of the research groups in big tech places. Like if you go to a place like Google Research or Microsoft Research, it's really not that different than an academic institution. Their main output is really papers, right? In journal papers, conference papers, so on and so forth. And it's really just a different way to do almost academic research,

kind of the classical Bell Labs model. And maybe, I mean, they do consult on internal projects and so forth. But I think in the quant world, it is much, much, much more applied. So I think typically the kind of thing would be like you think, you know, maybe someone comes to you, a vendor or you identify a data set that you might, that you think might have some relevance. You start looking at building various models of trying to predict prices or, you know, things that are relevant to prices. You try and pair in some different machine learning kind of techniques. But I think from the beginning, it's really oriented around concrete things like let me build a price for, let me build a model, sorry, for what the return of this asset is going to be over the next

month, right? Or let me build a model for how I should efficiently trade large blocks of stock over the next 15 minutes. Broadly speaking, it's much less of the sort of blue sky research. That isn't to say that some people don't do that. I think people do. But the incentives aren't there because, you know, for the most part, speaking for 99% of practitioners, there's no publishing, right? And I think people are extremely paranoid and sensitive because if your IP leaks and other people do similar things, maybe what you do will stop working as well. And so there's not that much of an incentive to do that versus the very kind of visceral incentive of, you know, making money, having, you know, outperforming in the market in the short term.

So research in the quant world, for the most part, tends to be much more applied. I have a sort of related question, but why is quant investing, or why are quants so secretive about everything? Or, I mean, I don't want to call them weird, but there is this sort of like odd culture around quant investing. And you think of places like Renaissance and Citadel. They're all sort of shrouded in mystique. I once heard that Citadel had an original enigma machine from World War II in one of its offices. I don't know if that's true, but just the fact that people are saying this kind of thing tells you something about how they regard these big story quant companies. Why is there this very specific culture, mysterious, secretive culture?

So I think broadly speaking, people in the by-side people in the hedgeman industry, are generally secretive. But I think with regards to sort of their internal IP and processes, but I think the nature of IP in the quant space creates incentives for people to be more secretive, right? So again, just, you know, pulling our hypothetical kind of Bill Ackman example, if he identifies some asset that's undervalued, he's going to be sort of very quiet about it until he goes in and accumulates the position you want.

because he doesn't want other people to know and other people to front run him and to sort of take that opportunity away. Now, once he's amassed that position, perhaps he'll actually start even advertising it, right? Because now people sort of follow him works through his benefit and he'll push prices in the way that he wants. The quant space doesn't quite work like that. Like, again, any individual trade is a very short horizon, maybe a couple of weeks, right? Trades are sort of very small and diffused across many, many assets. But the idea of the trade, the data source coupled with whatever is generating the signal, the utility methodology and so on, that has lasting value.

That might work for the next six years. Again, year on year, the performance goes down as anomalies disappear, but it has multiple years of value. So the general feeling is if people sort of figure out what you're doing and where the opportunities are and what data sets you're doing and so on, they will also do a similar kind of thing. they will copy you and then those anomalies will disappear faster. You know, at least in my experience, because of the longer time horizons over which this IP decays, people are more paranoid about being extremely secretive.

And that's not only for outsiders, but that's even within firms. So many firms are siloed down to the level of individual quant researchers where you may be, you know, you may have a team of a couple dozen people, all, let's say under a single PM, all working on the same overall strategy, but you won't know what the guy next to you is working on. And if you pass data sets across, maybe you label them in sort of random ways and so on.

So nobody sort of maybe has the full picture except a handful of people on the top. And again, the idea there is that over time people quit or leave or whatever, you want that the firms would like them to have as little of the IP as possible in terms of not decaying the value of their own IP. Now, I think famously, Renaissance does not operate this way. So Renaissance is one example I've heard where a firm which is, I think, very difficult to

get into in terms of being hired. But once you're in there, they're quite open in terms of what are the different things we've tried, what are the things that are working now, what are things that haven't worked before, and so on and so forth. And I think actually from the perspective of research that works much, better. Quant researchers tend to, believe it not, tend to be kind of social animals. And it's always more fun to work on things with other people rather than just sort of sit at your desk with the blinders on and so on. You know, interesting about Renaissance is how they've been able to

manage it so that very, very few people have left. And it seems like, you know, they have not had the kind of IP loss that other people worry about. So Renaissance famously just puts up extraordinary numbers year after year after year and the sort of the trick or one trick besides there being a bunch of mathematical geniuses is a having this sort of open culture of collaboration and research and be somehow preventing a lot of exodus so that no one else has really been able to replicate their approaches in any way how hard is this so you think about like someone like i don't know like you hear about other managers, like, you know, Steve Cohn is like, oh, I want to allocate money to Kwan. How hard is it, and this is sort of something I want to explore more now is like,

how hard is it to sort of ante up into that game and to sort of start being competitive if this if you're sort of starting from zero right now? I think it's a tough place. It's a competitive game. Maybe not so much anymore, but over the past five, seven years. My general perspective is that the buy side active managing sort of hedge funds have been shrinking overall. The one sector that has not been shrinking is quant. And so I think there has been an entrance of kind of new players there. Now, Steve Cohen, you specifically mentioned, he's actually been at it for a while.

He's been in the quant space since the early 2000s. On the order of 20 to 30 percent of his assets are actually quant, something like that, like a non. You know, people mainly think of him as a long short kind of guy, and that's probably mainly what he is. But again, you know, maybe a third of his assets are in quant space through Cubist and so on. Now, he operates very differently. He operates, his quant funds operate in kind of like traditional long short guys operate, wherein you hire individual PMs.

You watch them very kind of carefully. They make money or they lose money. If they're not making money quickly enough, you fire them. And you sort of, you kind of have a portfolio of these individual managers who are doing their own thing, who are tightly siloed. And, you know, you try to manage that. And that's the way his quant operation manages. So there's, you know, again, a whole bunch of small, let's call them pods or whatever, of, you know, two or three people each kind of doing their own thing in an uncoordinated way. You know, that's, again, quite a different model than, let's say the Renaissance, which is, you know,

you know, one kind of open strategy. And I think the advantage of the Steve Cohen model is that, you know, it's easy to hire people. The HR process is very easy. You don't have to care when people come and go and so on, because you're not really investing in any of their individual IP. Right. When someone leaves, or like let's see you fire someone, it's because they didn't do well and whatever they have is maybe not worth that much. And they don't know anything else about what your other PMs are doing. And so that process is very easy. But I think the downside is that what we're sort of starting to see is throughout the quant space, like the broader technology industry, we're starting to see that there are a lot of increasing returns to scale.

That as you get bigger and bigger firms are able to build advantages. And one kind of concrete source of this is around trading costs, right? When you're thinking about, like, let's say, on an individual trade-by-trade basis, do I want to get into this trade, you have a prediction of how much you're going to make if your models are correct, but also there are these costs that you're paying, these these transaction costs. And if your prediction doesn't exceed your costs, you shouldn't put on that trade. Because even in the best case, you're not going to make your money. Right.

So what's happened is that as more and more people have gotten into the quant space and more of these anomalies, sorry, are identified and markets get them more efficient, the signals have gotten weaker, right? And so just to sort of give maybe a concrete example, one signal that's sort of a, Quite well known throughout the quant industry and academics of published papers and so on is order book imbalance, right? If you go out and you look at an electronic order book and they're more buyers than there are sellers in terms of the resting limit orders, it's more likely that the price will go up and go down. You can go out and try that. That has a predictive value now.

However, if that's all you know, you won't make money because you might think the price is going to go up a, you know, a tenth of a basis point just to throw out a number. But your transaction costs are two basis points. And you can't exceed your costs. So the transaction costs to a first approximation, they're kind of like on a trade-by-trade basis, a fixed cost that you have to exceed. Now, if you're in a world where you have many, many signals, maybe tens, maybe hundreds, maybe thousands, and you're adding them up and they're independent, and you trade when they're all aligned, now you can have sort of signals that are weak individually.

And nevertheless, when you combine them, when you aggregate them, you are able to exceed transaction costs and monetize them. So that order imbalance signal that I just sort of talked about, if you're sort of one guy in your basement and that's all you knew, you can't make money off that. But if you have 20 other signals and you're going to put on a trade anyway, in some sense the transaction costs become a sunk cost. And that point one basis point that you're going to get because of this well-known signal, that becomes free money.

So as you get that kind of economies of scale because of fixed costs, I think it becomes harder and harder to have quant strategies where you don't have a lot of people, you know, in a very kind of coordinated research process where you have people working essentially independently. The kinds of, you know, places that are structured like, let's say, Renaissance, again, where you might have like 200 quant researchers all working on different aspects of the thing. And then, you know, these things combined to one sort of overall view of the market, I think that is able to better monetize a lot of these signals in this kind of more competitive world.

So on that note, if you are running a lot of these strategies, getting a lot of these signals, and you're able to lower your transaction costs because of that scale, and at the same time, quant investing has these big barriers to entry because you have to have these technological outlays, you have to hire a bunch of PhDs and things like that, does that mean that the industry is inevitably sort of trending towards a monopoly? Are we going to get a situation where there is just one or maybe two or three really big quant investors because no one else can compete with them effectively? I think we're kind of there. I mean, I think there are only a handful of quants. Most of them have been doing it for a long time. I mean, you know, Renaissance, D.E. Shaw,

P.D.T, two sigma. You know, there's a, there's a handful of others. I think it's harder to see, Maybe there are some exceptions in terms of funds that have launched more recently, but it's difficult to see people of that scale with similar track records. So I think we are seeing some degree of consolidation. I don't know if it's going to come down to one firm. I think probably not. There's probably room for kind of more competition. But I think it will be harder to have sort of either more independent managers

or like kind of a siloed model of places like, you know, SAC and Millennium. You can get the news whenever you want it with Bloomberg News Now. I'm Amy Morris. And I'm Karen Moscow here to tell you about our new on-demand news report delivered right to your podcast feed. Bloomberg News Now is a short five-minute audio report on the day's top stories. Episodes are published throughout the day with the latest information and data to keep you informed.

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Get the reporting and the context from Bloomberg's 3,000 journalists and analysts we're all over the world. Listen to the latest from Bloomberg News Now on Apple, Spotify, or anywhere you listen. If I want to start a quant fund, what are we talking about in terms of how much is just going to cost for computers and data just to even get in the game? Don't do it, Joe. I feel like this whole conversation is about how you shouldn't be doing that. No, I realize. I realize that it's a bad idea. But let's say I'm an idiot and I try anyway. Like, what are we talking about? So I think things have gotten over time much more expensive, things like data feeds and so on. The exchanges have constantly been ramping the prices on these things. But these days, what's become one of the biggest costs is actually just pure computation.

And this is also a trend we see more broadly in technology. You know, if you look at kind of the state-of-the-art models for things like computer vision, object recognition, for, you know, playing games like chess and go and so on, these types of models leverage approaches in machine learning that are really based on having a lot of data and doing, even more than that, doing a lot of computation. And so the spirit there, you know, coming out of places like deep mind at Google or Open AI and stuff, Open AI, you know, artificial intelligence company, their main model is literally like, we're going to do simple things, but we're going to leverage it to massive scale computation, right? And so I think you're starting to see that in finance as well, where you need to do things like, let's say, you need to backtest a trading strategy, but you have some parameters. and you want to try tens of thousands of combinations of those trading parameters, and each one involves a simulation over, you know, 20 years and so on and so forth. You need a lot of computers. So someone told me anecdotally that at a major quant shop, each quantitative researcher is given kind of a quote-unquote budget of 10,000 CPUs, right?

So at any given time, they can use up to 10,000 individual kind of processing units. And just to give you a sense of what that costs, you know, if you were to go, you know, buy that on Amazon at AWS, that would be order of magnitude on maybe a million dollars a year, right? And this is just for, this is just for research. This is not to actually generate the trades or whatever. This is just to, you know, tune all the parameters and then sort of really optimize your performance. That's really interesting. It kind of makes me wonder how good, I guess, academic research is at gauging quant strategies if the outlays just to run a few experiments are so massive.

But on a slightly different topic, I wanted to ask you, I guess this question is kind of inevitable whenever you talk about algorithmic trading or systematic trading. What value do you think quant investing actually creates for society? So, for instance, when we talk about traditional investing, that's supposed to channel capital in the most efficient way possible to good companies, and that should, in theory, benefit the entire economy. but quant investing, as we've discussed, isn't really about that. It's about arbitraging these small differences. So maybe it makes prices slightly more efficient, but is that worth the enormous

infrastructure investment that we've been discussing being spent on it? So I think there is, there are some benefits. You know, it varies based on the strategy and based on really the instant of time. But I think a lot of, you know, to a first of approximation, if you see a price move in a direction that's unusual, it could continue or it could revert. To the extent that you think it's going to revert, you're going to sort of bet against it. And what that amounts to is basically supplying temporary liquidity to the market. So I think the positive aspect to quantitative investing is that I think a lot of it is supplying liquidity to the market on a horizon of, let's say, days to weeks.

right now the flip side is if you're if you're really it's more of a momentum that you might be accelerating the trends you're taking away liquidity you're competing for that liquidity but as you said maybe you're making a prices more more efficient so I think on balance I think net net probably there is some benefit I think it's probably small admittedly is it worth all these you know very smart people being drawn away from other fields and so on I'm not sure but probably as much or more resources are spent at places like Facebook and Google getting people to click on ads, right? I'm not sure that that's as positive either. It's depressing thing about all these people, you know, looking for signals to squeeze out

three basis points in the market because there could be some great innovations in squeezing more ads onto a mobile phone that they'd be working on. There you go. Kind of a sad allocation of resources. See, you think Joe's joking, but he probably isn't. So here's one thing that also always tends to come up. It's this idea of this type of trading reaching the limits of available technology and pushing the strategies to sort of greater extremes, but those extremes eventually have limits. And so I guess I'm just wondering, is there a limit to quant investing? Is there a point at which quant sort of arbitrage? everything out of the market and the signals are no longer useful or the algorithms themselves are impacting the market in some way. And on that note, what's the next big thing in quantum

investing, I guess? Yeah. So, I mean, I think there's a constant balance. These efficiency inefficiencies are being identified in arbitraged away because there's money in it. And so as are arbitraged in it, the money sort of disappears, and then you get sort of fewer people kind of doing it. But so long as there's, you know, kind of traders out there who are not paying attention to this stuff and, you know, the Robin Hood traders or whatever and are kind of leaving money on the table, there will be people there who are trying to sweep up the crumbs. In terms of where it's going, what the next big thing is, I think it's pretty hard to predict, but I think broadly a shift towards things that are even more black box, even more computationally driven,

and not so much have like kind of nice structural explanations. Again, sort of following a lot of what's going on in the tech world as we shift to ideas like deep neural networks and reinforcement learning. and so on and so forth. You know, again, you have these systems that work great for, let's say, I'm playing Go, but it's really hard to explain what's going on. And I think we're starting to see that in the quant world as well, again, leveraging a computation, but really ending up with things that are, you know, black boxes that, you know, just are completely not transparent.

So in other words, you know, like you could look at something like satellite images and say, oh, there's a lot of cars parked at Walmart, and then predict that Walmart stock is going to be up. But the next generation of things to watch out for is this works and it works consistently, but we as humans can't really articulate why. Exactly. That's super interesting. Well, on that note of humans not really even being able to explain what they're doing,

seems like a perfect place to stop. Thank you so much for joining us. Thank you so much. Tracy, you know, as a media person, I have my own experience with the sort of alpha decay that CMAC was talking about. Do you know what it is? Did you build some sort of algorithm to take advantage of like Google ads or something and then it stopped working? No, no, there's nothing so sophisticated. But back in the early days of like blogging and stuff, I remember this phenomenon where you would come up with some like headline construction. You'd be like five things you need to know today. Oh, yes.

Or remember like the old upworthy headlines. They were like, and you can't guess what, you know? And then those work and those generate like excess traffic and they get shared on Facebook. And then everybody discovers that these headline cliches work. And then everyone does them. And then people stop clicking on them. And you need to like find, I don't do clickbait anymore. But I always thought at the time like that was like a very similar process to, uh, to this sort of

quant approach to investing, this sort of search for alpha and alpha decay of blog headline. Any more was the key word in that sentence about clickbait. But I think it's a really good analogy. It is a good analogy because like the usefulness of those headline constructions decays over time, as you point out, because more people are copying them. But it also kind of gets to that point about the limits of this type of investing. There are only so many ways that you can construct a headline. and eventually people kind of catch on to different ones and they become not so enticing. And I kind of wonder if the same thing could eventually happen to quant investing. So obviously there are many, many more possibilities in quant investing and it's possible that

markets are always changing. And so opportunities for arbitrage and identifying these signals are always coming up. But it does make you wonder. It certainly does. And what he's talking about at the end where maybe the signals of the future are just thinking, that work but can't be articulated is just like a super kind of fascinating phenomenon to just like wrap your head around. Yeah, I feel like that's a good microcosm for maybe the human experience in the future. Like we have the technology. We're not entirely sure how it works,

but we're just going to sort of let it run and hope for the best. One other thing that sort of interested me is like a sort of thing to watch going forward is, okay, so we talked about a huge aspect of that was just the costs and how like you might be able to identify. identify a profitable anomaly, but unless the cost of getting the data and executing the trade is lower than that, it's useless. But you know, you also have to wonder like, okay, right now, like a certain handful of exchanges, say, control a lot of the trade data costs. In theory, that seems like an area where maybe new entities will come and find a way to provide data cheaper. Amazon Web Services, you know, presumably computation costs are going to keep coming down. And obviously that was a big breakthrough from probably the old days where you had some sort of mainframe on-premise services.

You know, computation has gotten cheaper. So there's probably always going to be new opportunities to squeeze out even smaller profits because there are ways to shave costs in sort of your research, your work. Yeah, maybe. The other thing that was really interesting was the idea that quants, I think CMAC described them as actually social animals, which kind of, flies in the face and think of a lot of stereotypes. But I'm really curious. I would love to be embedded in a firm like Citadel and just observe how they work together and what's considered a good algo, a good systematic strategy versus a bad systematic strategy. Obviously, you want to make money, but are there certain things that are more valued over others, maybe cheapness

to execute or, I don't know, risk management, something like that? I'd be so curious to see how all works. I'm sure if we just walked in, they just let us in the door and let, we could just hang out there for a while. Yeah, I'm sure. They wouldn't mind at all. No, let us see their whiteboards, stuff like that. Citadel, if you're listening, we would like to compensate you. Okay, should we leave it there?

Let's leave it there. This has been another episode of the Oddlots podcast. I'm Tracy Alloway. You can follow me on Twitter at Tracy Alloway. And I'm Joe Wisenthall. You can follow me on Twitter on Twitter. Twitter at the stalwart. And you should follow our guest on Twitter, CMAC Malemi. He's at CMX. Follow our producer on Twitter, Laura Carlson at Laura M. Carlson. Follow the Bloomberg head of podcast, Francesca Levy at Francesca Today. And check out all of our podcasts under the handle at podcasts. Thanks for listening. This is Tom Keane inviting you to join us for the Bloomberg Surveillance Podcast. It's about making you smarter every business day.

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