Odd Lots - Two Researchers Explain How Quants Are Going To Revolutionize Long-Term Investing

Episode Date: December 11, 2017

When we think of computer-driven or "quant" investing, we often think fast moves, algorithms making buy and sell orders at incredibly short timeframes. So in theory, the likes of great long-term investors, like Warren Buffett, should be safe from the robot revolution. But maybe not so fast! On this week's Odd Lots podcast, we speak to John Alberg of Euclidean Technologies and Zachary Lipton of Carnegie Mellon, about their new research on the next generation of quant investing. Alberg and Lipton explain a recent paper in which they used machine learning to forecast the future fundamentals of companies, and the opportunity that offers in terms of beating the market over the long term.See omnystudio.com/listener for privacy information.

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And I'm Tracy Allaway. Tracy, I really liked last week's episode with Andrew Lowe talking about quant stuff and his sort of evolution of the efficient market hypothesis and where that might go. Yeah, I did too. I really like the ecosystem analogy, the idea that you have all these different players with different motivations and they're constantly evolving and adapting to the market. That point about adapting. And of course, that's the name of his book, Adaptive Markets, is the name of his hypothesis is really key because one of the main points that he made that I

loved was this idea of hedge funds as sort of the R&D laboratory for all of the financial industry. Right. The hedge funds are where innovative new techniques get to be sort of hash out without doing usually too much damage, I guess, to the wider ecosystem. Hopefully. Right. Not always. There's certainly examples of hedge funds actually having done major damage from time to time. But ideally, you know, what the evolution seems to be that some new idea sort of starts

in the hedge fund world and eventually makes its way to the broader world. And I think the most obvious example of that that we could cite these days is a lot of this sort of popular smart beta strategies, ETFs that are built on things like momentum or value or other factors, sort of quantitative ideas that for many years were only available to, you know, researchers at hedge funds. Right. So these sorts of quantitative investment or trading methods were usually the purview of

sophisticated hedge funds who had the time and resources to develop them. And then you had a bunch of ETFs who kind of caught on and matched. to replicate them, and now we can all trade like hedge funds for zero percent fees, right? Exactly right. And of course, once everyone can do it for very few fees, I think it's safe to say those strategies aren't going to produce the same returns, and hence the market is forced to adapt again. Right. Presumably, the hedge funds are always trying to stay one step ahead as well, right? Exactly. So which raises the idea of like what will be the next thing. If anyone can sort of invest in a crude momentum strategy for virtually no fees, then that requires the people on

the cutting edge, the people doing the R&D of this industry to, you know, figure out what the next big thing it's going to be. Do you know what the next big thing is going to be, Joe? Can you share it with your fellow partner at Odd Thoughts LLC? Sadly, and unfortunately, to all of the Odd Lots fan. I myself do not know what the next big thing in quantitative strategy or sort of advanced investing is going to be. But I'm hoping that our guests on today's episode might be able to shed some light. Oh, who are they? Okay, so today we are going to be talking to John Elberg. He is the founder of Euclidean Technologies, a quant firm, as well as Zach Lipton, a professor at Carnegie Mellon University, in the business.

school, an expert on machine learning, they recently published a paper titled Improving Factor-based quantitative investing by forecasting company fundamentals. So what I think that means, and we'll talk to them, is we talk all this stuff about price and computers and algorithms figuring out what signal we can get from price. But maybe the next generation can actually tell us something about the fundamental workings of the company itself. And maybe this could be sort of, the next wave of where a quant investing goes. And this sounds absolutely fascinating, Joe. Let's bring them on.

John and Zach, thank you very much for joining us. Was that a reasonable characterization of sort of where your paper and where your research is taking things? Yeah, I think it is. So first of all, machine learning has been kind of on a rocket ship of innovation for the last 10 years or so. And with the advent of deep learning, you know, computers and machine learning have been able to do things that, you know, historically have been very challenging, like image captioning and language translation. So we, Zach and I, you know, a couple years back, thought of the idea of collaborating to apply deep learning to the problem of long-term investing. So how did you actually go about doing that? And what exactly do you mean by deep learning?

That's exactly what I wanted to know too. Deep learning is sort of the rebranding of neural networks research. Say I had some data about a company, right? Like I had machine learning we call it a vector of features, but what we mean is just like a list of attributes, each of which is somehow like been made into like a numerical quantity, whether it's like their income, their number of assets, whatever.

One way of deciding how to predict what the, say, what the price will be or something is we say, well, we're going to have this long vector features and then we're going for every single company, you know, at every single time, we'll have this vector features corresponding to the state of the company at some period of time. And then we'll have some target that we want to predict. This could be a binary quantity, like will the stock go up or down in the next, you know, time unit of your choice, whether it's the next day or in the next month or in the next year? Or you could try to directly predict, say, the relative price.

So like, you know, the percent improvement or decrease based on. on sort of the available features. So one of the simplest ways you can make a model is you say, hey, I've got a bunch of features. What I'm doing is I'm going to take a weighted sum of these features. The way, like, you'd calculate a score to see, like, what's your risk of a heart disease? Maybe you take, you know, four times your cholesterol plus two times your age minus one times, you know, your amount of good cholesterol or something like this.

If you come up with some formula that's expressed simply as a weighted sum. So that would be a linear model. Where deep learning make things different is that you have. many different layers of computation that you basically are learning very complex patterns that maybe couldn't be expressed as a weighted sum. So maybe you're uncovering interactions between all of your features. So for example, if you want to learn to recognize a dog versus a cat in an image, there's no weighted sum of pixel values. It's actually going to tell you this because it's just the patterns too complicated. So in that case, you need some more like heavy duty machinery.

So what you do in deep learning essentially is that you learn multiple. successive transformations of your data such that after applying many such transformations, you know, could be two, four, five, ten, whatever, you come out at the end of a representation of your data where you actually can learn a very simple model on top of that. So we sometimes call deep learning representation learning because essentially what we're doing is we're both learning how to featureize our data, essentially how to transform it and how to classify it at the same time. So one of the things in sort of traditional quantity, a lot of of quantitative investing focuses a lot on price. And sort of listening to your characterization,

it seems like price, and this is relatively speaking, of course, price is a fairly, you know, sort of easy idea to capture. So you can come up with some definition of what momentum is and then sort of say, okay, these stocks are experiencing momentum right now or these stocks aren't. And And then what does history tell us the stocks are going to do next if they're sort of meet these characterizations? Your paper really looks at what can you do with this technology for sort of looking at future fundamentals. So looking at the sort of characteristics of the company and not just trying to see where

price is going, but where those characteristics are going. So explain sort of what your research specifically attempts to uncover. So one thing that deep learning allows. a researcher to do is look at kind of more raw features, like Zach explained in the image case, you're looking at raw pixels. Now, if you think about most quant funds and most quant models, the features that go into the model are highly engineered, and they include things like price and maybe book value, price divided by book value, price divided by earnings, and then maybe some momentum features. The interesting thing about deep learning is it allows you to potentially let it

uncover what the best features are. If you over-engineer features, you may not find the ones that are best to predict what you're interested in predicting. So that, you know, allows you to potentially find features in the data that you wouldn't find through a traditional feature engineering process. Yeah. And, you know, to directly address your question, your point is that the very most obvious thing you could say now, if I have this, I have this learning machine, I have a bunch of features and I have to choose, what am I going to predict? The very most obvious thing to try to predict is the price, because if you can actually do that perfectly, then you're done, right? If you actually know which way the price is going to move in the next year, then you can make

the perfect choice. So the problem is that's not so easy because the markets are quite capricious, right? So one problem that we found is we actually did these models where we were trying to predict price directly. But among the other things that you have is that one, it's hard to learn models that do a good job of this that are sort of robust across different time periods. So you might have like, hey, I'm going to train on these like decades of data and I'm going to try to directly predict the price. But then I come into periods of time where the market's behaving a little bit differently. And we call this non-stationarity, basically. Like, your model does a great job of uncovering the pattern that's present in the data that you gave to the model.

But that data is anchored to some period of time. And the future data that comes in, you know, the patterns changed a little bit. And so the kind of like function that you've learned no longer does a great job. So what we do instead of directly trying to predict price, the idea that we had was to think, well, this core idea behind a factor model generally, right, is to just say, hey, I'm going to sort all the stocks according to some reason of idea of, hey, the price of the company should be tied to its income. Any company is somehow is justified by like it's the long-term, discounted cash flow as well. Let's just say a factor strategy just something very simple. It says, well, let's just look at the current income divided by, say, the current price

or current income divided by the current market cap or enterprise value. Some notion of income and some notion of financial performance divided by some notion of of company size. And then I'm going to sort the stocks according to this. The ones that come out highest are like most cheaply priced. So let's buy those. So the idea is to say, hey, well, what if I told you, so we actually know that this does pretty well in back testing, whether or not the patterns will hold in the future.

But, you know, many people have made a lot of money for many years. So there's an idea of if you knew the income, this is a good thing, a reasonable thing to try to do. Our question that we asked, unfortunately, John, because he's actually in finance and I'm not, has this really great set of like industry great tools that, unlike most academic papers that look at like one stock over a short period of time or something, we actually had, you know, 40 plus years of financial data and can actually simulate like in a plausible back test what's going on. We said, well, what if you did a factor model, but someone gave you a crystal ball? So basically, instead of dividing the current income divided by the current enterprise value, someone gave you next year's income. And so you sorted the stocks according to next year's income divided by the current enterprise value,

something like this. So you're able to peek into the future. you know how the company will be performing next year and you're saying is how is it's next year's performance is that based on next year's performance is it currently priced cheaply or not so it's what we call like a clairvoyant factor model like you don't actually have such a crystal ball but if you you know give us some license and you imagine that you did what would have happened if you went back in history and you had this crystal ball and you traded based on a clairvoyant factor model and it turns out that the clairvoyant factor model just crushes it. So it does really, really well. And not surprisingly,

the more clairvoyant the model is. So if it knows the performance of the company six months out versus now or 12 months out versus six months out, it keeps getting better and better and better. So what we decided was, well, maybe trying to predict price directly is a bit subject to, you know, a kind of fickle market. But the patterns present in the fundamental reporting data itself is more stable. So in our method, what we do is instead of just trying to predict a return, we try to predict actually the fundamental reporting data itself just so we're given these features for like a trailing window of time corresponding to the companies like financial reporting. And then we're trying to predict what they're going

to report next year. And then based on what they're going to report next year, we sort the companies according to a value factor? So in essence, you can pick out of that future prediction, the components of the factor model, whether it's future predicted earnings, and you can take that out of the future predicted fundamentals, divide that by current enterprise value and sort. And then you have basically a factor model, which you are using instead of trailing 12 months earnings, you're using the future predicted earnings by the deep learning, the deep neural network. So as I understand it, the deep learning or the neural networks are used primarily to forecast the future fundamentals based on historic performance. Is that right? Historic fundamentals, yeah. Okay. So walk us through

how you actually develop an application that's able to do that. Like what are those neural networks looking at and what sort of information are they drawing in other than, you know, past predictive data to make those forecasts? There's two parts of that. One is the data that we use, and then two is the technology we use to build the deep, you know, neural network models. So on the data side, what you use is historical fundamentals on all companies, you know, that have ever, you know, been listed in the U.S. for the past 50 years. And so what do historical fundamentals mean? Well, it means earnings, book value, anything you could find on an income statement and balance sheet going back in time. In addition to fundamentals, we also use as inputs to the model,

you know, momentum over, you know, one month, six months, 12 months. So then, you know, if you think of it as like a big, you know, spreadsheet table where each row, is a point in time for a specific company. And then you can think of sequences going back through time, you know, IBM in March of, you know, 1985, and then all of its fundamentals in one row plus its momentum, and then that going back five years in time. So those sequences, both the fundamentals and the momentum,

are fed into a neural network, and all of those sequences. for all companies at all time are fed into a neural network and are trained to predict what the fundamentals will be, you know, one-time step out in the future. 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.

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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. So just to sort of summarize it all up, you know, it's like if you have all these strategies, if you have all these funds, chasing things like earnings quality, earnings growth, momentum, all kinds of stuff like that, your goal is to anticipate, today what those funds are going to be buying tomorrow? Is that a fair way to characterize it?

I think that's a fair way to characterize it. I think what we're really just doing is trying to build a better factor model, a better factor model in the sense that, you know, as Zach explained, if you had a clairvoyant model where you actually knew what future fundamentals were and could plug that into a factor model, you'd do substantially better than what you could achieve with a value factor model today. We're not like directly considering the psychology of the other players in the market in this particular approach, right? No, sure.

But it's essentially saying like maybe the way to characterize it is if you want to invest on some fundamental factor like earnings quality or earnings growth, bottom line is better to look at future 12 month results rather than trailing 12 months. You look at the trailing, but you're trying to predict the future. So there's two components, right? You could say, like one is we have the component that is trying to predict the future fundamentals. You know, imagine that I came from the future and I got out of my time machine and I gave you the earnings reports from the future, right? So the first thing you need is how do I get an approximate time machine, right? Which in our case is a predictive model that has a good guess about what the feature will look like. The second thing is you still need a way of executing on the strategy once I,

You still need a way to decide which stocks to buy, right? So based on this future information, like, it's possible that if I come from the future and I give you the earnings report and I tell you what the future income will be, well, it's possible that the income is going to go up, but the stock price is going to go down. You know, like, say it's Apple and, like, they made a lot more money, but it was also, like, announced that they had a major plant failure in the iPhone 14 or whatever they're up to is going to be delayed. So these two components are a little bit modular. Like, we could come up with, John, I think, is more the domain expert. So I'm more the machine learning

guy. Like, I'm sure John could come up with, you know, a million other ways that you might imagine that someone would try to execute on this information. In our case, what we're doing is we've adapted a factor model to work with this kind of future guess. So one other example. So again, in our case, what we're doing is taking the predicted future fundamentals and feeding that into a value factor model. But you could imagine using, let's say, the deep neural network said, you know, a company is going to do 100 million, but consensus estimates in earnings, let's say, but consensus estimates said it's going to do 75 million in earnings. Well, you know, that might be, You can imagine devising a strategy around that where you'd want to go, you know, bet on those guys

and ones where consensus estimates are above what the deep neural network is predicting, you'd want to bet against. Right. Assuming the current price is pricing end of out. That's a really, you know, Johnny shouldn't give away our secrets. That's a really good idea. So are these kinds of machine learning driven predictive models the future of investing? You think is that the way that we're heading?

I think what this paper showed is that there's a lot of potential in using deep learning to long-term investing. I think that there's been some debate about whether deep learning, which requires a lot of data to build successful models, whether in finance there's enough data or whether you even need these kinds of complex models in finance. I mean, a lot of quant people feel, you know, linear, simple. factor models are the best route to go. And I think what we showed here is that if you're trying to predict price changes, that might be true. But if you decompose the problem into first trying to predict fundamentals and then later, you know, through a factor model or some other method, trying to use those predicted fundamentals to predict price, deep learning has a lot of potential and does substantially better at predicting future fundamentals than what you could do with

a linear model. There's a sort of a technical reason to recommend the way we've cast a problem also without going too far into the weeds. Basically, you take really, really powerful machine learning models like deep neural networks. The thing that you worry about is John was talking about how people agonize over can you bring this to bear on long-term investing because you don't have as much data, right as if you were looking at the you know microsecond kind of trade frequency then you'd have you know trillions of trade examples or something you get one but if you're if you're looking at you know your your time tick is i have a data point you know once per month or once per year suddenly and i only have i have thousands of stocks not millions of stocks you don't have such a huge

amount of data um so what you worry about is that a model given given a super powerful model like a super overpowered model and then not too much data, that there's a propensity for the models to do what we call overfitting, which is the model basically, it does a really good job of memorizing the training data it's seen, but it learns kind of a spurious pattern that doesn't generalize the future data that it hasn't seen. So one cool thing about the way that we're casting the problem is that we're not just trying to predict the factor of interest. We're actually trying to predict all the factors in the future. And this means that the model has to simultaneously get the income right and get the assets right and get the debt right and get all these different

factors that are available so john was a 15 target factors that we have that we're trying to predict yeah 16 so so in this case this sort of like this is this is what we call multitask learning and the machine learning literature and one nice effect of multitask learning as it has a generalization effect in that it's it's harder to fit a spurious hypothesis because you have to come up with a representation that is good for task one and also good for task two and also good for task three and the probability that you come up with a pattern that's that's good for solving all of these tasks that is not the true pattern is much smaller than if you're only like trying to solve one task where it's easier to just kind of memorize those data points so we have

like essentially 16 times as much training data in in some relevant sense so i have to ask in the abstract of your uh paper or in the uh intro you see say that with this approach, you can improve your annual returns pretty substantially over a standard factor model in a back test, 17.1% versus 14.4%, which is pretty big beat. But as we know, and as a lot of people pointed out, there's a lot of strategies that seem to work in academic papers. And then when they're put into practice, they don't seem, the results don't seem to arrive as easily. John, in your firm, are you seeing the results of your research that on paper look very compelling actually play out in the market? So this paper, we have not put this model to test,

so to speak, in a fund yet. But, you know, we're very interested in doing that. I will add, though, here that many of the back tests that are done in the industry are done. done where you just run, you know, thousands of back tests on a data set over some time period, 10 years, 20 years, 30 years. And there's no out-of-sample testing, meaning that they don't then take that and then apply it to a new data set. One thing that machine learning, one technique that is used in machine learning to prevent overfitting and that we do here is we train or we build the model on one data set and then test it at a sample on another on another data set during a different time period and the results we present there are at a sample out of sample always being sort of ahead right in the future

you could so the model is the model at every given time is trained on the past data so we're simulating like what if you know if you train the model back then based on that it was only available up to that point I think more broadly, there's a good question there of, it's hard to say which patterns are just, you know, especially I think with short-term investing, it's very obvious that any pattern that exists on a scale of seconds is something that could be sort of traded away. It's not as clear. And I believe, I mean, John can speak more to it, right? But I believe part of the ethos of long-term investing is very much that rather than interacting in a place where the most price movements are due to the, behavior of the high-frequency traders, when you're in the long-term space, the price movement is more tied to the actual fiscal performance of the company, and that's maybe a more

durable pattern. So Joe and I were talking about financial players and how quickly they adapt to new markets and new situations at the beginning of this episode. From your respective viewpoints, how fast are these sorts of technologies and models and applications being developed? And for how long would something like, you know, a clairvoyant factor predicting model actually give you an edge for until someone else, maybe an ETF came along and copied it? I think that's a hard question to answer because, again, it gets back to how you would use this model, right? So in the paper, we give one very specific. example, we use the deep learning neural network to predict fundamentals and then we plug that into

one kind of factor model, right? In particular, operating income, predicted operating income over enterprise value. But as I suggested, you could use it to, you know, figure, you know, figure out whether consensus forecasts are good or bad. So, you know, I think that just saying in general, deep learning applied to, you know, investing is going to. to get used and then a year later is going to be arbitraged away, misses the point that, look, you know, you can use deep learning in a myriad of ways to attack the problem of long-term investing and presumably trading as well. To address your question about how quickly is this kind of technology getting adopted, my sense and based a little bit on an outsider's view as an

academic machine learning person talking to colleagues who've either gone into fintech or who've flirted with it or who've tried to recruit me into it. The sense that I get is that actually, and obviously a lot of people aren't talking about what they're doing, right? But my sense is that there's a lot of people doing this kind of stuff in the high frequency space, not maybe on the scale of, you know, fractions of seconds, but but on a pretty short time scale. And the reason why is because it's easy to collect a lot of data. If the patterns are very different a year from now, well, you have enough data.

If you're trading at the scale of months or years, then you have to look back 20 years, right? You have to look back 30 years. If you're trading at the scale of seconds, then your whole universe could be formed by the previous four days. There's a very fast cycle of development. So if you're in it and you just want to, you don't you don't have any kind of strong beliefs about finance you're just a machine learning

person throwing your hammer at finance and then going in the high frequency space gives you or the comparatively high frequency space gives you like this sandback box to just really quickly test off validated see if it works my feeling and when i've talked to friends who are doing this kind of stuff but what we're doing is that i think almost no one that i've talked to out of a lot of people doing this stuff for finance is looking at the same kinds of times scales And John might be able to speak to that because he might actually be deeper in the, I mean, he's definitely deeper in the finance community than I am. But my sense is people doing deep learning for finance and there are many. It's on the rise, but they're not necessarily looking at it in the same way and certainly very few on as long a times go.

Yeah. I mean, I think if you look, you know, the AQRs and the DFAs of the world, which are, you know, these huge, you know, quantitative shops, they do, they certainly do, long-term investing. But there's not a lot of evidence that there's a ton of machine learning, deep learning going on there. But, you know, I think if stuff is successful, you know, it's likely to be adopted. So probably won't be true forever. John Elberg and Zachary Lipton, that was a fascinating conversation. So much to think about and I'll wrap our heads around. Really appreciate you both coming on. Thanks for having us. Thank you guys.

Tracy, we didn't really plan it that way, but I really do think that was sort of the perfect follow-up to Andrew Lowe last year. No, Joe, you're supposed to pretend we did plan it that way. So everyone will think we're really organized. Isn't that so good? I mean, like, we should continue this series on quantitative strategies and new ways to evolve to beat the market. Let's continue this. Let's continue this. Yes, absolutely. Okay. In all seriousness, yes, it was fascinating. I really like the idea of, well, who doesn't like the idea of a clairvoyant robot who can predict how well a company is going to do in the future and then apply that to a factor-based investment model. If someone comes back in time and they're like giving me hints on what the

stock market is going to do, it's like, just give me the winning stocks. You know what I'm saying? Like if you're time traveling, don't like be a tease. Just give me the winning stocks. No, but in all seriousness, A, I felt like several times of that conversation, it's just like the level that they're operating and thinking about the market on is so high above anything that you and I typically talk about on a day, like several times. I felt like I had to catch my breath. Speak for yourself, Joe. Because it was just like absorbing all of that. And, you know, obviously there's probably a lot that I didn't get. But then the other thing, I really thought that last point was very interesting about time frame. So obviously, going back to the adaptive

framework for thinking about markets, you know, if there's a lot of it. You know, if there's a lot of is a strategy that works over a day and you can get it and you can, you know, just have to backtest four days or whatever. It's very easy to see, okay, this works, this doesn't. Let's go with the thing that works. And then everyone can sort of figure out the things at work and then it doesn't work anymore. But this idea that maybe a quantitative approach to long-term fundamental investing, you don't get that sort of instant feedback on whether it's working as fast. And so maybe winning strategies might prove to be a bit more durable. Yeah. And presumably it's much more difficult to actually develop them and see them evolve.

It's like, I guess it's like if you bred successive generations of like rabbits, right? Like it takes like a year to, well, less than a year. You could breed like a hundred generations in a year. Yeah. Right. Or I guess like, you know, it's like laboratories use mice because they do, like they can get so many so fast. But if you had to sort of, you know, breed hippopotamuses, you wouldn't know for a much longer period of time whether down the road you had sort of created the master hippo.

Yeah. Does that make sense? Wow, this podcast just went weird. Yeah. Okay, let's leave it at Master Hippo there. Okay. All right. This has been another episode of the Oddlots podcast.

I'm Tracy Allaway. You can follow me on Twitter at Tracy Alloway. And I'm Joe Wisenthal. You can follow me on Twitter at the stalwart. And you can follow our guests on Twitter. John Elberg is at John Elberg. There's just one L in that. Zachary Lipton is on Twitter at Zachary Lipton.

And you can follow our producer Sarah Patterson on Twitter at Sarah Pat with two teens. Thanks for listening. On April 4, 2023, around two in the morning, a man was found stabbed multiple times on a sidewalk in downtown San Francisco. Hey, who did this to you? What happened next turned the story into a political firestorm. Reports have identified the video. as Bob Lee, the founder of Cash App. From Bloomberg Podcasts, this is Foundering,

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