Leigh Drogen - Quant vs Traditional Investors and How Alphas Become Betas - [Invest Like the Best, EP.41]

This podcast is sponsored. I know firsthand how complex the tech stack is for asset managers and seemingly every new tool and data source makes the problem even worse. Adding more complexity, more headcount and more risk. Ridgeline offers a better way forward. One unified platform that automates away all that complexity across portfolio accounting, reconciliation, reporting, trading, compliance and more. All at scale. Ridgeline is revolutionizing investment management, helping ambitious firms scale faster, operate smarter and stay ahead of the curve. See what Ridgeline can unlock for your firm. Schedule a demo at ridgelineapps.com. The Chartered Financial Analyst Credential is the most respected and recognized investment management designation in the world. The views expressed in this podcast do not necessarily represent the views of CFA Institute. Hello and welcome everyone. I'm Patrick O'Shaughnessy and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, methods, stories, and of strategies that will help you better invest both your time and your money. You can learn more and stay up to date at InvestorFieldGuide.com. I've often joked that this show should be called This Is Who You Are Up Against because I am so often having conversations with brilliant people across the investment landscape who are effectively my competition and yours. This week's conversation fits that description because it gives you an inside view into how things work among some of Wall Street's most competitive investment firms. My guest is Lee Drogan. who has worked as a statistical arbitrage portfolio manager and who founded and now runs Estimize, a data company which works with some of the world's largest hedge funds. Our conversation centers on the massive shift from what we call discretionary portfolio management, basically stock picking, to a landscape that is increasingly dominated by quantitative investors of various types. We talk about how any investor might hope to earn alpha and how doing so is harder and harder.

There are so many great stories in this episode told by someone with the perfect career experience to know how the system actually works. After many episodes where I've been learning on the fly about topics like venture capital, permanent equity, or even health, this episode marks a return to my world of quantitative investing. I think you'll learn a lot and that you'll likely finish with an even deeper appreciation of just the type of investors that we are all up against. You can find show notes for this episode at investorfieldguide.com forward slash drogan. And now, please enjoy my conversation with Lee Drogan. All right, Lee. Well, I've been really excited for this one because I have intentionally avoided most discussion of quantitative investing on the podcast, just because I've spent most of my 10 years of my career doing this. And I thought it'd be fun to branch out a little bit, but I'm now ready to dip my toes back into the water. And we met very briefly at a, I think it was a quant conference, actually. I was standing talking to Meb Faber and we started chatting about something very specific, which we'll get to in a minute. But then I also came across a piece that you wrote on LinkedIn, which I thought was one of the most thoughtful pieces on kind of the current state of the investing spectrum between traditional deep fundamental work and quantitative investors and kind of how that balance might look in the future. And that's really where I'd like to focus today, because I think it is a fascinating shift that we're watching real time, and that a lot of big people, even famous people, are being left behind by this move towards quant. Or they're just simply opting out because of it. That's right. So I'd love to start with what we were talking about when we first met, which was, I said something to you like, yeah, we used to use this IBIS data set and look at analyst revisions, and it totally stopped working as a factor. And you said, Yeah, that was me. I did that. I was the one that armed that away. So can you just use that story as a way of describing kind of your early career and background in investing? And then we'll get into all the details. Yeah. So I actually, my...

My educational background is in behavioral economics and war theory. I actually thought I was going to like Randcorp, CIA, like State Department, that route. And it was interesting. I got an opportunity to do an internship at a quantitative hedge fund between my sophomore and junior years and loved it. And it was incredible. And at the end of the summer. basically I stayed in New York instead of going back to school and finished up at night at Hunter College here. And that world was so interesting to me mostly because it's all just numbers and letters, right? You don't have to deal with the irrationality of people. You just get to deal with like, there's a variable and it's either correlated or it's not. And if you can find that correlation and you back test it and the out of sample works and the P values are good, like you have a strategy and you can make money and it's all very kind of straightforward. Look, it's science. And it's the difference between science. and what politics is, which is not science. And the not science part kind of frustrated me. And so I started my career at this hedge fund, Geller Capital, up in White Plains, doing the reverse commute from New York every day. and fell in love with it, just fell in love with the process. And then from there, and we were doing exactly that. We were doing analyst estimate revision models, earnings acceleration, history surprise, beat rates. We were basically attempting to arbitrage the inefficiency in that sell-side analyst estimate data set. Can you describe in some detail what that inefficiency was? Because I think it's a really great example. into how a stat-arb strategy or an informational edge or some weird inefficiency in the markets was first identified and then alpha sort of extracted from it. So if you could describe in some detail, because I think it's a great example of how this works. Yeah, so a lot of the stuff that we did was not necessarily cutting edge. It was just we did it well. And these strategies had been around for, I don't know, the academic research had probably been done 10, 15 years earlier. And so the basis of this is that the sell-side analysts are inefficient in how they produce their estimates, largely coming about because of institutional biases, corporate access, investment banking pressures. They also tend to not update their estimates in the last couple weeks before the earnings report, partially because they're lazy, partially just because compliance is really hard to get new research reports out the door. And they also tend to love to allow the companies to beat their estimates because they want to get on the earnings call and be like,

They're like, ah, great quarter, guys. But nobody pays attention to the fact that they moved their estimates down 15% in the last five or six weeks. So you can see all of the patterns in this data and the average movement in estimates up or down, what percentage of the time does the company beat or miss. And then just very simple linear regression models of like, What happens if a company beats four quarters in a row by more than a normalized 10%? The next six months, the stock is going to outperform on a momentum basis. It absolutely will. And we've known this for a very long time. We know that companies which beat their consensus numbers by a significant amount will see post-earnings drift. And you can collect residual return in the three to five days afterwards very easily. We know that revisions are highly correlated to equity direction on a normalized basis. did was we basically attempted to normalize everything and then basically z-score the entire universe and go along the top you know deciles and short the bottom deciles in a you know market neutral strategy and this works incredibly well and so I was there from basically 06 on through the crash and I saw a lot of everything. And it was really fun seeing this thing work and eventually kind of ran one of the strategies. And that was the start to my career. So one of the key points in the story is when things stopped working. At the turn. So at the turn in late 07, when Momentum all stopped working because we were, you know, we were coming up the crest of what was going on, everything stopped working for like six months. And then it all started working really well again. Yeah, we lived through in 09, any quantitative manager that used Momentum. at all, especially those that used it exclusively, got absolutely destroyed in 2009 with a complete inversion whipsaw. And when we looked back, there had only been really five periods in the last 80 years that were as bad as 09 was for momentum investing of any kind. So it's amazing to watch stuff work really well and then stop completely. So this is crazy. The manager at the fund went completely to cash about halfway down in 08, halfway down the crash.

08 and we had made a bunch of money at the coming out of that turn on the short side he went completely to cash and I watched him trade index futures for the next six months incredibly well too like by hand just by feel and he caught he didn't catch so like we had that the next half of that first big drop and he caught a decent portion of that then he was out he didn't catch the turn because he was completely out and then he caught the move back up and then he caught the move back down into March and then he was and then unfortunately shut the fun down. And my mentor, he eventually passed away from skin cancer, you know, shortly after that. And it was, you know, it was just a... It was crazy watching him do that in the twilight of his life, like just operating so well. So I'd like to get into this actual framework now and maybe hopefully have you kind of build it on the fly. So I always think in terms or everyone always talks in terms of sources of edge being informational, analytical, behavioral. So first question before assuming you agree with that framework is, do you think that those are. the proper dimensions to separate any sort of potential alpha edge into? Yeah, I mean, I think the informational edge is becoming more arbitrage these days. Although it's interesting, with more information out there, new data sets like ours, new data sets all across the universe. of different types and we're at a new inflection where you need to be more aggressive about arbitraging that informational edge because, I mean, we've look rent tech buys everything. And so if you're not buying some things, at least, you know, you're going to not perform well relative to them. The analytical part, there are not enough quants out there to do the research simply. Like we just literally don't have enough of them out there, especially when the discretionary world is attempting to hire them now as well. So. Yeah, you can have an analytical edge by having a really good team. And then I think the behavioral one is the most important one, especially for the discretionary guys. Quants, we do see some irrationality on their part sometimes where they'll test something.

They will look at the out of sample and they'll look at P values and the P values will be really good. But then maybe the last three or six months of the performance of a 10 year track record will be like not quite as good. And they'll be like, well, we want to wait a little bit to put something into production. Like, no, you shouldn't do that. So there is some irrationality on their part when they turn things on, when they turn things off. But really, it's the behavioral aspect on the discretionary side is probably the most important thing right now. Can you dig into that a little bit more? So what do you mean by the most important thing? For a firm to have an edge, they need to behave well, meaning they don't... have style drift. They don't shift in and out of strategies, things like that. God, I mean that too, but I think it's more just, how do you make decisions? Like what is your, what is your decision flow process? And I like to use this really specific story and it's a, it's a very famous behavioral study and it's in Michael Lewis's new book, which is really good if people haven't read it, the undoing project. And the story is basically about a study that Thaler did with a, with a colleague where they give a set of x-rays to these very famous oncologists and then give the same set of x-rays to first-year medical students. And they ask the oncologist, how do you determine whether it's cancer or not? And they give them basically a 10-point rubric of how they determine and very quantitative structure of things. And they take that rubric and they give it to the first-year medical students and they say, tell us whether it's cancer. And then they give the same slides to the oncologist. And the oncologist end up being no better than random at guessing whether it's cancer. And the students are like, 70% hit rate. Why? Because the oncologists who are experts don't use their own damn rubric. We find this is the exact same way in the discretionary trading world where if you ask any of these guys who run a billion dollars in discretionary long short money, they will tell you exactly how they're supposed to be making decisions and then they just simply won't follow it. So I think firms need to seriously codify the flow of decision making from...

stock selection to position sizing to market timing to risk management to just the whole thing and i don't see many firms that have actually codified this very well i'm actually waiting for somebody to put together a good piece of software and sell it to them to like modify the behavioral heuristics that they operate against because man i mean i could pick like half a dozen off the top of my head that these guys are just so exposed to and and most of them are availability heuristic. They're going to operate on whatever information is like readily in front of their face the most that day. And they just shouldn't be doing that. And that's the difference between a good quantitative strategy based on science and what some of these guys are doing, which simply isn't working anymore. The Michael Lewis story reminds me of, you know, Atul Gawande and the checklist manifesto and my belief that, you know, as an investor, the only interesting strategies are ones which are repeatable. and therefore follow a pretty specific process. We'll get into in a little while how I think you could find really interesting advantages where it's almost a quantitative-like process with more subjective fundamental inputs. So fundamental company models or whatever, but still very strictly following a decision process. Or even doing the thing that we do on this. We have this app called Force Rank, right? And the whole point is it doesn't matter how you get to the decision of saying on a quarterly basis, you believe amongst these 20 stocks, we do 10, but let's just say 20 in a specific industry. These 10 will outperform the other 10 and you force rank them in order. It doesn't matter what your fundamental inputs or technical inputs to that decision making is. It's just that you ranked them. And if you can rank them correctly, well then just go long the top five and short the bottom five and you're going to generate alpha. But it's just like that portfolio construction and adherence to a model is so hard for some of these guys because they want to be correct more than they want to make money sometimes. And that causes all sorts of perverse things. You mentioned some terms in a process earlier that I'd love to have you flesh out a little bit more just so everyone can be on the same page. So viewing the quant research process as sort of the scientific method, basically, I mentioned Z score, P value, all these things. But the basic idea... Sorry, I'm incredibly nerdy. This is the world.

I think you're preaching to the choir in many cases here. But to define this process a little more cleanly, because I want to talk about data sets, it would be time well spent. So if the basic idea is you've got some smart people who know how to look at data, you've got a data set, we'll call it a generic data set, and you've got some set of future returns. Basically, that's what a backtest setup looks like. So from your perspective, how is good research accomplished? So if you've got those really basic three inputs, which is usually the basis for all this. What is the actual process? So first, you need to start with an ex-ante hypothesis for something. We can't just go and say, well, let's run a bunch of regressions and see what works. Because if you don't know why it works, you won't know why it stops working. So I think this is one of the biggest mistakes that I see everybody make and why there are certain products in the market right now for discretionary managers. which I will not name, that I don't like because they skip... The two biggest steps that I think are really important. And the first is the ex-ante hypothesis. Let's just say you want to see what happens when oil goes up 10%, right? And you just start running regressions across the board to different assets and stuff. And you say, oh, wow, look, if oil goes up 10%, then these things go down or whatever. Well, if you didn't start with a, I think if oil goes up, then these things will go down. You won't know why that happens and why it works. And it'll eventually stop working and you won't get out of it. So you need to have a... hypothesis for why a data set is actually has some kind of causality so we start with that then we simply take our time series and we split it into two or we can divide it up into chunks yeah simply we like to if you have enough data you just divide it up into two halves if you have a 10 year data set five and five run the regression on the first five then run the regression on the second five and then look at the p values and the p values are basically like does the performance from the first half equal the second half roughly and if the

p-values are good you know that you have not overfit your strategy in your in sample and your out of sample works and if you're out of sample works then you can be pretty sure that going forward it's actually going to perform well if you don't run that out of sample and you're just like wow look the regression works in the first five years let's go put it into production you have zero clue whether this thing's actually going to hold up at all and I see on the discretionary side again that step is getting skipped a lot. And that's why the efficacy is simply not there for some of this stuff. This may be a little too in the weeds. And if we get there, we can always just cut it out. But I'm fascinated by this stuff. So as you pointed out, there's lots of ways you can do this. If you've got a long enough time period or enough data, you can literally just chop it in half. But what do you think about the order of those time chunks? You know, is it always older half for second, newest half second? Do you do kind of random sampling, bootstrapping, that kind of stuff? Yeah. So what are your thoughts on some of these particulars? At Estimize, we keep it pretty simple with our quant research team and our process. And we have enough data now on the core data set, about five and a half years, to just do first half, second half. When we were early on and we wanted to figure this stuff out in 14 when we wrote our first white paper, we did chop it up into quarters. And then we took every other quarter and then we looked back at the other every other quarter. And that worked fine as well. These data sets, and I think this is stuff that people need to think about these days, in crowdsourcing, the panel changes. It's going to grow. It's going to morph different people, different reasons they're there, different types of people. And so it actually is good to go first half, second half, to see if the regressions that were run in the first half. And I'm talking not just about alpha generating models, but our select consensus model. How do we overweight and underweight certain analysts? And behaviors might have changed. Our platform has changed.

The interface has changed. The heuristics associated with how they make the estimates might have changed. And so you want to see if the same things there hold up. Because if they do, you know that there's some inherent quality of their decision making that is at play instead of just some heuristic that we're affecting negatively or positively. So there's a lot of different ways you can do it. We like now to just go for first half, second half. Talk about what Estimize is and does. So it's a relatively new business. I think 2011 founded it. So just describe the data set, describe the idea. It's related to the story you told earlier about analyst estimates. So just a quick history lesson on Estimize itself would be great. Yeah, the concept is basically that if you crowdsource all these expectations from a broad community of buy-side, independent, non-professional individuals, industry experts, corporate finance professionals, you get a more diverse data set, less bias, less hurting. You get a larger data set relative to the sell-side stuff. takes out a lot of those inefficiencies and bias associated with that that one and so we collect specifically eps and revenue estimates for publicly traded u.s companies we also do economic reports so gdp cpi oil inventory stuff like that and then we do we do force rank as well which i talked about a little bit before and then what we do with that is we run a bunch of models through it we overweight certain analysts we score and rank everybody and then on the back end we output all the raw data to institutional clients we have a big front-end data visualization platform, a bunch of emails. And then we turn the raw expectations data through our own quantitative research process into factor models so that both discretionary and quantitative managers that don't want to use the raw data can get the direct alpha out of it. And those factor models are relatively shorter term kind of weeks, days to weeks kind of models. And there are some that are going to be coming out that are more like earnings yield focus, which are longer, you know, quarterly rebalance type stuff. How do you incentivize the people providing the estimates? Yeah, so that's the billion dollar question here and why I think my background in behavioral economics is specifically relevant to what we do.

So historically, the buy side discretionary guys will call around to each other before an earnings report. And, you know, like this is known historically as the whisper number. Right. And then they'll call their equity research sales guy, Goldman or Morgan Stanley and say, hey, what are you hearing from your buy side clients? And this game is just like I knew guys before I started this thing that would share Google Docs amongst each other at different funds. And all of this is totally against compliance, by the way, of all these funds. It's certainly it's not illegal, but it's completely against compliance. You're not supposed to be sharing information outside the firm. But they have to. And the reason they have to is the fundamental premise of fundamentals-based trading is I believe that a company is going to earn X over the next year. It trades at some Y multiple now. I believe it's going to trade at some different multiple then. I multiply my fundamental expectation by the multiple, and that's my stock target price. My alpha is the delta between whatever the market is pricing in in terms of consensus and some kind of terminal multiple. But the problem historically is you don't know what that true market consensus is because the sell side stuff, especially going a year out, is like not representative at all. So they come to our platform and the deal we basically make with everybody is we're going to cannibalize the industry by allowing you for free to see everybody else's data. if you contribute to that specific stocks, specific earnings report. And then obviously people contribute to like four forward quarters or eight forward quarters, and then they can see all that data. And that's the deal we make with them is like, maybe there's $400 million of revenue a year in our industry total earnings estimates. And maybe we've eventually cannibalized that to a hundred million because we're giving away a lot of it for free. And then we get to own that data, obviously on the backend and sell it to everybody. But for them, they get to pseudonymously, without putting their real name up there, just under an anonymous account, analyst 459236, put their estimates up, get scored and ranked, use it as a utility for themselves to understand where they are relative to consensus.

Yeah, that tends to work pretty well once you have a critical mass of data. And so, you know, we started this thing in January of 12. We're five and a half years in. The thing really tipped sometime in like mid-14 when we had enough data and the system was well enough known that people just like flooded through the door. And then it's, you know, just grown exponentially from there. companies do you cover for a typical company? Let's pick a large cap one. How many estimates are there quarterly? Give me a sense of scale. Yeah. So we'd cover with three or more estimates, about 2,100 companies now. And we have about 1,400 companies with 10 plus estimates. And for a name like Apple, we'll have 1,000 estimates. But I think more importantly, so there's this interesting effect that takes place. For large cap names, the sell side will have 30 or 40 analysts covering a name. But for like a mid or small cap name, you might have seven or 10. The relative difference between 40 and 500 is like it matters, but it doesn't matter. It's totally diminishing returns there. And our data set is far more accurate. It's 70% of the time our consensus numbers are going to be more accurate than the street. But really where it matters is The street will have 7 or 10 for, like, a mid-cap growth name, and we'll have, like, 60. And that is really where the gold is. And there we're going to be, like, 75%, 80% of the time more accurate and just so much more representative of that expectation. And we also find in all the models more alpha in the mid- and small-cap stuff. And that's the case for most stuff because, you know, the large-cap stuff tends to be more efficient. But, you know, the coverage is basically it's, like, 97% of the market cap of the U.S. universe. We don't do REITs and we don't do community banks. And those are the only two things we really don't do. Could you tell that story about, in your piece, it was about cars and data on cars as an example of informational edge and why you need to be a part of this arms race if you hope to survive? Yeah, I mean, look, everybody's just like running to catch up to each other at this point. So I was at a conference in London back in March. And one of these data sets that's up there, it actually wasn't presented by the company itself.

presented by Tamar from Quandl, being one of these platforms that has a whole bunch of data sets that you can buy. And they have this data set where they're getting the new... insurance registrations on a daily basis for like all cars in the US from this one insurer. And this one insurer has a big enough panel that they represent like a good enough sample of how many cars are being sold. And so now you go from getting the car sales number once a quarter or once a month or something like that from the companies themselves to having a daily look at how many cars are sold. Well, I mean, if you're a car analyst or you're trading cars, like if you don't have this data set now, you're screwed. And these kind of data sets are coming out all the time, especially with location. You've got all these apps now. This is crazy, and a lot of people don't know this. It's just one of those things that data nerds are getting into now. there are these companies that basically have an SDK that they will put into all these different apps and they will pay the app to put the SDK into the app. And I'm talking like thousands and hundreds of thousands of apps. And so the likelihood that you have one of these apps on your phone is very, very high. And what this SDK does is it sends your location data back to this one company. And they now have a large enough panel of everybody to know how many people are walking into Urban Outfitters every month or whatever. And it's just we're getting to the point where the data is outstripping the ability for funds to actually just do the science and correlate it to outcomes in the market. But man, like next 10 years, this is where the stuff is. So the card data one's an interesting one because probably started as a huge source of alpha. So the first batch of people that bought this data set and started incorporating in their trading strategy were crushing those that didn't. And then it becomes this arms race where, well, everyone has to buy it. So it's like table stakes. But the alpha can go away very quickly in the informational game. You know, I get asked often.

Isn't all the informational ads just going to be gone? And then you look back at market history, and it's just always progressing, right? There's always the next level of edge. It's like, are we all not going to have jobs? No, we're going to have jobs. There's going to be jobs. We're not all going to get put out of work, and there's always going to be another data set. My favorite story from a past episode was... short sellers that would hire recent college grads to go sit at the Library of Congress that got the 10Ks earlier than everyone else and just run to a payphone and call and you'd be a week ahead of everyone. I have a good friend that runs a company called Reorg Research. And his whole company is predicated on having these people in the distressed debt courts, like when they do the filings. In the actual court, right? And then they have this product that pushes out a feed of this stuff. And this dude went from zero to $10 million in revenue in his business in like a couple years. It was incredible how fast this company grew. And it's, yeah, there will always be new data sets. And there will always be new heuristics and inefficiencies that the humans operate on relative to the data sets and the bad decisions that they make with the information available. So it would obviously be a very good problem for you to have as the owner of Estimize. What about the same scenario playing out here where everyone's buying estimized data and any alpha that's in it is arbed away? So I like to, so we obviously get asked this question at basically every meeting. Some data sets have obviously different capacities than others. So that card, you know, the car insurance, you know, stuff, it's just operating on cars. And there's like a small set of companies. And certainly that thing will be arbed out like within a couple of years. IBIS, the earnings estimate data set, took and still is not ARB'd out completely, but it took like 30 years to ARB. Ours should go faster than that, but is it 15 years? Is it 20 years? Is it 10 years? It'll happen eventually if we're successful enough at distributing our data everywhere. Now, it's interesting, companies, and I think traders and investors should know what they're using in this frame.

when they look at a data set, where is that company in the progression of that distribution model? Which is why we always get asked, how many clients do you have and all that stuff? Because they're trying to figure out where we are. So as a data set gathers more data and becomes more useful, it has more and more alpha. And then at some point, there's this like crest where enough people are using it that the alpha kind of like tops out. And then you go down that slope on the back end. By that point in time, like if you're really getting arbed, Like the company's making a lot of money. Like you're a very successful company. Like I said, good problem. Yeah, it's a good problem to have. So I will say we are still on the upward sloping part of that curve where there's more alpha today than there was yesterday. How long that lasts? I can't say. Maybe it's another four or five years. Then the value in the data set starts to come down. And that company has to go through if they want to instead of selling. And a lot of these data companies get to like $15, $20 million in revenue a year. And then they sell because it's hard to get past that. The reason being you have to go through this trough. And the trough can take years to get through. But if you get through that trough and out the other side, you become a must-have arbitrage data set that becomes table. And that's where IBIS is. And that's where a data set, like the short interest data set that market owns now called, and I'm forgetting the name that is it's table stakes to have that data set, the short interest stuff. And if you can get through that trough, you can, you can become a hundred million dollar revenue company, really, really large. Not many companies obviously make it through that. What's the most interesting data set that you've got your eyes on, or maybe before you even answer that question, set the stage for how this world works. I always joke that what this show really should be called is this is who you're up against to try to discourage people that think they have some insight on Apple stock or something from ever doing anything about that. So you've got a good sense into the worlds of firms like Millennium and Citadel and now 0.72 and Ballyasny and these.

extremely sophisticated firms that are a part of this arms race, some of which are purely quantitative, maybe a rent tech, some of which came up as discretionary, but now realize the importance of quantitative, whatever you want to call the hybrid, come up with some name for it. I keep hearing this term quantum mental. Well, the 0.72 guys, Matthew Granad, who runs their big data group, likes to call it systemental, which I actually think is the best term for it. Frankly, it just sounds weird. Yeah, so we'll roll with System Mental. So maybe set the stage for, before I ask the question about some data sets, for how this world works. So you could pick a firm, you could describe it however you want, but what has the evolution been in these very sophisticated sort of hedge fund platform type companies? Yeah, they're shifting a lot right now. So let's just take Millennium, for example. Historically, it was known as like a hedge fund hotel where they gave you some technology. and basically said, here's some cash. We're going to manage risk on you really tight. If you have one bad, terrible quarter, you're out. But you have to go do all your own research. You have to buy all your own data. You have to do all your own stuff. So that's how it kind of used to be. Then you had firms that were more kind of like one book or discretionary firms that were tighter together. And they would buy data and have an infrastructure and have a process all together. And then you had kind of the quant firms are very much the same way. You've got a firm like AQR, which is just like one research team that runs a whole bunch of different portfolios, both short-term and long-term stuff. And you have... Teams like Balazny, which runs pods of quants, or Paloma, which runs pods of quants. They're all separate, but the firms will give some kind of resources to those pods. And then you've got the third kind, which is the most recent, which I think is really interesting, which is WorldQuant. And WorldQuant is unique amongst the entire industry. They decided they're going to have a centralized risk management and portfolio management team, and they're going to centralize the data purchase and infrastructure part. But then all the analysis and alpha generation is going to be done by a group of like 500 analysts all around the world that they basically contract to do this.

And it's incredible. WorldQuant was one of our first customers, and I can actually say this publicly because they allow us to, and they use our data. I went to their conference in Puerto Rico a couple years ago, and I'm walking around and meeting all these people. And all the analysts know about our data set. And then I meet some of the PMs and they're like, never heard of you. And I ask the head of data over there, I was like, what's up with this? And he's like, the PMs don't know what's actually in the alphas that the analysts put into this bucket that the PMs just pick out and create a portfolio out of. It's an incredible way to do it because it's like infinitely scalable and which is why they've been so successful. So there's a lot of different ways to kind of set up the firm. What's changing now, I think, is a millennium. is now realizing they have to build an infrastructure to get more people onto their platform and support them. So they're building a big data thing to support people. They're bringing in new data sets. You're seeing discretionary firms attempt to build these overarching kind of infrastructures as well. But it's moving slowly on the discretionary side. I think there's still, people are trying to figure out which steps to take before they start running. And some of them have taken some false steps forward. So do you think that the best... way if you're a discretionary firm or even a new firm, right? Because we were talking before we started recording how there's this funny dynamic, as you see everywhere, it's almost like Thomas Kuhn, like progress happens one retirement or death at a time, that some of these older firms are just not going to adapt. Even if they were enormously successful and super smart and had an edge, that the nature of edge changes through time. So if you were designing from scratch a firm, would it look like basically like WorldQuant? You've got this centralized data kind of team and infrastructure that's then spit out. It makes me think of Numeri. Maybe you could describe what Numeri is. Yeah, that's a really, well, so I can't even really describe accurately what Numeri is. I don't think many people can, frankly. WorldQuant, I think, is the, yeah, I mean, I think they got it right. And it was a crazy idea. Let's hire 500 people in India and Hanoi and all around the world. Some of these guys are like rice farmers.

that had a mechanical engineering degree. And it's incredible, but it's very much along the lines of our philosophy, which is crowdsource all this stuff, pick the best out of the haystack and like go with that. And if you have enough people giving you models, then you'll find the models that work. I don't think many firms can re-engineer what they've done. It's a difficult thing. And there's a data... procurement thing that feeds the heart of that engine that is just incredible. It's a machine. There's another company called Quantopian that I think is really interesting run by a friend of mine, John Fawcett. which basically attempts to go even further than WorldQuant in crowdsourcing and disrupting the whole idea of, okay, they built this massive infrastructure platform that they just put on the web instead of keeping proprietary and allowed anybody to go in there and run regressions and build quantitative models. And then they built a hedge fund on top of it that Point72 invested in, where if you run your model out of sample for six months and it works out really well, they may pick it out of the bucket, put money towards it, and give you a product. percentage of the returns or something like that. I don't know exactly how it's set up with how you get compensated, but that's really interesting. That can scale incredibly large. And then this Numeri thing, I honestly, like I've met the guy a couple of times. I think he's super interesting. He was at the conference in London that I was talking about before. This is Richard Craig. Yeah. I can't even explain. There's a bunch of machine learning involved in it. It's also crowdsourced. It's also models. There's a cryptocurrency involved there. Please explain because I would love to understand exactly what's going on. I probably don't know any better than you, but here's where I am. This is the simplest explanation. The basic idea is at firms like Rentech, to use the example everyone uses because they've been so incredibly successful, you've got... incredibly smart, talented data scientists who are doing the process that we've described, which is finding data sets. understanding the relationship of that data set to future returns and some asset class and building trading strategies around them. And I want to come back to how you know when something stops working because that's so important. We didn't get as deep into that as I'd like to. But that's the basic process. And I think Numeri's interesting insight, just like yours, is this idea of crowdsourcing, which is that, okay, so you can have how many – even if –

Renaissance can have, I don't know, a thousand of these brilliant people. What if you had a hundred thousand? And so what Numera has done is create a data set, which frankly, I can't understand because the number of data points in it is something like 240,000. I also didn't understand this. What is the training data set? So, so it looks to, it looks to me just cause I work with some of these data sets like copy stat, like the, like the number of. observations and forward returns what is it observing we don't know we don't know so yeah but so here's it keeps it secret so the interesting thing is that so you've got your you've got your variables let's and they're you don't know what they are they're normalized so it's like zero to one or zero 100 or decimalized i can't remember so it's a factor right so it's some factor of something it's some factor but it's not 12 where you might think it's a PE. It's some number that they've scaled. So it's kind of blind to you. And then this is the thing that I don't really understand. The outcome, the thing that you're trying to correlate it to is binary. So you've got whatever stock identifier one, two, three, you've got factor score X, let's say it's 0.5, whatever that means. And then the outcome, which normally in our world would be returns. is not returns. It's zero one, which makes no sense to me because the magnitude of the return is everything. Right. So. I don't know what they're doing. Well, wait, so no, no, no. So it's interesting. It could be event-based stuff. No, no, no. It's actually interesting because that corresponds a bit to the way that we run Force Rank, right? It doesn't matter how much more accurate you are with your rankings. It just matters that your rankings are correct. I don't care if your number one stock went up 20% and the number two stock only went up 10, just as long as you got them in the right order. And if you get them in the right order, supposedly over time, and this is the case with this, it doesn't have to be. is that if you get enough of these right, the returns will work out correctly if you have the yes or no. But it doesn't have to. So that makes more sense. Yeah. Anyway, there's a data set. You build basically an algorithm that relates the inputs to the output.

And if you do that really well, and you're near the top of the ranking or whatever, you get compensated. Now, the funny thing here is... So this is the part that I didn't understand. There's some kind of cryptocurrency now involved? So now we get into a whole second world. I don't want to get into Bitcoin too much. But the basic idea is that there's all of these new cryptocurrencies being created and then used as compensation for whatever. And the value of that cryptocurrency is related. Oh, I get it. you get paid in what's called numeraire. Do I have to go buy this too now in my Coinbase account? I don't think they supported that Coinbase. But anyway, it's an interesting idea where you're not even being compensated in cash. You're being compensated in this new cryptocurrency where, again, there's a network effect where if more and more people do this, the cryptocurrency becomes more valuable and so on and so on. So I actually find this really interesting and it's awesome that I learned this today. The idea behind, not to get too deep into it, obviously, but Bitcoin is like there's an amount of work that gets done to verify the ledger. So I guess this is the work. So the work is, did you produce an algorithm? that that is correlated to outcomes outcomes right that's that's really interesting yeah so it's so it's fascinating and you can see how this is evolving, right? That there are new firms coming out that are taking advantage of this crowdsourcing idea. There's this hunt for new data sets. I'll finally get back to that question in a minute. But you mentioned Ballyasny and I came across a great quote. I think it's public. It's from his letter, but it came out in Bloomberg. So this is from Dimitri Ballyasny. A long short manager 10 years ago might have been okay with a couple of good analysts and a chief operating officer. Competing today requires a significant investment in technology, infrastructure, data, recruiting, corporate access, portfolio finance, compliance, investor relations, trading, and more. So this is a guy who's obviously been super successful. I think Bal Yadsney manages $13 billion or something like that, maybe more than that. And I think that's spot on, that it has become so incredibly competitive, which finally brings me back to my original question, which is, what's an example of a data set that you've come across recently?

you use it or not, that's really interesting. And where did you find it? Because it seems like the search for data sets versus individual pieces of information is a new source of alpha. So maybe tell a story or two about an interesting data set. Yeah. And Dimitri's right on. It's actually hilarious. I can mention this publicly because they allow us to. They're clients of ours. And I was floored when Dimitri in a meeting said, make sure you directly tell me about anything because he realizes he totally gets all of this. And normally the CIO of a massive whatever billion dollar fund would not be like, you data company guy, make sure you email me directly instead of going through my CTO sync ad or somebody else. So he's right on the ball. Data sets that are really interesting today. So, you know, some data sets are directly derived and some of them are derivative of other stuff. I really think that the satellite data is interesting from a lot of different levels, mostly because it's so hard to use. But if you can use it well, it's so valuable. And many people won't be able to use it well because it's really hard to normalize it. And there's this company called Orbital Insight that just raised an enormous amount of money, mostly because they're selling a lot of stuff to the government. But they're also selling stuff to hedge funds. every oil storage tank in the world is, you should be able to do better. So that one's pretty cool, but it's going to be difficult to parse it. And I've talked to a bunch of these quant funds that are trying, and they're moving slowly. And the universe of things you can use it on is not incredibly large. The location data is just... That's fascinating. Just really fast. Because it's everything. The credit card data is being used just across the board now. That seems table stakes at this point. It's table stakes at this point. And I actually have... I question how much value... you the discretionary guys are actually getting out of it though because I feel like it's funny like you talk to one firm and they're making a different inference on the stock based on the credit card data than another firm is making which means none of them are actually doing science they're all just kind of saying like well the credit card data for Chipotle is going in this direction so I'm going to trade in that direction the other firms

doing the exact opposite thing. So yeah, is there any efficacy to it? I don't know. So the credit card is like table stakes at this point. The location data is really cool. I really want to see somebody put together like a better front end for the location data because it's all just raw stuff right now. And most people don't even know it exists. Yeah, so that would be pretty cool. And then there's, I think even price and volume now are getting a new treatment with nonlinear methods where There is a company, and I'm forgetting the name of it right now, that has run some really interesting nonlinear models through just basically price and volume. And they're coming out with some really interesting factor models. And so even stuff that we think has been arbed or classic stat arb stuff has too crowded, I think these nonlinear methods are going to end up working pretty well. In the piece, you talked quite a bit about... the the spectrum that we referenced earlier which i guess on one end would be pure like shoot from the hip discretionary stock picker which is a breed that's dying on the other end is pure quant meaning you run models only there's no pm override meaning if something comes through the model you're buying it no matter what the PM might think about it. And in many cases, the people running those kinds of strategies don't have, we'll call it domain expertise, whether it be a sector or an individual set of names or an industry or an asset class. They are data people, not consumer discretionary stock people or not material stock people. You talked about the potential for a hybrid, which can outperform or has better return prospects than either of the extreme camps. Could you talk about kind of what your thoughts are there and why, what, what might make that true? Why, why would there be a case when some discretionary input improves on a just purely quantitative process? Yeah. And we're actually at SMI is up against this attempting to make this shift ourselves actually from being purely quantitative to that, that mix. And it takes,

more experience, it takes more industry expertise. So the basic premise of this actually comes out of a really good example that happened the other day where the AlphaGo team from Google that produced this incredible machine learning, artificial intelligence, very nonlinear algorithm to play the game Go has been crushing every human for like a year now. And nobody can touch the damn thing. But they recently gave AlphaGo to a mediocre Go player and put it up against AlphaGo. And the mediocre Go player crushed the AlphaGo algorithm in and of itself. I think this is an interesting and useful anecdote because it shows that... a human with a machine can beat another machine. And the reason is because the basis of a linear algorithm is to figure out how to fit something to an entire universe of stocks. And we know that not every stock in every market cap in every sector performs the same way relative to a given data set. And so I'll take something that we do specifically. So the post earnings drift model, which we have a factor model for, is basically. If a company beats their estimated consensus number by a significant amount, which is relatively normalized to the average variance in beat size, we find that over the following three days after the earnings report, it will drift in the direction of that beat or miss. But the thing is that not all sectors perform the same way. So industrials don't care about the earnings report, really, because they operate off of peak earnings. They don't operate off of what's the next quarter or even the next year. And so we know that in the industrial sector, the model doesn't work as well. But our factor model doesn't do that. It's like it puts it on a Z-score across everything. And so what we could do is say, well, de-weight the algorithm in... the industrial sector or the utility sector, which also doesn't really matter, so that you're not using that signal as much in your Z-score. So you're not going to put those into your portfolio on either direction. They may just fall out right in the middle, and you'll never actually use those scores. This is a really good example of how, as a pure quant, it's very hard to normalize for all those given little characteristics that you need the industry expertise to understand what are the variables impacting whether a model will work or not.

Because most quants need to trade across S&P 500, Russell 1000, Russell 3000, like whatever it is. And they don't literally have the time to go in there and like cherry pick the variables that you de-weight or overweight for certain sectors, industries, or other Fama French factors. So I think the discretionary guys do have the time because they're mostly operating in one specific sector normally, or maybe a couple of sectors, tech and consumer or stuff like that, energy materials, utilities. And so they can use the expertise of the analyst and the PM to understand, well, I know that industrials operate off of peak earnings, so I'm not going to run a post-earnings drift model on this thing, even if I have a factor that says that. Or I'm going to go create factors that are... I'm going to go do the research specifically on variables that I have an ex-ante hypothesis for that are strong. And I think they can, in their niches, over time, they can beat the systematic quants because the systematic quants are hitting for singles, and they can hit for doubles and triples on a regular basis instead. So the way that that manifests, the singles versus doubles and triples, I think, is portfolio construction. So if a quant... has hundreds of positions or wants to keep the max position below a fairly low threshold because they're not trying to place bets on just one name, a discretionary manager might have a 10% position in a single stock. If all the lights line up green. Right. Because of that sort of more context-oriented experience that they know, if there's an industrials analyst, they've got some set of understanding of how these names trade to know which data points are relevant and irrelevant so they can make bigger bets. Is that kind of the basic idea? That is exactly it, yeah, for sure. And then they can manage risk around those positions at different times during the quarter. So the systematic algos will do this. So some of our models will only be used in the couple weeks before and week after earnings. But the majority of these factors that the systematic quants use, they need to work on a regular basis. But man, I mean, if a discretionary guy has a factor that only works...

in the post earnings drift, right? And he sees that that thing is like bright green. Well, I don't know, go overweight your position by a lot, right? Like for that one stock. And you can do that because you don't have 500 positions on, you might have 40 and you can pay attention to each one and all the signals coming out of it. Back to when something stops working. So I'm going to use a big broad generic example, which is value investing. So one of the original factors, maybe the original factor, probably something that over decades and decades and every geography. By the way, value investor is the original quant. Right, for sure. Ben Graham was the original quant, right? The original factor. So here is something where, and I like to think in terms of half-lifes of a signal. So how long can you expect something to have alpha? You mentioned IBIS is being arbed, but it's taken maybe longer than you might've expected. So value is one that's fascinating, right? Because it's not an informational edge. There can be an informational component to it. But you can measure value better. You can have an analytical edge there, too. But the basic idea is that at extremes, markets overreact. So they get too excited about glamour stocks. They get overly despondent about cheap stocks. And this seems to be much more human nature than anything else. So arguably something that has an indefinite half-life that's just going to work forever. But you go through periods, we're in one right now, where value investing stinks for a long period. It could be, I think, in this case, seven, eight years of underperformance that wears even the most disciplined people out. And so the question is, and we've started to get it, we're value investors at heart, is how do you know if something is irrevocably busted and broken? And my answer to that question for value is the reason why, is the ex ante reason why it works, which is, well, this is a behavioral phenomenon. And as long as people are people and are to some degree pricing securities, it will exist. It may shrink, it may get lumpier. But if you have like a real long-term horizon and you buy a basket of very cheap stocks, I'm very confident that you will outperform over some decently long horizon.

So how do you think about that question of when something is broken? What are the markers? Because I've got the ex-ante idea behind value, but it hasn't worked in seven years. Seven years is a long time. So how would you think about something super simple like value? I think you have to, we look at it as there are intrinsic properties and there are properties that come about because of an informational edge. Value, momentum. growth, these are intrinsic properties of the market, and they will never go away. They will come in and out of fashion, and I think the difference is one is behavioral and the other is informational. And they can change. They can move back and forth sometimes, but some of these have been around forever. In fact, at this point, we just call, I mean, Fama friends, these are betas, and so stuff moves from alpha to beta. And I guess one of the questions that's going on in the industry right now is, is beta levering alpha if you get it right? And I think there's a big debate going on right now because 90-something percent of people have just been leveraging beta. And if you can do that at the right time, great, you have a great outcome. But more often than not, people end up blowing up eventually by doing that. I think factor timing in general is, or you call it beta timing. If you think about these different things as betas, which I don't know if I agree with, but... I actually, I think factors are the better word. So people call it like smart beta. Nothing is smart beta, right? This is all just factor investing. So the timing of those things is, it's a great, interesting thought exercise, right? Because it's this kind of game theory, competitive edge, contrarian. There's all these interesting components into how might you time factors. Whenever we've looked at it, the answer is you can't. You eventually blow up. You can get caught on the wrong side of it. Get caught on the wrong side. You end up over trading versus like a stupid naive. It's amazing how often we find if you have things that work, if you just equal weight those things, you end up doing and just leave it alone. You end up doing better than the fanciest, most overfitting tactics. One of the questions I love to ask everyone in kind of different contexts is for the most memorable individual day.

of your career in finance, investing, and markets, a day that stands out? Flash crash. Can you talk about why? So I was actually working at StockTwits at the time. So my career went from working as a PM to running my own small fund. At the same time that StockTwits was getting built, I was running product and then biz dev over there. And then I eventually put the fund down and started Estimize. I remember sitting at my desk and I just remember the couple of days before you see the market leaking, leaking, leaking. All the distributive signs were there. Like the market was in distribution. Thing was going back and forth for a while. The volume on the downside was much higher than the volume on the upside. It didn't look good. And you could tell like something was going on. And then you come in that morning and just like there's no bids. And it was incredible just watching this thing drop and drop. And the whole office, like, we got no work done that day. And we have nothing to do with the market. Like, we weren't allowed to own any stocks at the time, like, in our personal accounts. And the thing just keeps leaking and all of a sudden just gives way. And the thing that I remember is one of the lessons that my mentor, the first PM that I worked for, taught me is when something crashes, it always retests. Always retest the bottom. And so the thing ends up bouncing over the next couple of days. Everybody's like, oh, okay. It was just like a one-time thing. I'm like, no. No, no, no. This thing's coming all the way back down. And it eventually does come all the way back down. And roughly kind of bottoms out right around the low point that day. Many of the other stocks, which had just massive liquidity issues that day, didn't eventually come all the way down. But the index did. And it was just really interesting. Because I remember on that day, I'm like, I'm going to get to confirm a thesis that I was taught like years ago over the next couple of months. And I'm pretty sure that he's going to be right. And lo and behold, like absolutely correct. These things always retest. And it's because like, that's where support was fundamentally. Like that's where fidelity.

The CIO ran to the PMs that day, and he's just like, at that level, you have to buy everything. No matter what it is, just buy it. And it's not like the random person, like the random retail trader out there. It's like Fidelity and Wellington and those massive long-only funds were just like, I don't care what's going on. Buy it. And that's where their limit orders were or buy stop orders. And you know that when it comes back down there, they're going to buy it again because that's where they feel the intrinsic value of the companies are. So, yeah, that was interesting. Maybe we've kind of already answered this because of our discussion on kind of man plus machine versus machine only. Looking forward, given how much more of market orders are based on algorithms, on quant traders, on systematic processes, do you think that we'll see a lot more unforeseeable dislocations like that? And I guess the ultimate stabilizing measure is there will always be someone that says, this is insane, go buy everything. I mean, so this is back to the intrinsic versus informational edge. Liquidity will always be an intrinsic property of the market. And that will change over time, like what is adding or removing liquidity. I actually think the stat arb guys in many ways add liquidity. If you look at many of the models that they're running, they're liquidity takers. And that actually negatively. affects their models but they are the market makers will shut their things off like whenever they can so the more systematic guys we can put in that are not simply making markets and trying to jump in front of each other i think the better it'll be for the market and the less of these things you'll get because they tend to don't they tend not to shut their things off when things get volatile because they actually make a lot of money when things get volatile. That's what they like, whereas the market makers just don't want to be involved at all. So I think there's a shift going on right now where the high-frequency guys are going out. In fact, many of these old high-frequency firms like TradeWorks and Jump, not Jump still makes a ton of money in high-frequency, but they're turning into StatR desks because the guys there know how to do it. It's all the same work, basically, just different timeframes.

And so I think the market is actually going to have more liquidity. You're going to see less of these dislocations going forward. The better question or the thing I'm thinking about is, are there going to be any stock pickers left at Fidelity to say no mas at some point if it does happen relative to like just all the passive strategies? And a lot of people talk about, well, will passive strategies cause more volatility because nobody's actually there to like bring stuff back into line on a fundamental basis? I don't think that's an issue, honestly. I think the issue is really in a dislocation, is there a human there to actually just be like, I want to buy a million shares of this stock because there's no way this is overvalued? It's an interesting question where as we approach whatever, maybe there isn't an actual equilibrium point, but there's some... active passive balance that will settle out in some range, right? Where you need a certain amount of active, you need price discovery, you need liquidity. So the interesting question is, how will, let's say we're at 40% passive today, something like that. 33, 35, 40, whatever it is. There was a Bloomberg article the other day or yesterday that was just like, and they literally timed it out on the current growth rate. When will it When will it be the whole thing? Which it can't be. But let's say it gets to 75% passive or something like that. And... There's some massive disruption, some breakdown. There's a total lack of bids. And everyone isn't selling their GM or selling their IBM. They're just hitting sell, SPY, sell, Vanguard, sell, whatever. It's really interesting to think about what those little short-term periods of time and who will be the stabilizers, right? And maybe something like that happens and it results in a resurgence of... more active money and maybe it's cheaper or whatever. But, but it's an interesting, interesting problem to think about. I'm really interested to see. All of my money personally is in Betterment right now because one, I'm not allowed to own individual names given what I can see on the back end of our platforms. And two, I literally just, I don't think anybody should trade individual names if they can't put their whole like emotional effort towards it because it's such an emotional process unless you're running a systematic strategy and I just don't have the setup for that. I'm really interested to see what happens to Betterment and the behavioral things that they've built into that product during the next,

crash or whatever and go and drag that little risk yeah yeah i'm dragging the thing to zero right like am i gonna go in there and drag the thing to zero now personally like my whole philosophy on passive investing is that it shouldn't be completely passive We know that if you use some very easy like trend following algorithms with a long only strategy, you can get yourself out of massive drawdowns when basically the index drops below a falling 200 day moving average. Just get out. Super simple. Just super simple. Right. Like I would love it if Betterment put in just some super simple trend following algos or or and or allowed me to allocate towards a. smart beta, whatever, you know, quotations. They use value to us now. They do use, yeah, they do use value. So it's funny. I've discussed this. It's not your choice though. I've discussed this with John, the CEO over there. And our company also uses them for our 401k, which is an incredible product, by the way. I've discussed this and he doesn't want to break the glass box. And the glass box being, they don't want to give all these options because then they don't want to be. responsible for your returns because they feel like their responsibility is your behavior and this is what I'm actually really interested in in the next crash do people go in there and like against their own good judgment against their own best interests do they like move that slider to zero or can they prevent these people from doing that and I actually think the dislocation in the market and obviously like these platforms are a tiny percentage of assets but The behavior that takes place on these platforms will roughly represent the behavior that takes place in all the passive money. And so the next one would be the canary in the coal mine. And I hope they release some kind of report on like the market dropped 20% over three months. And we only saw an outflow of like 1% of our assets or something. And if that happens, we're safe. If everybody takes their money out, we're all completely screwed. My opinion on this, because everyone asks these questions of the various automated advisor.

platforms? How will they manage behavior like the best financial advisor in the world might manage their client's behavior in the next downturn? My answer is it's impossible to. Because if there is a real downturn, meaning like a crash, a big market drawdown, 30%, 40%, something really catastrophic, people always think, well, everyone sells at a bottom. I kind of think of the other way around. Bottoms happen because everyone's selling. So I think by definition, If we're in a really bad situation, people are going in and dragging the risk meter down to zero. Well, or it was a flash crash kind of thing that had to do with liquidity, but that should be relatively quick. That's right. Yeah, that'll be, that's actually a really interesting way to look at it. Bottoms happen because people sell. Yeah. Yeah, so that's going to be an interesting thing to watch is how behavior is managed. And then you could, if Betterment does better than Wealthfront, then all of a sudden they're loaded with material to win customers for the next several years. It's interesting that you bring that up because one of the things I talk about in that piece that I wrote, which is on LinkedIn, is... I think one of the reasons that there's just generally less alpha in the market today is because you have less of these just retail investors and you have less. Yeah. I mean, that's that's the Goldman term for it. And I'm parroting them. You have less Muppets in the market and you have you have less buy side Muppets, too. Right. Like and so if you have less behavioral. less bad behavior in the market, will you ever actually get that selling, right? But if everything's passive and nobody panics, will you ever actually get another crash? Or will the market become much less volatile? And I actually, this is a hypothesis that I have that is not in any way backed up by any long data set at all. But it's starting to seem like one of the reasons why you're seeing lower volatility in the market against everybody's better judgment and intuition. is because you're seeing more data sets that allow people to manage risk, that allow you to predict what's going on in the individual companies on a smaller interval, right, instead of waiting for the earnings report, being surprised, and then having to rejigger expectations. You have less active money in the market and more passive money, so you have less of these behavioral dislocations.

All of these things should add up to higher equity valuations because of lower volatility, and you should get a lower vol market in general. There will also be less alpha because that comes from the vol and the dislocations. that's what it seems like right now. I could be completely wrong here. And in like three years, we could see just massive whips all over the place because like some other variable is we're not accounting for here, but it's like, that's what it feels like right now. I think that the other variable here is lumpiness. So if, if there is. on a 20-year period, an average amount of vol, how that's distributed within that period is interesting. Same thing with factor returns. So if value investing is going to work in the future, maybe in the past the three-year base rate was 80%, meaning value outperformed 80% of three-year periods. Maybe the magnitude of excess return is similar, but the base rate falls to 60% or something like that. And maybe the same is true of vol. Obviously, I have no idea what's going to happen. We're just going to have these very discreet one-week periods where Vol goes nuts. And then everything just goes back to normal. Everything just goes back to baseline. That would be crazy. So what has you most excited about the future? Obviously, you're a market junkie like I am and just kind of love this whole game. What parts of the market? What has you most excited? Look, our whole space is interesting because I think we're just at the beginning of it, honestly, with the number of data sets that we can crowdsource, which is, you know, that's just simply fun for me. Whether it's at Estimize going forward the next 10 years or whether it's in another vehicle or whatever it is, I feel like my career will probably be there. I want to get back at some point to running money because I love it. I just like this discrete period of time and history with stuff changing and the opportunity in my specific industry. put me in a position to do these kind of things. But I miss running money. It's just the exercise of it to me is fun and intellectually interesting and emotionally interesting because you have to keep your emotions in check all the time. And the emotions associated with running a startup technology company are just completely different across the board. So that's cool. Josh Brown, one of my friends, I think you know well, writes about this. And I've written about it for a while. I think there's a

pricing and disruption risk across the board in everything in our economy right now. Retail companies is a good example. Right now, that is the example that's taking place. But, I mean, you're going to see this in industrials. You're seeing it kind of in energy right now with what's going on with oil prices. Maybe. Who knows what oil prices are correlated to. But I think there's massive disruption risk. And I think you're seeing the mispricing of that in the market, both on the upside and the downside. And everything is shifting towards technology. Every business is shifting towards technology. And that's going to cause these massive dislocations in industries. One of my favorite shorts right now is AutoZone. And for a lot of different reasons. But, like, I would... I can't buy any individual names, but I would be buying leaps on this thing because this thing's a zero. Who can understand how to fix a car these days? It's just a massive computer. Not only that, but in five or ten years, you're not going to be driving a car. You're going to be rolling around in one of these driverless things that Tesla licenses out to you, and they're going to be fixing it because how do you fix a Tesla? Good luck figuring that out. So like AutoZone's dead eventually. And the question is, what are the inflection points for when to get, you know, short this thing? Is it now? Is it later? Is it whatever? And there's a lot of those kind of interesting anecdotes that I have a lot of fun trying to figure out. And then, you know, you place all the different like market timing aspects on top of that general thesis. And then you got to go build a portfolio. And these are the things that kind of, you know. these are the things I nerd out on. The AutoZone is a funny one because it's actually one of the classic examples of a name that screens incredibly well in a lot of systematic strategies, especially those that look at value, at capital allocation, share repurchases, all these things. And it's done ridiculously well in a lot of the periods that these models have owned that stock. And it is the big question, right? Is how do you price disruption risk? And how does that affect value investing? Because the period over which

we formed our belief that value investing has worked, not the ex ante, but the actual evidence is, let's call it 1926 to now. Really, it's 1963 to now, but we do have data on value going back to 1926. The question is, with an accelerating pace of change in technology and the fact that we tend to extrapolate in the wrong, we're not good at exponential extrapolation as human beings. The question is, does that change the story for value investing? Because value, if you look at a, if you go look at a best decile portfolio of value stocks, you're going to see retail, you're going to see AutoZone, you're going to see some of these, a lot of these stocks and industries where your concern is, is most manifest. So it's a, I don't know. Even look at the technology, like I even think that there's. There's mispricing and disruption risk of the disruptive technology companies. Look at Zynga. So Zynga comes out. It's generating a ton of revenue growth. It's doing really well. And people don't understand that there's another whole set of things behind Zynga that will disrupt Zynga because the innovation cycle in the Valley and just in general has sped up so much that business models come in and out of favor within a matter of five years. And so you can't even count on the high growth momentum names continuing to disrupt the old industry because there's one right behind it. And that may take a whole new view on what is value. Look at IBM. good luck getting IBM to like innovate out of this cycle. I just don't see it happening. And that would be a classic value stock right now. One of my favorite charts is that rolling lifespan of a company in the S and P 500 that shows this kind of, there's some cyclical moves, but the secular trend is very clear where it was, you know, 50, 60 years. Now it's. 12 or 15 years. It's pretty amazing. And then you get, and then you get Apple, which everybody's been kind of on the multiple basis betting against forever because they're like, well, they're not going to sell another set of iPhones this season because eventually everybody, and lo and behold, they sell more iPhones like every quarter and it's incredible. My closing question for everyone is to ask what the kindest thing that anyone's ever done for you. Oh man. Well, I mean, I'll, I'll.

Well, it's very simple. My co-founder, Brian Smith, I think it's the luckiest thing that ever happened to me was finding him. So a lot of people don't know this. Some people know it, but a lot of people don't know that the first year of this company was just terrible. Like we did everything we could possibly do wrong. And the company almost died. In fact, there's a... There's an article at Business Insider from our launch day in January of 12 where I have shingles because I have not eaten or slept and I'm just like freaking out because we have to get this thing out the door. I fired my original co-founder who was a very nice guy, but we just did not work well together at all. And we just barely got the platform out the door. And I was just a wreck emotionally, mentally, physically. And they had to airbrush my face because one side of my face is like all puffy and everything. So the article is great. The image is ridiculous because one of my eyes is like almost closed. So we get the platform out the door, and our intern at the time was an incredible engineer, held the thing together for three months. And I find this guy who lives in San Diego, where I went to school, who I met on AngelList, you know, of all places, which we've hired a ton of people from since, and it's an incredible platform. I meet Brian, and on a whim, he flies out to New York for a week and stays with me in my apartment. and we go at this problem. Okay, so we have this platform out the door, but it's like, is this going to work? It's very much just still the experiment. And he spends a week, and he goes home, and he emails me the next day, and he's just like, I'm in. And we paid him basically nothing for the first six months while we did this experiment, and this guy could have worked anywhere. He was a former Google cryptography expert, incredible guy. just did this on a whim, like the idea, me, whatever, he bet on, he just bet on all of it. And without him, like, this thing never would have worked, and I wouldn't have gotten to do any of this. I would have gone back to running money or something. And I think it's the luckiest thing that happened in my life, and I wouldn't have taken that bet. Like, it was a disaster. Like, it was really, the operational aspect of this thing was a disaster up until then. And he comes in, and the thing goes, nothing goes perfectly well.

Like, you know, we've been on a very upward trajectory since then with very minimal kind of hiccups. Awesome. Great place to end. Thank you for your time. This has been a blast and a sign to me that I need to do more episodes on Quant because this is really fun. Yeah. Hey, everyone. Patrick here again. To find more episodes of Invest Like the Best, go to InvestorFieldGuide.com forward slash podcast. If you're a book lover, you can also sign up for my book club at InvestorFieldGuide.com forward slash book club. After you sign up, you'll receive a full investor curriculum right away and then three to four suggestions of new books every month. You can also follow me on Twitter at Patrick underscore Oshag, O-S-H-A-G. If you enjoy the show, please leave a quick review for us on iTunes, which will help more people discover Invest Like the Best. Thanks so much for listening.