My guest in this episode is Jason Thomson, a portfolio manager at the William O’Neil family office.

On paper, Jason doesn’t seem like a particularly good fit for this podcast.  He runs a highly concentrated discretionary portfolio of growth equity names.  He can be levered long, net short, or completely out of the market all at his discretion.

What becomes rapidly apparent is that while Jason has ultimate discretion, he adheres closely to a disciplined, rules-based process driven by the empirical research of an in-house quant group.  The core framework of that process retains the spirit of William O’Neil’s original CANSLIM methodology, but now has nearly a half-century of learning and nuance layered on top.

As a quant, it is tough to hear “growth” and not think “expensive.”  Jason dismisses the idea that growth investing is all about headline-making, high-flying stocks, though, and emphasizes the importance of valuations.  In fact, about a quarter of his holdings are turn-around plays.  

We talk about the role of investment themes, the importance of position sizing, and how Jason thinks about managing risk in a portfolio with less than ten names.

The idea of managing a portfolio the way Jason does definitely put me out of my comfort zone, but our conversation made me reconsider what I think I know about growth investing


Corey Hoffstein  00:00

Okay 320 Wait, wait, where’s my beer? Alright, cheers. Cheers. Let’s do it. Hello and welcome everyone. I’m Corey Hoffstein. And this is flirting with models the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.

Narrator  00:23

Corey Hoffstein Is the co founder and chief investment officer of new found research due to industry regulations, he will not discuss any of new found researches funds on this podcast all opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of newfound research. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of newfound research may maintain positions in securities discussed in this podcast for more information is it think

Corey Hoffstein  00:54

My guest in this episode is Jason Thompson, a portfolio manager at the William O’Neal family office. On paper Jason doesn’t seem like a particularly good fit for this podcast. He runs a highly concentrated discretionary portfolio of growth equity names, he can be levered long, net short or completely out of the market all at his discretion. What becomes rapidly apparent in our conversation, though, is that while Jason has ultimate discretion, he adheres closely to a disciplined rules based process driven by the empirical research of an in house quant group. The core framework of that process retains the spirit of William O’Neal’s original cancelin methodology, but now has nearly a half century of learning and nuance layered on top. As a quant, it is tough to hear growth and not think expensive. Jason dismisses the idea that growth investing is all about headline making high flying stocks, though, and emphasizes the importance of valuations. In fact, about a quarter of his holdings are turnaround plays. We talk about the role of investment themes, the importance of position sizing, and how Jason thinks about managing risk in a portfolio with less than 10 names. The idea of managing a portfolio the way Jason does definitely put me out of my comfort zone. But our conversation made me reconsider what I think I know about growth investing. Jason, welcome to the podcast, super excited to have you here talking about something that’s a little bit different for our, like a quant podcast, we’re actually gonna be talking about growth investing, which is maybe one of those factors that exists, it’s a little murky. And there’s not a lot of literature on it other than maybe some negative literature, thinking of it sort of as the opposite side of value. You’re here to talk all about growth. So maybe to kick off, you can give the listeners a little bit of background about yourself and the firm that you work for.

Jason Thomson  02:54

Thanks for having me here. Yeah, so I’m Jason and I work for O’Neil Capital Management, which is a family office fund for William O’Neal. And I guess a little bit of my background here. So I’ve been on the buy side for nearly a decade now. And I’ve kind of always been here. So I’m really fortunate for that. And one of the ways I got started was if you think of your typical junior year in college, and you’re getting a summer analyst position, most of your friends in the finance program are going out for an investment banking programs. I originally went out and was fortunate to get an internship on the buy side, which later turned into a job. And I maintain that status for a couple years. Now what was interesting is that when I was on the buy side with the first fund back on the East Coast, I was tasked with looking at new and innovative companies. And that pretty much fits hand in hand with growth investing and the William O’Neal mythology. So I looked at that I researched it, I really liked it. And I decided, okay, I want to work for the one group. And that’s fast forward today, maybe six or seven years later, and I’m at the William O’Neal companies. So again, to clarify, I’m a portfolio manager, a discretionary one, I manage a growth portfolio with a long equity bias. And essentially, with a portfolio I have full autonomy. So what that means is from the security selection, asset allocation, portfolio construction, managing risk and expected returns, I have control of all of that. And the way I’m able to kind of navigate, as you mentioned, these murky growth waters is that we have a 50 year legacy at the company. And within that you have a lot of research. So for example, our fundamental team is very deep. Our quantitative team is very deep. So I’m able to leverage both of those aspects in order to achieve some pretty fun and interesting results in the market.

Corey Hoffstein  04:40

I think that was one of the most interesting things when I first started talking to you was when we were chatting over over drinks you were telling me about being a discretionary Manager, which is very foreign to me as a quant, but the more I spoke to you, the more I realized the depth of the quantitative research that you had at your disposal, and I think that’s going to come out a lot in this conversation before Are we dive into that, though, I think the whole idea of growth, at least in the quant world is often conflated with expensive or glamour stocks sort of being the opposite of value. I know a lot of indices are even defined that way that growth is literally the opposite of value. How do you define growth investing at its core?

Jason Thomson  05:17

All right. So I will try to keep this somewhat short, because this could be a pretty lengthy answer. But I will say let’s break their questions up into three different segments. So the first segment will be growth and value and how they can converge, meaning growth and value, it shouldn’t necessarily be looked at in a binary sense, meaning access growth, why is value and they can’t be the same, because in fact, a lot of the things in the market are overlapping. And then next, we’ll break into the actual definition of growth or what I think the definition of growth is. And then finally, maybe let’s talk about a slightly different or contrarian view in order to fully answer your question. So the first thing is, is like you mentioned, growth is related to expensive and it’s kind of a pet peeve of mine, because growth does not necessarily mean expensive, there’s different ways value can converge in the market. So the punchline is basically something that is temporarily expensive today doesn’t have to maintain that status. In fact, it could change very significantly. And if you’re screening out investing ideas, because something doesn’t necessarily fit nicely into your value bucket or to your growth bucket, then you’re gonna miss out on a lot of opportunities. So two different ways actually value and growth can converge together to have actual similar meanings. But they start off in different places is that the first example is, let’s say you have a stock price that is flat, but earnings are growing faster than expectations. So what that means is your multiples will eventually compress and your valuation will become more attractive. And so this means certain scenario can be temporarily expensive today, but on forward looking basis, maybe over the next 12 to 24 months, that growth metric will become less expensive, and it will fit more lines to your value bucket. So food for thought on that example. And then the other examples, what we see it’s a little bit easier. And this kind of happens because of your loss aversion bias that takes place in the marketplace, and essentially, is your earnings are going to maintain their strong growth status. So by growth, I mean 20 or 30%. But due to market correlations, the stock price can increase significantly. So what that means is that when the market corrects your stock will correct, but earnings maintain its strong status, so the multiple compresses, and therefore it becomes more like a value driven name. So you’re in the name, it’s expensive, it becomes less expensive. And so now you have the argument of well is this growth value. To me, that’s not the right argument of what it is or what it’s not, I think we should look at growth and value as that instead of them being binary that could actually go hand in hand in many instances. So that’s the first perspective in terms of growth and value converging. Now, what’s also interesting, and this will maybe stir up a little bit of ruckus here is that if you look at some of the best performing stocks ever in the marketplace, from the time they had initial price move to like a topping process. If you look at what their valuation was initially, in the beginning, most offensive more than half the time you’ll find that evaluation, let’s say valuation driven by let’s say PE to use as a benchmark becomes significantly higher, it’s high in the beginning, but it becomes cheaper. So what you’ll see is, for example, if we take a couple of stocks by decade to illustrate this point, further is that in the 1960s, you had Xerox had sold for originally 100 times earnings before it went up 30 300% advance and then later the valuation became much more attractive and etc, and maybe would fit into your, your value bucket after Rose 33%. Now, that was in the 60s. So if you go a little bit later to the 70s, you have the syntax example, you know, they have the best product and marketplace, it was original birth control. And the PE was selling for 50 or 60 time earnings before it tripled, I guess if we want to use a more recent example to kind of wrap everything up. But it’s the Google example. So Google’s PE was, again, like in the 50 or 60 range before the stock price tripled. So my argument that I’m making is, yes, valuation is important. But sometimes having maybe one stock in your portfolio, or even two, that might temporarily be expensive today will not always be expensive. And ultimately, if we’re discussing your portfolio manager, this is what it comes down to, you have to have a differentiated view in the market. If we’re all agreeing on what value is or what it will be, then it’s hard to generate alpha. So in terms of having a different view, I think that’s what I think of growth investing from a valuation standpoint. And I realize I’m taking a long time to answer this but going into your definition of growth. I like to think of growth in a couple of different aspects. So we’ll break down growth from a persistence standpoint, from a sustainable standpoint and from a reoccurring standpoint, we’ll look at the different sources of growth. So growth and valuation by definition, I think growth is going up well into the 20% category. So 20% earnings, 20% sales and everything of that nature, then you’re going to get into a persistence category where we’re going to analyze growth and make sure it’s sustainable. So what that means is you want your growth to be reoccurring to have sustainable growth patterns. So that way, it’s predictive in nature. And that way, when you allocate to a stock in your portfolio, and you have, let’s say, because our portfolios, from my perspective, we tend to have like a year and a half holding period of 24 month holding period. So if the only way to hold a stock for a year and a half or two years is to be able to have some type of sustainable metrics, so if you have sustainable earnings, meaning no negative earnings surprises, or they’re just basically the company’s reporting very aligned with their earnings metric, it’s easy to hold it. And if not, then that creates a lot of different issues. So growth, by definition, again, is 20%. We look for a reoccurring growth pattern, sustainable growth patterns, and then we could get into sources of growth, which we talked about a little bit earlier, it’s not something that I think of individually, I think of sources of growth in terms of a sector exposure, so for example, certain sectors, and we’re prone to have various or put out various different growth metrics. So the most obvious one is a retail space, retail companies can grow by m&a standpoint. But that’s not necessarily organic growth. So you can have big growth numbers for the wrong reason. That’s not necessarily something we want to allocate to our portfolios. So organic growth comes down to just having essentially the best product in the marketplace, the most superior product. And if we’re able to have that, then you’re going to be able to have more predictive reoccurring revenue streams. So we talked about growth and value converging, we talked about the definition of growth. And the last point I’ll make is going to be this contrarian viewpoint. So the value camp always likes to use Buffett example. So I guess we’ll play with one of these analogies here. So the value camp from a different country and standpoint, we’ll use the Buffett toaster example. So Buffett and value says, Well, if the stock is $50, today, but it’s generally around $40, tomorrow, then that 20% discount, we would prefer to buy that because it’s cheaper today, not knowing that what’s cheaper today can continue to fall in price and can become much cheaper. So that’s the value camp. The other end of the spectrum is a growth campus, maybe something that’s $50 today, and assets being priced at $50. But tomorrow at $60. So generally, why would we pay up a 20% premium? Well, there’s a lot of different reasons. So let’s discuss potentially one of them. One of them is that a new growth company that is selling for premium today, could it be selling at a premium, because it has the best product, and the way it has the best product is that you know, because the last three or four quarters, their earnings are growing 2030 40% In some cases, and they’re essentially becoming a product market fit where it’s becoming dominated, you see the increasing market share. But in order to get that product to market, they have to have a high SG and a expense. So for example, they’re paying out a lot of their profits towards their sales team to sell that product to get it to the market, we saw this in the most obvious and classic example of Apple, once it becomes a fit to the market, they pull back the SGA expense. If I’m describing a scenario of what Amazon did, then that’s pretty much a model that a lot of companies follow. They have the best product, they’re paying a lot of overhead, they pull back the expensive and all of a sudden, you have those big growth numbers. So you’re paying a slight premium today, knowing that that scenario is a possibility. And again, going back to what I originally was saying about that you have to have a differentiated view in the marketplace. Well, a willing to pay a slight premium is a willingness to take risk. And for that you need to get compensated right. So that’s one of the different examples. Another thing is that if the street is expecting your company to only grow at 20%, for one or two quarters, but you think it will grow at 20% for the next three or four quarters, then that’s going to be a significant mispricing in the market, which will therefore create an inefficiency where you can capitalize on now I don’t think this ladder analogy falls into the what we discussed earlier, the greater fool theory where what I pay today, someone will willing to pay me a higher price tomorrow, I think it kind of falls into the category of the momentum anomaly where it’s obviously become more prevalent. And, again, we talked about this before, but the value and momentum camp is like this great new thing over the last five to seven years that everyone’s talking about. We’ve kind of had that same framework for the last three or four decades. At a minimum of wanting that momentum anomaly on our side, we just overlay it with a growth metric instead of value.

Corey Hoffstein  14:29

So a lot to unpack. There are lots to chew on. And I know for myself as a quant when we first started talking, my head was spinning because it’s so unlike my traditional framework, but the more we got talking, the more it did seem like a lot of what you were saying was aligning with frustrations I’ve had in the past. So we’re talking about this dichotomy of growth and value. If you look at every major index provider, whether it’s s&p MSCI, Russell crisp, they all do the same thing with growth and value which is When they define their value indices, it’s companies that are cheap by some sort of valuation metric price to book price earnings. dividend yield is one of the ones that they use. And then they also simultaneously have to have really bad growth. So often they look at earnings growth, sales, forecasts, earnings trends are another common one. And so what you end up with in your value portfolio is almost things that are justified, cheap, it’s cheap, and it’s contracting and fundamentals. And then on the growth side, you have not only to things have to be growing, but they have to be expensive. And so you almost get justified, expensive, but if it’s growing, it should be expensive. And so you’re not, you’re not trying to capture any edge there. So the idea that growth and value are opposites of each other doesn’t necessarily make sense to me, in preparing for this podcast actually found one piece of literature that showed that growth as a quote unquote factor might work. It was actually from MSCI. And what they showed was, if you just took sort of basic, really simple growth screens, it was a disaster. But if you controlled for these different variables, all of a sudden, it became a really impressive factor. And I think that’s probably what we’ll get into as we get going. You started to go down this road, though. And I think I want to back up a little bit, you started to go down this road. Often, when we think of a premium in the market outperforming the market, we ask where do we think this is going to come from, so it might be a risk premium, and you sort of mentioned that maybe you’re bearing a risk that the market is going to pay you to bear, it might be a Behavioral Anomaly that you’re exploiting, or it might be sort of a different type of framework. And this is sort of the Mobis in framework of you’ve got better information, you have an analytical edge, you might have some sort of behavioral edge, maybe it’s a structural edge, there’s someone in the market that just has a different utility function than you that you can exploit. When you look at growth. Where do you think the edge is coming from?

Jason Thomson  16:47

Right? Yeah, so there’s a lot to answer that framework, right? Because you have rear information, all your analytical, your emotional, structural, etc. So I guess before I get into that, let’s go over to quick analogies to set the tone of where these various sources of edge can come from. And then we’ll package it up together of how we actually capitalize on that. So the first thing of what’s an edge in the marketplace? And how do you define it? And then more importantly, how do you capture that? The first thing is that being a discretionary Portfolio Manager, and capitalizing on these inefficiencies in the market is very, very difficult. So you have to have a process in place to be able to do that, which I’ll touch on in a second. So what I like to say is being a discretionary RPM is like climbing Mount Everest, if you’re able to do it, but if you can, it has a big payoff. So you’re taking an asymmetrical bet on your life and in your career. Now, another layer on top of that analogy is that in a different sense, a discretionary Portfolio Manager is more like being a startup founder and Silicon Valley, where you know, the odds are against you that most of them fail, but the ones that do succeed are very successful. And in terms of like an absolute dollar amount, it’s very meaningful, right? What’s also interesting before we get into these edges is that some of us are genetically predisposed of wanting to accomplish this impossible. So knowing that it is possible, what are some of the ways that we could get the edge? So the way I’ll think of the general framework of how to get an edge in the marketplace? is I like to start with what’s the best and how did they accomplish that? And then work backwards from there and see generally if my strategy matches or if it’s in line with that so quickly, some of the best investors in the marketplace like Stanley Druckenmiller, George Soros, Paul Tudor, Jones, William O’Neal, how do they achieve these big 20 to 30% CAGR hours? Well, pretty much if you deselect these turns, which you know, spend pretty much endless amount of hours doing is that they’re able to capitalize on the markets that are conducive towards their strategies, and sit out in markets that are not. So they’re very hyper aware of that. And I’ll touch on the quantitative metrics and make you more aware of that briefly. But essentially, what that means is that they’re able to swing out the fat pitches in the marketplace, and then to sit out the ones that aren’t. Now realistically, in order to do that, you’re going to have to have an incredible amount of patience. But more importantly, your capital base, your investors have to have even more patience, because that means you’re going to have some periods where you’re doing well and some periods where you’re not. So to put this into perspective, if we break down that edge returns over 10 years, if we just take a random 10 year sampling period, what this means is that, let’s say half the time, so 50%, or five years out of those 10 years, you’re going to have inline market returns three years, we’ll have a 0% return, meaning you’re flat versus the market. So usually in these type of markets, the market is negative, it’s having a drawdown, so the market can be down 510 or 15%. But you’re at flat, are you at a 0% return. So on a relative basis, you’re outperforming and then again, these investors in order to achieve those big K gars is one or two years out of every 10 years, they have really, really big years, I mean, either 50 or 60 or 70%. And I know on the surface that seems a little bit ridiculous, but it’s not when we get into some of these things of how to implement it. It’s something that I was able to Do you and take advantage of a couple of times now, it’s following this framework, knowing that some of the greats can do it. And it’s available to us in the marketplace, the way to actually get there now to get those returns, is by going through some of these edges. So I know that was a little bit long to set the tone of like how these edges are possible. But they are possible give some of the reasons why are some examples? Now we’ll get into definitively what are the sources of those edges. So I think the source of the edge from a discretionary standpoint, basically all starts with a process the investment process, and needs to want to be able to produce alpha, it needs to be sustainable. And it has to be something that you can follow in a consistent manner. Now, for me, I like to take this philosophy and apply it to life in general, not just the market, because I think if you’re able to have a process, something that’s sustainable and repeatable, then you’re able to do well. And then just frankly, I think the days of being a discretionary PM, where you have this willy nilly process that it’s not exactly concrete, and you can’t back it up with empirical data that’s over with no one’s willing to pay for that. Moving forward, I think that we’ll continue to go along the same path. So as Bill would say, in terms of the process, you would always hear him saying the office rules, rules, rules, and what does that come down to it comes down to a process. So Cory, I know when we’ve talked in the past, and I’ve gone over some of the personal strategies I follow, you said, you know, this is more or less a quant strategy, and you’re just deviating slightly from the weights, when you think it’s worth taking those any of syncretic risk. And my framework is Yeah, I don’t know, like, why should be classified as entirely growth, if anything, our strategies these days, and more like, I don’t want to use the word quantum mental, but that’s kind of what they fit into. An example of this, or another analogy is that it’s the fighter pilot analogy. So we know that before a fighter player goes to take off, he goes through a list of checks and procedures to make sure everything is running correctly before they take off. And it’s something that they do every single time, day in and day out. And what that allows them to do is to be able to have something that’s consistent that they know that works, that is able to accomplish their mission or their job, the same thing can be applied in the marketplace. So we’re just taking it maybe a little bit further with some of these things that we do. So now the sources. You mentioned, the Bates approach, right? Michael Mobis. And he does a terrific job on this. So I’m not going to try to elaborate anything that I can say that he has no, he’s done a terrific job. So I’m not going to be able to touch on all those factors. But maybe I’ll touch up on one or two factors that I think where you could significantly get an edge in the marketplace. The first one probably arguably the most important is the behavioral aspect. Now there’s a ton of research out there that shows that behaviorally, there’s a lot of things that goes on in the marketplace. One of my favorite papers to illustrate a really fun and interesting point is that it’s the genetics of the investment biases. Essentially Segal and another author, were able to say that even though all of us are predisposed to all of these investment, different biases, some of us are more predisposed to certain factors that others and what they illustrate this point is they have two identical twins that are given in the same environment living conditions etc. have entirely different investing results simply because of the predispose experiences, they have meaning like individually and use and critically experiences. And that was able to produce significantly different investment outcomes. Now, just simply if I could happen to identical twins, imagine the dispersion you could get between managers. I think also when you see behaviorally, some investors are just more prone to be more like quant some are meant to be more discretionary. Some of them should even be investors. So if you’re trying to be a discretionary manager, but you have a hard time following processes, you’re just setting yourself up to failure. So it’s something I think genetically, you have to have something that’s there. And that’s available for you. And I guess outside the behavioral aspect, if you think of the informational aspect again, quickly, just off the top of my head here. Pre reg ft. Yeah, sure. I think there are sources of edge information only post Reg FD, I think you could see this and many different examples, that informational edge is pretty much it’s gone. Not gonna say it’s gone entirely away. But it’s definitely decreased significantly, because most of us are using the same information. And I think almost all of us are using the same tools. So that doesn’t seem like an area that you want to compete in. Frankly, if you look at information ratios over portfolio, especially how they diverge over time, I think you could kind of see this, I think, generally you want to have an IR in a portfolio, that’s net positives, because it does lead to that you have some type of edge, but doesn’t necessarily have to mean it’s information. Really, I think a big one that I’m starting to realize more as I get deeper and deeper into my career is the structural aspect. So I’ll deviate slightly from the Bates approach from a structural standpoint, and I’ll go into the infrastructure. And I’ll just say this, the infrastructure of what you do and what firm you’re at is very, very important because it’s going to tell you if you’re able to scale your ideas or not. So again, the punchline it’s very important. You need the ability to be able to go to the marketplace, have a hypothesis, go and test it to see if it’s significant. or not have a reiteration process that you’re able to do quickly and efficiently. Now, with this process, there is a big financial cost that goes into this quarry, I’m sure you see it all the time and more. So being involved with this directly myself, there’s a big time element involved. In fact, I think recently, which really hit home for me is when I was reading one of the OCM papers or tweets or something along those lines, they mentioned a research graveyard. And I just resonate really well with that, because I think of any good research process. And again, ours has been going on for about 50 years, now you have to have a very deep research graveyard. And it’s just frankly, expensive. So from an edge, yeah, infrastructure is very important. And I guess the last one to touch up and wrap up the whole beats or sources of edge is that so for forecasting error? There’s a lot that goes onto it when you’re forecasting or predicting a stock. So I think Philip Tetlock has done some of the best work on this in terms of forecasting, again, the punchline for this will be it’s very difficult, if not mostly impossible. So you want to be careful with how you make predictions, I would actually say there’s a big difference and prediction versus interpreting the data that you have. So from being a discretionary Portfolio Manager, you want to be able to interpret the data and be reactionary underneath it, and terms of like a bait and easy and cents, versus forecasting too far out. And the future and just extrapolating what’s good today will be good in the future, because that’s not how things unfold. Again, I know that’s a lot for the sources of edges. But I just wanted to make sure I touched upon some of them. One of

Corey Hoffstein  26:30

the things you touched upon there, that definitely resonates with me, I mean, talking about the Oh, Sam research graveyard, is the institutional legacy. And I start to see that now, we’ve only been in business for a little more than a decade. But I can start to see a little bit of a snowball effect of saying, No, I’ve done research in this area, or there’s an idea that creates some lateral benefit in another portfolio. And you can see the benefits accumulate over time. But I would say the vast majority of things you research end up in the graveyard as they should, markets are pretty efficient. And if all these ideas were working, we’d all be ultra wealthy. So starting with the null hypothesis that the markets efficient, and you would expect that the vast, vast, vast majority of your ideas should end up in the graveyard, which is depressing. But it speaks to your point, there’s a huge time element in being successful here. Talking about the legacy of a firm that we’ve mentioned William O’Neill’s name a couple of times, you work for the family office managing money, William O’Neal, for for the listeners, maybe you can give a little bit of background on him. For those who are at least remotely familiar. Or if they’re not familiar, the name they might be familiar with, for example, IBD, or they might be familiar with the whole cancelin methodology. So maybe you can give a little background on William, his sort of investing thesis and how much of that lives on because I think that original cancelin idea was published in like the 1950s, if I’m not mistaken. So maybe you can talk a little bit about how that legacy lives on through the firm.

Jason Thomson  28:03

Right? So this one will not be as long of a rant and it will be a little bit more fun. Yeah, essentially, William O’Neal, I’ve said this, we’ve been in business for over 50 years, he originally started off being the youngest person to purchase a seat on exchange. And he put a couple of million dollars in r&d making these growth models back in the 60s. So if you use present value terms, that’s pretty significant. And there’s something to be said about having a four decade headstart in the marketplace. So answering your question of what were the growth metrics that he originally invented? And what do we use today? I will say this, like any good research process is going to involve or evolve rather, and that’s what we do at William O’Neal is we always try to make sure the process is continuing to better itself. So at its core, he’s most famous for cancelin, right? That’s original growth models. And I would say a lot of investors kind of misinterpret it because they think it’s just an acronym. And it’s a weight of how to invest. I would say incorrect. It’s a way to perhaps select stocks. But you want to think of the khamsum metric and the growth models that were originally published four decades ago, as being the foundation of like a good growth strategy. So a foundation meaning as in like a foundation of a house, let’s use a physical example. For every house, you have a foundation of flooring, which we’ll call it cancelin. And then you’ll have a roof, plumbing and electricity. So there’s a lot that goes into the house besides just the floor of the foundation. Same thing with the growth models of what I use today. It’s a lot more than just an acronym, most importantly, the portfolio construction process, how to optimize a portfolio. And I know we have a couple of questions that hopefully we’ll get into in terms of what those metrics exactly are in today’s day and age, and maybe why did they change and how we optimize them. So I’ll touch upon that in a second.

Corey Hoffstein  29:55

For the listeners that don’t know can you actually say what cancelin is?

Jason Thomson  29:59

Right? So Can’t send as a growth model that we follow of how to select stocks. So starts off with like current earnings, you want current earnings of being 20% annual earnings around 20 25%. Most importantly, an acronym is that n. And that stands for a new product and innovation. What’s interesting is that we’ve mentioned that the cams flame has started around in the 60s, one of the letterings is the N. And I think it’s one of the most important it’s a new product, a new innovation. I know of some research, some controvery research has been controversial research has been published on this recently, where you have the school of thought from the HBS camp of Porter’s Five Forces where you have all these different bargaining powers, etc. And then you have the paper that’s published on from the Chicago Booth camp that says, that’s not exactly true. Let’s quantify this. And we’ll show that basically, the most significant determining factor is who has the most superior products, because if you have that, then everything else falls in place. And that’s kind of what we figured out as well, it’s just a little bit longer ago, and then you get into a market direction. So there is a slight market timing model to it. So originally, I said, What’s interesting, the last five to seven years, you have value and momentum becoming a thing, meaning the value camp said, Hey, we generally want to be in things at attractive prices, but we don’t want them to continue to follow on price. And maybe if they’re an uptrend, that will produce a some type of higher expected return and will capture premium for that. And it’s true, it holds well in the marketplace, it’s been tested. And so our market timing component just essentially does the same thing. But with growth, it’s that we generally want to own growth companies and up trends and not downtrends, because it produces a higher expected return. And we test this historically, it’s a significant factor. So generally, I guess in a holistic manner, that is what cancelin is.

Corey Hoffstein  31:40

And that market timing aspect is something I want to touch on a little bit, because I do think it’s really interesting part of what you do as a discretionary Manager, which is you don’t have to be fully invested. In fact, you were very willing to wait for your pitches and sit out the market. But to touch on your point, your value example there. I know DFA for example, they added in subtle momentum timing, they obviously didn’t want to admit that momentum was a factor. But they’ll say, Well, you know, what, we’re gonna defer the purchase of value stocks until it no longer has really negative momentum, or on the other side, they won’t sell something just because it’s gotten more expensive out of the minor bands, they’ll extend that band. In doing so they’ve helped eliminate a lot of the negative momentum factor what they do, which has helped them reduce dragging the portfolio versus the market. So trying to build a portfolio that accounts for those making sure you’re not buying into a negative factor is important. We’ll talk about that, where I want to start. And and this is something you mentioned a little bit with the new products idea. And you mentioned a little bit earlier, the idea of growth, investing in sort of broader themes. When you think about building a portfolio, how much of growth investing to you is in the name and a single security and getting that security call Correct? versus how much of it is just being in the right theme? Could people build a growth portfolio with all these thematic ETFs that are coming out nowadays?

Jason Thomson  33:07

Okay, so I answer that question in two parts, not three, but two, will give you a choice. So yeah, let’s look at this unbiasedly. What does the data say? So if you look at the data from our original studies show that about half so meaning 50% of a stock’s move is directly correlated to its industry group. And I’ll define that a little bit more clearly or in depth. So I prefer to look at the general sectors and generally a firm does, it’s not the overall sectors, we’ve been collecting data for a long time. So we’re able to parse this in many different ways. One of the interesting ways that we look at this data is that we take the general sectors, and we break them down into about 200 different industry groups. So what that allows us to do is simply is just knowing where the strength and where the weakness is Korean, where is it good was about at any given moment. And when we study this, over the last couple of decades, and no further look back period, shows you that again, half of the stocks move is tied or directly correlated to the industry group.

Corey Hoffstein  34:01

How often do you have to revisit those industry groups? Because it seems to me like almost a new industry can be coming out all the time. So I think

Jason Thomson  34:07

the industry groups now this is where it’s gonna get fun. I think those industry groups come down to like product cycles in that. I don’t think we should think of industry groups as necessarily in terms of like themes, industry groups, we could think of tack but what’s within tech? Well, we have software, well, we have software that was originally physical and in terms of like selling seats, and then it become Software as a Service, and then Platform as a Service. So that’s how we further break it down. And then what I will say is, in terms of answering your question definitively, of how to construct a portfolio based off of knowing these metrics is that that actually makes it a little bit easier because what that means is generally you don’t have to pick the absolute winner in a stock, knowing that if you could just land in the right themes or allocate towards the right themes that you’ll generally have a higher expected return versus not. And then what we try to do is we try to use a traditional bottom up fundamental research effort to narrow down those themes to The airport picks some of the best securities. But usually, if you could land in some of the best performing industry groups, you will be able to have a higher expected return.

Corey Hoffstein  35:08

So let’s talk about some of that picking individual securities aspect of it. Because again, I know in talking to you in the past, this is not a, but it is bottom up fundamental, but it is very quant driven from my perspective. And one of the things that really shocked me, the last time we got together is we were talking about the characteristics that call the universe down from call it 3000 securities or 7000 securities or whatever you want to call your big wide world. And you said, well, actually, once we sort of apply our initial screens, it’s more like it was like 50 stocks that you can ever really look at, which I thought was really fascinating. So maybe you can talk a little bit about those initial screens and the constraints that you face and trying to deploy capital. And then from there, once you get that universe called down to a pond, you can fish and what are some of the sort of metrics and characteristics you are looking forward any given time and trying to pick a security, you know, what makes a good candidate. So

Jason Thomson  35:59

I think in terms of like the listeners, right now, this is probably going to be one of the most interesting aspects because this is getting into the skin of the game and part of what we do. So if we just use an example of the US domestic markets, we start with the screening process of about 7000 unique securities. Once we overlay our growth models, it gets narrowed down very significantly, like you mentioned. So let’s define some of those metrics. First is that a general rule of thumb, the definition going back to growth is that you want to have about 20% increase in earnings 20% increase in sales, and a little bit lower than 20% for your margins, like your return on equity. And then you want to see an acceleration and these numbers most significantly, and needs to be weighted in the most to recent quarters, we could get into why that is. And then perhaps maybe that’s more of a momentum fact versus like a fundamental fact or, etc. But that’s something that we generally look for. And then we look for margin expansion. So just with those factors alone, if you narrow down the database start from 7000, any given time period, you’d probably end up with less than about 100 stocks, but then it gets reduced significantly when we add in liquidity factors. So everyone has different liquidity parameters and risk tolerances that they were able to deal with, I will maybe give an example of what’s available in the marketplace, and then how I’m able to kind of navigate with it. Because the liquidity aspect, the more I get involved with being a portfolio manager, the more I realize it’s one of the most significant factors, because you can have a really great idea that you’re able to find it’s just it’s very illiquid, it’s tough because then you’re getting a premium for being the liquidity risks that you’re not necessarily wanting your portfolio, especially your capital allocators. And if it takes you a long time to put on an exit a position that creates a lot of other different metrics. So the quarterly aspect of generally I use a parameter of having a company that trades $100 million a day. So the average daily dollar volume needs to be about 100 million. Now you could deviate from that, if that’s kind of like the preferred habitat theory and maybe fixed income, you’re gonna stay in your preferred habitat. But if you get a premium outside of that, then you will deviate. So I like to think of that as like the 100 million dollar benchmark of what I like to navigate in. So out of the 7000 unique stocks in the domestic database, about 500 companies trade more than $100 million a day, it’s not a lot. If you narrow it down further, about 175 companies trade more than $250 million a day. And then 75 companies generally trade a half a billion and only 25 companies, it’s actually 24 trade over a billion dollars a day. I mean, I create another subject I won’t deviate too far. But in terms of looking at proxies of markets and all these different things. It’s would you rather look at the Dow or would you rather look at 25 or 30 stocks, I trade a billion dollars, that would probably give you a good indication of what the market is doing. So back on track, I use $100 million on a daily average dollar volume and the rule of thumb, it’s really simple. For risk parameters, if you want to be 5%, or maybe say a little bit more aggressive 10% And the average daily dollar volume, that’s only $10 million building up position, it’s very small, insignificant amount, and then knowing it might take you 1520 25 days to establish that position. That’s kind of where you get these numbers from. It’s from a risk framework. Now outside of that, what’s an really interesting one that I know has a lot of edge and it’s one that I use a lot is the institutional sponsorship that we talked about. And it’s something that doesn’t get a lot of talk for whatever reason, but it’s something that’s very prevalent. So institutional sponsorship, what I mean is, one, how many mutual funds and how many long short funds are in your stock and then to the quality of them. So we rate these funds on a ton of different metrics. But basically we know what are good funds, meaning higher returns What about funds, meaning don’t produce any returns that don’t outperform the market. So generally we know whether the good funds are buying our stock, the higher rated funds and then to fund other analysts that we know, typically, the set group have long only mutual funds if they tend to establish a position, and they have a multi year time horizon. And they follow similar growth models in us. And we see through public filings that if they establish a position that generally that liquidity is on our side, and more often than not these funds are going to continue to buy one way to reconfirm this is that if the next quarter updated, they’ve added to their position. So now all of a sudden, we have our Fund with money in the stock, a couple of other funds with it. And then now we might have a handful of the really big, large mutual funds on the stock that helps with the conviction building process, because some of these growth names can be volatile. And if you know your stock is down maybe 10, or 15%, and a couple of weeks timeframe that hey, fidelity contra fund has $110 billion in a single growth equity strategy, they’re gonna step in and allocate to the position because they’re just not frankly buying at highs.

Corey Hoffstein  40:54

Well, that’s something I want to touch on a little bit later as we go down the rabbit hole of your process, because I know that’s part of your risk management thinking. But that was definitely when you first mentioned that to me different than I think a lot of a lot of people say I think a lot of the value guys at the more Nishi firms tend to prefer under followed companies because they say, well, that’s where you can get a real discrepancy and market view. And you can think differently and maybe really find value, you actually almost go a little bit the other way from a risk management liquidity management perspective that you can build more conviction, if a funds out there that are good performing funds or acquiring your security can be a positive sign for you, which I think is pretty interesting. I want to talk a little bit about go back to something you mentioned earlier, which was the sectors, the industries, the themes, one of the critiques that often comes up with, like very naive value strategies, for example, is that the same metric doesn’t apply across different industries. So enterprise value to EBIT up, for example, that’s like a cashflow metric that’s more capital structure neutral, but can’t necessarily be applied across different companies that have different capex needs. When you think of applying these different metrics across different industries, do you have to think about reweighting them for particular industries? Like do you think about, oh, this is a really asset, heavy growth industry, this is a really asset light growth industry? How do you think about characterizing that,

Jason Thomson  42:16

so yes, you’re going to want to wait them according to the industry that you’re in. So if you’re involved, or allocating or looking at maybe a tech company, recent example software company, that 20% growth margins are going to be pretty light. Why because almost all of them have like 80% growth margins. Now, the other end of the spectrum, if you’re looking at maybe like a telecom provider, or a retail company, their margins aren’t going to be nearly as high, their earnings aren’t going to be growing nearly as fast just by the dynamics of the market. And so you need to have a proportional weighting system in place for that. So generally, I mentioned the 20% numbers just to simplify it, but you want to have those in relation to the certain industry group. So the punch line is, the more I guess, risk and growth oriented the industry group is that the higher the benchmark becomes, and then the lower the risk and growth is the lower the benchmark really comes. So we have a framework to work around, but we use adjustment factors.

Corey Hoffstein  43:14

So I want to get into the nitty gritty of your portfolio. Now a little bit. One of the things that I’ve always sort of believed differentiates quant managers and discretionary managers is in the way we manage risk. So quant managers tend to not know what’s in their portfolio. And we tend to think that’s a positive thing. We don’t sort of get fall in love with our positions. But we manage risk through diversification, we tend to hold a broader basket of securities. And our metrics might be a little bit more obtuse, but hopefully it sort of the incorrect positions are getting canceled out. I know you hold it like an incredibly concentrated portfolio, five to 10 names type of portfolio, and you watch them like a hawk. How do you think about position sizing? How do you think about building that portfolio that is so heavily concentrated?

Jason Thomson  43:59

All right, so my favorite part of this discussion by far, and hopefully one of my last amount of horizontal provide for today. So position sizing, and concentration within a portfolio. It’s something that I think about every single day, and it’s something that I think I have thought about every single day for pretty much the last decade is positioning towards theme, and I’ll get into the reasons why. Basically, if I get a chance to talk to any significant investor in the marketplace, first question I ask them is how do you position towards a certain thing? What about carrying values, notation of values? What about sector weights? How do you deviate from that, etc. So hopefully, I’ll be able to answer some of those questions. To set the tone, I will say the appropriate position, size and concentration in a portfolio is like wearing the correct fitting pair of running shoes in a marathon. If it’s too big, or if it’s too small, you’re not going to be able to complete the race. But if you have just the right running shoes, just the right size, you might be able to finish the race. So translate that into the portfolio if it’s too large. If your position size is too large or too small, you’re not gonna be able to finish the race. You need to have an optimal position size. Now to get away from that, and I’ll get into the metrics of how to optimize it,

Corey Hoffstein  45:06

I have to say that that might be the healthiest metaphor you’ve given. So far today,

Jason Thomson  45:09

I’ve a lot of these things that translate, it’s just how we look at or how we’re able to simplify a lot of this stuff. So what we do know is that being a discretionary Portfolio Manager, how are you compensated? How are we incentivize? Well, by definition, I get paid, if I have have returned higher than the market, right, my author. So in order to do that, it’s very difficult to have a ton of stocks in your portfolio, we’re not a mutual fund, we’re not going to carry 100 to 200 stocks, which essentially you become correlated to the market and you are the market, you need to have a low number of stocks. And there’s a ton of research on this generally less than 30 is fine. The from all the data that we have looked at on empirical basis, generally, a 10 stock portfolio is fine. And this is with basically any dollar amount that you could come up with. So when I say I have a 10 stock portfolio, or even a six stock portfolio, immediately a lot of my value friends immediately say too risky, you’re taking way too much risk, that concentration is not necessary to produce the highest return. And I say, Okay, well, you know, it’s interesting, this buffet guy, he has half of a trillion dollars, a couple 100 billion allocated to the market, he has 80% of his portfolio is allocated and 10 ideas. 62% is allocated in the top five, and he has a single position with a 22% weight. That’s pretty much what we do. So why is that not risky? And but what maybe a growth manager does is risky. And then the counter argument is oh, well, in that sense, maybe we could start to see why. So what I will say is that one of the biggest sins and investing is having your biggest position being your smallest percentage of equity. So you want to be able to combat that. And some of the ways that we could get into having like an optimal position size are going to be the quantitative metrics. So a rule of thumb for individual position sizing is it’s generally okay to start with the Kelly criteria. So you start with that, it’s going to be based off your window loss hit ratios, like for example, if your batting average is really high. Fortunately, mine is recently so it says I need a 40% position size. Obviously, that’s ridiculous by our standards, use some adjustment factors. Now there’s a lot of different ways you could use adjustment factor, some people just use half of it, I like to be a little bit more quantitative in that and use a series of like checklists almost. And so that way I have, let’s say if my my starting position size, generally 15%, which is kind of most often it is I’m not going to deviate too far from that. If all of the things lined up, I might go to a 17%. Or I might go to 13%. And

Corey Hoffstein  47:42

what’s something like that’s on that checklist that would make you go up or down,

Jason Thomson  47:46

there’s a series of quantitative attributes, and there’s a series of qualitative attributes. So the quantitative attributes is, the more strong it is fundamentally, and there’s a ton of different fundamental variables that the higher ranking it will get. And then that overlaid or weighted with a colleague formula will tell me to deviate slightly to the upside, meaning I’m able to take more, or I should take more risk based off all these factors. From a qualitative standpoint, what I like to think about is what’s the reality of the current conditions? Well, if my portfolio is up 20%, in the year, I’m only 70% invested mean, I have a lot of capital to deploy, I have the ability to take on a little bit more of a higher position size, because my portfolio is doing well I have that risk. So you have the quant metrics in terms of like the Kelly sizing, and then you have some of these qualitative attributes to change it. Now, that’s for individual position sizes starting. And there’s a lot more that goes on to besides starting throughout the position, I mentioned, an optimal holding period for US based off of all of our model books is around 15 months. So your position size is going to deviate from your starting amount based off your carrying value notational value,

Corey Hoffstein  48:55

can you go into that, that 15 months? Where where’s that come from? And how does that sort of tie back to the ideas of growth.

Jason Thomson  49:00

So that comes from our internal model book studies. And it’s actually one of the things we’re pretty popular for, from like, on the buy side, at least I remember, when I was starting out in the business, just ironically, the fund purchased these models, originally in like the late 70s from our group, and I didn’t know it at the time until six months later. And they were saying, oh, yeah, these things are great. They give you a good benchmark of what is realistic, what is not going back to the Michael Aubusson example with his expectations framework is that you have this general benchmark of what you think is possible, and then you see what the reality is, and then you kind of adjust your ways accordingly. So we know through of empirical data that internal studies that we’ve done, that generally if a stock fits our characteristic, it’s going to go on for about 15 months. And then we have time periods based off certain other factors that will go on for 24 months, meaning by the time you enter this position, it should continue on for two years. And this is based off a lot of different factors. And then what I was just saying before that was that, knowing that you’re busy Shouldn’t size is going to deviate from that based off its carrying value, I will say it’s something you’d have to get more aware of as the size becomes larger in your portfolios like actual dollar amounts. Because all of a sudden, let’s say, if you start, let’s use recency example, that the last two years in the market space software has been doing really well, you have to 20% positions in software, well, all of a sudden, that’s 40% Your portfolio but based off the carrying value to doing well over half your equity, and software, well, you need to trim that back, you need to take off some of that exposure, because you know, it maybe in certain environments, it’s okay, but most of it, it’s not. So you need to be aware of what your carrying value is, and what are some of the factors to reduce that carrying value, which I’ll touch upon, because I think those are really interesting. And then another point I will make based off the position size is, look, we’re in a day and age that we want to use all these different quantitative metrics of how we’ve narrowed down everything. And I probably use them, let’s just say I use them a lot. So something that’s different, I will say is no one what type of market you’re in. And you have to kind of be in tune with this through, again, a series of checklists. But for example, having a 10 stock portfolio in some markets might be okay, but having a five stock portfolio and certain markets will be okay. So easy examples that, you know, recently I hate it, but like the Fang was doing really well, well, you can make a strong argument of being correlated to just a couple of names and having a very high expected return. Well, that just doesn’t exist in the last couple of years. And if you check every market, there’s different scenarios that will tell you to have maybe five stock or 10 stock portfolio. So a recent example, that was a very concentrated portfolio was that and 2004 to 2006, you have the apple Google Market, you could have half of your equity, these two very stocks, and your expected return relative to the risk that you’re taking is still very, very high. And maybe 2018. This recent year, we’ve had in the first half of it, maybe not today, but the first half of it, we’ve had a lot of different industry groups. So doing walls, it doesn’t make sense to have a three stock portfolio, it makes sense to perhaps have a 10 stock portfolio. So it’s different environments will tell you what to allocate towards.

Corey Hoffstein  52:01

So because you brought it up, I want to talk about Fang for a second, because I think that might be one of those cliche anchors of this is growth. This is what I imagine a growth manager does they just buy Fang stocks all day long? And I know that’s not true and talking to you, how do you differentiate growth from just hey, I’m gonna go buy Facebook, Apple, Amazon, Netflix, Google,

Jason Thomson  52:22

it goes about what the model is. And just frankly, a lot of the valuations are very, very, very high today. So they’re not going to fit those models. So they’re not going to be in the portfolios. But what I will say is that building a portfolio might be maybe to use an easier example, I won’t get into these crazy metaphors. But it’s like playing golf, you can’t have a perfect round of golf. But you could try really hard to so building a portfolio, it’s not going to be perfect if you know what is acceptable to take in terms of risk and what is not. Now if you want to deviate from that you have to have, you know, series of events that leads to that. So for example, let’s say you have a 70% growth oriented portfolio that fits exactly towards your models. But based off certain conditions, you notice that the certain groups or the certain stocks, like the fang group is doing well? Well, if one of those stocks fits into the portfolio in terms of a context, that where you could look at the portfolio holistically and say it still matches the growth portfolio, then I think you’re still being true to your mandate and having that total portfolio context, where it’s still growth, and still fits a growth in model. The reality is, though, if you add in one of those stocks in your portfolio, it throws off a lot of the other metrics. So all of a sudden, your beta is high or your volatility is higher, the valuation metrics are high. So now you need to have something that’s countering that so you’re allocating towards the mean, that has lower growth, low beta to reduce the volatility in your portfolio. So it sounds good in theory, it’s just hard to implement. And I guess the punchline is, that’s acceptable. But as long as it’s fitting into relation of the total portfolio context,

Corey Hoffstein  53:52

one of the things I just realized we, I don’t think we’ve talked about it all. So we mentioned your pm at the William O’Neal group. And we talked about this hyper concentrated portfolio, we should mention for listeners that you are not the only pm and that there is capital spread among multiple of you. So you might all have very concentrated portfolios, but it’s not like the entire William O’Neal group is tied up in five names at a given time.

Jason Thomson  54:13

Yeah, so for disclosure purposes, that makes a lot of sense, right? So no, we allocate on a global basis. So there’s 10s of 1000s of activities in our database to choose from. We have a discretionary team that only does discretionary management. We have a quant team that only runs algos. And then collectively, we have an absolute return strategy that we use on an internal basis. Some of our portfolios do take on a lot of risk. But it’s like you said, everything pretty much has to be right in order to have these elevated risk levels. And the reality is, is when they stop working, we pull back very quickly. It’s one of the things we’re able to do. It’s part of the strategy. It’s part of the edge is being able to sit out the markets and just having past conversations with you. I mentioned something that I think maybe you perhaps thought that was a little bit interesting was that I mentioned I studied the way people blow up portfolios blow up

Corey Hoffstein  55:00

This is where I wanted to go because I wanted to eventually get into

Jason Thomson  55:04

I’ll just say the portfolio blow up thing. So what’s the number one rule in investing capital preservation? Well, how do you preserve capital by not blowing up? Or what leads to blow up? Right? So your long term capital management, all these different funds, may be Ackman or einhorn ran into trouble from time periods. How do they get into those time periods? And, yeah, I guess I worth stealing a little bit of thunder from another segment, but it’s about leverage. So using too much leverage, and it’s about a behavioral aspect, it’s a beige thing. It’s not admitting when you’re wrong. So all of your quant metrics will tell you that you’re wrong. But you deviate from that. And that’s when you start to get into trouble. So you have to be very objective in what you do. And that’s why I use a fighter pilot analogy, a series of checklists. Because if you have all these different things, that says, hey, you should pull back the position and you still don’t well, that’s really on you. And you shouldn’t be using that as your process.

Corey Hoffstein  55:55

So let’s keep talking about risk management, maybe talk about some of the considerations you think about on a risk management side before buying a security. So like you mentioned one already, that whole concept of institutional buyers being in the security or starting to acquire, and then maybe think about, maybe you can chat about sort of risk management, after a security has been bought, what happens when a position does start to go against you? How do you handle that sort

Jason Thomson  56:19

of thing, whether we’re buying it before or after the fact, I think when you think of risk, you have to think of it in terms of one a framework and then to a system. So a framework with a specific metrics that you’re going to be accountable to like the hedge fund space max drawdown. So you do that in different various simulations over various events, and then a system as in, if x happens, then I will do why or this certain event happens. And this is how we’ll react to it. So certain scenarios are going to be able to create an elevated risk tolerance level where you’re able to take that risk, and some scenarios are going to be able to where you’re not going to be able to afford to take that risk. So the framework, let’s break the framework down into a qualitative stamped and a quantitative stance. So from the quant framework perspective, there’s a ton of them, maybe I’ll just add value towards a couple of them. One of them is total portfolio exposure. What I will say that’s interesting, and I know this is going to go against the grain in terms of what the quant world says. But a lot of the research, you know, I’ve been reading recently is saying, well, one of the best ways to optimize portfolio results in terms of achieving higher expected return is that let’s lever up portfolio instead of having these individual stock bets. And, okay, I’m not disagreeing with that. But just in practice, that’s very difficult to implement. And I’ll tell you why. Because if you have a levered up portfolio, and a handful of names, or let’s say, even 10 names, the volatility of that portfolio is going to be very high. And going back to the running shoe analogy, it’s going to be difficult to hold. And if you can’t hold it, then there’s no point. So from that perspective, there’s different ways I guess you could combat it. So one of the ways you can combat it is that instead of levering up an entire portfolio, maybe lever up a couple of names. So maybe have a 15% position size or a 20%, position size, and what often times I find in practice, so again, skin in the game stance is that if you’re able to, I do well, with having a levered up idea, one idea, versus having a portfolio that’s using leverage in general, in terms of various risk metrics, you’re able to just control it better, it’s more predictable, and you know, you’re not going to be as correlated to the market. So that’s what you want, you have the same expected return, but less correlated to the market. And the volatility by those names is actually less because you’re not having the entire portfolio related to the market. So from a total portfolio perspective on exposure, that’s where I think my value add will be different in my differentiated view will be from that, then from it, once you’ve narrowed down the total portfolio exposure, we’ll think about sector exposure. So I already mentioned it, I won’t harp on it too much. But basically sector exposure, you need to be hyper aware of how much exposure and risk we have related to groups, because all of a sudden, if you’re an earnings season, which I’ll touch upon in a second, if your software names are not performing well, then all of a sudden, you’re out of favor, and now you’re out of tune. Now you’re having to rebalance your portfolio,

Corey Hoffstein  59:00

and you care about sectors relative to the market. Are you just talking about absolute exposure in your portfolio?

Jason Thomson  59:05

Well, I mean, it’s both in terms of I’m trying to not answer this question in 10 different ways. So on a relative standpoint, yes, you want to be in the best names relative to what’s going on. But also, there’s certain time period, just an absolute sense that hey, these names are the best right now or these groups are the best for various fundamental reason is there persist like this. So even if the markets deviating from this, it doesn’t matter? These are names that we have in our convictions very high in it, because you know, empirically, when we’ve implemented the strategy over the last four or five decades, that’s produced an expected return. So from the quant framework perspective, in terms of risk, I’ve touched on a total portfolio exposure sector exposure. What I will say I think about a little bit more often these days is beta in relation to the portfolio. And I won’t say a beta, either portfolio, but I’m beta aware, and it’s based off again a series of checklists. So, low beta versus high beta stock generally holistically. You do not want to have a high beta portfolio for various Just reasons

Corey Hoffstein  1:00:00

which I think just saying like that. That was another thing that caught me very much off guard. When you think of growth names, you typically think high beta. And you said to me very explicitly in the past, like you’re not looking for high beta names necessarily.

Jason Thomson  1:00:12

Yeah, so my favorite screens are low beta, we have this one proprietary metric that goes off earnings consistency, it takes the various reoccurring dissects like the income statement, balance sheet, all this tells you based off the metrics that we’ve tested, approved significance, how reoccurring some of these things are, how or not, how it deviates from what the management is saying versus what actually happens. And then it waits versus what the expectations are in the street. So it gives you this metric, and I screen off of that, it tells us how consistently this stock potentially could be that their earnings results. And it’s one of my favorite metrics I use, but essentially, I’m looking at low beta, low valuation, very high percents in the earnings growth, and an industry group that is relatively performing well. So that doesn’t match in line with a high beta, high expensive,

Corey Hoffstein  1:00:56

it almost sounds like a turnaround in many ways.

Jason Thomson  1:00:59

So if you look at our models, about one out of every four stocks is a turnaround story. So I know the preconceived notion on growth is at this high fire high beta name that is going to be a fad versus a growth name. And fact we’re sometimes I guess, that perhaps is the case. But that’s not something you’re allocating more than one sock to your portfolio. Again, going back to one of the four scenarios that there’s going to be a turnaround scenario. So I know you know, some of the retailers have done that recently, over the last couple of years, they went through a big drawdown, and all of a sudden they’re starting to come back. So then earnings from a negative basis is going from negative to positive, it’s you’re capturing that 20% growth metric there. And then they will fall into the bucket of the growth category to where you can consider them as investment ideas. So yeah, I’m beta aware, if I am allocating, maybe let’s go back to the Fein example, if I’m allocating one of these names, that’s higher beta valuation is high. Again, it’s allocating it towards my portfolio making sure I don’t have a whole basket of these stocks, it’s only one stock in my portfolio and holistically, when an allocator is looking at it unbiased ly, they could say definitively that this is a growth portfolio based off of all these different valuation metrics, based off of all these things that we’ve studied. And also with that respect, if you added like a thing named to your portfolio, you need to have something that’s going to counterbalance it to keep you within that mandate. So it’s very hard to build a whole portfolio around that, because then you’re just not following your mandate, you’re not being a growth investor. Now, the last thing I will say about the quantitative framework, and surprisingly, I don’t hear about this a lot. And I don’t see a lot of research on this besides like hedging strategies. It’s not something that even like a lot of the well known famous investors talk about. But it’s how to deal with earnings events, anyone who’s been involved in the marketplace, that’s an active investors know, these events are just frankly, difficult and more often knowing than not, so it’s something that I’m going to have to deal with throughout my entire career. So you have to be able to handle these very well. And from a risk standpoint, basically, these earnings events for holding the stock for a year and a half, two years, you have a lot of them that occur. So you want to be able to handle them well. So basically, what it comes down to is you’re going to have this quant model, something that I use, that tells you how much risk this event based off of these various different scenarios is going to be in your portfolio, how much of an impact this event is going to be on a relative basis meaning like what percentage of portfolio is at risk based off of this one event. And he uses different scenarios like your base case, worst case best case and applies a multiple on that for the underlying volatility, etc. And it says, it gives you an output, knowing that you can adjust that. So hey, if my carrying value in this position is at 25%, but the stock has a huge implied volatility. And historically, the last eight times it’s reported it’s done this and forward looking at should look like this, then most oftentimes, I look at that number and I say, Wow, I gotta pull that back. So it fits into my total portfolio risk tolerance. So it’s that earnings events from a growth perspective is something that’s very significant. Oddly, it’s not talked about. But being able to handle those events is very, very important, especially early on in the stock move. So what’s most critical is the when you have a position to establish it, the first or the second order of their earnings report is going to dictate how you’re going to be able to carry that position moving forward. Essentially, if you’re avoiding the hiccups and beginning everything else is so much easier, you’re able to take those risks. So that’s the current framework of some of the things that I’m adding value on. Hopefully, the last one will be a qualitative framework. And I’ll just leave it very simply as this from a qualitative standpoint and risk. You almost want to be counter intuitive to what you think. And so I’ll use an example from Stanley Druckenmiller, but 30% CAGR guy for 30 years. Unbelievable. So one of the talks he was given, or he gave said that the question from the audience was, how did you achieve these 30% CAGR hours? Well, he answered it with what I said initially, in the beginning, he’s able to swing at those very big pitches, he’s able to sit out the markets that you’re not capital base has to be okay with you do that. But what you did say is that something that he does very differently and you see this in investors returns is that when he has a huge year, first half of the year is up 40%. By any standard, that’s absolutely amazing. And most managers they want to walk in that year, they want to pack it up, go on vacation. Why? Because they know next year, those sweet AUM numbers are going to increase, they’re going to get higher upper management fees, etc. Stan said, No, that’s not what you do. Again, this is where it’s getting into counterintuitive. It’s when you have the 40%. Up here, you want to pull things back, understand that this environment is temporarily favoring your strategy, it’s conducive towards your strategy. And you were able to look at this definitively in an unbiased standpoint, because you have a couple of metrics that lead to that, like maybe you’re trailing Kelly, for example, saying your position size should be astronomical, when it’s, well it really shouldn’t just means you’re everything you’re doing is worth game. So you have to be aware of that. And so he says, hey, when I’m up 40%, I’m going to look for the next pitch, I’m gonna have my entire team, look for that next pitch, because that way, if it does come, I have the ability to size it proportionally. We’re now all of a sudden, I could have maybe a double the return that I expected to have. And that’s where most people do not do. It’s not It’s against human nature. But if you want to have those big keggers, that’s something that needs to be in your wheelhouse.

Corey Hoffstein  1:06:20

So you mentioned with Stanley drunken Miller there, the idea of your styles in favor, obviously factor timing is a big hot topic over the last couple of years. But is this something you see on the discretionary side as well, that there are times when growth investing is easier? Or it seems like it’s in favor, and you can do no wrong? And there’s other times where just as a style, it seems to be very out of favor?

Jason Thomson  1:06:41

Yeah, I think to answer that is both? Or maybe it depends. I hate answering questions like that. But I feel like answering almost all questions to be truthfully, it’s like it depends in certain scenarios. But if we look at it from a correlations base, yeah, you could see when certain metrics are growth factors that are working just by correlations. And then you could also see, again, like I mentioned, these trailing numbers that you have that determined certain factors in our portfolio. So again, if all of these metrics are saying, hey, you need to take a lot of risk in the market, it’s not that you should take risk for any given reason, it’s because that all things pushed to the side is that your strategy is being favored. So it’s something that you need to be aware of.

Corey Hoffstein  1:07:18

So what will actually make you cut a position? When things go against you? What are you looking at to say, you want to know what this idea didn’t work? I need to get out.

Jason Thomson  1:07:26

Right? So I’ll just touch up on a couple of these, most of them are pretty well known. But again, it goes back to the process, you have to follow these. And what’s really hard. So the first one is, are the first two I’ll say is a position stop, or a regular stop loss. Plain Jane, very simple, everyone knows it. Okay, well, why do we have these positions. So the one that I like to use, which oddly is going to be very tight for some of our listeners is that generally, I want to have like a 7% stop loss. And the reason for that is because terms of law percentages, if I have a 7% loss, then I only need about 7% Return roughly slightly more to get back to even. But all of a sudden, if I have a 33% gain, then you need like a 50% plus return to get back to even So how often do you have those 50% returns not often for most managers, so you want to be able to keep things tight. And the reason for that is again, going back to percentages, but also you want to avoid drawdowns in your portfolio. It’s something you have to be aware of, especially in today’s day and age, the capital allocators, they’re allocating a ton of money towards strategies that they’re able to say, Well, hey, this is what the equity curve is going to look like on any of these given simulations for so from a discretionary standpoint, you know, that’s what they’re doing, you have to make your portfolio look like that. And if you’re running a concentrated portfolio, you have to follow some of these position limits or stop limits, because if not, then your equity curve is not going to look like that, and you’re going to get capital allocated. So the 7%, or 10%, portfolio limit is what I like to use, something that I use as well is a time stop. So most people don’t talk about a time stop for a couple of different reasons. And I think I know why. And it’s because that you invest a ton of money, a ton of time into a particular idea. And it’s not working, and you’ve been with it for a while, maybe a quarter two, and you’re still with it. And that eats into your opportunity cost of capital. And the reason is behaviorally is you have so much invested. So outside of position stops and regular stop losses. Time stops are very important, especially when you’re getting in good markets. Because it’s a relative metric. It means Hey, it doesn’t matter how good you think your research is, or your thesis is, the market is not rewarding it so you’re effectively wrong. So you need to reallocate that capital to the most optimal source. And, look, I’ve done it a lot of times you work on an idea forever, and then all of a sudden, everything aligns it says, Hey, you have to sell this position and it’s not fun. It’s usually not a good day in office, but it’s something that keeps you in business. It’s something that keeps you always being in tune with the market too. If you have a position that gets too far away against you, or if you’re just Sitting with a position that’s eating up on terms of your opportunity cost of capital, then that’s where you run into trouble. The other metrics that you can use as a simple percentage off high your portfolio of when to reduce exposure, this can lead a series of steps, it can be a step back process or meaning. If your X percent you reduce exposure, if you’re an additional percentage, you reduce it. And what that allows you to do is kind of like a trend that factors that, hey, if everything goes really bad, and it turned into a bear market, your exposure is going to be very light. Now, the other side is that is, in theory, this does increase your portfolio turnover slightly, but it’s something that you have to think about as being investors, would you rather have a slightly higher portfolio turnover in order to minimize those draw downs and have a smoother equity curve versus the other side of that,

Corey Hoffstein  1:10:47

and that’s one of the things I find really fascinating about your processes, it’s not just long only buy and hold concentrated investing, you touched a little bit on the idea of using leverage, you guys will go flat. And you’ve mentioned to me in the past, you guys will even short some of the portfolio managers, so there really is a full flexibility for you in terms of not only managing position risk, but many different ways managing entire portfolio holistically the risk going along with it. So let’s actually look at the other side of the coin here. We talked about when things go wrong, how do you manage risk? And what makes you sell? How do you take a position that’s doing well for you What’s and decided to get out of what is like a positive sell criteria look for you. When you say, Okay, this position did its job, I’m happy with the return, I’m ready to get out

Jason Thomson  1:11:31

behaviorally, when it’s time to sell, you don’t want to because everything’s working? Well, you’re making a ton of money. And you think it’s going to persist when all the metrics that you use, that’s why use a quantitative process tells you Nope, you’re wrong, you have to sell based off of these factors, because empirically, if we look back, all of these factors can do simply say we showed, and again, they proved to be significant. So from a valuation base, a sell roll, one of them is going to be a multiple expansion. The punchline is that if a stock doubles, generally in a short period of time, you’re gonna want to sell it. Why? Because it’s going to revert back to its longer term average. And if you have an oversized position, again, wanting to minimize a portfolio drawdown that’s going to help offset that drawdown in your portfolio.

Corey Hoffstein  1:12:11

Are you talking about double in price? Are you talking about double in fundamentals like a P E ratio, so both

Jason Thomson  1:12:16

so you could take your current P E, apply multiple on it based off forward earnings. And if you use an adjustment factor, maybe a double adjustment factor 100% growth in the PE expansion, you will sell it. And that will actually give you a price target. It sounds very simple. But when you go back and look at different look back periods, that actual doubling of the PE multiple is an interesting factor to look at, I will say the data that I’ve studied on that is in the last like decade or two, it hasn’t been something that you want to use, because it really leads to a lot of false positives, or just like negative metrics in general. But before that, it works pretty well. So maybe in the future it does. And then going back to the other side of that coin is on price. Yeah, for stock doubles in price, then, again, it’s kind of like the factor if it’s gone up too fast to quickly. And yes, we want to have the momentum anomaly on our side. But that’s just not sustainable. So you can still have a position in the stock, but you want to pull it back in terms of managing your total risk of your portfolio. Now, the other thing that we could talk about is that earnings surprises and breaking expectations. So we’re talking about when to sell a position in terms of like when it looks good. And when it looks bad. Well, if all the data, it says it should do X, and all of a sudden it does why? Well, that’s a sell roll, that’s something that’s going to pop up on your screens that says, Hey, we should sell the stock. And it gets into the, again, the breaking expectations thing. And one way to get into trouble with portfolios, all of a sudden, you have all these rules that are starting to break your expectations. But you’re still with idea. That’s a negative sign. So all of a sudden, the company is reporting 2030 40% earnings growth for several quarters. And then they have a negative surprise, where they’re where they report, let’s say half of that 15% growth number. And all of a sudden you dig into the fundamentals. And you say, Well, yeah, I see why I missed this quarter. And I see why it’s going to persist over the next few quarters. So I probably want to at least trim this position down. Now a really basic one, in terms of like a sell roll, it can be just, this one’s a little bit easy to follow and makes a lot of sense, right? And it goes into like a trend effect. And if you study a model books, you see this a lot. It’s basically when a stock becomes too high or too far stretch from a longer term like moving average, for example, generally, you’ll know that it’s going to revert towards normal price levels or long term price levels. And if you overlay that with the fundamental factors and its earnings growth, most often they go hand in hand if their earnings have been growing at triple digits for several quarters, that’s not sustainable in the future. It’s going to revert usually the price has followed at seven now it needs to revert. So yeah, you’re using a kind of like longer term moving average strategy, but you’re overlaying it with earnings. So it’s like that blend of quantamental

Corey Hoffstein  1:14:56

I think you’ve used the phrase quantitative metrics at least a dozen times. In this conversation, which, again, for me, everything you talk about as having a discipline rules based process, some sort of checklist, I know your firm has so much of what you do is supported by empirical research constructed by a quant team at your firm, you’ve got all this data at your fingertips at the firm has accrued over the last several decades. When you take a step back how much of what you do, do you think can be systematized at the end of the day? How much do you think can be captured by a quant?

Jason Thomson  1:15:34

I think most of what I do, on a personal basis can be systematized. I’m not exactly sure the exact numbers but maybe 80% of it can perhaps even more. And then you’re thinking of well, then what’s the other 20%? Well, the other 20% is what we get paid for. And in terms of the absolute that’s our alpha. And in terms of an absolute dollar amount. It’s very significant. And what makes up that 20% Some of the most, I guess I’ll finish with going back to the sources of edge, I think it comes down to the position size of the portfolio and your bets. If you break up your core metrics from your act of return of well, how much does your weight in your portfolio deviate from the benchmark times your expected return? And then take all of these different factors? And well, yeah, it says what the position sizing your portfolio will lead to different results. And if you have two portfolio managers that are trafficking, and the same exact names and a portfolio, but they weighed them differently, you’ll have drastically different returns. So I think part of being compensated as a discretionary Pm is knowing when to increase those weights or decrease them. And I think that’s maybe only to turn it into, like remaining 20%. But an absolute sense. It’s very significant. All right, Jason,

Corey Hoffstein  1:16:43

last question of the podcast. This is final question of the season for everyone. And it’s a bit of a hypothetical. And the hypothetical is, I’m going to ask you to sell every investment you have, and you can only invest in one thing for the rest of your life. It can be an asset class, it can be an investment strategy can be whatever you want, but what are you investing in and why?

Jason Thomson  1:17:07

Okay, that’s a tricky one. So So I sell everything I have no exposure, you’ve got zero exposure, then I’m probably going to like an all world global index or something like that. I need some type of expected return that’s not going to blow up. I’m not going to take too much risk outside in strictly to em or mostly em, but I will. Yeah, I’ll say a combination of everything. Maybe this is a slight hedge answer, but a global portfolio, maybe long short.

Corey Hoffstein  1:17:30

Well, Jason, this has been a lot of fun. Thank you for joining me.

Jason Thomson  1:17:33

Thanks for having me on. It was a lot of fun.