Full Transcript: The Excess Returns Weekly Wrap - May 10th, 2026

Jim Paulsen, Ian Cassel, Chris Mayer and Elena Khoziaeva on Market Concentration, the Bifurcated Economy and a Lot More

Jack: Welcome to the Excess Returns Weekly Wrap. I’m Jack Forehand, joined as always by my good friend Matt Zeigler. Matt, how’s it going?

Matt: It’s going great. You drinking your Kash Patel signature brand bourbon yet?

Jack: I haven’t got that yet. Bourbon is something I can’t handle. It doesn’t go well. I’m more of a tequila guy. Bourbon is not my thing.

Matt: Well, I’m sure we got another government official who’s in the tequila trade.

Jack: Well, the All-In guys were in the tequila trade. We had some outrageously priced tequila bottle.

Matt: There you go. If you can’t have Clooney, go All-In.

Jack: Maybe that’s what we need to get into, Matt. We need to do the high-end liquor based around our clip show. I assume there’d be an incredible number of buyers for that.

Matt: So basically should we just be doing great alcohol? What would the Excess Returns equivalent of this be?

Jack: That would be the perfect fit.

Matt: We’ll do like a $3 bottle of alcohol — $3 moonshine — as opposed to people doing the high-end stuff.

Matt: All right. We’re sourcing names now for the Excess Returns coveted liquor brand. If you have good titles, put those in the comments. I can only imagine what we’re gonna get in the comments.

Jack: We got some great clips this week. We’ve got 100 Year Thinkers, which you and Bogomil did with Ian Cassel and Chris Mayer. We had Ian replacing Robert for one episode and it was really awesome. We had Jim Paulsen — his usually monthly Jim Paulsen show — had some really cool insights. And then we had Elena Kozyeva, who’s the co-CIO of Bridgeway, on as well. So we’ve got some really good stuff and we probably should just get into it.

Matt: Let’s get right into it. If you haven’t watched these, this is your chance. We’re picking out some of the best clips so that you can see them. Go back and watch the full episodes. That’s the game here. Who are we starting with today?

Jack: We’re starting with 100 Year Thinkers. We’ve got Ian Cassel, and he’s talking about this book, The Art of Execution by Lee Freeman-Shor, and some really interesting data in there about how often the best stock pickers are correct. So here’s the clip.

Ian: I recently had a wonderful conversation with Lee Freeman-Shor and Claire Flynn Levy. They wrote a book called Stock Market Maestros — just came out in March. And a lot of people maybe read Lee Freeman’s first book, The Art of Execution, which was another great book. It’s an easy read. You can fly through it in probably a couple hours. In that first book, he allocated a billion dollars across, I think, 45 different managers across the world. A lot of them are high-profile fund managers that we would all know. Kinda allocated 10 million, 20 million here or there. But he told them to only invest in their top 10 best ideas, and then he basically analyzed their trade data over the next 10 years. The two or three takeaways from that first book were, first of all, the best stock pickers in the world that he allocated to — they were right 49% of the time. Their hit rate was 49%. It’s about a coin flip, whether they’re right or wrong on an individual investment. The second thing that was interesting — he kind of came to the conclusion that the ones that did outperform, 80% of them were lucky, not skilled. They bought Costco 20 years ago and held it. They bought some company a long time ago and just held it. And I would agree, because I’m a concentrated stock picker — that takes skill to hold onto something during that rollercoaster. But his point was, when you analyzed all the other trades from a skill perspective, it didn’t show they were really that skilled. So he kind of viewed it as those folks were lucky.

And so the second book — what they did was he teamed up with a data analytics firm, Essentia Analytics, which Claire Flynn Levy owns, and they basically analyzed 10,000 active funds and created an algorithm to look at those seven skill sets: when to buy, sell, position sizing, adding — all this stuff. And then basically identified 12 managers they could identify that were actually skilled based on their execution of their decision-making. Then interviewed them — pretty nice interviews about how they average up, how they average down, what portfolio rules they have, what constraints they put on themselves, how they capture more of the wins and cut most of their losers before other people. It was really just a fascinating book, and I’d recommend everybody go buy it. Another easy read.

Jack: A couple of things in this. This principle is true in everything — in life. This idea that outliers drive your returns. He’s talking about this idea that the best stock pickers are only 49% right, and the reason that works is because they’re way more right when they are right than they are wrong when they’re wrong. At the extreme, in venture capital, those people are right maybe 10% of the time and can still have huge returns. And even what we’re doing with the podcast — I’ve been posting clips on Twitter and some of them go crazy. This idea that outliers drive everything seems to work across all aspects of life and business.

Matt: Yeah, it’s a magnitude game. The old baseball thing — even the best hitter only hits .300, or whatever. But it’s still the magnitude. They’re the best hitter because of those three out of ten at-bats where they made contact. Whether it’s slugging percentage or on base or however you’re looking at it, those drove the things that make it memorable. You could have a guy batting .600 on the crappiest Double-A team, and at best what’s he gonna get these days — a TikTok or something? They’re an outlier, they’re an interesting story, but until you do it at the highest level, it doesn’t really matter. When we’re looking at these investors and we’re saying they have a 49% win rate — and then teasing in the luck factor on top of that — it’s the magnitude of those wins that put them in that top tier. Very humbling to hear what that statistic is. But the magnitude is what fixes all problems.

Jack: The 80% lucky thing — for the quant like me — is the thing that’s really interesting. Let me give you an example to illustrate this. Let’s say we’ve got two stock pickers — Jack and Matt. Let’s say we’ve had the same return, crazy outperformed the market by a crazy amount over 20 years. Jack did it because I owned a 10-stock, low-turnover portfolio — Costco, Amazon, something like that. And let’s say you were making a bunch of trades over time. You got to the same place, but you did it with a lot of trades and without the big outliers. A quant like me is gonna look at you and say you have skill. They’re gonna look at me and say I was more lucky. But that’s because to find skill, you need data. And I don’t have any data based on what I did because I held these companies for a really long period of time. So really what we’re saying is — we’re not saying you’re lucky. We’re saying we can’t say statistically that it’s skill. When he says the 80% thing, that’s basically what he’s saying — if someone just bought Costco and Amazon and had great performance over 30 years, they very well might be an elite stock picker that can identify great companies. But I can’t say that with the data. And that’s what makes it so hard to analyze those types of people as a quant, because you just don’t have enough data. We’re not saying they didn’t have skill. We just can’t prove it the other way.

Matt: And isn’t the maddening aspect of this that it just follows through into every other aspect of life? We just lost Ted Turner — I don’t know if you saw this in the news — he just passed away. One would probably argue skill in all these things: inventing CNN, and even a lack of skill — or at least a lack of luck — with the Time Warner stuff and some of the extreme cases of hubris, or his track record with his marriages. There are lots of ways we can judge this guy’s success rate. But the idea of actually being able to say what is skillful or unskillful, what is luck or unlucky — it’s just extremely humbling to the idea that you’re actually gonna tease this out to the degree that you think you’re gonna make it replicable.

Jack: And even if you think about what we do with the podcast — when we have episodes that are successful versus not — how much of that was skill in terms of being better interviewers, and how much of that was stuff beyond our control? That’s just the way everything works. And as a quant, it’s hard when you want data to say definitive things — it doesn’t work, because sometimes you just have to accept that you don’t know. This guy that’s picked 100-bagger stocks — I don’t know if it’s skill or luck. I just can’t say it with the data.

Matt: Here’s to not being able to say some stuff with the data. Take that, you quant. For all the poets out there — keep struggling, keep struggling. Heal those broken childhoods and your adult track records.

Jack: So this next clip — we’re gonna get into the data a little bit more because this is Jim Paulsen. He’d had a great piece about inflation. I’d recommend everybody check out paulsenperspectives.substack.com. This idea of inflation and comparing it to the 1970s — some people think we’re in a similar period, and some people think the opposite, which is where Jim falls. Here’s Jim talking about that.

Jim: Historically, going back centuries, across the globe, the probably most important factor that drives growth across economies is the rate of resource growth in the economy: land, labor, and capital — primarily labor. Those economies that have the strongest growth in labor force or labor supply have generally the strongest sustainable real GDP growth rates, and those that don’t are the opposite.

In the developed world — from Europe to Japan to us — we’ve all been talking in recent years about how hard it is to get growth because our labor supplies are drying up. If you wanna look at inflation in the 1970s, that thing was totally the opposite of what we’ve had since 2020. That was an inflationary environment that came about from an excess demand situation, where we stimulated demand in the economy far greater than supply capabilities, and we had excess demand driving up price inflation over that period of time.

This chart kinda gets to that. The blue line’s annual inflation, the red line is the four-year average annualized growth in labor force. You can see that coming out of World War II in the early ‘50s, we had labor force growth that was very modest — 1% or less most of the time — and we didn’t have much inflation as a result, because we didn’t have much growth. That’s basically what it was. Then starting in about 1965, labor force in this country surged. As it did, so did inflation — because demand, everyone got a job, everyone got an income, everyone got desires, and we added leverage on their credit cards and household spending in the ‘70s. We had way too much demand for supply, and we had massive runaway inflation for almost 15 years.

In that environment, when you have excess demand, the correct policy is to slow demand — slow it down so it’s more in line with curtailed supply. That’s what Volcker ultimately did — he killed off demand and brought inflation down.

It’s very different from what we’ve had in recent years. We haven’t had, for 20 years — we’re back to the early ‘50s where we can barely get 1% labor force growth a year. If you have zero productivity and 1% labor force growth, you know how fast you can grow? 1%. So we’re back to that. There’s no way we get excess demand out of 1%. We’re actually growing labor force in the last several years about half a percent a year. There’s no way that’s gonna create excess demand over supply capabilities. This is a hugely disinflationary environment we’re in, where the ‘70s was very much the opposite.

Now, how did we get inflation then? This time it’s been supply-side problems. A pandemic that shut down global supply capabilities for a short period, causing prices to go way up. Then we had a tariff, which was supposedly gonna push inflation up — another supply-side problem. And now a war, which has created a supply problem in commodities, causing their prices to go up. But are any of those demand-driven? No. And sustainable? No. They’re both temporary, and more important than that, they’re not tied to aggregate demand.

Jack: This gets into this idea of comparing periods to the past. The important thing is when you say any period is like a previous period — inflation is like the 1970s — you want to dig in and look at more specific criteria to say what’s the same or different. With inflation, there are a couple of questions you can ask yourself. One is: is the inflation supply-driven or demand-driven? That’s really important in distinguishing a lot of things about it. The second is: is it temporary or permanent? Is what’s causing it something that might go away, or something that’s gonna be with us for a long time?

Jim is arguing that in the 1970s it was much more demand-driven, and it also had much more staying power. So he doesn’t think comparing right now to the ‘70s is a good comparison for those reasons, and he thinks inflation’s gonna come back down.

Matt: I think in Jim’s case — another case of Jim having an incredible data perspective on this stuff. I love Jim explaining something like this — actually imposing inflation with all its quirks over labor supply growth, with all its quirks, rather than just using unemployment. That’s extremely valuable. Coming to that point where — what did he say in the clip? Basically 1%. He’s like, “We’re in a deflationary environment when you lay these costs and the supply shock against where labor growth is.” Is that what he said in this clip?

Jack: I believe so.

Matt: So it’s fascinating for me to think about this. This is the constraint. This is as much of the reason that my entire career in this business has not been hating on the Harry Dents of the world — the population people and whatever else — it’s like you’ve got the right idea, but you’ve gotta zoom it in to understand it over periods. What Jim is settling on here is that we are probably still in a deflationary environment so long as the labor pool is still as large as it is — with people delaying retirements, there are a million reasons we can get into for why this exists this way. You could have your supply shocks. We are still a consumer and consumption-driven economy. People have jobs, they spend that money. It’s really hard to get runaway inflation, a ‘70s scenario in this. And it’s also really hard to get a supply shock that tears the whole thing down. I don’t see anybody else with this perspective.

Jack: And obviously the temporary-versus-permanent question is something we’re all dealing with right now because of the Strait of Hormuz. Even Jim would admit in the podcast — if a year from now the Strait of Hormuz is still closed, we’re gonna probably have some problems on our hands. And we don’t know — that’s in the hands of politicians and leaders beyond our pay grade. But the big thing here is just break this down: what’s driving it? Is it temporary or permanent? That’s a great way to look at these things.

Matt: Yeah, without using the word transitory. There you go.

Jack: I purposely did not put the word transitory in my outline because I don’t wanna say that. I used temporary. Temporary was a better word.

Matt: And let me be clear — I am only here to get my Strait of Hormuz updates from the one, the only Jack Forehand. The Oracle of Hormuz, as I call him.

Jack: I literally just talked to Claude right before we started about it, so I’m like one of the world’s leading experts now. I know all about it.

Matt: I can at least find it on a map now. I couldn’t do that before.

Jack: So I’m moving in the right direction.

Matt: Tony Greer said to me the other day, “Every day I wake up and ask Grok if the strait is open yet.” That’s the first thing he wants to find out every day. But I no longer trust any expert to tell me what’s going on, because this is probably the most important question for the next year.

Jack: And a lot of opinions from people who knew nothing about the Strait of Hormuz.

Matt: Everybody’s got an opinion, which is just nauseating. What do we got next?

Jack: So the next clip — we’re gonna get into the quant world a little bit, but it’s something that applies to everyone. This idea — the factor nerds like me have been beating around for a long time — which is this idea that there’s a small cap premium. If you go back to Fama and French’s work, there was this idea that there is a premium associated with small cap stocks. And then there’s been a lot of work since then that either it doesn’t exist anymore, or it never existed in the first place, or depending on how you slice and dice it, it changes. This is one of the more interesting takes we’ve had on that. This is Elena Kozyeva, co-CIO of Bridgeway. They wrote a recent paper — Andy Berkin wrote it along with a co-author — about this issue. Here’s what they found.

Elena: This paper was published very recently, and it’s by our head of research, Andy Berkin and Christine Wang. It’s a great read — simple, intuitive, and the results really speak for themselves.

Let’s think about how academics define size. Small size is a well-documented phenomenon, but there’s a lot of criticism because recently it’s been out of favor. Is it no longer an alpha factor? Is it now only a risk factor? How do you think about size?

Academics typically — when you look at the Fama-French studies and all the other studies — define size as, for example, small minus big, the most famous Fama-French factor. You basically take the smaller half of the stocks minus the larger half by their size. Some Fama-French factors use the top 30 minus bottom 30. Some use quintiles, deciles. It’s pretty standard.

What this paper did — Andy and Christine looked further into the definition of size and questioned how we think about small companies. What they’re suggesting is that not only do they want the stocks to be small now when we’re ranking them, but they want the stocks to have been small a year ago. The name of the paper is “I Know What You Did Last Summer” — very creative name. It basically says, “I want to look at the names that are small now, and I want to invest in names that were also small a year ago.”

In addition to requiring the names to be small for two periods of time, they also removed names that were N/A’s a year ago. That could be stocks on pink sheets or OTC — over-the-counter. There could have been IPOs, and there are papers proving that IPOs don’t do well in the beginning. So they only formed small portfolios from names that had been small, that did not become small this year, and that existed last year and were small.

That adjustment showed some really improved small size premium. There’s some intuitive reasoning behind it. It basically relates to negative momentum — names that are going down tend to continue to go down, so there could have been negative momentum in those names. There could have been weak profitability. Why did those names become small a year ago? Interestingly, they repeated that with shorter periods of time and a longer look back, and they also shifted it every quarter. The results were pretty robust, indicating a very interesting idea for investors: invest in names that are not only small now, but were small before. That’s the nature of the study, and it’s an interesting paper.

Jack: This one was interesting to me because I hadn’t seen it done this way before. A lot of what people say is, yes, there is a small cap premium, but you have to make some adjustments for it to exist. For instance, if you look at the S&P SmallCap 600 — which only has profitable companies — versus the Russell 2000, it does a lot better over time. Just that change of requiring some profitability works out. But what they found is when they looked at the small cap universe, if you took out two things — one, large cap stocks that became small cap stocks (which obviously doesn’t happen for good reasons), and two, IPOs — the small cap premium comes back.

And I just hadn’t seen it done that way, so I think it’s very interesting. Those are things where you can make a strong case before you even look at the data. If you said to me, “Do you wanna take out large caps that have fallen into the small cap space?” I’d say, “Yeah, there’s probably a good reason for that.” Do you wanna take out IPOs? Yeah, the data on IPO performance is not great. So I thought it was very interesting from that perspective.

Matt: So what I wanna really commend — do you remember the name of that paper?

Jack: No. You probably would’ve had a field day with it, whatever it is.

Matt: They titled it “I Know What You Did Last Summer.” Amazing. I’m applauding you, Elena, team. Fantastic. Chef’s kiss. Beautiful. This idea of name-checking late-’90s slasher canon. In a slasher, you basically have murders that need to take place — there has to be some prior sin that they’re avenging. This idea of the large cap company that has committed the prior sin, this injustice, and now they’ve invaded the area — it’s a really thoughtful metaphor. I approve of the naming of this research piece.

This is another one of those cases where it sounds obvious. This is up there with Cliff Asness’s “size matters, but mind your junk” stuff. You’ve gotta scrub some of this stuff out. It helps if you put a clever name on it, because we all miss the small cap premium. We hope it comes back with us someday. But it seems so obvious when they lay it out why this would add value to the factor itself.

Jack: Now I’m regretting not having you do the interview, because we could’ve done 10 minutes on the derivation of the title.

Matt: We could have parsed the nuance of whether this was the correct movie angle to take, or how deeply anyone thought about it. Because these are important questions, Jack.

Jack: She would’ve been like, “I didn’t see that line of questioning coming at all.”

Matt: I was delighted by that part. Who do we got next?

Jack: We’re back to 100 Year Thinkers, and there’s been a lot of talk about software companies and AI — how far they’re down and how much they’re gonna be disrupted. I like talking to Chris Mayer about this because Chris actually owns software companies, so he’s living in this right now, trying to decide how much AI is gonna be impactful and what he wants to do. Here’s Chris talking about that.

Chris: The only software companies I own are what you’d call vertical market software. It’s just a label, but what it means is software dedicated to very specific verticals — very specific industries, whether it’s running an auto body shop or a transit system or whatever. Those systems have particular advantages. You also hear the term “system of record” — that’s the place where truth lives. It’s what auditors rely on, it’s where regulators go. If companies are sued, that’s where the legal system goes. There are lots of reasons why you wouldn’t wanna mess with that.

So you’re deep in the individual processes of that vertical. You have the system of record. And a lot of times these are tiny, narrow verticals. This is very different from horizontal market software, where you have one software product that can go across a lot of industries.

The vertical, mission-critical stuff tends to be vertical. What does mission-critical mean? That’s stuff you need to run your business. If your customer relationship management software goes down, you’re inconvenienced, but you could still operate that day. But if your vertical market software goes down, you can’t see patients that day. That’s the difference between what your employees log in to every day to do their job versus what’s nice to have.

I was very confident that those vertical market software companies would be fine in AI — and plus they’re using the tools themselves. It’s not like an AI native is facing incumbents that are using the tools themselves, plus have all the deep vertical knowledge, plus the data and the trust and all that. So I’m still surprised that space has drawn down as much as it has. I think a lot of people sort of just threw all software out together in a bucket, and now we’re entering a phase where people are gonna start sorting more carefully through who the winners and who the losers are.

There’s a great difference between Wix, which does web development, or Zendesk versus Salesforce versus vertical market software within Constellation, for example, running transit systems or something. The AI story — even though share prices have only really collapsed since last summer — has been around a while. They’ve talked about it at every annual meeting for at least the last two, maybe three years. So when it came, I felt I was pretty well prepared for it. But I certainly didn’t expect this kind of drawdown. Given the history of markets, this happens. Getting cut in half — even the best stocks routinely get cut in half on their journeys. In some ways, we have to expect it.

Jack: The first thing that struck me here is — and we’ve talked about this on other clips about software companies — Chris knows these businesses really, really, really well. And I think that’s probably the key to this whole thing. If you’re trying to figure out which companies are gonna have major problems and which are gonna end up being incredible investments — because they’re being driven down for no reason right now, and they’re actually gonna take advantage of AI — the key to distinguishing those is to know these businesses inside and out. And you could tell in that clip: Chris knows the businesses.

Matt: Chris knows his stuff when it comes to this software space, and you can hear it in this. And we get deeper into this in the full conversation — this has come up with him a number of times too, so see prior 100 Year Thinkers if you wanna dive into the software topic.

This idea of vertical versus horizontally integrated companies — for you to disrupt these vertical companies who are actually using AI to help — if you’re a dental office and this is basically the way your operation runs: the people come, they have the meeting, they have the cleaning, you do the X-rays, whatever it is — they have a vertically integrated tech stack where the lights barely turn on the next day if you don’t run this thing. It’s really hard to disrupt that. You can disrupt it from the middle out. You can replace the entire vertical structure, but that’s really hard for AI to do.

What is happening is those vertically integrated software companies are getting compared to the horizontally integrated companies — companies that do lots of different things for lots of separate verticals. Those are the companies having a lot of trouble, a lot of stress, a lot of questions about where AI can replace a piece. AI can break up that vertical stack across the horizontal axis. So this is maybe the most compelling explanation for why — if you can find some of those vertically oriented software companies that actually have an edge — AI probably increases profit margin for those companies over time. Fascinating. Of course Chris sees it this way, because it’s such a long-term view of where a 50% decline is potentially an opportunity.

Jack: Was transit one of the examples he used? I forget.

Matt: Basically any industry where you have a core piece of technical infrastructure that the business is essentially dependent on — the business doesn’t operate in the modern era without this one vertical component of their software stack. Transportation would be a great one, especially logistics planning.

Jack: Yeah, and also — what happens if it goes wrong? In those types of industries, you can’t just have some vibe-coded thing. If things go wrong, you’ve got major, major problems. So they have much more of a moat than people probably think.

Matt: Yeah, exactly — and this is where you still have a software moat, because your ability to turn the lights on at the business tomorrow if you screw this up goes away. That’s different from an RIA deciding who their new CRM is. You’re not gonna vibe-code your new CRM if you’re at a firm of more than a couple of people, because you probably have the SEC or somebody else to answer to. But there’s a giant middle space right now where you can solve a bunch of problems with something like a CRM for lots of industries where it’s not the critical core piece of infrastructure. And those horizontal structures — this is a really interesting question.

Jack: And I think — I might be wrong on this, but I believe it was Amazon — we’ve seen some examples of this stuff going off the rails. I believe it was Amazon who now requires that a person review the code before it gets implemented because something crashed. We’re gonna see more and more of that. These things are gonna go off the rails a little bit and I think we’re gonna take a step back and realize — all right, this could do all kinds of stuff, but at the end of the day, some human being is gonna be accountable for these changes.

Matt: Yeah, and that’s especially true for the Amazons of the world, where you are already this kind of business where you touch the horizontal space. And I’m sure you saw Amazon making the full-on move of saying, “Come work with us, work with our supply chain for delivery” — the full-on attack on FedEx and the UPSs of the world is now underway. And a big part of it is saying, “We’re not gonna screw this part up.”

Jack: Our next clip — we’re back to Jim Paulsen, and this is something we’ve talked to him about a lot. It’s been eye-opening for me, because you obviously have your overall data in terms of what the economy’s doing, but within that it’s very bifurcated. There are two different things going on. Here’s Jim talking about that.

Jim: Today, we have an economy where about 87% of the economy is outside of the new era. At most, 13% is new era, and that’s accounting for almost a third, which means there’s only about two-thirds left for the other 87%. The bifurcation is getting so big that it’s starting to create two entirely different economies — one booming, one busted right now.

How much is the rest of the economy growing — not the new era, but how much is the rest of the economy growing? If you go back to the bull market of the 1990s, even though you had a very concentrated bull run in tech stocks, the other parts of the economy — about 80% — were still growing at roughly 3.5% annualized in real GDP terms. It was not only a boom for tech, it was a boom for non-tech as well. The early 2000s bull still had good growth in the rest of the economy — 2.5% annualized growth. Very bad growth everywhere after the great financial crisis in 2008-09. The growth rate over that entire bull run was 1.8% per annum — despicable growth. That’s why we kept interest rates at zero in this country for so long and had big deficit spending — we were trying to lift the growth rate. Then in the post-pandemic bull, we had good growth again because we stimulated everything so hard.

But look what we’re doing now. We’re back to almost stall speed — 2.1% growth in the 87% of the economy that’s not new era. And that’s getting deathly close. That’s not just in the recent period — that’s for the entire bull market. There’s a reason why the concentration of this bull market in the stock market was so extreme: because 87% of the economy hasn’t participated, or virtually very little. And so the stock market reflects that with extreme concentration, and much of the rest not doing well.

Jack: This is just this idea — and he’s talked about this a lot — that tech is driving the economy. One of the mistakes I made is I talked about this whole AI infrastructure thing to Jim and said, “Oh yeah, it’s AI infrastructure driving the economy.” But what he’s pointed out is this is not a new thing with AI CapEx spend. This has been going on for a very, very long time. We’ve got an overall economy — that 87% — growing at a very, very low rate. We’ve got this new era, as he calls it, growing at a much higher rate. And it’s just important to understand that as you analyze the economic data.

Matt: This is where the money’s being spent. Seeing that this has gone to 87%, understanding where that sits historically — this is such an important complementary signal to the relative concentration in your large cap indices. You wanna understand where this money is being spent inside of these and being recycled, because it’s a different awareness of what type of narrowness this market is exposed to.

Jack: And this is all the Ks everywhere stuff, right? We’re seeing Ks in every part of everything we do.

Matt: Yeah, from the clickbait episode. It’s Ks all the way down. This is yet another example of where you have this imbalance — you have the event, and then you have the arm and the leg of the K. And as Peter Atwater will tell you, it’s not just where the event is and where the arm and the leg are — it’s that growing separation between the two, and you’re looking for when that is continuously widening when you’re still in that K shape. We’re seeing this everywhere. This 87% indicator from Jim is yet another one to keep track of.

Jack: And what’s interesting is a lot of people would use this as some sort of doom and gloom thing, and Jim uses it the opposite. He thinks we’re gonna get policy support, he thinks we’re gonna get a broadening, and he thinks the rest of the economy’s gonna come up and catch up to tech. He doesn’t think we’re gonna have a situation where tech’s gonna roll over and we’re gonna have 2000 all over again.

Matt: Yeah, he just thinks that we’re gonna take that spending, and as the other sectors start to benefit from it, the spending slows — at least on a relative basis — and you see this giant lift on the floor from the benefit of all this stuff. That’s not a bearish way to correct this. That’s probably a different take than what you would apply to, say, income inequality or something.

Jack: No doom and gloom title for me on that one, unfortunately.

Matt: So close.

Jack: I’ll get ‘em next time, Matt.

Matt: Get ‘em next time.

Jack: So this next one — we’re back to Elena Kozyeva again, and Bridgeway does some really, really interesting research. This idea of people like you and me constantly talking to our clients about the concentrated market — it’s this ambiguous thing. What does that even mean? What does it mean historically? They brought in this index — I’ll call it the HHI because I pronounced it so wrong in the episode. In the episode, I said it the wrong way, and she’s like, “I just call it HHI.” I should’ve done that too. That’s a life hack right there. It would’ve been a much better way to do this. So here’s Elena talking about that.

Elena: It is a very simple and creative way to measure concentration, and I’ll give you an example. The paper was done by Andy and Kai about a year ago, and we called it “How Many Stocks Are Effectively in the S&P 500?” We all know the S&P 500 tends to have about 500 stocks — maybe 502, 503, currently 504. But does it really matter that there are 500 stocks in there? What drives the returns of the index?

They did the study going back to, I wanna say ‘92 or ‘94 — taking several decades of data — using this index. The way this index works: let’s say you have a portfolio of 10 stocks, and each one represents 10% — they’re equally weighted. The index will be 10. If you have a portfolio with about 90% in one stock and nine other stocks have about 1% each, then the index will be close to one. It’s effectively the impact of one stock in the portfolio that’s driving the returns.

So they calculated the index going back to the early ‘90s. The highest effective number of stocks was in 1994. Now we’re talking about less than 50 — we’re at 46 as of kind of the end of last year when they did the study. So it’s effectively less than 50 companies in the S&P 500 are driving the returns.

What does it mean for investors? It means that you think you’re getting diversification — you think you’re investing in a diversified market index with 500 names — but that’s not the case. It’s only those few names, the mega caps and then the next 30, that are driving the returns. The risk exposure of your portfolio is actually higher than you might perceive, because you invested in what you thought was a market-diversified index.

The possible solutions: you could do equal weight on the S&P, but then you’re getting a small cap bias, which may not be what you want for your core portfolio. Some of the suggestions in the paper were to diversify — look for other allocations. Add an allocation to small caps because they’re a diversifier of these big, growthy companies. And by the way, the growth characteristics of the S&P 500 right now are just incredible — we’re at about 27 times earnings right now, which is higher than historical levels. Another alternative is to invest in international markets. Look for other opportunities that are not as correlated, that have low correlation with the large core holdings in your portfolio.

Jack: I love this idea because it gives me a number now. When I wanna talk to people about what’s going on with concentration, I can say — based on what they showed here — that the S&P 500 is acting as if it were 46 companies. That is a great number to give somebody who wants to understand concentration. On one hand you’ve got 500 companies in the index; on the other hand, it’s behaving as if there were 46. And by the way, that’s the lowest number it’s ever been — the 46. To me, that’s a great thing I could use in client conversations, because now I can explain it in a way people will understand.

Matt: I thought this was fascinating — right below the slasher conversation, but up there. The idea that we could actually put a number on it is amazing. I just wouldn’t have ever thought about using the same HHI math for understanding if something is a monopoly in an industry — which is where I’d encountered this math before. This is like why AOL Time Warner, when you look at it, you’re like, you’re ruining who you compete with in a bunch of ways. And so this way of looking at the entire S&P 500 — rather than talking about Mag Seven or two-stock attribution or all the other things we’ve been talking about, which are all relevant — “What is this behaving like?” It’s kind of amazing. It’s like we’re all just converging back on the Dow almost, because we’re only 17 stocks away from basically being the Dow again inside of this construction. And it’s gotta make your earlobes itch if you’re benchmarking to this index.

Jack: Yeah, and it’s not necessarily a doom and gloom thing. People like to turn market concentration into a doom and gloom thing. It’s just the reality of the situation. The way I look at it is more like a risk management thing. All right, you’ve got this S&P exposure that’s acting like 46 companies. Maybe some diversification into other things, given the level of concentration, might make sense — not like the S&P 500 is about to fall apart tomorrow.

Matt: And that doom and gloom resistance is the important part here. This goes back to Jim Paulsen’s idea with the 87%. Slow down, look at this, and think about not just the doom and gloom. The problem with most doom and gloom forecasts is you look at this, and then you just assume it ends badly and everything ends. In the doomsday scenario where everything ends, you haven’t imagined any of the recovery scenarios. This is yet another one where it’s like — we might be overly economically sensitive to these outcomes because of these companies, but that may be the very thing that helps lift us into the next turn of this market. And that’s where Liz Ann Sonders and the rotation thesis and some of the other stuff comes into play — this could end in some very interesting, very positive ways, not just everything crashes and burns and S&P zero.

Jack: On the doom and gloom thing — a great thing to do is go to some of these doom and gloom YouTube channels, click on videos, and sort by most popular. First of all, you’ll see all kinds of negative stuff. But it’s funny — you get the lesson of how useless that stuff is. You’ll see something that’ll say like, “Recession to come within six months that’ll destroy the economy” — dated 2023. And the recession did not come. But it has like 100,000 views on it. People love that stuff. But if you wanna find out how it’s not doing you any good, look at those actual doom and gloom forecasts over time that were popular, and see how they worked out.

Matt: One of the great gifts of YouTube is being able to pull up the total videos on a channel and sort by most popular, especially on the channels that have strong opinions. You can see these things in real time. They’re not helping you. Not that you can’t learn stuff from it — you wanna be able to imagine risk as “more things can happen than will happen.” Try to understand some of those things you hadn’t really thought about. That goes a long way.

Jack: And the idea of that channel is just make a video about everything that can happen — whether it will happen or not — and it all works out.

Matt: You tell me when I’m making my S&P zero video. I’m ready to get the spiked shoulder pads.

Jack: Maybe if things fall apart for us, Matt, you and I could do doom and gloom if we have to.

Matt: We can get in there.

Jack: We can get in there.

Jack: So moving on to the next one — we’re back to 100 Year Thinkers, and this is a great topic for me because I’m a quant guy, and I would never do this under any circumstances. But here’s them talking about management — meeting management and what role they play in the process.

Chris: I’ve been in meetings with other professional money managers, and sometimes I’m shocked at the questions they’ll ask — just wincing, like, “That’s in the filing.” Management teams can sometimes be a little dismissive, or they come into the meeting with a certain preconception of what you are — the American hedge fund manager — and it takes some time to win them over. But if you do the work and you show that you know some things about their business, and you’re asking insightful questions that aren’t necessarily in the filings or something they’ve already addressed, then they are willing to spend more time with you.

I’ve had really good meetings that way, and it takes a while — it’s not like the first meeting you win them over. Maybe not even the second. But you’re there, and two or three years later you’re still there, and a lot of things have happened, and a lot of the other people he’s talked to are gone. I’ve had meetings where the CEO popped out of his chair and started writing on a whiteboard, giving me all this good information about how the company and the organization works.

But you have to change your expectations a little bit. If you go in thinking you’re gonna learn something from management — like they’re gonna tell you some kind of secret that nobody knows — you’re gonna be very disappointed, because that rarely ever happens. In fact, if it happens, it’s kind of a red flag: if they’re leaking out stuff to you, who else are they leaking stuff out to?

What you really gain when talking to management — and this doesn’t apply for all businesses either — is just a better understanding of the business. If you really wanna be a long-term investor and own the business, sometimes it helps to have the CEO walk through how this business actually works. How do you really win business? You get into the particulars of how things work. And the more you know about that particular business, the easier it is to hold on later when you’re tested — when the market tests you and you’re down 50% or whatever. That’s really the value in the work — it deepens your understanding of that business, and it makes it easier later to know, when news hits, whether it’s material or not, whether it matters or not. On your own, not relying on what the media says or analysts say.

Ian: I kind of view every position at the start the same as building a relationship. The first time you meet somebody, it’s kind of like the first date you had with your spouse. And the key is continuing that relationship and putting in more reps with the CEO — you need to get to know them. To your point, Chris, the edge isn’t necessarily informational — it’s relational, in some regards. Especially at microcap. It’s the same as in any relationship: your spouse doesn’t have to tell you that they’re mad at you — you just know, because something changed in their tone, their cadence, whatever the case may be, because you know them well enough to know who they are.

That’s the benefit of not just doing one meeting, but getting to know management teams over time — especially in an area like microcap, where the base rates are they have shorter shelf lives than small caps, mid caps, or any of the larger ones. The advantage in that relationship is not necessarily so you can build the conviction to hold longer than other people — 80% of the time it’s so that you can spot the signs of your thesis cracking before others, and sell. It’s kind of a two-way street when they’re these smaller, more fragile businesses.

Chris: There’s one other thing. There’s another side to meeting with management as well — you can get charmed by them. You can start to like them, and it clouds your judgment. I don’t want people listening to this to think suddenly they have to talk to management teams to perform well, because that’s not true. There are lots of investors who actually make it a point not to talk to management. I always think of Walter Schloss — he never did, and he did about 15% for, I think, five decades.

Ian: And Joel Greenblatt writes about it in The Magic Formula book — he tells individual investors not to talk to management for that reason. You can get snowed, you can get charmed, and then you’re stuck. You don’t have to. I think it helps in certain situations.

Chris: In most situations for Ian — he’s dealing with microcaps where there’s often not a lot of research out there. The research you do is original research. But if you’re covering a larger company where there’s a lot of coverage, a lot of experts, a lot of people you can talk to — then maybe it’s not as important to talk to management. It really depends on the situation.

Jack: So first of all, this is funny because throughout my whole career we get asked, “What do you think about good management?” And our answer is always, “Well, if the management’s good, it’s gonna be reflected in the fundamentals we’re using as quants to invest in the companies.” Which has some truth to it — but not necessarily on a micro level like these guys exist in. In their world, you can see where the value is in knowing these companies really well, knowing what to ask management. And you can particularly see the value on the microcap side with Ian. People are probably not calling up that management. Very few people are talking to them. You probably can get more information. People aren’t following the stocks. So I can see the value when you look at it from that perspective.

Matt: I can also propose a different genre for the paper on this one, Ian. I would love to see 50 First Dates as, like, a microcap manager meeting —

Jack: Is that Adam Sandler? I forget who was in that.

Matt: Yeah, Adam Sandler and Drew Barrymore.

Jack: Okay. Yep, I do remember that one.

Matt: See, sometimes I know this stuff.

Jack: Very rarely, but sometimes. I even remembered Sarah Michelle Gellar when you were talking about “I Know What You Did Last Summer,” so —

Matt: There you go. You are the Buffy of Excess Returns, Jack Forehand.

Inside of this, though, is this idea — Ian brings up the first date thing, which is real. You’re gonna get the best representation of these people. They’re on their best behavior, they have manners, they’re being nice to the waiter. But then what you’re constantly doing is this pairing exercise: what’s the market saying — to the degree they’re trading in Ian’s world, but in Chris’s world they definitely are — what’s management saying, and what’s my own independent idea? The manager’s doing well — I can make my own independent assessment. But triangulating for these concentrated value managers, that is part of the gift. Because if the market is saying this company is puking and everything’s going wrong — back to the software stock example — but you’re talking to management and they’re going, “We’re actually not seeing any interruption with any of our clients, and AI is actually building this out further. We think we’re getting lumped in baby-with-the-bathwater style” — and then you can independently confirm that — you can see how that turns into an edge.

Jack: So one of the things I realized going into this next clip is we’ve been doing a lot of comparing things to previous decades. People have been talking about the ‘20s and the ‘30s, but the ‘90s is another thing that’s come up a lot. People are comparing what’s going on with AI to the ‘90s. Here’s Jim Paulsen talking about why he thinks that’s not a valid comparison.

Jim: I wouldn’t conflate today with the 1990s. In some ways it’s true, but there are major league differences. The 1990s had a big boom in technology — but as I showed earlier, ex-tech growth in the economy was phenomenal in the 1990s. 3.5% annualized growth during the ‘90s bull market in real GDP ex-new era. All boats in the harbor were going north during that boom. In part because all other parts were benefiting from what was happening — it wasn’t on the come, it was happening right then.

I wouldn’t say that’s the case anymore. So far there’s not much evidence that a lot of the innovation in this bull market has yet been transferred to other parts of the economy in a beneficial way. It’s not showing up. Now, that said, I think it will — there’ll be net benefits that come out of this eventually — but maybe it’s gonna take longer. You could argue that growth in jobs is already being held back by AI. I don’t know if I see that clearly, though. The one place you do see that pretty clearly is in the technology sector itself — jobs are going down in that sector. But I don’t really think that’s obvious in a lot of other sectors. We’ve got other issues there, like lack of labor force growth and too-tight policies, not stimulating those companies. So I’m not saying there’s not gonna be any positive fallout from this innovation cycle, but I think it’s very different so far from what we saw in the 1990s.

Jack: One of the things that stuck out to me here — and I knew this but hadn’t thought about it — is that economic growth was way better in the ‘90s overall. And that is a big difference right now. Going back to Jim’s earlier clip, the economic growth in the non-tech area right now is very, very anemic. It was a lot better in the ‘90s. I don’t know what necessarily the conclusion from that is, but it’s important to dig back and understand what was actually going on versus what’s going on now.

Matt: Yeah, there are just so many things that are plainly different about periods, and that’s normal. This is every single period. This is when people make the Great Depression examples, and it’s like — lots of parts of society were wildly, wildly different. We don’t have the same types of global conflicts we had in the ‘90s. Europe was a very different continent than what it is now. We have lots of different factors to look into before we try to make some historical analog match. And that means any boom that comes out of the environment we’re in now is also risky to compare to the boom that came out of tech stocks in the late ‘90s and the economic place it was born of.

Jack: This idea that history doesn’t repeat, it rhymes — it’s something you just have to keep saying to yourself. Because what you realize is: whatever I’m looking at in the past, it’s not gonna be the exact same thing. But if I dig into the details, there probably are elements I can learn from and apply today. That’s true for the ‘70s, the ‘90s, whatever else. The doom and gloom people are the ones going to the ‘30s and stuff. But no matter what you’re looking at, that’s the way to approach it.

Matt: Yeah, and it’s okay to learn from those lessons and look for what rhymes. Just be careful not to carry it too far, because that’s where you’ll get yourself in trouble if you’re positive it’s gonna look exactly like something else. It’s fine to say, “These details rhyme with this prior analog, and here’s what happened, so we wanna be sensitive to these things.” That’s useful — really useful in client communications too. Here’s what’s similar to the ‘90s when we last saw a giant tech build-out, but here’s also what’s extremely different. Parsing that helps people get a little more comfortable with the actual uncertainty in front of us.

Jack: So this last clip — we’re back to 100 Year Thinkers, and this is something I think about a lot: as a stock picker, or as someone who’s an investor, what are the moats that still exist? AI can do all this stuff, we’ve had tech with us for a long time. Here’s Ian and Chris talking about that.

Chris: I don’t know that I have much of an edge on the analytical stuff. We all do the same thing — we all look at the free cash flow, look at the margins, try to get a handle on competition. A lot of that is out there. I think the edge that I really have is just the ability to look out longer than most people and hold on longer — and really be a long-term investor. A lot of people still say they’re long-term investors, but then they’re parachuting out the first time you get an earnings miss and the stock’s down. And then they concoct reasons why the thesis is broken, and they write this stuff in their letters to their investors.

I look at it and say, “Well, you could’ve sold for that reason at any point in the last five years.” It’s not a sudden thing. Being focused on the long term — then a lot of things fall aside. They’re not as important.

I wanna write a piece about: it’s not the stock, it’s you. The key idea is — what do I need to know to stay invested in a company? I think especially for Chris, but for Ian as well — you wanna know enough so you can have the conviction to hold it through the tough times, when it goes down, when it goes nowhere, when everybody questions it.

Bogumil: What do you need to get that conviction? It’s a curious place to be. Everybody’s looking at the same things, everybody’s going to the same meetings. One walks out with conviction. The other one has no conviction about the idea. For me, because I invest in small companies, I think the relational aspect of management is important to me.

Ian: But I also think it’s important across all market cap classes — when you’re analyzing a business and doing that maintenance due diligence on a constant basis — is to find some way to verify the trajectory of that business independent of what management’s telling you. Trying to figure out exactly how to do that. It will change from industry to industry and company to company. But combine those two things — have an independent view despite the stock being down 30% year to date or up 30% year to date — I think that’s the key.

Jack: A couple of things here. This idea of patience — the ability just to sit there and endure the pain is an edge. And I think it’s an edge that will remain because of human behavior. It’ll be with us forever.

Matt: This is an edge in life in general. Seth Godin calls it “the dip,” which has always stuck in my head as the perfect framing of this. Everybody is super excited when they start, and then there’s the realization: “Oh, I’m gonna start a podcast. Wow, this sucks. This is hard.” There are a lot of things to learn, and even when you’re finally coming out of the dip because you’ve endured all the pain and frustration and hard lessons that you can only learn by doing something — do you understand if you should keep with it?

A lot of people get to that bottom part of the dip, and that’s when they check out. We see this in investing all the time. I bought something, it went up a little bit, then it got creamed, and then I stepped away from it. But on that climb back up out of the dip — which is where it gets the hardest, because you’re enduring the most pain after you fell into the hole — the climb out extra sucks because you broke a bunch of limbs. There’s probably another slasher analog inside of this, too. But in that climb on the way out is where you finally realize, “Here’s where the peace is on the other side of this.” And so to hear Ian and Chris both explain: “I don’t have an edge in the analytics. I can look at all the same stuff just as well as anybody else, or ChatGPT for that matter. But where that edge is gonna come is my ability to endure the pain, the drawdown, the suffering — and make sure if this is still on track, all I have to do is climb out of this hole, and then I’m at a whole new level.”

Jack: Your podcast example is a great one. And I don’t know if our podcast will end up working out in the long term or not. But this is what we’ve been through. We talk to a lot of people who ask us, “I wanna start a podcast.” And the one point I make to them is: you’ve got to enjoy doing this. Because you’re gonna put in a ton of effort, do a great outline, do all this editing, put the thing up — and four people are gonna watch it. One of them is gonna be you, one is gonna be your mom. And that’s gonna continue for an extended period of time. Very few people are gonna keep putting out something that gets watched by four people. It’s just really, really hard.

And we’ve been through it — you see the fluctuations behind the scenes. There have been times where we think we’ve figured everything out, and then right back down. We lost — I think towards the beginning of this year — our views and subscribers per month went down like 70% or something from where they were. Now they’re coming back up again, but they’re gonna go back down again. It’s just the nature of it. Building any business is that way, but this is a particularly challenging business because it’s not a very lucrative business. And it’s one that requires enduring a lot of pain. And it’s public pain to some degree, because people see how many people watch your videos. You have to be able to endure that. The “Oh, no one watches your videos” thing — it’s hard.

Matt: This is the dip, this is the tragic gap, this is all of those things. And this is why anything worth doing — you’ve gotta make sure you love doing it, and you’ve gotta make sure you can do it well.

When Chris and Ian are talking about not having the edge, but having those table stakes — just because you don’t have an edge doesn’t mean you’re ignorant of it. It’s not that you’re ignoring the analytics. That’s table stakes — you make sure this is a viable entity that you can invest money in. But the endurance, the actual seeing it through that whole cycle until you get to the other side — that’s where all good things come. It may not be a 100-bagger. It might just be you get to the next turn and realize, “We’ve kinda tabled off here. It can’t go further than this.” But that awareness of all that it takes to get to the other side — that’s the life lesson. “Excess Returns” probably won’t ever become a self-help podcast.

Jack: No. And probably not a 100-bagger either, although you could argue it was worth about $1 at the beginning, so maybe it already is a 100-bagger.

Matt: Depending on what you put the price on. Yeah, it might be.

Jack: Yeah, depending on the level of optimism. But you could argue “Excess Returns” is a 100-bagger.

Matt: We’ll ask Ian if he thinks we’re pink-sheet-worthy or something, and then we can know just how much we’ve grown.

Jack: One thing we do not need is this trading publicly. You can imagine me running analytics and tracking the daily — we don’t need that.

Matt: It’s a good thing we didn’t do this in like 2019 and become a meme coin or something. Then we would —

Jack: Yeah, yeah, exactly.

Matt: All downhill from there.

Jack: So that’s probably a good note to wrap up on. I’ll let you bring us out like you always do.

Matt: Make sure you check out the Excess Returns Substack. One of the coolest things we are doing is taking a lot of these interviews — not only giving you the transcripts and whatever else, if you wanna run your LLMs on them, but we’re doing posts like “the five lessons we learned from great investors” and a bunch of other things. Please go subscribe over on Substack if you haven’t already. Otherwise, let us know what you think about these recap shows. We’re loving pulling these clips and talking about them with each other and with you guys on here. Comment, subscribe, all the things below — and we are out.