In their latest episode of the VALUE: After Hours Podcast, John Huber, Jake Taylor, and Tobias Carlisle discuss:
- Warren Buffett’s 90% Rule
- Build Your Stock Watchlist Like A Baseball Fan
- Finding Opportunities Using The NCAA Pairwise Rankings
- Compare Companies Using Munger’s Head-To-Head Comparisons
- SVB Did The Fed A Massive Favor
- Peak Performance Age For Investing
- Prisoner’s Dilemma Between The Fed And Government
- The Genius Of John von Neumann
- Why JPMorgan Are Great Capital Allocators
- Ted Weschler’s Investment In Dillard’s
- Is ChatGPT A Threat To Google Search?
- Stocks Can Go Nowhere For A Long Time Before They Become Multi-baggers
- The Biggest Problem Coming Down The Pike For Banks
- SVB Clearly Not Thinking Like Owners
- Bank Credit Tightening
- 10:3 Inversion Steepest Ever
You can find out more about the VALUE: After Hours Podcast here – VALUE: After Hours Podcast. You can also listen to the podcast on your favorite podcast platforms here:
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Full Transcript
Tobias: And we are live. This is Value: After Hours. I am Tobias Carlisle, joined as always by Jake Taylor with special guest, John Huber. How are you, John? What’s happening?
John: Good. Yeah, I don’t know how special, but I appreciate the invite, guys. Thanks for letting me tag along and I’ll do my best to fill Bill’s shoes, which is impossible task, but we’ll see how it goes.
Tobias: Just for folks who don’t know who you are- [crosstalk]
Jake: That’s no one. Come on.
Tobias: -tell us a little bit about who you are, your firm. You’re a value investor. You’re an investor, your style of investment.
John: Yeah. So, I run a firm called Saber Capital. We do separate accounts. We have a fund and it’s modeled after the Buffett partnership fee structure. I would describe my style as a value investor looking for high-quality companies. So, I’m trying to find companies that are durable, that have long-term moats. I like what Todd Combs said recently about companies that are 90% likely to have more earning power in five years. That’s in a nutshell how I would describe the universe of companies that are on my watchlist. I try to find companies that I think are going to do better, are going to be improving.
I believe in the idea that companies are either getting better or they’re getting worse. They’re not staying the same. So, you have this dynamic list of companies out there, and I try to separate the universe into companies that are in that first cohort, companies that are improving and getting better over time.
So, yeah, in a nutshell, I’m looking for good quality companies at a fair price like everybody else. It’s not a unique strategy, but I do my best to implement it successfully.
Tobias: I thought– [crosstalk]
Jake: My humble estimation, John is one of the most process-conscious investors who I’ve ever come across, and I mean that in the best way.
John: Well, I appreciate that, Jake. Yeah, well, we’ve been sharing a lot of notes lately and a lot of conversations and it’s been fun to tag team the efforts that you’re working on with Journalytic, which has been a huge benefit to the process. But yeah, I’m a big believer in the idea that investing is one of these games that I call long feedback loops. You put in the work today and you don’t get any feedback on that effort until, let’s say, five years from now, really. Maybe three, four, five years down the road, you start to get feedback on the work that you’re putting in today.
So, you have to have some sort of process to help you make decisions on a daily basis, help you guide you on what type of work you’re going to do, what type of companies you’re going to prioritize. My personality is just process driven to begin with, but I think investing is one of these games that you have to have some process enabled to do productive work and to make inroads over time. I’ve always liked that. Running is another thing I like to do for fun. Running is very similar to investing, where you have these long feedback loops. You got to put in the work day after day after day, and then over time, you start to see noticeable differences. But it’s the daily process that is– [crosstalk]
Tobias: Are you still getting faster?
John: No, I’m getting slower.
[laughter]Jake: I’m trying to delay that. I’m doing my best to delay that process. So, I’m getting slower and weaker, but I’m doing– [crosstalk]
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Peak Performance Age For Investing
Tobias: At least the investing would probably get better. That’s the nice thing about investing. You should get better as you go along.
John: Yeah, you should. I’ve often wondered your peak VO2 max– For running nerds, VO2 max is, basically, how efficient you are at processing oxygen into your bloodstream. Your peak physical ability as a, let’s say, you’re a miler, you’re going to peak at like age 25 most sports. If you’re a basketball player, you might peak at age 26, 28, something like that. That’s your peak physical condition. I’ve often wondered, what is your peak mental age? I think Charlie Munger talked about this recently, how there is a noticeable degeneration of your mental capacity. He’s 99. At some point, you begin. But I would think in your 60s, you’re probably better than you were in your 40s, I would think.
Tobias: No, the chess players. The chess players say it’s late 30s, early 40s.
Jake: Is that right? Yeah.
Tobias: Yeah, you lose the computational ability after that.
John: Interesting.
Tobias: [crosstalk] count so much.
Jake: There’s a speed component to that though that I don’t think is necessarily a constraint on the investing side.
Tobias: But that’s the pure calculation. That’s the pure calculation part. If you’re obviously investing says very large, [crosstalk] component to it too. The more reps you get in investing, the more experience you have. The less you’re doing calculation, the more you’re just comparing [crosstalk] before.
Jake: Pattern matching.
Tobias: Yeah.
Jake: Right. Yeah, I think a big part of it is experience and pattern recognition. I would posit that Buffett’s a better investor now than he was in his 20s. But the other interesting thing is his record was the best in the 50s when he was in his 20s or 30s, because he had a small amount of capital.
Tobias: Opportunity set.
Jake and John: Yeah.
John: So, he’s better now than he was then. But you’re also governed by the amount of capital you have to allocate.
—
Tobias: John, I got to give a shoutout to all the people who– Because we’ve got a good spread.
Jake: Where are they calling it from?
Tobias: Dubai, Cincinnati, Camas? I hope I’m saying that right. Toronto.
Jake: Camu?
Tobias: That was C-A-M-A-S.
Jake: Oh, okay.
Tobias: I think it’s Washington. Vestavia Hills, Alabama. Banana Bend. That’s not a place in Australia. [laughs]
Jake: Yeah. [laughs]
Tobias: Belle Plagne, French Alps. That’s a nice one. Poland, Pittsburgh. I’ll come visit that one. London Town. Milton Keynes is back. Omaha. Nice. Offerton in the house. Nashville. Rochacha, Oslo, Bucharest. That’s a good spread.
Let’s talk a little bit about your process. What is your process? How do you do it?
John: Well, my process is I like making slow decisions. So, I tend to build a watchlist of companies that I follow for quite a long time. My process is basically come in every day and try to expand that watchlist of companies that I follow. That expansion process takes a long time. I might only add 10 companies to the list every year, but my goal is to continue to build that list of companies that I understand and that I feel like fits that durability test.
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Build Your Stock Watchlist Like A Baseball Fan
I have four-part– my investment process has four components. One is durability, one is growth potential. It doesn’t mean fast growth. It’s just companies that are expanding have what Nick Sleep called a destination. So, companies that are moving forward, heading towards some far off destination, and they have room to improve. So, potential prospects. [crosstalk] Yeah, runway, right? The third would be capital allocation, which is like a management factor. And then, the fourth is valuation. So, those are the four things that I’m looking for with every investment.
Jake: Maybe talk about the baseball analogy that you have for your farm team. [crosstalk] had to be a nice analogy.
John: Yeah. My Journalytic page is filled with all kinds of baseball jargon.
Jake: [laughs]
John: In fact, the topic that I was going touch on today is also a sports topic that I’ll try to manufacture into some investing insight for us, if you guys want to. My watchlist, I’m a baseball fan. My A list is like the big-league list of stocks, and there’s probably 150, 160 companies on there. So, these are companies that have met those three criteria in terms of business quality. And then, the fourth criteria is valuation. My process for valuation is basically, I think about it a little bit differently, I think in terms of rate of return. So, on that spreadsheet, I have my expected rate of return for every stock on that list. And so, that is the main list.
Then I have, like you were referencing, the farm team is basically a list of companies that I’m learning about, wanting to study in more depth, trying to figure out if it’s a company that fits those criteria. That’s a huge list, because anytime if Jake and I are talking and he gives me this idea, I might put it on a list. I rank them. I move them up and down. I try to focus on no more than 10 companies at a time. I try to focus on even fewer than that, but I try to rotate through and focus that list and prioritize that list. I have little systems in place. Like I said, I have this little thing I was going to run by you, guys, but little systems in place to try to organize and prioritize my research process. But the process in a nutshell is build a list of companies and then wait for those companies to hit the price that I think will allow me to meet my objective hurdle rate of return.
Tobias: Do you want to go through your little– your–?
John: Yeah.
Tobias: Sports–
John: I figured it was part of the job to prepare some a veggie segment [Tobias laughs] to get into the Value: After Hours lineup. So, I came prepared. I think Jake might like this. Like I said, we talk a lot about sports and investing, and we share this common desire to build investing analogies around various topics of sports. And so, that’s what this segment is and I’ll touch on that. But basically, I’ll loop back and we’ll talk about portfolio construction, which I thought might be interesting and more specifically, opportunity costs.
A month ago, when Silicon Valley Bank had their run, I started going through a list of bank stocks. I was just curious because I wanted to see– One of the lists on this spreadsheet I was describing earlier, I have a list of bank stocks that I’ve followed over the years and they’re typically like smaller regional banks, community banks. But for those of you familiar with the US banking system, it’s a very fragmented system. The architecture of banking system can be traced back to our distrust, like the original distrust of centralized power in America. It’s like part of our DNA. We don’t like big government, we don’t want centralized power, we’ve always been skeptical of money that’s concentrated in the hands of the few. So, when we designed our system, we didn’t really design it, it just happened this way that we have like 4,000 banks in the United States, and there’s like 600 or 700 that are publicly traded.
So, it’s a tall task to try to go through them and you need some system to try to filter through which ones you want to pass on. As I was going through this list, really quickly you can pass on, I would say, four out of five are just immediately you pass because of a variety of reasons. You might not like the loan book, you might not like the leverage, you might not like the liquidity, you might not like insider ownership or management quality or a variety of reasons, but you still, even for those one out of five that, look interesting, my original idea was there’s going to be some babies thrown out with the bathwater here because there are a lot of issues in a lot of different banks, but there are also banks that are flush with liquidity and huge amounts of cash on their balance sheet. A lot of the stocks trade at five PE, six PE. They’re very, very cheap.
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Finding Opportunities Using The NCAA Pairwise Rankings
So, I was just curious to see– what I really wanted to do with this little mini-project was figure out how the management teams allocated capital. So, in 2020, 2021, banks got huge amounts of deposits from the fiscal stimulus and the monetary stimulus that took place. It’s very interesting to look at the cash flow statements, which is a forgotten aspect of bank financial statements. But the cash flow statement you can use to see, “Okay, where did the money come in? How much did they get in deposits? And then where did it go? Did they put it into loans? Did they put it into bonds at 50 times earnings?” which is what a 2% yield is. Is that’s what Silicon Valley Bank did.
Jake: Oops.
John: Did they hold their fire? Did they keep it in cash or in T bills, which right now looks like a great decision, but at the time, at 0% interest rates, that was maybe a difficult decision to make. So, it’s just interesting to see how these management teams allocated capital. So, I’m going through this list and I’m thinking there’s just too many– You get analysis paralysis. I’m sure you guys have probably experienced that from time to time. I don’t do a lot of screening, but I do look at a lot of A-to-Z lists, whether it’s industry groups or just random lists that I’ve uncovered from time to time. I used to go through Value Line and I have a whole list of the Value Line stocks. I’ve always had these little systems to, almost like a hack, try to like, “How do I filter these?”
So, coincidentally, I was talking to a client of mine a few weeks ago, and we were talking about the Frozen Four, the way that the Frozen Four, which is the collegiate hockey tournament, how they rank their teams. And so, everybody knows about the Final Four. The lesser-known cousin is the Frozen Four, which is NCAA hockey’s version of bracket. There’s 16 teams instead of 64, but it’s very similar. It’s a single elimination tournament. So, picture 16 teams in a bracket. But what’s interesting about it is there’s a system that the NCAA uses to rank all 61 NCAA Division 1 hockey teams, and it’s called the pairwise system.
And the pairwise system is a very simple comparison tool that uses three categories, and you get one point for each category. The three categories are head-to-head, common opponents, and RPI. Head-to-head is self-explanatory. That’s like Michigan versus Minnesota. So, if Michigan and Minnesota are being compared in this pairwise comparison, let’s say Minnesota beat Michigan. Okay, Minnesota gets one point. The second is common opponents. So, how did Michigan and Minnesota do against– Let’s say they both played Ohio State and Boston College. And let’s say Minnesota won both and Michigan won one and lost one. So, Minnesota had a better record against common opponents, so they would get a point. Now, it’s 1-1.
The third point is RPI. And the RPI is a little bit more complicated, but it’s a rating and it’s based on winning percentage and strength of schedule. Let’s say Michigan has a higher rated RPI. So, they would win that pairwise comparison 2-1. And so, what’s interesting about the system is you can rank all 16 teams. The system is used to select– It’s a little complicated but once you have the 16 teams selected, the system ranks 1 through 16.
I was thinking like, “This would be an interesting way to–” After talking to my client and I’ll give him credit for this idea, I started thinking like, “Maybe there’s a way, just for fun, to use some your own internal checklist to rank stocks.” I was in the midst of this little research project I was doing and I was thinking, “Okay, I need something like this to filter– If I have a hundred stocks that look interesting– My style of research, I like to do deep dives, so I want to try to meet with the management team if I can. I want to make phone calls and really do some research. You have to have some way to prioritize what you’re going to work on.
So initially, I thought, “Well, I could adapt this system to my own four-point checklist.” So, you could have a three-point checklist. You could have business quality, management quality, and value. So, I was thinking you could rank stocks in your portfolio using a three-point pairwise comparison, and you could compare any stock in your portfolio. If you have O’Reilly and, let’s say, Fastenal, you could compare those two against each other and see which one’s better, which one should get the higher weighting.
Again, this is just for fun. This is not to automate decision making. It’s more just to aid decision making. It’s really just a tool to– like a check and balance, it’s like food for thought. I started using it just for fun to use my own four-point checklist that I just described to you, guys. It’s durability, its growth potential, its capital allocation, and value. Since I have four, you could have a 2-2 tie. So, I use valuation as the tiebreaker.
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Compare Companies Using Munger’s Head-To-Head Comparisons
So, I thought of three ways that you could use this little system. One is you could just use it for head-to-head comparison. If you think about opportunity costs, Charlie Munger had this– My favorite way to explain opportunity cost is to just rephrase what Charlie Munger said about Wells Fargo years ago, and this no longer applies because they sold Wells Fargo. But he used to say, “Our best idea is Wells Fargo. And we would compare every stock that we’re looking at to Wells Fargo. If it’s not better than Wells Fargo, why not just buy more Wells Fargo?”
So, you could use this system to compare it to your best idea. Whatever your best idea is, you could run every prospective investment against that idea. I actually think it’s more practical to compare it against your least favorite idea. So, let’s say you have 10 stocks in your portfolio, if Wells Fargo is your favorite, you’re not going to sell Wells Fargo to buy the new investment. You’re going to sell the number 10 idea to buy the new investment. So, you might want to compare it to– But you can compare one stock against each other. That’s one way.
The other idea that I wrote down that you could use this for is portfolio weighting. You could use it just like the NCAA hockey to rank– again, let’s say you have 10 stocks in your portfolio. You could rank all 10 through this system and determine which one’s one, which one, 1, 2, 3, all the way up to 10. It would be interesting to do because you might find that let’s say you have a 15% position and a 5%, again, O’Reilly and Fastenal to use those two, let’s say you have 15% in Fastenal and only 5% in O’Reilly, and the system tells you the 5% position should be number one and the 15% position should be number seven. It might give you food for thought. It’s like a check and balance.
The interesting thing is it’s not using random inputs. It’s using your own self-selected variables that are important to you as an investor. So, the system is telling you based on the criteria that you’ve deemed to be important, here’s the ranking. And so, it’s just a fun exercise. It doesn’t really work, if you have a lot of stocks. If you have 30 stocks, you’d end up with close to thousand different pairwise comparisons. [crosstalk] Yeah, it would probably be too much work for the trouble. But if you have a concentrated portfolio, it might be interesting to do that.
The third way you could use some sort of a system like this is the way I’ve been using it, which is to prioritize your research efforts in a list of stocks that you’re looking at. So, I’ve designed a little bank-specific pairwise system to rank the stocks that I’m looking at in that industry. It’s bank-specific criteria, like there’s a liquidity test in there, there’s a leverage test, there’s a profitability test. There’s seven or eight different categories. I assign a one through three ranking or one through three-point system. The goal here is, it’s to do nothing more than filter which ones I should dive into deeper. So, those are the three practical applications that you might be able to use this pairwise system for.
Tobias: That’s great, John.
Jake: The guys over at Ensemble Capital, shoutout to Sean and Todd, they do something similar where they do, I think, it’s zero to three. They’ll basically convert a bunch of qualitative assessments into a quantitative assessment, and then be able to compare things against each other. Everyone makes their own assessment and then they talk it out as to arrive at like, “Okay, we’re going to agree that now this is like a two out of three,” when maybe you thought it was three coming into the meeting. But I think that’s actually a very intelligent way to bring some process into the conversation. That way, you’re not talking past each other about these qualitative things that can be all over the map.
John: Yeah, that’s really interesting. Yeah. I think making it simple is the key. The college hockey system is so elegantly simple. There’s like three points. You get one point for each category. You can compare every team against any other team using that system. And again, the goal here is making better decisions. And so, I think having tools– Jake and I talk about Journalytic a lot. I’m a big fan of writing down my ideas. You’re trying to improve your process, increasing the efficiency of your thinking and your analysis and your research efforts and all of that. And so, these are just tools that you can use to help you– [crosstalk]
Jake: I’d be careful though, John. You’re on a slippery slope that leads you to running a quant value ETF.
[laughter]John: Yeah.
Jake: Something like– [laughs]
John: Yeah, exactly. That’s the thing. I don’t know, what do you think about this, Toby? But for me, this is more, again, it’s to help me increase the efficiency of the research effort, not to make any decisions. Because the way I would look at this is you’ve already put in the work. You’ve already selected the stocks. You’re just trying to determine– Basically, Charlie Munger said, “Is it better than Wells Fargo?” What this is forcing you to do is explicitly explain to yourself why it’s better than Wells Fargo. You might know just intuitively or just through common sense that stock A is better than stock B. But this forces you to itemize, again, based on your own criteria that you’ve created.
Jake: Yeah, show your work.
John: Yeah, show your work. Exactly. Show your work.
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The Biggest Problem Coming Down The Pike For Banks
Tobias: In doing little bank project, did you look at the composition of the loan books in terms of how much commercial, how much–?
John: Yeah.
Tobias: That’s my question, really. You can look at any chart about occupancy. It’s 50% of where it was pre-pandemic. That’s going to create huge problems for– If you’re a business owner, you’re not going to need as much floor space. You’re going to reduce the floor space. That means smaller tenancies. You’re giving up a lot of space. If you own a commercial office tower, you’ve lent against it, then you’ve got bad debt problems, you’ve got lots of issues coming down the pike that may not be currently reflected in the books. Do you have any thoughts on that?
John: Yeah, I think commercial real estate is probably going to be a big problem for certain banks. And perhaps, it could be a problem– [crosstalk]
Tobias: A lot of the regional ones have this huge exposure to commercial from what a lot of the regional– [crosstalk]
John: Yeah, a lot of the community lenders are lending against– They make loans to typically one of two broad categories, small businesses. And so, their CNI books are worth examining. And then, the commercial real estate component of their books tends to be larger than, let’s say, the money center banks, which are extremely diversified and in my view, very diversified and probably quite safe. In fact, they’re gaining market share, I think, in this turmoil.
But yeah, I think it’s interesting to look– So, there’s broad differences though from bank to bank. I’ve looked at countless numbers of these banks recently. BankRegData is a really good site. Shoutout to Bill Moreland who runs that site. It’s an incredible tool. If you’re interested in banks, that’s a must have in terms of– It basically filters the call reports. So, you can look through call reports. What he has is a system that allows you to parse through these bank balance sheets and these loan books and who are the customers, how concentrated are the deposits?
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Why JPMorgan Are Great Capital Allocators
But I think there are some banks that are really poised to do well and take advantage of the current market environment. There are banks that allocated capital really well and then there are banks that allocated capital poorly. There’s so many differences. A big bank like JPMorgan is an example of one that has allocated capital very well in my view. They had about $800 billion of deposits flow in, just a massive amount of deposits in just two years. They put just $50 billion of that into long bonds. $50 billion sounds like a lot, but that’s only about 6% or 7% or whatever of the total amount of inflows that they achieved. They had some loan growth, but the majority of that was just parked at the Fed, earning zero in T bills or just in deposits at the Fed. Those are now earning 4% or 5%. And so, they have over a half a trillion dollars in cash and they’re poised to do really well.
There are a surprisingly large number of community banks that have done quite well. It’s a minority. It’s a small percentage. But just given the fact that there’s so many banks, there are a lot of banks that have really high-quality loan books, there are banks that have a lot of cash as a percentage of assets, which is one way to just– it’s a quick and dirty way to assess liquidity. I don’t make a lot of investments in banks. I don’t have any investments in small community banks, but I like to look at them. There’s a notion out there that I’ve been reading in the last month that, “Oh, you can’t invest in banks. This is why you can’t invest in banks.”
Jake: You are– [laughs]
John: Yeah. Bill Ackman’s out there shouting about how risky banks are and fearmongering and all this stuff. I was thinking like I’ve never seen research on this, but I’d be very curious if anyone has any. But I’m not so sure that the failure rate of a bank or the probability of failure at a bank is higher than the probability of failure at, let’s say, a retailer or a manufacturer-
Tobias: It’s a good question.
John: -or an industrial or the technology company, because we know the stats overall in the American economy. Most companies– actually, I think a majority of companies over 50% end up going to zero. So, most end up failing at some point. I think that the thing that always gets everyone’s attention is banks fail overnight. You can have a run on the bank. But I agree with Buffett when he says banks can be very good businesses, if you are a low-cost attractor of funds. If you’re a low-cost operator in a commodity business, you can carve out a moat. In banks, money is the commodity. And so, if you’re the low-cost producer of that commodity, money, meaning you can attract low-cost deposits, then you can gain an advantage. What he says is banks can be a great business and a great investment, if you don’t do stupid things on the asset side.
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SVB Clearly Not Thinking Like Owners
What I would say is, what happened to Silicon Valley Bank was not– A lot of attention has been given to the concentration of the deposit base. I don’t think that’s actually why the bank failed, although that was a risk factor. But why the bank failed was they did stupid things on the asset side. They bought bonds at 50 times earnings. Just like if you buy Coke in 1999 at 50 times earnings, you’ll eventually make your money back. It’s good credit. But it doesn’t mean it’s a safe investment. You can suffer a 50% mark to market loss. In the banking world, if you’re a leveraged institution, then that’s a problem. So, Treasuries and AAA rated MBS securities, they have no credit risk, but it doesn’t mean that they’re safe investments.
So, I think they made unsafe investments on the asset side and that led to a situation where their deposit base became a problem for them. But their deposit base was there for 40 years. I don’t think that was the problem. I actually think what the problem was incentive structures put in place to incentivize short-term earnings. Because if you get $10 billion of deposit inflows and you say, “Okay, I have a choice to put $10 billion into 0% earning– park it at the Fed and get zero, or put it into these 2% yielding securities that gives me $200 million, I’ll take the 200 million, because my bonus is affected by the net interest margin that I produced this year,” or whatever it is.
So, I think looking at the cash flow statements, you can see which management teams are incentivized to create earnings now and which think more like owners, like Jamie Dimon at JPMorgan. There’s, again, a number of community banks that we won’t need to go into them but– Buffett’s, another example. He kept his money parked in T bills for all those years, because he’s thinking like an owner. If he was incentivized to make money this year, he’d put him into something yielding more than zero. And so, I think that’s interesting to consider.
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The Genius Of John von Neumann
Tobias: Hey, we’ve just clicked over the hour. JT, do you want to do your vegetables?
Jake: Sure. Absolutely.
Tobias: Heavy serving of vegetables today. It’s just vegetables.
Jake: Just veggies all day. [laughs]
John: Oh, mine was probably more dessert. Dessert first. So, we’ll get to the healthy stuff now with Jake.
Tobias: [laughs]
Jake: Well, I don’t know. Maybe, we’ll see. So, we’re going to be talking about game theory. Giving a little bit of background on– Do you guys know much about Johnny von Neumann? He was a mathematician, scientist, researcher, physicist, polymath. Well, all right, I’ll give you a quick rundown of him.
Tobias: Did he make rockets?
Jake: Yeah, he did help make that. He was on the Manhattan Project as well. Born in Hungary in 1903 to a Jewish family. Again, was another child prodigy. It seems like we come up a lot. By the way, where are all the child prodigies today? I don’t feel like I hear about that as much.
Tobias: YouTube.
Jake: Oh, they’re on YouTube. Yeah.
[laughter]John: They’re influencers.
Tobias: That’s it.
Jake: Yeah. Ouch.
Tobias: Mr. Beast.
Jake: Yeah, that’s a good point. So, at six years old, he could divide two 8-digit numbers in his head. By eight, he was doing differential calculus, spoke several languages, read Greek historians in their original language as a kid. He had a perfect recall of everything he read, basically. Even all of these people that he came into contact with, like Einstein, Hans Bethe, all of the preeminent scientists, all of them said, “Oh, yeah, he’s smarter than all of us here. He has a very special brain. This guy’s just insane.” But he made early contributions to a ton of different fields, including economics which we’ll get into, quantum mechanics, nuclear physics, like I said, Manhattan Project, linear programming. He’s the father of a lot of computer science stuff.
Immigrated to the US in the early 1930s. Designed and promoted the policy of mutually assured destruction. This is part of that game theory that he brought to geopolitics. Worked on the idea of self-replication as a more general concept before we really understood DNA at all. A lot of his ideas hinted at how DNA ended up working. And then, 1949, he designed a self-reproducing computer program, which is considered the world’s first computer virus, which is kind of interesting. Created the first world climate model software, did the first numerical weather forecasts. So, before, I think it was like you just went outside and licked your finger or something.
Apparently, he could recite Gibbon’s Decline and Fall by heart, which is insane. Claude Shannon called him the smartest person he ever met. What else is interesting personality wise, he loved to eat and drink and tell dirty jokes. His wife said of him that he could count everything except calories.
[laughter]—
Prisoner’s Dilemma Between The Fed And Government
Jake: So, he founded the field of game theory as a mathematical discipline. He teamed up with this economist named Oskar Morgenstern, and they brought serious math to economics for the first time. I stumbled across this interesting example of game theory that I don’t know if you guys have ever read about, but it’s a prisoner’s dilemma for the Federal Reserve and politicians. This came from work from Alan Blinder, who was an economist. Was, I think, a vice chairman of the Federal Reserve at one point. So, what you have to imagine is that, is there coordination that’s possible or even desirable between the Fed and politicians?
So, on the one hand, the Fed, you have monetary policy, which is involved with the control of the short-term interest rates and the money supply. And then fiscal policy, which is the government deciding how much balance is there to the budget, how much do they spend versus how much do they bring in tax revenue. The Fed authorities, they’re perceived to have control overinflation as their primary responsibility, which makes them favor economic contraction over expansion. So, that’s their goal seeking. We’ll see if how much this truly applies in a very messy world, but just abstract and let’s make things simple for a minute.
Then, the Fed also serves very long term. So, each member of the board of governors is appointed for 14-year terms. The idea of that is that they’re supposed to be independent of political pressure. Now contrast that with politicians, they have to run regularly for election, which leads them to favor economic expansion now over contraction. And also, Congress serves two-year terms, senators are six-year terms. Compare that to a 14-year term. So, we have different time horizons that are competing.
The object of the game in this is to get the other side to make the tough decision. The Fed, they don’t want to have inflation and they would prefer contraction, if possible, to avoid inflation. And then, the other side wants the party to keep going. The Fed would prefer a budget surplus to keep inflation tamped down and then keeps their names out of the paper, but that’s up to the politicians to decide. And the politicians who are worried about constant reelection would prefer the Fed to keep rates really low, keep the money flowing, keep the party going so that it stimulates business activity and unemployment. So, less unemployment. So now, there’s no need for them to run budget deficits. They can just do whatever they want, so they don’t get in trouble.
So, it sets up this weird game theory matrix where you have do nothing or contract over expand on either side. What ends up happening, this is the classic prisoner’s dilemma. The Fed’s preferred outcome is that they do nothing and then the politicians contract. The politicians are never going to want to do that outcome. The next best for them is that the Fed contracts and the politicians do nothing. And the third best is that the politicians contract and the worst outcome for the Fed is that both them and the politicians are expanding, because that’s the one that’s going to lead most likely to inflation.
Now, if you look at the politician side of things, their preferred outcome is that they do nothing and the Fed expands. So, it’s not their problem. And then the worst outcome for them is that, both the politicians and the Fed are contracting, which creates socioeconomic problems and now they’re not going to get reelected because the economy is in the shitter. Okay. So, actually, what ends up happening in this is the Nash equilibrium point of that is that the Fed contracts and the politicians expand.
So, I can’t help but wonder is that like a little bit where we find ourselves today is you have the Fed talking a stern hawkish game of contraction to try to tamp down inflation and just keep expectations low while the politicians are, I don’t know, we’re running trillion-dollar deficits every single year. It seems like this is probably what you would have predicted based on game theory of what they’re looking at. Neither party can really afford to look neutral and then therefore take the blame. That’s the prisoner’s dilemma version where neither person rats out the other person. So, we end up in this–
Actually, what is of the total nine squares of outcomes, we end up in the 7th best one. It’s towards the least good outcome because of game theory. So, I thought that was an interesting little exercise that I hadn’t really ever come across before.
Tobias: Would you say that Bernanke and Yellen, they are accommodative, right?
Jake: I know. That’s why I’m saying, we got to suspend a little bit of some of the nuance here. This paper that highlighted this was written in 1982, and it very much explained the Reagan era, where you had the government running huge deficits, and then you had for Star Wars programs and, I don’t know, whatever the hell we were spending money on in the 1980s. Trying to outspend the Russians, I guess. Then you had a very controlling Fed with Volcker. And so, it explained perfectly at that time. I don’t quite understand where we ended up for the last 10 years, maybe before Powell, where it did seem like both sides were just drunken sailors, but I don’t know. [chuckles]
John: Yeah, that’s super interesting. I just actually read Volcker’s book, Keeping At It. He talked about that era, the Reagan era. I’ve read a few books, either by or usually they’re about different Fed chairmen and just the Federal Reserve in general. It’s interesting. One of the broad takeaways I’ve had is how intertwined they are with politics, despite the fact that they say they’re neutral. We have tried to set it up in a way that in theory, it should-
Jake: Insulate them?
John: -result in neutrality. I remember when Trump was pestering Powell nonstop, he was doing it in public. But what’s interesting is everybody’s like, “This is unprecedented.” But really it was like, “No, this actually has happened.” The only presidents that I think did not really meddle– I don’t know that Obama really did, and I don’t know that Bush really did. But every president before that– Volcker says Reagan came in. He was called into the White House for a meeting one time, and Reagan said nothing. But his Chief of Staff said, “You are not to raise interest rates.” Volcker wrote that he was directed essentially by Reagan to act in a certain way and he was so flabbergasted by it. He was ready to tender his resignation.
Yeah, that prisoner’s dilemma thing is super interesting. I don’t know how separate they are in terms of their incentives. I think they both, at the end of the day, want to– Politicians are maybe more so incentivized to be accommodative, but I think they’re both incentivized to lean accommodative.
Jake: I wonder how much of it is that there’s been a lot of, I would say, ideological creep in a lot of economics, especially if you’re a PhD at the Fed level of economics, where it’s like deflation is the absolute boogeyman. You cannot at any cost have deflation happen. Although when I look at the history of especially the United States in the 19th century, we had a very gradual soft inflation that just led to a higher standing of living for the average person. It was a relatively beautiful outcome, I think, from a societal standpoint. So, I personally don’t quite understand all of the hatred for deflation as a natural, general, technologically advanced deflation. But that seems to be an anthem to– Maybe I’m not smart enough to understand why. I’m not a PhD economist.
John: Yeah. Well, we’ve had steady inflation over the years. We’ve had certain deflationary forces like in technology. Maybe that’s what you’re talking about, JT?
Jake: Yeah.
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SVB Did The Fed A Massive Favor
John: Our productivity has increased our standard of living. I think there was a period in American history where when we were on the gold standard, we had these spikes of inflation and the spikes of deflation, and it was very painful. But the money supply was fixed more or less, and we really had no inflation over like a hundred-year period. The value of goods and services didn’t really move, which is interesting. So, that’s sort of a foreign concept to us now, once we detethered our money supply from gold. But yeah, it’s interesting.
I was thinking about this current– just in the last month, the banking issue with Silicon Valley. They probably did the Fed a favor because, to your point, the Fed, they know that they need to raise rates to stop inflation, but they don’t really want to do that, because that’s going to cause the economy to slow down and perhaps, crater. This is like a gift to the Fed because the banking system now can do the Fed’s work for it, and the Fed can absolve itself of any blame, because of course, the Fed was perhaps responsible for causing the crisis in the first place by raising rates. But the banking system is really the neutral entity that can create money. Money is created by the banking system, not really by the Fed. [crosstalk]
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Bank Credit Tightening
Tobias: I saw some headlines today that it’s very hard to borrow as a small business at the moment. Small business conditions are exceptionally tight since SVB failed.
John: Yeah, I’ve heard that anecdotally as well when I talk to different banks. It does seem like credit has tightened, which makes sense. I think if you look at– the Fed puts out a weekly release that is a banking system overview and you can see there was a huge amount of cash buildup at banks, which essentially was a product of– Deposits flowed from small banks to big banks initially, but what really happened, which is interesting, is banks began to borrow at the discount window and through some of the new facilities that were recently created just last month. And so, banks have been hoarding cash. You just think if you’re a banker right now, you’re not buying back shares, you’re not making new loans. You’re battening down the hatches and you want to be prepared. And so, that’s going to inevitably tighten credit, I would think.
Tobias: Do you think that precipitates anything? Do you think that causes–?
Jake: Not slow things down–
John: I think it causes a slowdown. Yeah, if that comes to pass– Who knows? It’s beyond my paygrade to try to predict where it goes but it certainly will cause a slowdown, because, again, the banking system is the mechanism that our economy uses. It’s the transmission mechanism to create money– Loan growth creates money supply, which allows consumers and businesses to go out and spend and work on new projects. That’s how the economy grows. And so, if that’s working in reverse, which it might be, then that will cause a slowdown. So, it’s tough to predict how it all shakes out.
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Warren Buffett’s 90% Rule
Tobias: Before we came on, we were talking a little bit about Buffett’s 90%. Is it 90% confidence interval that firm’s earnings will be bigger in five years’ time? Is that how he characterizes the business will be bigger in five years’ time?
John: Yeah.
Jake: He bought exercise that they do in terms of– [crosstalk]
John: Yeah, it’s a super interesting little simple thought experiment that I thought was really neat. It’s basically like the first test is, is this company 90% likely to have greater earnings in five years or are you 90% confident that this company–? So, it’s companies that obviously within your ability to estimate that or predict that, but are you 90% confident that the company is going to be stronger in five years is how I would think about it.
Tobias: We were talking about some examples of that. Before Amazon, who did you mention before Amazon?
John: Well, we were talking about Dillard’s before we– [crosstalk]
Tobias: Was it Dillard’s? Yeah.
John: Before we turned it on. Yeah.
Tobias: Run us through that thought exercise, the Dillard’s?
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Ted Weschler’s Investment In Dillard’s
John: Well, yeah. And, Jake, you may have more– I don’t know if you want touch on it. I was just making a comment that I looked at Dillard’s. Ted Weschler invested in Dillard’s. I did a little case study on this one time and was trying to reverse engineer what–
Jake: Nobody see– [crosstalk]
John: “What on earth did he see in Dillard’s? Why did he buy Dillard’s?” If you look at it, their numbers are just unlike anything I’ve ever seen in terms of a retailer that– The profit margins just exploded. I think their operating margins went from 4% to 16%. What’s interesting is their sales didn’t really grow that much. I think their sales went from $6.5 billion to $7 billion. So, over a couple of years, their sales were up modestly, but their margins exploded and their profitability exploded.
They own their real estate. They have some assets that are valuable, I think. I think that’s what Weschler saw is this downside protection. I don’t think he could have ever imagined the success, because the stock went up 10X, because the margins have exploded– [crosstalk]
Tobias: [crosstalk] to do it and nothing happened for the first five years.
Jake: Yeah.
John: Yeah. It went up 10X during COVID. From 2020 to 2022, I think it went up 10X or 12X or something. It was because the profits exploded, the PE multiple expanded, and-
Jake: Share count was– [crosstalk]
John: -and the share count, no idea.
Jake: [crosstalk] first five years.
John: Yeah. This was like a $25 stock when Weschler bought into it. I think they did $50 per share of earnings last year. So, just astronomical. What really happened there is they were preparing for the Great Depression, and they got the biggest boom that they could have ever imagined. If you think about it, businesses– that just never happens. You never have a situation where you’re preparing for the worst and you end up getting the best, and that’s essentially what I think happened at Dillard’s. And so, it’s just an interesting thing to observe.
Jake: My big takeaway is that there is no ability to time when the market will wake up to a situation. There’s nothing that was materially that different over the course probably of his ownership, but the price just absolutely mooned. How could you ever predict that kind of thing? You really can’t.
John: Yeah. Like the old saying, “Good things happen to cheap stocks.” He was buying a cheap stock. My suspicion, I read some of the annual reports and I was thinking, again, trying to just figure out what he was looking at, you could see they had a lot of assets. So, they had asset value, and then I think there was downside protection. It was a very cheap stock. It was probably trading at 20% earnings yield or something. And so, if anything turned around, then you’d get some good things happening, but I doubt that he ever would have imagined–
In 2020, you couldn’t have looked and said, “We’re going to get a physical retail spending boom.” Perhaps, you could have predicted it, but I doubt many people did predict that to the extent that we got. Because if you remember, and I know you guys do remember, it wasn’t that long ago that physical retail was dead and COVID was like the nail in the coffin. And so, this was not like, “Hey, these guys are going to–” The management team was preparing for the worst too. You could see how they tighten their belt and that’s why their margins exploded, because they ratcheted down their cost structure so well. Then, when they had a little bit of bump in sales, they had this astronomical operating leverage.
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Stocks Can Go Nowhere For A Long Time Before They Become Multi-baggers
Tobias: When you look across your portfolio, can you deduce anything about what the next few years looks like for the rest of the economy? Does that factor into your decision making?
John: I don’t know. Jake, you want to take that one?
Jake: [laughs]
John: I’m not good at predicting the economy. It’s very hard to do.
Jake: I think I’ve got about– what are we, 200 episodes of evidence that we have– [crosstalk]
Tobias: We can’t–
[laughter]Jake: -these kinds of things?
John: Yeah, it’s tough.
Tobias: Yeah, but maybe we’re going to get lucky one of these days.
John: [laughs]
Jake: So, you’re telling me there’s a chance.
John: Blind squirrel finds a nut. Yeah, it’s bound to happen. I think when I look at the portfolio, what I try to do is just invest in companies that I think are going to be positioned well for the next decade or the next, let’s say, seven years. On my spreadsheet, I have a six-year rate of return estimate. I mentioned this is a chat for another episode, but the way I think about valuation is rate of return and there’s a few inputs into that estimate. But I look out six years, more just because it’s simple math, a double is 12%, a triple is 20% in six years.
Jake: Also, you got the jump on all those people who are only doing five years out.
Tobias: [laughs]
John: Oh, yeah, I’m going a full 12 months further. So, that’s that time arbitrage [crosstalk] trying to capture. Yeah. So, I’m just trying to look for companies that I think are going to do well over, like I said, 5 to 10 years. Knowing that we’re going to have adversity in the economy, we’re going to have tough times, to me, those tough times come– Like you were just saying, Jake, if you guys have read the book, 100 Baggers, one of the takeaways from that book is that when you look at those stock charts, there’s periods of years where the stock goes nowhere. You have these incredible winners, and you think, “Wow, I just got to find one of those.” But the reality is you have to sit through periods of 5, 10 years, even longer sometimes, where the stock goes nowhere. Very few people, I think, are willing to do that. So, you’ve got to have a long-term view on where the company is headed.
Tobias: Good call– [crosstalk]
John: You’re a long-term investor. You’re going to have a recession at some point, right?
Tobias: Is it overvaluation being worked off or is it just the underlying business is struggling for a period of time, or both?
John: Yeah, I think both. That’s actually the other observation I had was business results are not linear like we all wish they would be. It’d be nice to just put into a spreadsheet what the revenues are going to look like every year. But businesses go through periods where they have fundamental tailwinds and then they go through periods where they’re challenged. They go through all kinds of management changes. It’s just like life in general. You have all sorts of things happen in your life. In business, that are ups and downs and that’s just part of– I think long-term equity ownership is the reality that you are going to have– [crosstalk] The businesses you really like are going to have some tough years. It might be no fault of their own or it might even be self-inflicted. Good companies can overcome those self-inflicted wounds though.
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Jake: I was being a little glib. I do actually have some outlook on what the portfolio says. I think the broader theme is that the next 10 years will be different and harder than the 10 before them, and I want to have a portfolio that is built with resilience in mind and not optimization. Really, it’s kind of being the least wrong across all the potential rainbow of outcomes that could unfold over the next 10 years and not being overly indexed onto any one particular outcome if I can help it.
John: Yeah, that’s fine.
Tobias: I got a question for you, John, from the audience. Samson wants to know, “What’s your favorite tech/growth stock?”
Jake: Just say Tesla so Samson can– [crosstalk]
John: [laughs] I don’t really have a favorite tech/growth stock, I guess.
Tobias: What about something that’s more towards the– you’re relying more on the backend, the future?
Jake: [laughs]
John: Yeah. I do look at certain technology companies. I think Microsoft is a great company. I think they have a very sticky product base, very sticky customer base. There’s certain companies I like, but– [crosstalk]
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Is ChatGPT A Threat To Google Search?
Tobias: They got that AI too. They got that ChatGPT so hot right now.
John: Yeah, we’ll see. But that doesn’t necessarily mean that the stock is undervalued. I think it’s a great company, but I think there are some pockets of tech that I think are very cheap or could be cheap, perhaps. Google might be cheap. It’s interesting. Google’s moat has been called into question recently. I think the thing I’ve observed with investing over the years is just narratives tend to dominate in the short run, but the narratives often overexaggerate the reality. It oftentimes is directionally correct, but it overexaggerates. So, Google is not going to disappear tomorrow. Some of the rhetoric suggests that they might have a serious problem. I think the reality might be somewhere in the middle, but that can present opportunities.
But yeah, it’s an interesting market, because I think there are actually a lot of stocks outside of tech, perhaps even in tech. Tech is not like– I’m not an expert. I like businesses that have moats. But most of tech, I just don’t really understand or don’t really follow. But yeah, there are a lot of opportunities, I think, as a stock picker. So, that’s what I think is exciting is the next decade could be a stock picker’s market, which is the market overall might not do that well. Jake and I have talked about how the S&P 500 is not really undervalued and it might only– 20 times earnings, if you get 7% earnings growth, which is what we got last decade, we might not even get that.
But if we do get that, we’re going to have a headwind on the PE multiple. So, the three engines are PE expansion, growth, and share buybacks. You do the math on all three of those variables and we might only get a mid-single digit return at best over a decade. But there’s lots of stocks that are, like I said, trading at 15% to 20% earnings yields that are going to do really well, I think. So, I think it could be an interesting decade to be a stock picker, perhaps more so than the last decade. That’s my hope.
Tobias: Yeah, I think that roughly accords with what I think too, probably get 3% or 4% on the index.
Jake: If you can survive that depression that’s imminent based on the yield curve inversion, Toby? Is that right?
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10:3 Inversion Steepest Ever
Tobias: Yeah. Well, that’s right.
Jake: [laughs]
Tobias: Yield curve, yesterday, most inverted it’s been, going back in the data. You can go back earlier than 1980 and there’s some wacky stuff a long way back. But in the SEC website– sorry, the Fed’s EDGAR website or whatever it is– I’m blanking on. Which one is it?
Jake: FRED?
Tobias: FRED, yeah. Sorry. The FRED data. Yeah, most inverted it’s been in the data. There’s no relationship between inversion and the depth of the following recession. But in terms of calling them, it’s been very consistent. So, I don’t know what that means. But we’ve got Cam Harvey coming on in a few weeks’ time to educate us about it.
Jake: Help us sort this out.
Tobias: Let us know. That’s just coming up on time, John. Thanks so much. If folks want to get in touch with you, what’s the way to do that?
John: Well, my website, sabercapitalmgt.com. You can find my blog post there. I used to write a blog called Basic Investing, which I’m actually going to start to bring back a little bit. So, you can find me enough there. Yeah, I want to do more writing. The Journalytic experience has produced all these draft notes that I want to start publishing a little bit more for fun. Yeah, I’m on Twitter too. So, you can find me out there. But appreciate you guys having me on. It was a lot of fun.
Tobias: Yeah. Good to see you again.
Jake: Yeah. Thanks, John.
John: Yeah, likewise.
Tobias: Thanks, folks. We’ll–
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