In their latest episode of the VALUE: After Hours Podcast, David Trainer, Jake Taylor, and Tobias Carlisle discuss:
- ChatGPT: Is It A Mass Plagiarism Machine Or A Real Advance In Machine Learning?
- Tesla’s Embedded Assumptions: What Investors Need to Know
- Mauboussin’s Valuation Approach: A Way to Avoid Overvalued Stocks
- Investors Have Too Much Focus On The ‘r’ In (1+ r)^n
- 79%+ Of Institutional Investors Are Closet Indexers
- The Metric That Changed Wall Street Forever: Price to Eyeballs
- The Stock Market Is Pretty Inefficient These Days
- Why So Many SEC Filings and Disclosures Are Ridiculous
- Data Is There, But We’re Not Looking: What Will We Ignore Next Crash?
- GAAP vs IFRS: Which Accounting Standard is More Realistic?
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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Transcript
Tobias: And we are live. This is Value: After Hours. I am Tobias Carlisle, joined as always by my cohost, Jake Taylor. Our special guest today is David Trainer. He’s the founder of New Constructs. We’re going to talk to David about what New Constructs is right now. How are you, David? Welcome to the show.
Jake: [laughs]
David: I’m great, Tobias and Jake. Good to be with you. It’s nice to see you again, Tobias.
Tobias: Yeah, good to see you again, David. Tell us a little bit about New Constructs for folks who don’t know, what is New Constructs?
David: Yes. We’re a financial technology firm, which means nothing, of course.
Jake: [laughs]
David: We focus on building an integrated research stock ETF mutual fund, fixed income research platform. That means we source fundamental data directly from filings, and we process them all the way through to ratings and reports. And so, we do everything in between. I think we’re the only platform of our kind. I don’t know anyone else that’s actually collecting their own data and producing their own analytics on their own, from credit ratings to stock ratings to ETF ratings and mutual fund ratings. We do it for private companies as well. People have the financials for the private companies. We can analyze them.
The main goal, at the end of the day, the most important thing, our goal, our reason for being is to help people understand the true underlying profitability of businesses or the economic earnings of businesses. That’s what I built the technology for. I was on Wall Street before, during and after the tech bubble, and I’ve seen how the sausage is made. There’s not a lot of diligence in the sausage [laughs] as we’ve seen in the last few years, that’s come back to haunt some folks. And so, I felt like it was important for someone to build the technology that was going to go through these 200,000, 300,000, 400,000-page filings and pull out all the important information, so that people could have a clear and unadulterated view of the truth. It’s a lot of hard, boring work. Wall Street is not doing it and really nobody else wants to do it, so we built technology to do it and do it with scale.
Tobias: Yeah, I love it. I use it. I’m a big fan. I use it in all of my stuff. Just for folks, you get raw financial statements, and then you go through and you add stuff that’s in the notes, stuff that’s– You just look at the decisions that they’ve made in drawing the financial statements and you try to make them equivalent, so we can get a better idea of the– Is that a fair description, try to get a better idea of the actual economic earnings of the businesses?
David: Yes, the raw filings. And then of course, you need the balance sheet, income statement, cash flow statement. You need all that data. And then there’s the footnotes and the MDNA. There’s a very material amount of good data that goes to hide in the footnotes.
Tobias: [laughs]
David: That’s the hard part. A lot of folks can give you statement, balance sheet, cash flow statement. The footnotes is the hard part. As I teach all the analysts at our firm, there’s really two things we’re doing, Tobias. I think you mentioned them, but it was quickly. One is to make everything accurate and complete. You can’t be accurate if you’re not complete, so it’s really completeness and accuracy. But second is comparability. It needs to be apples to apples, because the stock market and investing, it’s a relative game.
Tobias: Right.
David: We don’t go in and think just because we did so much work one company. The more work we do, the better that investment can be. No, we actually have to do that kind of work on all of them to understand which are the cheap ones and which are the expensive ones, and ultimately play those two things against each other for any successful, fundamental strategy. That’s why it was important for us to build technology around this, because doing this work on any subset of companies is really inadequate, because you don’t know what you don’t know if you don’t do it for all of them.
Now that we’ve really cracked the code on that, we’re excited to offer this to all kinds of investors, sophisticated investors like you and quant funds who trade on our proprietary core earnings and earnings distortion data to advisors and even individuals.
Tobias: [crosstalk] Sorry, JT.
Jake: I was just going to ask, are there certain industries or sectors that historically you make more adjustments for or less? What are the outliers when it comes to industry? Whose accounting is the worst against the economic reality?
David: It’s very difficult to make broad statements alike about the financial statements, believe it or not. That’s what makes what we do so hard is that there’s just so much idiosyncratic noise and so much failure to actually follow disclosure. You realize that the SEC holds true to its commitment to review 10-Ks once every three years and no more, [laughs] because there are a lot of major disclosures that are just missing. And so, a big part of our business and our engine is intelligence around how to fill gaps in disclosures. An obvious one is off balance sheet debt. For some companies, it can be 10%, 20%, 30%, 40% of assets, and they just may go one quarter or even annual with not putting that disclosure in there.
So, if your model is going to just say, “Oh, well, that 20% of assets doesn’t exist.” It’s a zero this quarter because they didn’t disclose it, your free cash flow is going to be all kinds of wonky because you’re going to show a balance sheet volatility that’s not real. And so, we have to provide estimates and do things like that and do a lot of cleaning. But to really get to your question, Jake, I’d say the cleanest industry, believe it or not, it’s the financial sector.
Tobias: [laughs]
Jake: Ironic.
Tobias: Get used to the scrutiny.
David: Yes. And the reason I think that is because actually understanding the underlying economics of a bank or financial type company is already tough enough that they don’t really need to hide things in the footnotes because most people can’t read their financial statements anyway.
Tobias: How do you do it at scale? You’ve got thousands of companies that are reporting all the time. How big is your team and how do you get through so many?
David: It’s a great question. A lot of it’s time, we’ve been doing this for a long time. So, our team is around 20 people now. We’ve been smaller than that most of our life, but it started really simply, Tobias. I think this is a comment on all of machine learning. There is no such thing as somebody at least that I know of that can write an algorithm that’s going to teach a machine to do things that humans haven’t already done. And so, what I mean by that is in order for us to get good at pulling data from the footnotes, we had to do it a lot and do it with a machine tracking everything we did.
So, my original invention, you call it, in 2003, when we started New Constructs, was an application, proprietary application, it was a natural language markup tool. It integrated the filing and a human parsing in the database all in one. So, every time a human said, “Hey, I’m putting this phrase cost of goods sold in the cogs bucket in the database.” They do that enough times, then a machine could say, “Oh, the humans have put this cost of goods sold that shows up about this location in this area in relationship to these other line items into this same bucket 10,000 out of 10,000 times. I don’t really need the human to tell me how to do that anymore.” It’s really through that activity, repetitively, that we’ve been able to train the machines to automatically parse.
Right now, we were about 2% at the beginning of this year and we’re automatically parsing 25% .100% straight through process. So, we’re just at the tip of the iceberg in terms of that, building out that automation. We really hadn’t even started that at the beginning.
Tobias: So, that’s a new thing. You’ve gone from 2% to 25% and your objective is to get as close to 100% as you can.
David: Yes. We recognize that we will never probably be able to be there because of this idiosyncratic disclosure– [crosstalk]
Tobias: [crosstalk] idiosyncratic.
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Why So Many SEC Filings and Disclosures Are Ridiculous
David: It’s just ridiculous, like, what people companies do and not do. Even within their own, just from a Q to Q and a Q to K, the financial statements can be wildly different. It’s like, they got a different auditor for the annual report than they do the quarterly report.
Tobias: Is that on purpose, do you think? Or, is that just sloppiness?
David: I’m a paranoid guy.
[laughter]Tobias: Me too.
David: I’ve been doing this for a long time. I started doing this in 1996. When I started, the filings were 15 pages long. I’ve seen the long arc of filings and disclosures. I worked with FASB for five years on the investment advisory committee. And for sure, a lot of it is unintentional, but for sure, a lot of it has to because there’s a lot of things that just don’t need to be as hard as they make them.
Jake: I think Buffett said recently that or maybe it was five years ago that he feels like the reporting from 20 years, 30 years ago is actually better than today or at that time because of the– They just dump so much. It’s impossible. Like, a 500-page proxy tells you almost nothing because it’s just so much.
David: Yeah. That’s a big part of gets the existential purpose behind New Constructs. I think when you look at a 500-page proxy, Jake, or a 2000-page 10-K, what you’re looking at is our societies and regulators effectively attempt to try to get the truth out of filings or to get companies to faithfully represent what’s going on. It really reflects, I think, the inability for regulators to solve this problem. They lack the subject matter expertise, they lack the experience, and sometimes they lack the will because we all know that they ultimately want to get a job with one of these companies, right?
Jake: [laughs] Right.
Tobias: [crosstalk]
David: Yeah. And so, we’ve just seen so much stuff layered into these filings, so that the companies and the executives don’t get sued.
Jake: Right. We disclosed it.
David: Correct. That’s the rule. As long as I told you I was going to screw you over, it’s completely legal for me to do that, even if it was on page 277 in the fine print or whatever.
Jake: Yeah, on a Friday night dump.
David: Correct.
Tobias: Of course, a lot of that.
David: That’s true. That’s a real phenomenon. Yeah, I think at some point, someone had to break ranks with the rest of Wall Street and go and do what we’re doing and rat everybody out and go like, “Hey, we know how to do this work and we’re going to do it, and we’re going to let everyone see it.”
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Data Is There, But We’re Not Looking: What Will We Ignore Next Crash?
Tobias: After every crash, there’s always public soul searching about what financial disclosures we should have had. What do you think after the next collapse? What are they going to come back and say– What we’re going to add in Sarbanes–Oxley squared or whatever it’s going to be? What do you think it’s going to be this time around?
David: Yeah, who knows? The bottom line is that the data is there. It’s already there. Even going back to the financial crisis and the whole credit default swap boondoggle. I actually met with the Senate Banking Committee and some other members of Congress. Actually, the SEC and pretty much the whole alphabet soup to point out that for a lot of these big banks, the credit default swap liabilities were very clearly disclosed. That was ultimately the oil and the fly and the ointment that brought a lot of things down. It was there. Nobody’s looking at it. I know remember looking at–
In fact, I think I submitted a couple of companies and one of them was Bank of America. I only remember them for another story I can tell you, because they had a 3,000% increase in their credit default swap liability exposure. It was a red flag. But this was also the time when there were, what was it like, what were these mortgages, no signed mortgages, no money down.
Jake: Ninja. Yeah.
David: [laughs]
Jake: No income, no job.
Tobias: No job. [laughs]
David: Yes– [crosstalk]
Jake: No problem.
David: Yes. So, when that’s going on, they’re not going to care about it anyway. We’ve got meme stock trading. People are going to care about that. When we’ve got IPOs– [crosstalk]
Tobias: Crypto, NFTs. We’ve had a lot of bubbles that have come back to earth.
David: Yes, and we still got others. It’s like, we got bubbles on top of bubbles. Like, NFT is like a bubble on top of crypto. And the NFT bubble popped, but the crypto bubble has probably not yet popped.
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ChatGPT: Is It A Mass Plagiarism Machine Or A Real Advance In Machine Learning?
Tobias: Not looking good for the old NFT searches though. I checked the Google search every now and again, it’s getting a little bit sad. It’s down to three. They give you the 100 as the index top, and so it’s three off an index top of 100, so NFTs are pretty dead. ChatGPT holding pretty strong. ChatGPT has made a big recovery. So, maybe ChatGPT is the real thing. I don’t know.
Do you dabble in any of that sort of stuff? Given you’ve got a long background in machine learning, I’d hesitate to call it AI– I don’t know how much real AI is out there, but do you look at any of that stuff? Is it an advance on what we’ve had before?
David: It absolutely is. But I also agree with you, Tobias, that I would hesitate to call any of it, artificial intelligence. Intelligence to me suggests that there’s some ability, there’s some sentience there that can discern patterns that weren’t previously discerned. I think you’ve heard some folks call ChatGPT and these other things more like mass plagiarism machines. They’re great at copying.
Jake: [laughs]
David: But they’re not innovating. I think there’s a big distinction there. And so, yes, there are for sure much better machine learning tools out there, but it’s really about the processing power. If you look at what Nvidia brings to the table, it’s in that graphics processing unit in technology that was able to process things so fast. It lent itself. If you really talk to the AI experts, they’ll eventually admit to you that it’s really advances in computing power that are driving what we are calling AI today. There’s a lot of intelligence there, but it comes back to what I said in the beginning about how our system works.
There’s no substitute for the decades we’ve spent going through and having experts classify all the little different accounting items that look like one thing but mean another, or look different from another accounting item but actually the same, that over and over and over has built the training data set that our machines can plagiarize, so to speak, in order to say, “Okay, well, I’ve seen it done like this before. I’m going to assume this pattern applies to this company in this same sector.” That’s really what machine learning is.
Machines learn from someone really smart, telling them to do something sophisticated, or they can learn from someone doing something really simple like robotics on an assembly line. But it’s increasingly sophisticated instructions that machines can now follow and interpret. And at the end of the day, it’s the quality of the instruction that makes a difference, just like it is with any software. We think we built some really high-quality instruction around how to navigate footnotes.
Jake: I saw this interesting write up about– It was out of Bain, I think, and some other researchers. They did a study on consultants and they gave some of them– They established a baseline for them as far as productivity, quality of work, output. And then they gave some of them ChatGPT, some of them not. And then what they found out of the study was that, interestingly enough, the bottom portion of workers had the biggest productivity boost, but the high-end people didn’t really see that kind of boost. So, it was almost like a leveling of the output between poor or less experienced versus the top tier. I don’t know what that means for society, in general. If you raise the floor but not really push the ceiling any higher.
Tobias: That’s not what I would have expected. I would have said it was the other way around. That’s funny. That’s counterintuitive.
Jake: It’s very interesting.
Tobias: Do you know what they did? What the actual things that do?
Jake: I don’t know, I think maybe the– At the end of the day, it’s always about pattern matching. And so, the people with less experience have less patterns to match, potentially. And then this is giving them new ideas and new patterns to look for, whereas the other higher end people, maybe they already knew the pattern and so they didn’t really get as much out of it. That’s just my very unscientific guess about it, but very interesting.
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Tobias: Let me give a shoutout to all the folks. So, we always shoutout to everybody lets us know where they’re listening from.
Jake: Geography lesson time.
Tobias: Geography lesson. Toronto. Antigonish, Canada. Milwaukee. Stockholm. Savonlinna, Finland. Braunshweig. Braunshweig, I hope I’m saying that right. Tallahassee. Miami. Chester, UK. Saskatchewan. Cromwell, New Zealand, what’s up? Early start for you. New Brunswick, Canada. Nashville. Prague, Czech Republic. Salvador? Is that Brazil? BR? Cessnock, what’s up? Early start for you. Blockchain, Metaverse. Portsmouth. There we go. That’s it.
David: Well, Blockchain, Metaverse. Where’s that?
Tobias: [laughs]
David: It’s everywhere. [laughs]
Tobias: So, I’ve got a question here, David, from the crowd from BrownMarubozu. I just got that wrong, I know, but “Are there certain industries where it’s harder to predict outcomes?” Yeah, I guess that I’ll read you the whole question. “Are there certain industries where it’s harder to predict outcomes? For example, I find quant funds don’t care for a lumpy 15% very much. For example, how Morningstar analyzes Fairfax Financial.”
David: The answer to the question is absolutely. That’s the whole the high PE company or the high growth PE company has got more risk than the low growth, low PE company. Yeah, there’s a lot of different risk. And a lot of that risk is about the likelihood and magnitude of future cash. Our approach on all of that is, would you rather be a fortune teller or a critic of a fortune teller? We think Mr. Market is our fortune teller every day, and so we choose to be the critic. And so, all of our models, in addition to getting to the economic earnings in the past, we get to the implied economic earnings to justify the current stock price.
We usually break that down into revenue growth and return on invested capital over a particular period of time. We call it Growth Appreciation Period. Michael. Mauboussin calls it Competitive Advantage Period. It’s the same concept. There’s only a limited number of years over which a company is going to be able to grow its business with returns on capital, but the cost of capital. If you can do that into perpetuity, well, then you have an infinite value. So, law competition is going to catch up with everybody at some point.
David: So, yeah, we will break down, speaking of AI and AI stocks, like, within Nvidia. It’s got to grow revenue at something like 30% compounded annually for 15 years and raise its return on invested capital from around 30% or 40% today to over 1,100%.
Jake: So, you’re telling me there’s a chance.
David: Our clients call that. They say, it’s refreshing to have a math, logic driven narrative. I don’t have to say is it expensive or not expensive, or you should buy it or not buy it. I just tell you what the expectations baked in the stock price are.
Jake: Yeah.
David: You decide.
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The Stock Market Is Pretty Inefficient These Days
Jake: David, what do you think in general is the–? Looking at all this stuff, do you feel that the market is actually pretty relatively efficient then? What are your views on market efficiency?
David: I think that’s a really tough question, but I think it’s pretty inefficient these days. I think that what we want to track, if we want to get a good sense is where the information asymmetries and what’s the size. I think one of the ways we can measure that, which is very difficult and subjective. This is something that honestly, I spend way too much time thinking about because as really true, deeply fundamental folks and who’ve built technology and dedicated 20 years of my life to building a technology to give people reliable fundamental research, it pains me when I see things really disconnected from fundamentals.
I think one of the ways we can measure the degree of information disadvantage that manifests in the market today is the meme stock and Bored Ape phenomenon. That’s to me, a very large segment of the market basically, showing utter and complete capitulation and frustration to the fact that they’ll never understand as much about the stocks as their Wall Street insiders’ competitors. And so, they’re like, “You know what? The heck with it. This company should go bankrupt, but we are going to [crosstalk] short.”
Jake: To the moon.
David: Yeah. Because guess what? We can bully trade. That’s all we got, and we’re going to use it. To me, that in many ways is a symptom of people saying, “You know what? You got me, but I got you one thing and I’m going to use it.” I think it’s an ominous sign, because when that sentiment is controlling that larger size of the market that they can do things like that, we’re really beginning to undermine the integrity of the capital markets. We all know it. It’s a meme stock.
We’re effectively saying it’s not real, that’s not efficient in that place. And so, then the big question is, “All right, well, you got meme stocks over here and you got efficient markets over here. Where are we? Where does it stop being a meme stock and where does it start to be fundamental?” I think we’re getting over know.
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Tesla’s Embedded Assumptions: What Investors Need to Know
David: I think Tesla’s a meme stock. I think Netflix is a meme stock.
Tobias: Let’s talk about Tesla. Do you know off the top of your head, what are the embedded assumptions in Tesla?
David: Yeah.
Tobias: Can you look it up in that one?
David: That one what we like to do is actually boil it down even further. So, Nvidia is easy to think about cash flow, but the cash flow growth for Tesla is so absurd. It’s like, it doesn’t even make sense. What I can tell you is the implied vehicle sales. By 2032 is around 28 million, depending where the stock price is. 28 million electric vehicles a year. By 2030– [crosstalk]
Tobias: To justify the stock price.
David: To justify the current stock price.
Tobias: That’s globally?
David: Global. At margins that are higher than Toyota. And then the other thing to keep in mind is that I think the entire electric vehicle market is expected to be maybe 30 million in 2032.
Tobias: So, they’re expecting to own a lot of that market?
David: Yeah. If you assume a lower margin, if you assume a lower average selling price, I think we’re assuming something like the average selling price of a new car in the US or something like that, or I think it’s maybe even a little bit higher. You get to way bigger numbers, like, more than 100% market share. You think about firms that have that kind of market share, it’s not Mercedes, it’s not Cadillac, it’s not high-end fancy cars like what Tesla sells for the most part. It’s the run of the mill car. It’s got to be available to the masses. Tesla struggled in that area, struggled mightily in that area. So, those are the expectations baked into the Tesla stock price. It’s been like that for a long time. Then of course, the argument is, “Oh, they’re going to get in all these other businesses.” That’s been the argument for 10 years and it still hadn’t happened.
Jake: It’s a lot of capital to get from whatever they’re making now at a million cars or something to $30 million? It’s a lot of factories to build.
David: It is, and they’re nowhere near on pace to be at the capacity production level they need to sell that many cars. I think in our model, we assume some super conservative low capital allocation.
Jake: Expense. Yeah.
David: We do that mostly, like, especially in a lot of our zombie stocks and danger zone stocks, we just try to make the scenarios just so optimistic, so no one can say like, “Whoa, you did this. That’s why it looks expensive.” You don’t have to do that with a lot of these stocks. Didn’t do it at all.
Jake: [laughs]
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Mauboussin’s Valuation Approach: A Way to Avoid Overvalued Stocks
David: Like, with Nvidia, give them a margin that is 15 basis points, I’m sorry, 15 percentage points higher than what they’ve ever done, and grow revenue at 25 plus percent compounded annually for 25 years, 30 years, so the return on capital gets to anyway, that’s what it takes. That’s what a lot of our clients love about our system. I don’t know, Tobias, if you use that part of the tool or not, but that kind of scenario analysis where you can back into and understand the math behind the valuation, I love that part as much as I love the economic earnings part.
Tobias: I always go and look at the assumptions that the market is implying for the stock to try to understand. The stuff that I’m looking at though it tends to be just the way that I use the stuff. For the cheap stuff, I tend not to go in and look, because I’m not long-short anymore. When I was long-short, I used to use the short. I used to like to make sure they were expensive and deteriorating and so on. I don’t look so much at that. Now, I just look to make sure the assumptions are very, very modest for all the stuff that I buy. I always joke if they get up in the morning tomorrow, then thesis plays out.
As long as they survive for one more year, thesis works. So, that’s what I like to see virtually less than one-year growth appreciation period. Very, very modest assumptions for survival. And then if any of them materialize, then they should do fairly well. From that perspective. I really like that Mauboussin approach and your approach that you’ve adopted. I like that expectations implied. I think that’s a much better way of than trying to guess what is an appropriate thing. It’s much easier just to look at what is implied and say that’s possible, that’s impossible. That’s the way I’d rather think about it.
David: Yeah. Would you rather be a fortune teller or critic of a fortune teller?
Tobias: Yeah.
David: It’s a no brainer when you see how crazy the fortune teller is on, both the short and the long side. We see a lot of great stocks on our focus list long that just incredibly cheap. Incredibly cheap. I think that’s the crazy expensive stuff sucking capital away from the better stuff. And ultimately, that’s not good for society either. That lowers growth potential.
Tobias: I think part of it has been too that there’s been this long period where capital has been so available that the ability to survive or self-fund has been not at all important, because you can just go and raise another round of VC or whatever the case, just tap public markets, whatever the case may be. That might not be the case from here on or from the last 12 months onwards as the cost of capital approaches a real cost of capital and VC pulls back the ability to self-fund and survive is a much more important. That’s just my bias as an old value investor is that you want to make sure that the thing can survive on its own 2ft without requiring additional financing. But I don’t know. To what extent do you think that’s important? Is it too conservative? Is it that financing really is available all the time? There’s so much VC and public markets are so frothy.
Jake: There’s a lot of kindness of strangers.
Tobias: Yeah. I don’t like relying on it. Sorry.
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David: Yeah. No, I’m sorry. I agree with you 1,000%. That’s why we created a zombie stock list. These were zombie companies that were around only because funding was so cheap. I think a lot of these private equity guys, they wanted to get them off their books, sell them off to unsuspecting public investors. It’s just a recipe for disaster. It’s just going to end up being a big wealth transfer. The only reason these guys stay around is because they’re able to restructure their debt in the more recent times like Carvana. A really terrible business, never made any money. I don’t know that it ever will. I don’t think it ever will. They should have probably gone out of business a long time ago. They found somebody to restructure the debt. [laughs] I guess, that’s better than going to zero.
Yeah. No, it shouldn’t be. That’s not good, Tobias. It’s not good when markets are like that. I think that’s very unhealthy for society. I think that’s mal investment, and I think that capital wasted is capital lost. If it’s wasted on someone’s private jet and huge estate and whatever and not returned to society where it can in turn continue to grow wealth, that’s a loss. We don’t get that back. That’s, to me, why a new construct is important and why capital allocation is important. We need to be discerning about what we do with our capital if we want to continue to enjoy our quality of life. That’s the bottom line. And if we’re going to be reckless with it, we’re going to pay for that if we let it go.
Tobias: Well, that approach has underperformed for the last 10 years or so.
Jake: Now we figured out where we’ve been going wrong. [laughs]
David: No, you’re right. For 10 years that you’re right. It has been out the window. It’s troubling.
Tobias: You pin interest rates to zero. That’s the root cause or what else?
David: I think it’s also this huge new as some of my more blunt, institutional clients have put it. You got this huge influx of somewhat unsophisticated retail people, investors, and part of that’s driven by things like Schwab online, and Robinhood, and online trading, but also the move away from defined benefit to defined contribution plans. Everybody’s a money manager. Yeah, as that institutional client has put it in the past, there’s a new way to lead more sheep to slaughter. I think that’s what’s going on.
Look, it started with the tech bubble. I still remember. The purposeful small allocation to the public of an IPO, so that they could– [crosstalk]
Tobias: That would be oversubscribed.
David: Yeah, be so oversubscribed and that would be a bid versus Wall Street bids. And it’s like, they’re trying so hard to get a piece of these super sexy IPOs. Yeah, so, that’s it. I think technology and communication plays a role because I think it’s really easy to spin up a narrative and to drive a narrative that, true or false, can be very influential and captivate people and get them to do things they probably ought not do. We see this all over society, the news and everything. There’s a lot of false narratives out there. I think part of it’s just the stage of the human race.
I think the beginning of the information age is always the misinformation age where our ability to properly discern between noise and signal is not developed enough across enough people. And so, those three things. I think four things. Super low interest rates, a new investor, very unsophisticated investor class, and the propagation of misinformation, the speed at which that happens, and just the fact that this is happening when we’re really not ready to deal with it. All those things are kind of got us where we are.
Jake: I’d say, there’s a fair amount of moral hazard that you could mix in there as well.
Tobias: Just being bailed out, constantly bailed out?
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The Metric That Changed Wall Street Forever: Price to Eyeballs
Jake: Everybody thinks that they’ve got a backdoor. If things go sideways on them, somebody’s going to step in.
David: Jake, that’s a great point. I probably should have led with that. If you don’t think there’s any risk or you don’t think there’s a big penalty for what it is you’re going to do, you’re going to do it. That’s probably more than anything. It’s almost in some ways. A byproduct too of the other four, like, the low interest rates and the new investor class and the propaganda, just driving things up makes people feel like there is no risk. Buy the dip. Buy the– [crosstalk]
Tobias: That’s a fair point. That has been the singular strategy that’s really worked for the last decade is just every time there’s any pullback, any weakness, the most aggressive buyers have been the ones who’ve been rewarded the most, because we’ve recovered to a new all-time high that seems to have stopped. Probably, we topped out in end of 2021, beginning of 2022, and we’re still down below that high watermark. We were much, much lower last year in October, and we’ve rallied since then.
The thing that I find most amazing is that the way that the bull market is measured, it’s 20% off the low. Once you’re more than 20% off the low, you’re in a new bull market, even though you’re still below the peak of the last bull market. I never really understand that one. I think they just like the headlines and it brings that wave of investors back in.
David: It’s about the propaganda. It’s about the propaganda, so that you can bring more of these unsuspecting folks in, because they’re making a lot of money on. Having been on Wall Street before during and after the tech bubble and just really seeing a change in culture. And Credit Suisse, it almost happened overnight in some ways, because the day Frank Quattrone joined Credit Suisse, I remember that day. Our research department doubled, and we also had this big tech team. We didn’t really have much of a tech research team. Within days, The morning call changed. Michael Mauboussin used to run the Morning Call, and we worked pretty closely together.
I learned my methodology from him, and so I was implementing. So, we had an agreement that, “Look, if people weren’t using the David Trainer Credit Suisse model for return on capital and competitive advantage period, let’s not let them on the morning call.” “Hey, Mauboussin, take advantage of your power, man.” And so, gradually, we’d won the salesforce over and we had all the analysts really using it because it all makes so much sense. You’d be a little shame not to use it. I would get the salespeople to ask the analysts that I knew weren’t doing the work, those questions. [laughs]
So, we basically had this framework that I’d learned from Mauboussin and implemented into a model being used. It wasn’t long before we got a call from the top at Credit Suisse saying, “Hey, let these guys on the morning call, you idiots. They make us a lot of money. Come on, what do you think we’re doing here?” And we went from understanding economic earnings to market implied competitive advantage period, and expectations baked into stock prices right back to PE to price to sales to price to clicks.
Jake: Eyeballs.
David: Yes.
Tobias: The number’s not big enough. Just make the number go higher.
David: Yes, Jake. I don’t think back then they even had a way to measure that. I don’t even have really a good way to measure clicks. There was no Google Analytics. It’s like, Netscape was just barely working. Who’s measuring it? I don’t know.
Tobias: Self-reported. We’re getting lots of sure.
David: For sure. Impressions is a subjective thing. Anyway, they definitely didn’t have a way to measure price to eyeballs. I remember there were some people up in arms. The first time that was dropped at a morning call meeting, I remember the global head of research stood up and threw his papers on the desk and cut everything out, left the room.
Jake: I love it. [laughs]
===
Tobias: JT, he gives us some vegetables every week for our learnings. Do you have veggies, JT?
Jake: I do. The streak continues. So, I’ve been thinking lately about the return formula. In parentheses, you have 1+R, so your rate of return to the power of N. And N is the number of years that you keep multiplying that return. I feel like everyone is focusing more on that annualized return, the R and not the real magic, which is the exponent of the N. And so, what you should really be thinking about is the duration of your investment, even more so than the annualized return part. So, in order to get into that, we’re going to have a little quiz here on animal longevity, okay? I have four pairs of animals that I’ve put together, and you tell me which one is longer lived, okay? So, number one, the Greenland shark versus the giant tortoise?
Tobias: I think the Greenland shark is like hundreds of years. I think that the tortoise is like a hundred. So, maybe the shark. Maybe it’s a thousand years. I’m going to say shark.
Jake: David?
David: It feels like you got to go shark, because everyone knows the tortoises are really old and the Greenland shark is a little more obscure to me. So, it’s hard to see why you’d bring it up if it weren’t the right answer.
Jake: Correct. So, the Greenland shark, call it, 400 years. The giant tortoise like 190 years.
Tobias: Okay.
Jake: All right. Number two, a parrot versus a rough eye rockfish, which is commonly caught off the Pacific Northwest.
Tobias: How do those fish taste? No one knows. Too old.
Jake: Too old.
David: [laughs] Very tough.
Tobias: I’ll go with the fish.
David: I’ll go with the parrot.
Jake: All right. Parrot, 90 years. The rockfish, 200 years.
Tobias: There we go.
Jake: Isn’t it sad to think that you might have eaten something that was 200 years old that was just caught off the coast of Seattle?
Tobias: If it tastes delicious, that’s bad for it.
Jake: Oh, man, I feel bad about that. All right, next one. Number three, the chimpanzee versus the Laysan albatross.
Tobias: I’m going to go the chimp. I don’t believe that birds live that long.
David: I was trying to think what an albatross was.
Tobias: Aren’t they the giant birds?
Jake: Yeah, giant seabird.
Tobias: It’s bad to see an albatross. I think it’s bad luck for sailors to see an albatross.
Jake: That’s right. Rime of the Ancient Mariner.
David: Yeah, I’ll go albatross, just to be contrary.
Jake: Okay. David wins this one. Chimpanzee, 50 years. The albatross, 70 years.
Tobias: What?
Jake: All right. Number four. Last one. Humans versus tuataras? This is the tuatara, which is like it’s a lizard in New Zealand.
Tobias: Are there any long-lived lizards? Yeah, that’s not a lizard. I’m going to go humans. The healthcare.
David: Give the [crosstalk] Tobias is from that part of the world, I got to go with what he says. So, yeah.
Tobias: [laughs] I don’t know that particular lizard.
Jake: Yeah. So, we’re going to go with the humans on this one. Based on the oldest recorded human is this woman named Jeanne Louise Calment, I think it is. She lived 122 years old. The tuatara lizard in New Zealand only lives roughly 100 years.
Tobias: On average? That’s pretty good lifespan for those things.
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Investors Have Too Much Focus On The ‘r’ In (1+ r)^n
Jake: So, that’s what we’re going to get into now, more specifically, is this tuatara. They’re endemic to New Zealand, and they’re roughly similar to lizards, but it’s actually an entirely distinct order of lizard, of reptile, I mean. They originated 250 million years ago, but their order broke off, it branched off, and that split actually happened before lizards and snakes even diverge from each other, and it was basically geographically isolated.
So, a typical lizard, like, a normal lizard that we think of will live for five years in its natural habitat. Yet somehow, this animal lives for 100 years. There was Rory Sutherland, who’s one of my favorites, he was talking recently about some of the most interesting information lies in the outlier, not in looking at averages. The world always looks at averages, but when you notice something odd, it can really help you to understand even more if you can figure out what’s going on with that outlier.
He actually tells this funny story about, in Pfizer, when they were testing Viagra– So, they get to the end of the study. And of course, they were trying to test it for blood pressure or hypertension or something. The subjects were like, “I’m not giving these pills back.”
Tobias: [laughs]
Jake: They wanted to keep them. No one had ever really been asking, “I want to keep this little blue pill that you gave me and I’m not giving it back to you.” And so, they’re like, “Well, what’s going on here? They’ve never asked us before to keep the pill.” And obviously, it turns out it was helping in other ways. Anyway, this tuatara actually reproduces very slowly. It takes 10 years to 20 years to reach sexual maturity. And the females mate and lay eggs only every four years, and it takes then 12 months to 15 months from copulation to hatching, which is a really long time. In the wild, they’re known to still be reproducing at age 60. And in fact, there was one male in captivity, he became a father at 111 with an 80-year-old female.
Tobias: [laughs]
Jake: They have one of the slowest growth rates of any reptile. They continue growing larger for the first 35 years of their life. So, in the business context, investing context, I think everyone is looking for these hyperscale companies with these rapid growth prospects, get big fast, which in my mind represents having a very large R in that 1+R equation. But something that grows slow and steady, slow to reproduce like the tuatara might be providing a bigger N on that exponent of your equation, and actually might be the true driver of a very long-term return. So, there’s a little lizard and returns, hopefully weave together for you.
David: We got to open the tuataras fund.
Jake: 100%.
Tobias: The focus is on survival rather than high rates of growth.
David: At some point, we got to be countercyclical, do something a little different, because there definitely are tons of funds that focus on the R.
Tobias: Yeah, that’s true.
David: That is everything is about the R. I think a lot of people pay lip service to N and very few people actually build their strategy around it.
Tobias: Do you think Seth Klarman is an example of that focus on the N more than the R? I think that every time his letters come out, people are– shocked is probably too strong a word, but surprised that his returns tend to be so low.
Jake: Absolutely. Great example. I put Berkshire in the same category.
Tobias: The thing for Berkshire though is the returns are quite high. The R is quite high there. The skill of it has been to generate a very high R in a context of a very high N of a big N.
Jake: It was early. But I would that R has definitely come down over the years as any– You get so big. Of course, it’s going to be its own anchor.
Tobias: What is it now? 10%, do you think?
Jake: That’s probably the high end is what I would underwrite.
David: There are a lot of other things in the Berkshire business that have been able to help goose returns, for sure, especially– [crosstalk]
Jake: Yeah, leverage.
David: Leverage, and the deals they were able to do with Goldman and others during the– those great deals, right? Convertible debt at 8% or 9% and converting at a super low price. That’s the benefit of being Berkshire that you’re in a position to get that call and then to be able to answer it.
Jake: Yeah. Those bathtub Sunday night deals are usually pretty lucrative, if you can get them.
David: [laughs]
Tobias: Did he negotiate it from his bathtub? Is that the–?
David: I think Warren is drinking a Coke in his bathtub, hanging out.
Jake: That was the joke.
David: [crosstalk]
Jake: Yeah. “Mr. Moynihan, I have some money for you.”
David: If you don’t take my money, I guess you’re going to get to talk to– Who was the head of the Treasury at the time that blew up Lehman?
Tobias: Was it Paulson? Hank Paulson?
David: Paulson. Yes. Yeah, I got him on the other line. You want to talk Tim or me? [laughs]
Jake: Paulson’s money was probably cheaper.
Tobias: Yeah.
Jake: It tends to be cheaper.
Tobias: Who else is in that category of like an N type investor, very long-term? It’s hard to think of too many. Maybe Fairfax. Although Fairfax tend to hedge the book a little bit, so they’re always–
Jake: Yeah, that’s somewhat of an operational thing for them too. They’re taking pretty big liabilities often on the insurance side. So, you really need to keep a strong balance sheet. But yeah, I would say they’re pretty good N.
David: My experience is that there are really very, very few N investors out there, very fewer than even pay lip service to it. When I first launched New Constructs, before we had our own fund, I spent a great deal of time marketing to institutional investors.
===
79%+ Of Institutional Investors Are Closet Indexers
I remember reading a paper that Mauboussin gave me from a guy at Wharton. He did a survey of institutional investors, and this was in 2004. “79% self-described as closet indexers.”
Jake: Self-described even. Wow.
David: Yes. And 13% said, “We’re traders.” Only 8% self-described as value investors, which he’s more specifically described as concentrated. They had a portfolio of less than 20 stocks or something. And not a lot of turnovers. Highly concentrated, not a lot of turnovers. 8% self-described in an institutional survey. This was published in the Journal of Financial Accounting, I think, a prestigious journal. I’m happy to share it with you. My experience in the marketplace was very much aligned by that, except I would say, “Of the 8% that self-described, less than a third of them were doing more than paying lip service.” Calling themselves value and fundamental was marketing, not real.
Tobias: What would you say that number is now? Higher or lower?
David: Probably lower.
Tobias: Yeah, I would say lower too. There was that value boom that ran to, I don’t know, 2007, 2010. There were probably quite a few more around for a period of time there.
David: Yeah, I wonder if that was just not the off funds.
Tobias: More marketing that way?
David: Yeah. The number of people that I know that understand growth appreciation period, competitive advantage period N, and really apply, it is really pretty low, because it hasn’t paid. I remember in my hedge fund marketing, we were really positioning ourselves as being countercyclical and saying, “Look, there’s nobody doing what we do because guess what?” It doesn’t pay. Returns as high as they’ve been for so long, and the buy the dip strategy to your point before Tobias being so effective like, why do fundamentals?
===
That’s a big part of why we’re excited to have this deal with Bloomberg to be launching a family of index funds all about true earnings and fundamentals, because I think that there are people out there. Let’s say, it’s the Bored Apes and the meme stock investors, they want reliable fundamental research. They just know they haven’t been able to get it. And if we can make this available at scale, then we have a chance to maybe turn the tables on all this propaganda and misinformation.
Tobias: Can you talk a little bit about it? What’s the idea, and how is it going to be expressed?
David: Yeah. The name of the fund at this point tentatively is the Bloomberg New Construct’s True Earnings Index. And effectively, it takes advantage of our proprietary core earnings data to go along those companies whose earnings are most understated compared to what the street and what their reported numbers are. That those companies will, over time outperform, and the testing we’ve shown, the performance is really strong. This is my way of being able to go to the world and say, “Listen, you want to be on fair footing with Wall Street insiders? You want access to the same great fundamental analysis they do? Then this is the fund.”
Tobias: So, it’s long only? Long-short?
David: It’s long only. It’s an index.
Tobias: Then it’s not discount to your estimate of the economic earnings. It’s the ones who have the most understated– So, their own published earnings are understated for what their true economic earnings are?
David: Yeah. So, actually, what professors at Harvard Business School and MIT Sloan found was that there is a lot of noise in, both Wall Street and GAAP or any measure of earnings. There are a lot of companies who, when you take out the noise, the unusual gains and losses, their earnings are meaningfully understated or overstated compared to the correct or more correct number, what we call, core earnings. And the name of the paper, if you want to find it, is called Core Earnings: New Data and Evidence. It proves a few things. Number one, Wall Street’s not looking at footnotes. The stock market is not taking into account footnotes. And that you can actually make money on strategies that go long, stocks that understate their earnings and short stocks that overstate. They demonstrated that alpha and we’ve demonstrated in other areas.
With Bloomberg, we’re going to be able to bring to market something that takes advantage of that. Maybe the only way, the only antidote to all the propaganda, but also to the self-reinforcing process where well, if by the dip and momentum works when people just keep piling into it and capital allocation gets less and less discerning, because it’s not about capital allocation. It’s just about buying momentum and trying to make money here in the short-term, that works until everything blows up. I’m hoping that if people have an alternative to that, and they begin to see the risk they’re taking and just rolling into buy the dip, buy the dip, buy the dip, maybe that’s an opportunity for markets to become a little more discerning.
Tobias: Yeah, I like it. I don’t mind people chasing the returns though. I do think that it leaves opportunity there for value guys to dig around on the stuff that doesn’t participate. You just got to hope that at some point, I guess, that it participates, but it’ll get there. One name we forgot in the list was Tony Deden.
Jake: Yeah, that’s not a bad observation.
Tobias: That’s a good suggestion. That was Brian McCann. So, when do you expect your index or your funds to roll out?
David: Early next year. We are in talks with some of the largest issuers in the world and looking to bring it to market early next year. It should be the first of many. The first one is going to be basically for large cap, companies with the truest earnings.
Tobias: So, you look to do a large cap and then some other, but all domestic US?
David: Yeah. For now, yes. Our coverage is greatly expanding. I think I mentioned before that at the beginning of this year, we started looking to fully automatically take advantage of the machine learning technologies that are out there and take this training data set that we’ve been developing for 20 years and really use that to say, “Okay, where do we not need analysts anymore?” Because once you’ve parsed a couple of hundred thousand filings, we’ve got over, probably, millions of repetitions of the machines going through the filings and trying to figure it out. And then the human come in and say, “You get this right, you get this right, you get this wrong,” or you can even get the machine to grade itself against the previously parsed filing by human. That’s where we’re seeing a lot. That’s leading to a great deal of automation. I think by the end of next year cover, almost all the companies in the United States and then go global soon after that.
===
GAAP vs IFRS: Which Accounting Standard is More Realistic?
Jake: I was going to ask, how do you feel about the US GAAP versus IFRS? Whose do you think is better, as far as matching reality at this point?
David: I think it’s about even.
Jake: Yeah.
David: They both do things about different things well different things poorly. More recently, I think FASB has become a little bit too obsessed with the mark to market thing. I don’t think we want to mark these things to market. Like, the idea that you have hard enough time trying to value the entire balance sheet of the company, I think precludes the possibility of being able to value the individual assets. Exactly right. Like, come on. And then to what end? Who cares?
Jake: Especially on a quarterly basis, there’s so much noise.
Tobias: It’s crazy swings. Just makes no sense.
Jake: It’s noise feedback just back into the system. That reflexivity just seems boneheaded to me.
David: It is. Think about how many unsophisticated investors don’t know this. They don’t see this. And so, it’s really a disservice to society to throw gas in the flames of the propaganda misinformation. So, they all struggle. Part of the problem is that they’ve been mostly beholden to the corporations. They either come out of public accounting or come out of a big firm. For a long time, FASB was funded by companies. So, there’s an existential struggle sometimes between really doing what’s right for investors and making sure that their corporate overlords don’t get too upset. That was a frustration of mine being on the committee with just having to explain.
Jake: Yeah. How’d you feel about expensing options in that time period? It was like such a joke.
David: Exactly. Right. How long did that persist? For a long time until the bubble had popped, it was too late. It really never made any sense. It’s tough. I can count on more than one hand. The number of times, I explained to the board, “Listen, whenever a company tells you it’s too much work for them to disclose something, that is not an acceptable answer.”
Jake: [laughs]
David: The one that really got me was with derivatives. We wanted some more disclosure around derivatives. And the pushback was, “Well it’s too much work. We don’t really have this information. It’s too much work for us.” And I’m like, “Wait a second.” What a– [crosstalk]
Tobias: [crosstalk]
Jake: How’s that on your balance sheet then?
David: Yes, it’s like there’s one of two situations here and investors need to know either one. Either they really haven’t done the work and they don’t even understand what’s in their derivative portfolio. I need to know that. if they have done the work and they do want to understand what’s in their portfolio, it’s not a lot of incremental effort to share it. So, it was funny. I had that conversation more times than I’d like. I think it speaks to just a mentality there. It’s a challenge, because FASB really needs the companies to help them understand what’s going on. There are not a lot of investors out there that are willing to do that explanation, because in many ways, understanding that is their competitive edge.
Tobias: I think Buffett pointed it out that they’d have two counterparties to a derivatives contract, both of whom would be writing down that they were profitable on the trade. Because you paid, your bonus on the way through. You hit your targets on the way through, and then in year 10, you actually figure out what happened. “Ah, we lost. Sorry.”
Jake: Yeah. Blows up. Yeah.
Tobias: “Big [unintelligible 00:57:39]. We’re having a bad year, having a bad quarter. We’ll get it next quarter, fellas.”
Jake: [laughs]
David: So, there are some differences between IFRS and GAAP but it’s by no means overcomeable– or not overcomeable. The hard part is getting all the filings, and then translating them probably. There’s some really sophisticated translating software out there that’s getting better and better all the time. But there’s no central SEC for a lot of these other countries.
Tobias: You’ve got a local application of IFRIS in most countries too. There’s going to be a slight variation depending on which country you look at.
David: Yeah.
Tobias: Is there any suggestion of changing the valuation of the assets from quarter to quarter? Because that just does seem crazy, that flow through that– Berkshire’s results are always, just you can’t rely on the headline number. You actually have to go and have a look, because the move in the assets makes it just silly.
David: It used to be the right way. You had unrealized gains and losses would be in your other comprehensive income. That’d be in the balance sheet and the accumulator of the comprehensive income, and that’s where it went for all the right reasons in the world. You don’t need those changes in market value washing through your earnings, because they’re unrelated– [crosstalk]
Tobias: They are non-cash. Yeah, not operational.
David: And non-cash. They just changed it back the other day, and it was just like a complete like, “What the heck?” So, I don’t expect them to change it back again anytime real soon. You got to really wonder about motivations in those situations, like, “What’s the motivation? Tell me why that’s a good idea.”
Tobias: Well, what do you suspect there?
David: I don’t know.
Jake: [laughs]
David: I asked the question first, Tobias.
[laughter]Tobias: Fair.
David: [laughs]
Tobias: It’s fair. Well, David, we’re coming up on time. If folks want to get in contact with you or follow along with what you’re doing, what’s the best way of doing that?
David: Go to newconstructs.com or follow us on @newconstructs on Twitter/X. That’s the best way. We are actually rolling out here in the next few weeks, a new retail entry level membership. For the last 9 months, 10 months or so, we’ve been really exclusively professional. $999 a month, our minimum price. We’re going to bring back some entry level partnerships and memberships that we think are going to make it easier for people to get to know us and take advantage of what we do.
Tobias: I will link those up in the show notes, so have a look underneath and in the podcast, the audio notes as well.
David: Great. Thank you.
Tobias: David Trainer, New Constructs, thanks very much. JT, what about–?
Jake: Thanks, David.
David: Thanks, guys. Good to see you.
Jake: Yup. Flying off to Chicago, going to Ian Cassel’s, microcap conference, giving a talk. So, it should be fun. Go hang out with the boys out there.
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