Episodes

Dan Ferris: The 2024 Stansberry Research Conference and Alliance Meeting is back this fall in Las Vegas. And for the first time ever they've extended their early-bird discounted ticket pricing, which means if you reserve your seat today you can save $450 off your ticket. Head over to www.VegasEarlyBird.com to find all the details and get your discounted ticket. The Stansberry Conference is truly one of the best business mixed with pleasure industry events out there. Past speakers have included Shark Tank's Kevin O'Leary, Dennis Miller, and Steve Forbes. And of course all your favorite Stansberry editors will be there too, including yours truly. I mean, I hope I'm one of your favorites. [Laughs]

I look forward to this event every year. It's great getting the chance to meet our listeners from the show, whether it's chatting during a break or grabbing a beer at the end of the day or whatever. So, I hope you're planning to join us. It's a great event. Go to www.VegasEarlyBird.com to get your discounted tickets before prices increase. That's www.VegasEarlyBird.com. So, come on out and find me in Vegas and say hello.

David Trainer, welcome to the show. Good to have you.

David Trainer: Thanks for having me, Dan. I'm glad to be here.

Dan Ferris: Yeah. So, you're a new guest on the show and I'd like to sort of ask my stock first question for new guests because it seems to work. So, if you and I were just – if we met in a bar or something and we got to talking and finance came up and I said, "Oh, well, what kind of investor are you?" what would you like to – how would you like to answer that? What would you like to tell our listeners about that question?

David Trainer: I call myself a modern value investor. I think the old-school value investors were known for kind of trudging around and shuffling a lot of papers and going through a lot of data. We bring Ai to value investing. So, we're taking it to a new level by really using technology to sort through millions and millions of financial filings and data points to produce materially superior measures of profitability and valuation that takes traditional value investing to the next level.

And what that means is that you understand where value is and you actually move a little more dynamically than the legacy guys who kind of – the old-school values guys who are like "You own something forever." That doesn't really work anymore.

Dan Ferris: Oh, owning forever doesn't work anymore. OK. So, that's an interesting statement to me because I feel like that was the second era. The first era of value investing was cigar butts. And the second era was finding great businesses and owning them for a long time and compounding. But you sound like you're in – you're the modern guy. You're the third era guy. So, can you get into that a little more deeply? If owning forever doesn't work anymore, then you've got to know that really hard thing that so many people don't know, which is when to sell. That's what intrigues me about that.

David Trainer: Yeah, no. I think that that's one of the modern facts of today's trading, is that stocks can go from cheap to really expensive in less than a lifetime. So, you don't own them forever. There are points in time where they get too expensive. Nvidia is a great example. They flew too close to the sun and the wings melted. And so, it's become an overvalued stock even though the underlying economics of that business are very strong. And so, there are stocks that you want to sell in a lot less than a lifetime. So, you don't own them forever.

There's still those compounders out there. We've got some stocks on our focus list that have been on there for years and they continue to do really, really well. But if a stock skyrockets up 2,000 or 3,000% in the space of six months or a year, it's only smart to start to take some gains, take some capital off of the table, reallocate that back to stocks that remain cheap and still have good fundamentals.

And that's the idea, is that we've seen markets move fast. We've seen them turn on a dime, especially with some of four zombie stocks. I mean, one of our – we had a couple of zombie stocks go to zero. One of our danger zone stocks, Express, just filed for bankruptcy today. We've been warning about it for years. I think not too long ago it was $200 or $300... now it's about to go to zero. So, these things happen fast. Real fast. And as a modern investor you've got to be able to move a little more quickly. You can't just buy and forget like the old days.

Dan Ferris: All right. Now, the use of AI to do some of the analytical work or data collection or however you use it, that's really interesting to me too. I want to say how do you do it or what do you do because it's just such a – AI is this enormous topic that encompasses all kinds of things. But maybe you can tell us the one or two most important things that you get out of using artificial intelligence to scan documents?

David Trainer: Yeah. Dan, I could talk about this topic forever and we're happy to talk about it.

Dan Ferris: I know.

David Trainer: Because people are like "Oh, don't give up anything, David." I'm like "No, no, no." It's like Tom Brady can show you how to throw a touchdown pass but that doesn't mean you're going to be able to step on a football field and do it.

Dan Ferris: No. Yeah.

David Trainer: So, AI is a very misunderstood topic. I've been writing about it for decades. I've been doing it for 25 years before the terms "machine learning" and "AI" were even out there. And what people don't realize is that the core of AI is not sexy. The large language models and chatbots and stuff that sit on top of things, those are great technologies. But they're only as good as the data that goes into them. This is why ChatGPT hallucinates, because guess what? Not everything on the internet is true.

Dan Ferris: That's great.

David Trainer: What would you say, Dan? What percentage of the internet is true? What percentage of the data that goes into ChatGPT can be trusted?

Dan Ferris: I mean, one?

David Trainer: Yeah, exactly.

Dan Ferris: Really. Honestly. I mean, I've asked it all kinds of questions and gotten all kinds of absolute baloney. I mean, just baloney. So, yeah, not much. It's a low percentage.

David Trainer: Yeah. And so, think about the AI and the technology as the engine. The model is the engine. Data is the fuel. AI is a great technology. But if you're pouring low-grade gasoline into it you're not going to do very well. And so, what New Constructs started with was creating a better fuel because I knew the technology was always there. Look, from the beginning even the old-school value investor guys, they would tell you "Your model is only as good as the inputs." If your model for measuring whether or not somebody has a cigar butt was based on bad data, you're making a bad decision.

Dan Ferris: Well, this goes back to the '70s, the oldest computing stories in existing: "Garbage in, garbage out." GIGO. So, absolutely. It's – the point is well taken.

David Trainer: Absolutely. Garbage in, garbage out. So, what we created is a better dataset. And we have proof now that our data is superior from a paper published by The Journal of Financial Economics, one of the top peer-reviewed journals in the world, and a paper written by professors from the Harvard Business School and MIT Sloan School of Business – Sloan School of Management that proves our data is better. And that's really what I started with, because I was on Wall Street and – before the tech bubble. And that was back when they were sort of the 1.0 and 2.0 sort of value investors, Dan. They were like – they'd go through the filings, they'd read it, build models. And then, when the tech bubble came they threw all that out the window. And I know because I was advocating for that. I ran a special business within Credit Suisse that was like "Hey, we're building a better model." Because if you pay an executive based on accounting earnings, you can pay them to run the business into the ground. We've got plenty of high-profile stories going back all the way to WorldCom, Enron. They were great earnings. Business run into the ground. Valiant. Express. We're seeing this in more modern terms. Tesla is going to be another one of those.

And so, people were no longer doing the work to get the good data because it didn't pay. There was too much money to be made in getting that IPO out the door. We all know how that ended with the tech bubble with those analysts being busted for "Hey, this is such a great IPO... everyone should buy it" and then to my friends "Oh, I can't believe everybody is stupid enough to invest in this piece of blank." They caught them red-handed on that.

Dan Ferris: Yep.

David Trainer: So, I knew that there were – not only were people not doing that anymore... people didn't want to do it. And the number of people in the world that were able to actually read footnotes and build a model was quickly going to zero, because the people that had the education, experience, and expertise to do it were getting paid too much to do other things. So, that's where I got the idea that we needed machines to go through these filings because you just couldn't find people smart enough and willing enough to do it for extended periods of time.

So, it took – it was – it's a very, very sort of detailed, painstaking process to kind of carefully mark up these filings and teach machines to do things over time. And it's one of those things that it builds over time. Once you've taught a machine that "Hey, this is the revenue number and this is the gross margin number," you do that 10,000 times and the machine doesn't need a human to do it anymore. That's a bit of an oversimplification but we've done that millions of times over 20 years so that now we have this really robust training dataset that feeds an AI technology. And I would submit, Dan, that we are the only real AI that works. ChatGPT is great but it doesn't work because you can't trust it.

Dan Ferris: Right. OK. And because you've got whatever it is, a couple of decades or 25 years of learning and data behind what you're doing too, that's fairly significant and probably not especially easy to replicate, I would guess.

David Trainer: Bingo. I think the moat's pretty wide. I really do. I think you're right. And look, we've got a big lead on the market because not only is our data superior but with superior data our AI can produce superior financial analytics. That's proven by a paper from Ernst & Young. And then, we produce superior stock ratings, proven by a paper from Harvard Business School. So, we've got a pretty wide moat, high barriers to entry, for sure. And yeah, I think it's going to be difficult to replicate, Dan, because there's just not that many people who want to do it. How many people do you know that actually like to read footnotes?

Dan Ferris: Not many.

[Laughter]

I don't even know if I really like it, although it's something that I stick with because every now and then you find a really juicy one. But it's funny, right now, rather than asking you about what the machines are doing all of a sudden I want to know what the people are doing. So, if the machines are scrubbing through data, and as a value investor of course the standard posture is for me to sit here going through 10-Ks and 10-Qs and reading them and figuring out what's in them and producing my own spreadsheets and all of that. Sounds like a lot of that is automated, so what are the people doing?

David Trainer: The people at New Constructs?

Dan Ferris: The people at New Constructs. If the machines are doing all of this work, what are the people doing?

David Trainer: The machines do about 95 to 100%. They don't get it always right. And so, what we are doing in the process over the years, Dan, is in the beginning the humans did – they marked everything up. Now the machines can mark up a lot on their own. But the machines aren't sure of certain things. And so, we continuously have the machine only allow itself to parse things that it's 100% sure of and then we can typically validate in a number of different ways. And then, when stuff comes along that's not 100% sure, well, humans come in and say, "OK, this is the right answer. And here's why." So, we're constantly building on that training dataset over increasingly complex areas of filings. No. 1, we do that.

No. 2, filings are changing. The accounting rule setters are changing the rules all the time. I mean, one of the great tricks of the last couple of years is the whole off balance sheet debt thing. Right?

Dan Ferris: Oh, yeah.

David Trainer: The accountants said, "Oh, hey, we're bringing all the off balance sheet debt on balance sheet. Thank you. We did everybody a service." And then, what does corporate America do? Create new ways to keep the debt off balance.

Dan Ferris: Keep it off. Yeah, that's right.

David Trainer: And so, we have to build our models to collect these new data points. Before, there weren't these variable lease payments. There weren't these sort of prepaid lease payment type things that are now significant amounts, billions and billions of off balance sheet debt hidden in new vehicles effectively. And so, we have to go in, see how the disclosure actually manifests in the filings, build a collection system to get the data, build the models to calculate the data correctly, and then fix the – and fix the accounting.

So, there's stuff like that that's going on constantly. And then, we're always building the product. Several years ago we added ETF of coverage. We added mutual funds to coverage. A few years ago we added credit ratings. And now we analyze debt and credits as well.

Dan Ferris: This question is inspired by a story that I've told a few times on this show when we talk with – mostly with traders, like futures traders and stuff when we have – sometimes we have market wizard types on the show. And I just remember I visited one of those firms once, and of course they've got a whole system for spitting out trades and establishing breakouts and whatever they're trading. And it was – at the one firm I visited, it was a fireable offense not to trade the signal. They have all these Ph.D. computer scientists and mathematicians and physicists and stuff on one floor and then the traders on another, and the traders have to trade the signals. And I was left alone with a trader for about five minutes and he said, "See, we just got a signal here." And he looked at it and he said, "But do you really want to trade after three hours of this? I don't think so." And I was like "Oh, OK. I hope you don't get fired."

The point being does your system ever generate trades or recommendations or ratings that you look at and just go "No way. No way"?

David Trainer: I don't know about – I mean, I think shorting, we get a lot of stocks that should be shorted. And I think – we ran a hedge fund from 2006 to 2016. We launched in 2008. And one of the things I learned during that time was you can have the best short in the world and it can blow up in your face. I'll never forget there was an oil and gas conglomerate losing tons of money, terribly overvalued, and we put a nice short position on there, and then they announced that they had hired Bank of America to evaluate their strategic options, which anybody from Wall Street means "Please find someone to buy us before we go bankrupt." Well, the stock goes up 40%. We get knocked out of our short. And then, of course four, five months later it goes bankrupt.

So, I think that's part of being a modern value investor. It's like, listen, the fundamental signals can be so true, but as Warren Buffett says, "Hey, the market can be wrong for longer than I can be solvent." And so, I think, yeah, there are fundamental signals that may very well be true, but the market's not going to pay attention for a while. And so, you've got to sort of – you've got to be a modern value investor. And I think it's awesome to pair what we do with technicals. Make sure you're –

Dan Ferris: David, it sounds like the answer to my question is no, you really have a lot of trust in your system, because what you're telling me, it happens after that – what the market does, nobody controls that. There's no AI that controls that. That's what you're telling me. But it sounds like what you're – the real answer to the question is no, our system spits out really good ideas consistently.

David Trainer: Yeah, I mean, Dan, I mean, look, I'm not going to say our analysts have never made any mistakes.

Dan Ferris: Sure.

David Trainer: We've made a lot of mistakes over 20 years. But we've also got an automated system that every time we make a mistake we create what we call a data check and the system forever checks to make sure that kind of mistake is never replicated. And so, that's a process we've been applying for 20 years. But no, my answer to your question candidly is I believe in the fundamentals of what we do. And I don't believe anyone else can match it. That said, fundamentals are not always what you want to trade, long or short. But our fundamental analysis is by far, I believe, the best in the world and no one can match it. I mean, there's never been another research firm that has had papers from the Journal of Financial Economics, Harvard Business School, MIT Sloan, Ernst & Young published, proving the superiority of their data, models, and ratings. Never happened. And we've got a stock-picking track record on SumZero, which is professionals, we've been ranked No. 1 across five to 10 categories for 36 months in a row.

So, however you want to slice it, no one's done what we've done. We're breaking records, I think. We're breaking a new mold with this modern value investor technology. And we think it's going to revolutionize markets. And yes, I absolutely trust it and believe in it because we've got proof that it works.

Dan Ferris: Wow, OK. So, just to be clear, though, it sounds like New Constructs does not – am I correct, you do not manage money? You do not have other people's money that you're allocating?

David Trainer: We do not. We do not.

Dan Ferris: You do not. OK.

David Trainer: We do not. New Constructs has never done that. Now, we had some separate entities that were affiliated with New Constructs and they were subscribers to New Constructs' data but those closed down eight years ago. We do have a relationship with Bloomberg where we're planning to launch passive ETFs, and that should be happening, that should be announcing something along those lines in the next few weeks with a top five issuer. But that's just a data feed that they use. They're more like a client. We have some of the top quant funds in the world that take our data and use it to manage money. Bloomberg takes our data and uses it to create ETFs. We're going to launch an ETF with a top five issuer.

So, there are definitely money-management applications, whether it's our clients or whether it's some of our partners for sure, but I do not. We do not have a hedge fund, so we are – we're 100% independent, Dan. That's a big deal for us. We've always been independent. We don't have bias. We're not using our clients against themselves, you know what I mean?

Dan Ferris: Oh, I do know what you mean. Yeah. Yeah, that's – I mean, that's part of Stansberry's claim to fame, if you will. We're completely independent and we don't take advertising and all kinds of other ways to describe it. We don't manage money. We don't take advertising. We're completely independent. So, if we don't – it's just – we're in the same boat here. If you don't do the work that your clients need you to do, if it doesn't work, you're through. So, just the fact that you're still here and the fact that we're still suggests that we're both doing something right, I think.

Well, this is fascinating to me but I have to tell you as a long-time sort of value-oriented, fundamental, bottom-up investor it's a little troubling because, I mean, there's New Constructs out there. I'm like "Wow, is New Constructs going to put me out of business?" Because what you're doing sounds like it's so superior to anything any individual or even group of humans can do. And it –

David Trainer: Dan, I mean, that's the – that's like the same people that were in the business of carrying around rocks felt that about the wheel.

Dan Ferris: Yep.

David Trainer: And look, you're successful and you're smart because you know how to use tools. And humans over time have just been better at using tools. New Constructs is a tool. It's not going to replace everybody. That said, you're not the only one that feels that way. Look, since I started marketing this product to institutional investors, shoot, back in 2005 and '06 – there's a Harvard Business School case study that quotes me in some of my meetings with institutional investors, who said, "You know, David, your data is probably better. But as long as everyone else is using the same bad data, I'm OK with that." Or they would say things like "Yeah, you're taking away my competitive advantage." And my answer is "No, I'm giving you a new competitive advantage. Take advantage of what we provide to more precisely understand fundamentals and use that to augment the rest of your strategy." Because honestly, I don't for one believe that investing should be 100% fundamentals based only.

Dan Ferris: Oh, so I thought you did mention a few minutes ago, correct me if I'm wrong, you started to mention technicals and I think I cut you off. Do you want to talk a little bit about that?

David Trainer: Yeah. My point being that, look, it's a big, complicated world and if you're fundamentals only, you're going to get your face ripped off like I got my face ripped off on that short when we were in a hedge fund. And those kinds of things happened if you're 100% fundamentals. I don't think people should be 100% fundamentals but I also don't believe they should – they should not be 0% fundamentals. And if you're going to rely on fundamentals at all, well, you better damn well be sure that you're relying on the true fundamentals, because if your fundamentals are giving you the wrong signal then everything thereafter is wrong. Options, strategies, technical strategies, sentiment strategies, all of those things. If you can get all these things to align, well, then you've got a superpower.

And so, yeah, I think we take that into account. We also look at strategic analysis when we're writing up our long ideas. We've got a research, small research team as well and we do research where we will write up a traditional sell side analyst report on particular stocks and explain "Here's why." Those reports are like mini case studies on how to use New Constructs, which is, I think, really cool. But a big part of investing at the end of the day is "Do I agree or disagree with the expectations for future cash flows reflected in the stock price?"

And that's what all of our models are effectively positioning our clients to do. And that's a question that's not just about the numbers. That's competitive position, that's how markets are changing, that's technicals, that's the macro environment. And I don't believe that that investment decision-making should be made in any kind of vacuum frame any one of those silos. Ultimately, you want to bring all those to the table. But you need really high-quality product in each one of those in order to have a trustworthy investment decision process.

Dan Ferris: Yeah, I smiled when you talk about the expectations for cash flows being – whether or not they're baked into the current price. That is exactly what my partner analyst, Mike Barrett, and I have been doing in our Extreme Value newsletter for the better part of about 14 years or so here. I read a book called Expectations Investing by Alfred Rappaport and Michael Mauboussin, and I just sent it to Mike and said, "Maybe we should do something like this." And he built the model and we're doing it and that was it. And it works. It works very well, I have to say.

David Trainer: One-hundred percent. We are expectations investing applied. So, Michael Mauboussin and Al Rappaport, I worked with those guys when I was on Wall Street. Mauboussin was my mentor. I remember helping edit Al Rappaport's – I think the second version of Creating Shareholder Value. But we brought the reverse DCF model to Wall Street. I did it when I was a Credit Suisse. And New Constructs is the application of that. We do that at scale. And we give our clients the ability to play with these models. You can put in whatever inputs you want.

For example, when we put Nvidia on our buy list, the stock price implied that its profits were going to permanently decline by 50%. We were like "Oh, that's cheap". Well, right now Nvidia stock price implies that its profits are going to be greater than the GDP of Ireland. And about 20 times where Apple's profits are today. So, expectations, when you quantify them in this discrete way, I think it's just – it's a game changer. It's like "Wait a second. I'm not talking about whether I believe AI. Let's just talk about this stock." If the expectations are for cash flows to grow at 30% compounded annually for 30 years to some unbelievably large amount, well, then maybe I need to put the pause button on the buy here – maybe they take some gains – and go find another reasonably profitable company at a much better valuation. We've found a bunch of those for our clients, some AI stocks that people haven't discovered that have been cheap, that were cheap, and have had a lot more room to run.

Dan Ferris: Yeah. What really – one of the many things that sort of impressed me about the expectations approach is that oftentimes you look at the old-school, traditional sort of Graham and Dodd fundamentals, the things like price-to-book and price-to-earnings and price-to-free cash flow and all that stuff, and they can look unattractive. But then, if you do the expectations modeling correctly, that same stock that looks unattractive by those old-school fundamentals can look like a real bargain.

And we've done that numerous times in the post-2008 era of zero interest rates. Without that we'd have been sunk. I'd have been out of business because nothing would have looked cheap by the old ways.

David Trainer: One-thousand percent. Ratios are shortcuts. And shortcuts can get you into trouble. Any ratio is a shortcut. And yeah, that's part of our AI is to build a better fundamental dataset because whatever your discounted cash flow model or projection mechanism is, whatever you want to call it, if it's not based on the best fundamentals, then whatever you extrapolate could end up being wildly off the mark. So, that's part of our AI is a reverse DCF modeling capability that quantifies the expectations baked into the stock price. And we have the market implied competitive advantage period. We've been doing that – I've been doing that for 25 years.

Dan Ferris: Yeah. So, I mean, in extreme cases, like when you see something trading 60, 100. 200 times earnings you're not surprised when the model says, "Yeah, this thing is discounting the GDP of Ireland or whatever." But overall though, I'm just really gobsmacked at how many times that has not been true. And it really is a whole different, much more effective way of reading fundamentals. And I'm – it just – it warms my heart, David, that the guy who's doing all this AI stuff is – has expectations investing at the core of it. Of course, that's why people are writing papers and you're getting – setting records. That makes perfect sense to me. Thank goodness something about AI makes perfect sense to me. [Laughs]

David Trainer: Thank you, Dan. And it should. I mean, reasonable people can disagree about a lot of things, but the right way to do things for fundamentals is hard to disagree with. You need to do your due diligence –

Dan Ferris: Yeah, I mean, look – that's right. We all – we know what things are worth. They're worth those cash flows discounted back to the present. But you can't predict them. That's why, as you said, it's a reverse DCF that helps you get to the right value.

David Trainer: Yeah, I love telling all my new analysts, "Would you rather be a fortune teller or a critic of a fortune teller?"

Dan Ferris: Exactly. That's well – I'm going to steal that. I like that.

David Trainer: You're welcome. You're welcome to.

Dan Ferris: Yeah, I'm going to steal that for sure.

David Trainer: And so, yeah, Mr. Market is in the job of predicting fortunes every day for every single stock price, so let's just figure out what kind of fortune he's predicting and that's what we do at scale.

Dan Ferris: All right. So [coughs] – excuse me. So, we've talked about what the people do and what the machines do and sort of what your business is about. I wonder if – we do like to teach our listeners how to fish. We also like to give them a fish every now and then if we can, even if it's just like a sector or a general idea or just the way you're looking at the market right now? What do you see? What does the New Constructs system sort of tell you about what's going on in the world and what's attractive and what's not right now?

David Trainer: I just did a webinar on this: two hottest sectors and two most dangerous sectors. And we talked about – we like energy. And by the way, we have sector ratings as well. That's one of the benefits of the AI, Dan. We build all these models on all these individual companies and we can aggregate them into a model for an ETF or a model for a mutual fund or a model for a sector. And so, our sector models are flashing a really strong green on energy. And then technology is one we like as well, even though the sector rating is still neutral.

And we brought that one up in the webinar because it's important to understand that you can't paint any one group of stocks with the same brush even if they're in the same sector. Even though the overall energy sector looks attractive, I don't recommend all energy stocks. Even though the overall technology sector looks neutral, we don't hate all technology stocks. In fact, most of that neutral rating is a function of, you guessed it, six or seven stocks being really expensive. So, we think there's a lot of opportunity in the tech sector and in the energy sector, and we've been riding on a lot of energy names for a long time and they've done really well for us. Anything from master-limited partnerships to some of the big oil and gas producers – like, BP is one.

The least attractive sectors: real estate and utilities. Utilities, kind of obvious. In a rising rate environment you've got no real cash flow growth. It's just – and real estate's been really overvalued for a long time. We've been spending a lot of time giving away some of our – what we call danger zone picks and zombie stock picks because we just feel like investors just deserve – I feel like almost it's my public duty to help people just avoid some of these complete junk stocks that are circulating out there.

My most recent e-letter, I pointed out a company Root, ticker ROOT. The Wells Fargo analyst on April 11th raised her target price from $5 to $60 or something like that. I don't remember the exact number but it was a huge – without changing the rating. And the stock went – I'm going to look it up here. The stock went from – yeah, from about $10 up to $80. And then, around $65 she raised her target price. And it's now down – in the last week, last month, it's – or the last week or so, it's down 10, 20% since that time. And to me that was just an obvious pump-and-dump scheme. "We pumped it up to a big number and we want to give our institutional clients some time to get out. Let's sucker some more retail folks in and let them sell."

Dan Ferris: Wow, that is – I think that might be the most aggressive upgrade I have ever heard of in my life. A 12-fold increase in price target in one go. I mean, wow. I mean, if I were – I don't know, I could say things about the SEC, but if I were there and I saw that, I would stop doing whatever else I was doing and take a closer look at that. Just reflexively, just knee-jerk.

David Trainer: Yeah, I 100% agree. Yeah. And not surprisingly, the options activity was skyrocketing around the same time. And the funny thing – you bring up the SEC, Dan – there's a lot of things that have been going on that when I was on Wall Street right in the late '90s, early 2000s, I mean, people were getting away with doing things that would've gotten them perp walked. I mean, all these forward-looking statements that don't come true? I mean, that would've gotten you – I mean, the FBI would've been at that money manager's door and parading them in handcuffs to the thing with movie cameras if they say stuff like that.

So, there's all kind of – I think the SEC is hamstrung in a big way. I think they're afraid that if they call out some of this BS and the stock craters, they get sued. Part of that's because so many of these companies' valuations have become so disconnected from fundamentals that people are like "Fundamentals don't matter anymore." There was a time when the SEC could do this. Stock would get cratered and they'd say, "Well, now it's more appropriate to the fundamentals." Well, that argument doesn't hold water because the markets have been so disconnected from fundamentals for so many stocks for so long we don't really have any way to find sure footing anymore.

Dan Ferris: Right. Yeah. That yardstick definitely was broken by zero rates for the better part of, what, 14, 15 years, something like that. Well, from December 2008 to March 2022, most of the time spending at zero rates kind of screwed everybody up, I think. It just sort of pulled the rug out from under. There's no benchmark anymore, So – but we're back to 5% on T-bills. So, I feel like even though the effect may not be immediate and there's still plenty of mega-bubble valuations out there right now, I mean, this sort of gives us a better footing than 0% T-bills or 0.25 or whatever they were, doesn't it?

David Trainer: Yeah, 100%. When money is cheap or effectively free, of course you're going to move out on the risk curve, because you're not – what's your downside? That's why we saw all these zombie stocks born during that time, because it's like "Oh, well, I'm running out of money. I just go get more." And what we learn – what we lose, Dan, that I think is so important to society at every level is discernment. If we're not discerning about where we allocate our resources, society is ruined. And when you've got free money and people aren't discerning about where they allocate their capital, society eventually is ruined because if we become a society that that keeps throwing good dollars after bad, where does all that go? It goes to all the bankers and CEOs that are robbing from you instead of the companies that are growing and creating real value. The United States is founded, I believe – our success is founded on intelligent capital allocation, discerning capital allocation.

Dan Ferris: Yeah. And ultimately it leads to this sort of divided society where the upper 1% or whatever percent you like is seen more and more and more as a kind of exploitative criminal class rather than the most productive people. And there's still many, many productive people. But if it keeps going a certain way – for example, guys like Jamie Dimon effectively have their billion-dollar fortunes backstopped by the Federal Reserve because they're too big to fail.

And eventually – I think you're right – that does wear on people and you begin to see protests and violence in the streets. And we – and it takes a very long time, the way things go in societies over time, to sort of make the connection. But I agree it could be – return to a normal state if we just have more reasonable benchmarks. And I'm hoping that higher-for-longer interest rates will help us do that, but we'll see.

So, it's actually – it's been great talking with you here, but it is time for my final question. And it's the same question for every single guest, no matter what the topic. Even if it's a nonfinancial topic – sometimes we have nonfinancial guests – exact same question. And we can edit, so if you just want to take your time and think about the answer, that's fine too. But the question is simply this, David: If you could leave our listeners with a single thought today, what would it be? What would you like it to be?

David Trainer: If I would leave your listeners with one single thought, it would be to be more discerning. To be discerning in terms of what you put into your body, what you allow into your brain, what you watch on TV, and of course how you allocate your capital. Intelligent decision-making takes time. It takes gathering of research. It takes gathering of information. And I want to help people be more discerning. I want to improve the integrity of the capital markets, and we do that by giving people data that allows them to make more intelligent decisions. And it's there for the taking but people have got to want to do it. And I think what we are missing these days, Dan, is that they are too often tempted by zero rates, free money to gamble, go with FOMO, go with MOMO, believe that it's getting – you can get rich fast and easy, which is just not true.

Dan Ferris: I agree. The zero rates are gone but the habit's still there. Yeah. That's a great answer. Thank you. That's a really good answer. It's funny, we used to get all financially oriented answers but now we're getting more of them that are either completely just sort of philosophical in nature and only apply tangentially to finance, but we're getting more like what you did, which is it's about life as well as finance. It's sort of an all-encompassing answer that investors can apply and that they can apply to their life. So, thanks for that. And thanks for being here, man. It was really good to talk with you.

David Trainer: This is great, Dan. Thanks for having me on. I love talking about this stuff. And I love that you've read Expectations Investing. That is awesome. Major hat tip. That is – I mean, once you've read that you can never go back. It's like –

Dan Ferris: No, you can't. Yeah. Once you read that you're like "Oh, I've always known I couldn't predict the future, but now I know what to do about it." So, yeah. Yeah, you bet. I recommend that book to everybody. I've recommended it on the show more than once. Absolutely.

David Trainer: There's a short report that you would love. It was written in 1996. It's called the Neglected – it's called the Neglected Value Driver. It's like 10 pages. It's Mauboussin's – it's like the precursor for Expectations Investing. It's like – if you have people who don't want to read a whole book – and I can send it to you if you want, Dan.

Dan Ferris: Oh, OK. That'd be great.

David Trainer: But Mauboussin wrote it shortly after I got to Wall Street. And I remember I read it and I took copies and I handed it out to every single analyst on the floor. And they're like "What are you doing? What, are you some kind of kiss-ass for your boss or whatever?" I'm like "No, you read this and you'll be changed forever. It makes so much sense." But you'll love it because it's like 12 pages and it's Expectations Investing in 1996 as opposed to – when was Expectations Investing published, like 25 years later?

Dan Ferris: Yeah, I wanted to say 2012, but I don't think that's right.

David Trainer: It's a cool paper. You'll like it. And if you've got people who don't want to read a whole book, you say, "Well, just read this little paper" and it'll get them to the same place.

Dan Ferris: Great. I'd love to see it, yeah. All right. Again, thanks a lot, man. It was really good talking to you.

David Trainer: Thank you. I enjoyed it. Thank you, Dan.

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