How to Profit From Expectations Investing - October 2024 Training

I'm coming to you from sunny Las Vegas, where I have been attending and been a speaker at the Stansberry Research Conference. And I mentioned that because I gotta say, uh, it's my first non-institutional conference. I went to a lot of Wall Street conferences Hadn't been to many in a while, but I have to say that this conference was one of the most, if not the most value-added conferences I've ever been to. Probably the first— my number one conference was the first ever one I went to when I was at Credit Suisse and I was running the specialty earnings and valuation team. So it was my first ever conference and I had to speak and I was like 23 years old and scared to death. And that was an amazing opportunity. And I'd never been to a conference and it was all about my product, which was really cool. But this is, this has been the best conference I've been to since then. And I think that what I find, what I think that what strikes me is that at this conference, like they're really focused on figuring out like how to get people alpha. Like there's like some cutting edge interesting stuff and it comes from all different walks of the market. We've got people in healthcare, you got people in energy. People all over the place and tools. And then there's also sprinkled in some great guests. I mean, the Wall Street conferences were boring. I mean, it was really all just kind of a, I don't know, it was like, you know, Club Med for Wall Street people. They're just like, you know, all hanging out, pat each other on the back. But at this, this conference, they've had Billy Beane, they've had Michael Lewis, they've had a couple comedians. And even the speakers who are pitching some really, really, really good ideas, on stocks and investing. So yeah, awesome conference. Really, really impressed by the, the ideas, the presentations, the, the alpha. I can't say— I'm really surprised, really surprised. And speaking of alpha, we've been continuing to do research on our proprietary data set, and I'm really excited today to be able to share with you the results of that, of that research. Some really impressive Analytics that we've been doing. You know, as you all know, we you know we've got a proprietary data set. No one's ever done what we've done before. And look, give us some grace here. We're still figuring out how to make the most out of it. Sure, I had an idea about how to build a rating system twenty years ago, and it's worked. It's outperformed human analysts. We've gotten papers written about it. Does that mean that's our ceiling? No, there's a lot. And and I'm very fortunate to have partners. like Christian and Brett to help Kyle push Kyle and me and Cody to go look a little deeper. Hey, what if you do this? What if you do that? How's that look? He backtested this. And, and when we do that, we tend to find some really good stuff. And today we have another new discovery. And this just reminds me of back when we were working with Charlie Wang at Harvard about, you know, how we can potentially have like our own internal Research department to do nothing but backtest. In fact, I talked to Charlie about that about a year or so ago. Like, we wanted to invest in that, and it's just not been something that we've had the time or resources to really get after. But great idea. Fortunately, we're able to do more of that on our own anyway without having a dedicated staff and group on that. But we found something really cool. So here we are: how to profit from expectations investing. And I got a lot to say about expectations. is investing. I think it's the only way to look at the world. 20, 30 years ago, when I first got to Wall Street, my boss at the time— it was 28 years ago— published this really great paper called The Neglected Value Driver. And I made a copy and put it on every analyst's desk at Credit Suisse because I thought it was the smartest, most intuitive. Just, I don't know how you could do your work without it. And my mentor was Michael Mauboussin. He went on to write a book about this. We built a model together, like applying expectations investing on a one-company-at-a-time scale. And at New Constructs, when I started New Constructs, basically I built an enterprise system to scale expectations investing, to scale reverse DCF, reverse DCF analytics. And I'm super excited to bring that to you today. All right. Our typical disclosures and disclaimers. We are not advisors. We're not providing advice. You should get advice from someone who is a trained professional on that front. We are just information providers. We are publishers. We want to help people make the most informed decisions possible. We are not telling them what decisions to make. No recommendations here. All right. I want to hear from you guys today. Get into the chat. Let us know where you're from, what kind of investor you are. And then most importantly, if you want me to tell you the answer about one of your stocks, You got to put your ticker into the chat. Got to put it in there. And if you stay to the end today, I'm going to reveal the power of utilizing expectations investing. Great concept. Application is everything here. I'm going to show you how we do it. Also, the secret weapon that I used to call the top of overvalued stocks. How our team of analysts has discovered a new set of rare and valuable stocks based on backtested data. And how you can grab my latest top stock pick or recommendation based on our latest discovery. You guys know me. I am David Trainer. I am the founder of New Constructs. I've been doing this work for 28 years, really closer to 30 years now with real forensic accounting. Author of Modern Tools for Valuation and The Valuation Handbook. And I don't think there's really many people in the world that know more about robo analytics and AI than we do. Our research has beaten 16,000 buy-side professionals to claim the number one ranking for North American stock picking according to SumZero. I always like to point out buy-side is a big deal. I'm not competing against the sell-side because guess what? That wouldn't be much of a win. But professional investors, that's who we're measuring ourselves against. Real pros. Our work has also been studied and validated by prestigious institutions such as the MIT Sloan School of Management, Harvard Business School, Ernst Young, the Journal of Financial Economics. Name another research firm that has one paper from an institution that prestigious— one— and we've got 3. We've been amongst the first to recommend many of today's most valuable stocks. Including Apple at $1,950, Eli Lilly at $36, Microsoft at $42, Nvidia at $0.50 a share. If that doesn't get you interested, I don't know what will. We've also been able to recommend many lesser-known hidden gem winners such as Williams-Sonoma. It's up almost 500% since we first recommended it. Jabil up 266%. Sprouts Farmers Market 428%. We're not just out there calling out the big names, though we have been early on a lot of the biggest stocks in the market. We've also been early on a lot of the other best stocks in the market. And despite our research and AI being able to identify successful picks over the two— over the past 2+ decades, My team and I are always searching for new ways to find an edge in the market and spot the best upcoming stock— stocks based on our superior data. Last month, our team unveiled a brand new set of Super Stocks based on a unique metric we found in our database, and our community loved what we found. Today, I'm happy to announce yet another new discovery we found in our superior research. And I gotta give credit to where credit is due here. Our team of analysts spotted this just a couple of weeks ago after backtesting more data. This discovery allows us to identify stocks that are clearly undervalued in the market and have a high credibility— probability to correct and explode even faster than most stocks that are considered cheap in the market. It has to do with an important valuation metric, and I'll be discussing it in detail coming up. Once you fully understand its potential, you'll see how you can profit from expectations investing. If you stay to the end, I'm going to show you how to get my latest number one stock recommendation based on this new discovery at New Constructs. This new recommendation is not found in any of our current model portfolios. This is a new discovery. We consider it a special new pick as it's based on a combination of strict criteria. This recommendation should not be taken lightly because our track record speaks for itself. I've recently been ranked the number 1 stock picker of all time on SumZero, again out of 16,000+ buy-side professionals. I hold the number 1 spot in North America, number 1 in value recommendations, number 1 in consumer discretionary, number 1 in industrials, number 1 in consumer staples. And as much as I'd like to give myself credit for these number one rankings for 41 consecutive months, it's really all based on the proprietary RoboAnalyst technology and stock rating system. There's no way any one person, not Warren Buffett, not any two people, not Warren Buffett and Charlie Munger, can have that much information in their minds. That's why I created a platform to analyze all stocks so we could truly distinguish truly discern those that are the best and those are the worst. And of course, there's a bunch that fall in between. And in this training, not only will I test some of your tickers in our rating systems— our system— I'll be showing you how to grab my newest stock recommendation today. Why should you act today on my next recommendation? Well, I believe now is the perfect time to act in this market. If you've been following my latest content, you know that I believe the current market is overvalued. And the data, I mean, speaks volumes. The total market cap of US stocks as a percentage of global market cap is now at a record 48%, unprecedented highs. The US stock market is now worth $57.4 trillion, twice the value of the US GDP, 3 times higher than the market cap of major Asian stock exchanges as well as European stock exchanges. The S&P 500 is up 21% year to date and has hit 41 all-time highs this year. And the Fed is cutting rates for the first time since 2020, driving even more momentum. China is rolling out its most aggressive stimulus since the pandemic. Global markets, including the US, are reacting. I mean, they're just the— like, the world— the central banks are just pouring money, pouring money into the markets, which creates speculation. And then there's this shocking chart that no bull wants to see: a highly concentrated NASDAQ 100 index. 8 companies represent 48% of the NASDAQ. That just seems crazy, right? All of this is signs of increasing risk. In the markets, there's also a high market volatility that we're seeing as we get closer to the election. We all know it's going to be—it's going to be volatile. It's going to be crazy. Who knows what's going to happen? It's already been a really exciting, a really exciting year on that front. I expect a lot more excitement on this front before it's all said and done. With a combination of these factors, finding good stocks is just plain hard. If investors are chasing the herd or chasing the next hot stock, they are— they risk getting crushed when this house of cards finally cracks. However, we do believe there are still great investments out there, especially when you know the specific criteria that will help you find stocks that are truly still cheap and excellent companies. Now let's get into the background behind how we discovered the first set of criteria for these new stocks? It comes down, it comes down to a concept called expectations investing, along with a secret weapon I developed at New Constructs to call the top in overvalued stocks. The concept behind expectations investing is simple. Instead of making your own prediction about the future, you can analyze the market's prediction by quantifying the cash flows required to justify the current stock price. We're quantifying the cash flow expectations baked into the market's current valuation of a stock. You can reverse engineer what the cash flows have to be. Then you can make a more objective adjustment— excuse me, a more objective judgment about whether or not those expectations are realistic. We're not talking about a PE of 20 or 30. We're talking about requiring cash flows to grow at 20% compounded annually for 20 years or requiring cash flows to go down by 50% permanently. That's a much more tangible set of numbers to understand than some ratio that you, you don't even know, you know, for real what it means. Just a shortcut. Reverse DCF analysis, expectations investing is the opposite of the shortcut. It's the way it should be done. It was— this is a term that was coined by Alfred Rappaport and Michael Mauboussin in their book of the same name. And it can be incredibly effective. Again, I got this first idea, idea first from Michael Mauboussin when he published this report called CAP: The Neglected Value Driver. And if you guys want, I'll send you a copy of that. Just send us an email at support@newconstructs.com. We'll find you a copy. That report was written back in 1996. Amazing. And it was, it was the precursor to this book, which is all about helping investors understand How to read stock prices. My favorite quote: Investors who use expectations investing will have a fundamentally better way to evaluate all stocks, setting them, setting them on the path to success. I believe that. That's why we built a technology platform to do this work across thousands of stocks. And by quantifying market expectations across thousands of stocks as we do, it's easy to find pockets of irrationality and identify companies that are overvalued or undervalued. That's the beauty of technology and the level of insight you can get when you're able to look across the entire market. So this is a slide I created probably, I don't know, 20 years ago when we were launching our hedge fund and running our hedge fund. This is how I explained to investors how the new construct system works, right? Start with the blue line. That's the sort of— that's the distribution of valuation across the market. Right, right in the middle would be fairly valued, right? And I've got, I've got the market a little bit overvalued here, right? That— but the point being that the majority of the market is fairly valued, right? Most of the area under this curve, right, falls here. You got pockets of undervalued and pockets of overvalued. That's the point, right? Because you can know something is overvalued, but it doesn't matter how overvalued it is if you don't know how overvalued it is relative to other stocks. And you need to understand overvalued in terms of future cash flow expectations, real numbers you can sink your teeth into, not just some ratio. It's a PE of 25, 35. Is it this year's earnings, next year's earnings? And who knows, you know, what earnings number you're using. Is it a pro forma earnings number that somebody's made up? Is it a GAAP earnings number? Is it a Wall Street analyst forecast that's also made up? Right? No, our data, our analysis is going to be based on the true cash flows of the business, and we can compare those future cash flows to past cash flows to get a sense of how much of a gap there is between what the company's done and what the company's expected to do. Now, the other line on this chart is the distribution of profits. And so we see that some profits are understated and some profits are overstated. Where we see overlap between overstated profits and overvalued stock, well, that's how you— where you want to be. That's where you want to avoid, like get out short that. Or on the other hand, you want to be long undervalued stock and understated cash flows. So that's a conceptual framework for how you apply expectations investing at scale on the New Constructs platform, which also has the best data. for earnings and cash flows. Now, by knowing this core principle of expectations investing and combining it with my secret weapon, I'm able to help call the top of overvalued stocks. So what is my secret weapon? I call it a reverse dynamic discounted cash flow model. Our clients will tell you that it's not just any discounted cash flow model. It is the best in the world for 2 reasons. For one, it's unbiased. It's based on our superior data, and it's based on just a very objective reverse engineering of the expectations baked in the stock price. I see people, some folks raising their hands. Put your comments in the chat. Raising the hand doesn't really help us out because we can't let everybody talk. So if you have a question, put it in the chat or put it in the Q&A. The second reason Our model is the best in the world is that it's based on better data. And I've said this a lot at Stansberry, at the Stansberry conference. I mean, you heard, and if you guys have been following for a while, you know I say it all the time. A model is only as good as its inputs. Only as good as its inputs. You put bad data in, you're going to get bad results out. So we have the best data in the business. We get the best results in the business. And again, that's proven. Multiple times by Harvard Business School, MIT Sloan, the Journal of Financial Economics, Ernst Young. So unbiased model based on superior data. And the other thing we got to remember here is the technology to apply this at scale. Never been done before. Never been done before. The right way to use DCF models is not to try to predict the future. This is really core to the whole thing, right? We're not going out there and saying, I'm putting my cash flows in and predicting the future. Right? My favorite question for all new analysts at the beginning of training is to say, hey, ladies and gentlemen, would you like to be a fortune teller, or would you rather be a critic of a fortune teller? Would you like to be forced into the business of predicting the future, or would you just like to be able to be a critic of those who do make predictions? I don't know about you, but I think I'd rather be the latter every single time. I'm not really big in trying to make predictions about the future. But it's easy to be critical of people who do, especially when they're crazy. And by the way, when you're looking at the entire stock market, there are a lot of crazy overvalued and crazy undervalued stocks. But how do you find those if you can't stack them all up and compare them to each other? So the right way to use the DCF is to quantify the future that the stock price is predicting, a.k.a. expectations investing. Now let's get into how we used our DCF model on everyone's favorite stock nowadays. Nvidia. First, we loaded the DCF model based on the stock price at the time, or current stock price, of around $144. See the sections in the red boxes. What this shows you— this is from our reverse DCF model, and this gives you a quick and dirty on a quick summary from the reverse DCF model to tell you what's baked into the stock price. So this is the way you read this, is to see that to justify the current stock price, Nvidia's got to grow revenues at 48.5% compounded annually for 8 years and maintain an average return on capital versus, versus cost of capital spread of 635.8%. So we're talking about pretty stellar growth over a pretty long period of time while also seeing returns on capital Quadruple, triple. I'll show you even more detail on that, but a simultaneous huge growth in revenues and margins. Those are some high expectations. Now, if we tone down the growth a little bit from close to 50 down to 30, you see it takes 16 years. The reason I wanted to start with this slide is because I want to— I want you to understand that the role of duration In equity analysis is just as important as it is in bond analysis. You just don't hear anybody on Wall Street talk about it. Right? They don't want you to have tools like this. That's part of why our technology is so revolutionary. But you get the sense here, right? If I'm going to grow at a slower rate and a slightly lower level of profitability, well, it's going to take more time for me to grow, to generate the cash flows to justify the price. I shouldn't say me, the company, right? You know, so you can, you can think about the stock prices as 2 pieces: the piece of the stock price that is based on the existing cash flows and the piece of the stock price that's based on future cash flows. So there's a portion of the valuation dependent upon future cash flows. Those cash flows can be earned in a short amount of time if you grow really fast at a really high level of profitability, or it can take a really long period of time if you grow at a slow rate with a lower level of profitability. There's a million ways to slice that up. The point is, you got to have some kind of framework to take a look at them and understand them. Otherwise, you know nothing. Better to know something than nothing about expectations. And these are just 2 quick examples to give you a sense of a quick way to break it down. Now let's dive in a little deeper. In the optimistic scenario, again, it says to justify the $143 stock price, Nvidia has to grow revenue at a 49% CAGR while achieving an average ROIC minus WACC spread of 636% for the next 8 years. In the pessimistic scenario, to justify the price, Nvidia has to grow at 30% while achieving an ROIC minus WACC spread of 570% for the next 16 years. So we're clear on that. Now, when compared to the historical— Now we then compare historical revenue to implied revenue. Now This is where things really start to get interesting. So you can see here's past revenue. I mean, you can't even see like any real growth here, right? In the first scenario, we're seeing $1.4 trillion in implied revenue at the end of the growth appreciation period. The growth appreciation period is this number here, growth appreciation period of 8 years and 16 years. Right. So 8 years in this model, we have higher margins, right? So you're seeing cash flows and valuation go up faster with a faster growth rate. And that is $1.4 trillion is what the revenue reaches at the end of the growth appreciation period. In this scenario, it's over $4 trillion. Pretty big number. Pretty big number. Now let's put that in perspective. How big is $1.4 trillion? Well, Apple, the world's most valuable company's revenue at the end of its 3Q24 was only $385.6 billion. Revenue of $1.4 trillion would make Nvidia bigger than Indonesia, the number 16th ranked country in the world based on 2023 GDP. Almost bigger than Spain. Now let's look at historical ROIC and see what we're talking about. Again, Nvidia's got higher ROIC, man, like 208, nothing to sneeze at. That's amazing. But in the first scenario where the margins are higher, they've got to get up to almost 1,000% ROIC by the end of the growth appreciation period. That's a skyrocket improvement. Look at this. Right, while also growing revenue at 50% compounded annually, or close to 50% compounded annually. Skyrocketing revenue, skyrocketing ROIC. That's what we're talking about to justify this over $140 stock price. And in this scenario, it's about the same, 927%. Just, you know, you're a little bit lower, takes a little bit longer, and you're growing at a slightly lower rate. But the beautiful thing about this analysis is it really gives you a before-after. Right before is what this is. Here's what the company's done before or in the past. This is what we know they can do. Here's what the market price is saying they're going to do. So again, I think no better way to really understand what the stock price implies than these kinds of charts comparing the historical performance to the past, to the future performance, and really getting a sense of just how crazy expensive this stock has become. And again, why aren't you hearing about this on Wall Street? They don't want you to know. They just want you to keep investing based on momentum. So, some more perspective. How many times do we think in the history of the— in the last 5 years of the S&P has any company achieved an ROIC greater than 100%? Well, 11 times for 100%. Wow. 7 times at 200%, 3 times at 300%, 2 twice, once at 600%. The number of times anyone's been over 700% in the last 5 years? None. None. So Nvidia's got to keep, keep growing at an incredibly high clip and get more profitable than any company in the S&P's ever been in the last 5 years. Last 5 years, way more profitable. They got to get over close to 1,000% ROIC. We've never seen anybody over 700% in the last 5 years. Again, that's a way to put in— expect to understand expectations are really, really high, right? That's why I like to— that's why I've said I think it's more likely that hell will freeze over than Nvidia will ever get to an ROIC over 900% while also growing revenue at anywhere close to its current clip. I'll say it again, I think it's more likely that hell will freeze over than Nvidia will achieve the expectations baked into its stock price. Having the ability to quantify market expectations with this level of precision and based on the best fundamental data in the world is a huge competitive advantage for investors, for our clients. And while we can use our powerful weapon to identify expensive stocks, With lots of downside risk. You can also utilize this weapon. You can utilize this weapon to identify cheap but still very valuable stocks by analyzing our rating for the market implied growth appreciation period. The market implied growth appreciation period is defined as the number of years that a company's stock price implies it will earn ROIC greater than WACC on incremental investments. In other words, the number of years the company can grow its profits. Because if a company can grow its profits into perpetuity, it effectively has an infinite value. By the way, all the Wall Street analyst models— I shouldn't say all of them, the majority of them, almost all the ones I've ever looked at— they all have growth and perpetuity assumptions baked into their terminal values. I just got really geeky in there, on there with you, but that's for those of you who really know finance. You know what I'm saying is that the Wall Street folks are juicing. If they're using anything close to a DCF, they're juicing their numbers with unrealistic expectations put into their terminal values, unrealistic forecasts put into their terminal value assumptions. Because guess what? Nobody looks at those. Now, one of the key differences in our DCF model that I want you to understand that's very different from everybody else's— everyone else is like a 5-year model. They put every company into a 5-year model. That's their excuse for having a terminal value that captures everything thereafter. Right? And that's why they make it so big as well. I'm only going out 5 years because I can't forecast for more than 5 years anyway. So, I have to have some assumption that captures all the growth after 5 years. Well, they're so vague and opaque around what that post-5-year assumption is. That's why they can make up any number they want in a DCF. Our model is very different. It goes out past 5 years. It goes to 100 years. And again, we're not making predictions. We're just using that long period to help us find where the market's prediction is, right? So we just run our DCF model out. We don't use all 100 years. We don't have to. We use as many years as necessary to get the model to generate a value equal to the current stock price. So like for Nvidia, when we get to a stock price in 8 years and we can say, ah, okay, so the expectations for the future cash flows to justify Nvidia stock price in this scenario Is that 48.5% revenue growth with that 636% ROIC spread for 8 years? You put that kind of information into your model and then you get the market applied growth appreciation period. All right. And then we go farther out, 16 years, and you do lower growth rate, lower revenue, get to the same price. The point is there's a sliding scale there because guess what? Not every company should have the same competitive advantage period. Not every company should have the same growth appreciation period. In fact, the competitive advantage period or growth appreciation period is perhaps the most dynamic element of valuation. So forcing it into a 5-year or 10-year bucket by using a 5 or 10-year DCF model is probably one of the silliest things of all time. Wall Street loves it because they can hide all kinds of assumptions in those models and they're lazy. We're not. Yeah, we're not lazy about building technology to do it the right way. We don't want to do it by hand either. Trust me, I built hundreds, thousands of models like this by hand before I started New Constructs. It's a lot of work. And so we're very happy to have this technology to do it, and it's amazing, best technology in the world for this analysis. Cool thing about it is that when you use our rating system, you get the benefit. From this technology. The market implied growth appreciation period, it's a part of every single rating. There it is on the far right. We include that reverse DCF sophisticated analytics as a key element of our rating system. After identifying Nvidia's unrealistic valuation and conducting further research with other stocks, our, our team decided to run some backtests for stocks that had this metric and one other key metric in place. The metric being a low market implied growth appreciation period. When the results were finally in, we were shocked. Why? Because the stocks we found based on these new criteria tended to be able to reach their truer valuation in stock price much faster than nearly every other stock we analyzed. So look, look, it's not just about finding good stocks because look, if it takes 15 years for it to become, you know, for the market to figure it out, Eh, you know, we'd rather it happen faster. So this criteria led us to finding cheap and valuable stocks that eventually caught up to their true market price due to valuation distortions, often leading to triple-digit gains in a relatively short time period, such as TravelZoo, up 337% within a year. Global Ship Lease, 320%. in a year. Korn Ferry, 125% in a year. Major takeaway and key criteria number 1: companies that have a market implied growth appreciation period of less than 10 years were more attractive as stock picks. Now, having a market implied growth appreciation period of less than 10 years is only half the battle in our new discovery of our special set of stock picks. The other half— Secret metric in place. And if you've been part of our community for a while, you may know this secret metric is not that much of a secret anymore. So what is that secret metric? It's when economic earnings are greater than GAAP earnings. Now, how do we calculate economic earnings? This is a proprietary adjusted measure for us, also known as EBA. Again, anyone can calculate this. I'm giving you the formula. Formula is not tough. What's tough is having the right data, the right data to calculate the core elements of this formula. No pat invested capital. We do that better than anybody else in the world. So why are economic earnings better? Economic earnings better? Well, first of all, it's not just me saying this. You got Adam Smith saying this in Wealth of Nations. Ben Graham, Charlie Munger, Warren Buffett—they've been saying it their whole lives. And why do they like it better? Well, first of all, economic earnings are better because they're based on the complete set of financial information— balance sheet, income statement, cash flow statement, and the footnotes. Not just the income statement, or worse, not just some pro forma income statement. Why would a doctor ever diagnose a patient looking at just their ears? We do a full body physical at New Constructs on every single stock. Economic earnings are also better because they're standardized, they're normalized across all companies. So you've got apples-to-apples comparisons across the entire market. Number 3, they're a more accurate representation of the true underlying cash flows of the business. And last but not least, they're much better for finding hidden gems in the stock market. So here's how you calculate economic earnings. Groupon, 2015, NOPAT minus WACC times invested capital. They had negative $22 million in economic earnings. But they reported a positive $21 million. We all know what happened to Groupon. How about Bumble? Similar story. They were reporting a positive $318 million in GAAP earnings while economic earnings were negative $383 million. We all know what happened to that one too. The bottom line is, if a company is not generating positive economic earnings, it is not creating shareholder value and could go to zero. That's just plain mathematical facts. They're not creating any shareholder value, then the equity is worth nothing unless that's going to change in the future. If they never create any shareholder value, the equity value is worth nothing. Now let me show you how this worked in the past and have a look at the stocks we mentioned before to see the hidden metric in place. Domino's Pizza. This is just, this is just with GAAP earnings less than economic earnings. I'm sorry, or economic earnings greater than GAAP earnings. That's the only thing we're really talking about here. Domino's Pizza, when that metric was in place, it was up over 2,000% while the S&P was up 370%. Accenture, up over 1,700% while the S&P was up just about 500%. And AutoZone, up 732% while the S&P was up just 293%. Now, it's important to realize how rare this metric is. I'll have more on that later, and how large the true difference between economic earnings and GAAP earnings can be. The numbers— the problem is that the GAAP numbers can be legally manipulated to make earnings and profitability appear more impressive than they truly are. The accounting system gives people license, companies license to do this. Thankfully, over the past 20 years, we've trained our system to identify and close these accounting loopholes. To uncover the true distortions between economic and GAAP earnings. These stocks closely align with Warren Buffett's philosophy, where he highlights the difference between a good company and a good stock. I love this. Investors making purchases in an overheated market need to recognize that it may often take an extended period for the value of an even— of even an outstanding company to catch up with the price they paid. AKA Nvidia. It's an investor superpower. It's a superpower for you to be able to find companies earning significantly more than what the market perceives, because that's how you get an edge like the greats. But you can only do that if you have the right research and data to see where economic earnings are greater than GAAP earnings. Now let me give you a quick heads up. Google, ChatGPT, they're not going to give you an accurate list of companies where economic earnings is greater than GAAP earnings. Can't do it. Their numbers are based on faulty data, faulty data, and will not give you a good read on what's really happening. But we can. Here's what it looks like using our research platform. Type in a ticker, you go to the ratings breakdowns page, and we will actually have a chart showing economic earnings compared to accounting earnings over the last 5 years. We got thousands of these. And when that metric is in place, eventually those profits can catch up and boost the stock. The market cares more about economic earnings in the long term. Now, the beauty of these 2 criteria that we're bringing together is that we're going to find stocks that have both in place, or what Christian has coined— I like here— a high earnings distortion, meaning they're more profitable than they appear, and a high valuation distortion. Meaning they are trading at a substantial discount or a lot more valuable than they appear. The key is getting your money in ahead of the crowd. That's by looking at places that other people can't or won't look. However, in order to find these specific types of stocks, you need the right research to give you the true companies that have both earnings and valuation distortions. in your favor. And that's where we— and that's where we come in. The RoboAnalyst technology system. It's so good the Harvard Business School published a 50-page paper proving its superiority. It's the only stock rating system with an official U.S. patent behind it that I know of. It's how we identify the crucial valuation metric we discussed earlier: market implied gap of less than 10 years. And it's used by some of the top investors in the world. This AI has the ability to come comb through thousands of government filings with precision, analyzing every line and footnote. This has never been done before. It is revolutionary. And it's a superpower. Superpower is finding these earnings and valuation distortions across thousands of publicly traded companies. This is something that no one else can do. And when you find a stock that has both market implied gap of 10 years or less and economic earnings greater than GAAP earnings, you'll be able to find stock plays that could potentially crush the S&P in the near futures— in the near future, such as Penn Entertainment. It's got economic earnings greater than GAAP earnings and a GAAP rating of 1 or less than, which means the market implied gap is less than 10 years. We found that in place, the stock was up 341% in the next year. When those 2 criteria were in place for Residio Technologies, it was up 150% in the next year. When Huntsman Corp had those metrics in place, it was up 135%. It's important to note these specific stocks go above and beyond our typical rating system. This is beyond the existing rating system. They are considered outliers even amongst the attractive-rated stocks. We have not put these stocks into, into a monthly model portfolio. We haven't had time to do that yet. They are special picks that could get a new alpha rating. Why? Because not only do they have a short market implied gap, they also have this hidden metric that no one is able to see but us. We've been building and refining our data for over 2 decades now, which gets updated daily with new stock prices and any new SEC filings from the SEC. And at least once a quarter, we update our models on over 3,400 stocks with the latest financial data. That means there are always new ideas coming out of our research. However, we still get very few stocks with these special criteria in place. We're always updating our data and our models as fast as we can. When the, when the filing comes out, we're on it, which means we're able to generate a lot of new ideas. And that's why it's amazing that we're seeing so few stocks that meet these special criteria. And remember, they have these special criteria of market implied GAAP of 10 years or less and economic earnings greater than GAAP earnings. These stock picks closely align with Warren Buffett's philosophy. Here's another one of his quotes. Most people get interested when everyone else is. The time to be interested is when no one else is. You can't buy what is popular and do well. All right, live demonstration time. Let's go in here and open the site, look at some tickers. Give me just a second here, opening this baby up. All right, Kyle, what's our— what's our— what was, what was the most popular? Did not get a single repeat, so we'll just randomly pick some here. Uh, we First one being ADM. Archer Daniels Midland. So attractive rating overall. Not a greater return on capital, but decent economic earnings. And look, you got the market implied gap thing in place here as well. Uh, that's always good. So cheap valuation, decent quality of earnings overall, still gets an attractive rating. So not bad. If we want to look at the trend in economic earnings compared to accounting earnings, You can see economic earnings are not above accounting earnings. They're trending in a similar direction. At least economic earnings are positive, though at this rate doesn't look like they're going to stay that way for very long. If we wanted to look further and look at return on capital, you see it's headed down. If we want to go dig a little farther, margins are mainly the main culprit for why return on invested capital is headed down. It's the margins. Capital turns headed down too, but clearly not as steeply. All right, let's do our next ticker. What's our next one? Next one, UPS. UPS, neutral presentation. That's where we would, we would come and say, you know, hey, you know, vaguely right. The valuation looks about right. Nothing special. It falls in the middle. Not nothing too exciting one way or the other. A little green, little yellow, overall yellow. Yeah, this would be in the middle of the the curve or the middle of the normal distribution. All right, Kyle, what's the— what's our third one? Last one will be PayPal, PYPL. PYPL. We've been watching PayPal for a long time, and it took a really long time for this to get to where it is. It was an unprofitable business for a long time. It has turned profitable. I would say good company now and not such a good stock. So here you can see the market implied growth appreciation period greater than 50 years. That's red. That's really high expectations. And, and, and it's looking that way on the price-to-economic book value. And so yeah, I think PayPal is good company, bad stock. Very expensive, but you know, great profits. We love it. You know, what does this look like? All right, let's get in here. Economic earnings headed up. We love that. Headed up and even maybe a little bit faster than accounting earnings, but still below accounting earnings. So again, economic earnings are not greater than accounting earnings, they're below. And then return on capital trending up here nicely as well. Is that more about margins? It's capital returns, man. Capital efficiency. A lot of times people don't see capital efficiency. Everybody's looking at margins. Nobody's looking at capital efficiency. We're showing that's a, that's a good one. So that's, that's interesting. But again, pretty darn expensive stock at this point in time. Alrighty, that's it. Well, thanks everybody today for being with us. I love doing it. Loved having you on, and we'll see you next time. Signing off from Vegas.