New Constructs' Live Podcast: Intelligent Capital Allocation - 19 July 2024

We'll go ahead and kick this one off. Um, I want to just start up with a quote from David's latest e-letter. It is on our site and also we emailed it out. If you haven't read it yet, definitely check it out and we'll talk about it a little bit in this session here, but we'll jump into. Intelligent Capital Allocation, July 19th. We'll start with the disclosures, disclaimers. Main thing is we are not providing any recommendations or advice. We are publishing. On the agenda today, this is the podcast driven by our community once a month. You can get replays and slides in the community. Next month's podcast will be on August 9th. We'll be updating the Zoom after this, so you might get an alert to let you know that the latest date is in the system. Since our last one of these, we've put out a ton of stuff, a lot of performance and earnings stuff. Let's start with the stocks most likely to beat and miss 2Q24 earnings. Looks at overstated and understated, uh, Street earnings, uh, some new long ideas, some danger zone updates. Uh, there was a zombie stock that received a lifeline, uh, took it off the zombie stock list. Uh, David sent out multiple e-letters, um, with some stories from his experience, what he's seen in the markets right now, a lot of general market comments as well there. Model portfolio updates— just put out the sector and style rankings for 3Q24 with all the individual sector and style deep dives on the best and worst mutual funds and ETFs. Talk about a little bit later today the update to our free cash flow yield and stock ratings, some replays, and then all the model portfolios updates as well. After this, we'll talk about interesting headlines in the community, and then at the end, if we have time, we'll take a look at some of your stock requests. Instead of— we've in the past, we've looked at some good performing stock picks of ours. So send us in the chat or the Q&A any stocks you want us to take a look at. If we've got time, we can take a look in our rating system and see what the rating shows using our institutional membership. So get— be sure to get those in. And with that, we'll kick it off to the next one here. We got some special webinar content. Recently, David hosted a training, The Ultimate Retirement Solution. We've got a replay here, and we'll provide these slides. It is— will be up in the community later today. We've got a Let's Talk Long Ideas coming up next week, and also a members-only training where we reveal an AI stock pick next week as well. Um, which takes me to Headlines in the community. We'll start with an update to the free cash flow yield calculation and our stock ratings. There's a question on this in the community. We also sent out a blog post and email, but we made, made an update to the free cash flow yield calculation, which affects our stock and fund ratings. Previously, there you can see free cash flow divided by enterprise value. Now we're looking at a 2-year average free cash flow excluding cash divided by enterprise value. So it really gets at a free cash flow yield and free cash flow calculation that is more of the underlying business and is less impacted by one-off events, huge purchases, sales, raising cash through stock or debt, things like that. And we can really get a cash value that looks at the real actual free cash flow of the business. I don't know if you wanted to add anything on that, David. No, it's always big when we update the models.

Yeah, no, we have not changed our rating system in, gosh, I don't know how many years because, you know, you don't fix it if it ain't broke. But, you know, the funny story about this is that we started with a 2-year average free cash flow yield a long time ago and moved to 1 year so we could cover IPOs. We since updated our system to use what we call a greedy average, which is, hey, if you only have data for 1 year, then use a 1-year free cash flow. But if you have it for 2, then use that. So we can do either or, and we have that now. And so we're going with this 2-year average because we think it's better. And yeah, everything else we're doing with this is, is, is super cool. It's better, it's smarter. You know, we're learning, we're getting smarter all the time, but that's what we do in new constructs. And so take a look at the, at the note if you want more detail. Yep.

And that's on our site here. We've got a whole detailed rundown of exactly what we did. And then it'll also be available in these slides on Society when we post this replay. Another, not necessarily an update, but a new education page. we created was for return on tangible invested capital. This one's been in our model as a calculation on a return on invested capital, but it removes goodwill and intangibles, which is useful for companies with large acquisitions in the past that could have a large goodwill balance. And so it lets you look at those type of companies, compare them, and just compare ROIC in a different way. And depending on your use case and what you're looking for, you know, we're not trying to sell the only way. We're trying to give better data and better calculations on all that data. So we got a new education page with info on return on tangible invested capital, and then also some quotes on why it's, you know, important, and then some data on the actual companies with the highest and lowest return on tangible invested capital as well. With that, I think we jump out to society. We can talk about— I think, David, talk about your latest e-letter and some of the posts in the community on that topic, and then we can dive into some of the other ones as well.

Yeah. Yeah, that's great. You wanna keep driving and just go on, on, on into society there? Yep. We can do that. Yeah. Cool. A lot of good stuff. A lot of good stuff. I wanna say, you know, I'm really grateful for this community. I think they, we, we really talk about some very sophisticated concepts. There have been some deep dives, some case studies provided here that are, that are brilliant. I think it's really cool. That we have this online community. And as just a quick side note, we're not calling it, you know, anything special anymore. It's just the New Constructs Online Community, uh, because we just, we want to focus on, on that part of it. We don't think we need a special name there anymore. But yeah, so I, I published a new e-letter yesterday, and this— the e-letters are sort of really part of our, our new sort of marketing chief pushing us to, to communicate more on that kind of a level, more personal level. We've been a very institutionally oriented firm and We want to be a little more personal, let you understand more about what goes into what New Constructs does. And anyway, this is, you know, we did some really cool macro work. Kyle, if you want to, if you want to click on that post.

Yeah. Go to the, yeah, the e-letter here.

Yeah. I pulled in some, some data that you can get for free from the Fed website. There's links to it all there, but really just showing how much ridiculous amount of liquidity has been pumped into the economy. over the last decade or so, last several years, and actually like last, you know, even more than that, 20 years or so. And it's never come back out. So every time they put more in, they don't take it all out. They put more in again. And it's not just coming from the Fed. In fact, the Fed's starting to taper off. But while the Fed's tapering off, the federal government or monetary stimulus— I'm sorry, fiscal stimulus has been skyrocketing. And so what that means is that we're just awash in liquidity, which is a big part of what's driving stocks up so much. And so the Trump trade that I think no one's talking about is the fact that maybe the Republican Party is not so much about growing government and endless spending and frivolous spending that we're seeing and a shift away from that, which we all know we need to make, right? Like the deficit is at all-time highs and we're, you know, we're spending more than we're making and interest rates are up and the interest expense on the federal debt is potentially going to become the biggest line item expense in the entire, uh, government budget. This is all a bad thing. It has to stop. And if it does stop, then we could see the one thing that really does drive corrections, that we know drives corrections, and that's a reduction in liquidity. And so that's maybe why we're seeing the stock market start to stumble a little bit here, is that it's starting to discount the risk that liquidity is going to take a step down. And Julio in the community Added a really great chart to, and I wish I could have had it. I almost wanted to republish the the the e-letter with Julio's chart, you know, showing just another perspective on how much debt has is going up on the federal level. I think he's showing debt minus certain liabilities that you know, especially in particular, the Federal Reserve balance. And so you can see really how there's a net plus addition to liquidity from the fiscal side, more than offsetting any tapering we're seeing. So yeah, let's go to the next one, Kyle. We got a lot to cover. I'm going to— let's try to move faster and just jump in here anywhere you want.

Definitely.

Yeah, I think the viability of Chinese stocks, great question. You know, we, we struggle with this honestly at New Constructs, and we got burned a long time ago. We covered a bunch of Chinese stocks early on because people were pushing us to cover Chinese stocks. And we didn't have yet a category for suspension that we developed later, which was basically Chinese stocks because they don't— I mean, they don't follow the same rules. I don't think they have to have auditors. I can't remember. Jump in, Kyle, if you remember. Like, they, you know, one of the big issues is that they don't have to, like, submit to the same level of scrutiny in terms of auditors that everyone else has to submit to, which is everyone in the US. So it's like it's a double standard. Total BS. So I definitely recommend that you, you just, just be careful with Chinese stocks. Alibaba's a little different because it's almost like a global, well-known stock and company. And, you know, it's already kind of been through the wringer there because they took the CEO out, maybe sent him to some reeducation camp for a little while. I don't know. He disappeared and he came back. And is that— am I thinking about the right company or is that Tencent they did that to?

I think it was Alibaba. with Jack Ma, the CEO, founder. Yeah.

Doing a little too well there. Government didn't like that. Maybe you need to take a little vacation.

I think another one was Didi Global, the rideshare company that went public here. And then what was it, a week later, the Chinese government was like, hey, maybe there are some issues with that. We might need to put a clamp on these things. And the stock plummeted. I remember that being Big test case in the danger of Chinese stocks.

Yeah, we crushed that one. That was a dangerous zone from the beginning IPO stock, and it went down so fast we didn't have a chance to get it on our focus list. And so, yeah, you know, so when we show up, when we talk about our focus list short performance, I mean, we've missed a couple layups just because, you know, we're not sort of like trying to be too knee-jerk. And we are a little bit more now, a little more careful about making sure some of these Focus list, short danger zone ideas get added more quickly.

But yeah, that's—

we nailed that one. So yeah, what's the next topic, Kyle?

Let's talk about new constructs, trends, and then also the free cash flow. We touched on that a little bit already.

Yeah, so trends, you know, free cash flow is a trend in a lot of ways. Like, you have to have at least 2 years of history, which is why we— to calculate free cash flow for 1 year, you need 2 years of history. And so it's a bit of a trend metric, but the biggest one in terms of trends is the economic earnings compared to reported earnings. We used to publish a lot more detail around how we calculated things in our metrics for free. We don't do that anymore because of large language models out there that are just going to hoover up everything. And we just do so many good things at New Constructs. We don't, we don't need to make it easy for people to recreate it. So we put a lot of our education stuff behind the paywall. It's not free anymore. For good reason, because the education posts we have are amazing. I mean, I've literally had professional investors at major firms call me and say, oh, by the way, I learned everything I know about what I do now for you, and we want to give you business. I'm like managing several billion dollars at, you know, UBS or something like that, or whatever major firm, and I have it all to New Constructs to thank because I learned everything I know. I mean, it's a free MBA on our website in terms of education. Yeah, it's all these metrics. I mean, you want to know how to do it right? That's how you do it right. These are the— this is the best way. And by the way, best does not mean there's just one metric for return on tangible invested capital. You need to look at companies in different ways when, you know, you've got multiple situations because the world is so dynamic that, you know, there's just not always one metric that's perfect for every company in every situation. So yeah, in terms of trends, economic earnings versus reported earnings, we're looking at both the trend in those 2 things and how they compare to each other. So worst-case scenario, accounting earnings are positive and rising, and economic earnings are negative and declining. That's how you get the very unattractive rating for economic earnings versus reported earnings. So Good question there. Goldman Sachs casting doubt on AI. Oh, I love this piece. So, you know, we need to share in the chat the, you know, the piece we wrote years ago on AI in finance where we basically predicted all this stuff. And what we were predicting is that AI and machine learning and basically the tech, the intersection of technology and data is a lot less about the technology and more about the data. Meet the old boss, same as the new boss. There's nothing that's going to shortcut doing the diligence in the fund— in the filings. There's nothing can shortcut that. You've got to do that. Machines aren't going to figure it out. Machines have not yet figured anything out that humans haven't told them. Right? They do work that is directed by humans. That's what machines— that's what programs do. A program is programmed by a human, and that program is only as intelligent as the human programming it. So at the end of the day, we need to make sure any AI, any LLM, anything related to that is based on good data. And I think that's what Goldman Sachs is calling out here. Number 2, I think a great point is that because there's $200 billion plus being spent on AI, whatever company is at the top of the heap right now will not stay there. There are big firms that have big budgets that can spend tens and hundreds of billions of dollars on competing with those at the top of the heap. You know, I'm talking about here Nvidia. And so the returns on capital that we're seeing Nvidia are going to last. They can't last. And, and so that's just the nature of competition. But when you know that numbers that big are being, being spent, that just means that, you know, you can't make it that long at the top. And, and so it's a great report. And I really, I, I think that people need to understand that there's 2 stages to AI and we're in the first stage, at least 2 stages. The first stage It's sort of like, you know, we just, we go through this as a society all the time. We become enamored with the underlying technology. Like with the first stage of the, with the internet, you know, the internet, you know, that was the, gave birth to the tech bubble and a bunch of companies that ended up not existing. Turns out the internet is really just something that benefits everybody. And it doesn't necessarily create a lot of value for a lot of, you know, individual companies except for those that, you know, were able to build the business models that win on this new platform. Anyway, the first stage for AI is where we are now, where everyone is enamored. ChatGPT. Oh, it's amazing. It's awesome. You know, and is it? No. I mean, we don't hear about hardly ChatGPT anymore, hardly at all anymore. Why? Because it hallucinates all the time. Why does it hallucinate? Because it's based on bad data. Right? The, the LLM And the, the chatbots and all these things. Oh, they're amazing. They're great technology. But if they're not based on good data, what are you getting? Hallucinations, wrong answers. ChatGPT can digest the entire internet. Okay, that's great. Alright. But how much of the internet can we trust?

Yeah, I'd say digesting the entire internet would be bad for you.

Exactly. It is. And it doesn't really work. So That's what I put into the chat here is a report, a series of reports we wrote 5 years ago, 6 years ago, an ebook, you know, what it means, what machine learning and AI means for smarter investing. You know, and this is just, you know, part of, I think, the thought leadership that New Constructs brings to the table. Part of what makes these webinars and podcasts, you know, valuable is that when you're talking to us, you're talking to some experts in a lot of fields. We've been there. We've done that when it comes to AI and machine learning. And let me tell you, there's no substitute for hard work. And you guys can read that in all my e-letters too. There's no substitute for the hard work we did to go through the footnotes and mark them up hundreds of thousands of times so that our machine learning and AI can be based on reliable data. And we prove that every time we give somebody a report or a model. And as our institutional customers know, you can click through to the underlying footnotes. Transparency, I think, is important. We want you to know the kind of work we do for you, and we're willing to show it to you. And any other research firm that's not willing to do that, you should fire. Let's go to the next one, Kyle.

This is a topic you'll like here. I know we touched on a little bit with your e-letter, and then this was a nice long article from Yeah, man, this was a great one, Joseph.

Anybody who hasn't read this, you need to pay attention to this because this is absolute truth. And it's about how much equity preference drives future stock market performance. And another way to put that is to say, this is about the future, you know, at least the near-term direction of the stock price is going to be about how much people want to be in stocks in general. So what does that mean for Wall Street? Oh, they want to increase the demand for stocks. Well, what's one of the best ways they can do that? Well, it's to lure more unsuspecting investors into the stock market. Robinhood is one of the— and we wrote about this in our Danger Zone pieces on this. It is the sort of ultimate slap in the face to individual investors. It is a way for Wall Street to make money on you individual investors Cannibalizing each other because Robinhood is making money by selling your order flows, your trading intentions to Wall Street insiders, and then you're going to buy the stock. You're going to give them money to take advantage of you. That's what it is. Anyway, Wall Street's selling us on Robinhood because they want to lure more investors in. And how they do that? Oh, we make trading free. Let's just get more people in. Why? So we can increase The overall demand for equities. As long as that's growing, right, stocks are going to go up. And another way they do that, oh, let's change the traditional 60/40 stock-to-bonds model portfolio asset allocation model. We need to go to 70/30 because stocks have outperformed so much, right? Why are they doing that? They want you to buy more stocks. They want you to buy WeWork. They want you to buy Beyond Meat. They want you to buy CrowdStrike. They want you to buy everything. They want you to buy those IPOs.

They want you to buy anything.

Correct. Just want you buying. And, and, and this is, this is, and this, you know, and this affects stocks in, in general. And the other thing, you know, they want is the government to keep giving you money to buy. And, and as I wrote my e-letter, the government's doing its job on that front. And so this is, this is a great, just a great, another great example of how the community, I think, just adds so much value to each other. I mean, I don't— this is stuff, a couple posts we've talked about here, you know, it's stuff you just don't even see anywhere else on any, any of the news sources. And so I really believe this, this community is, is really adding value to our clients. And, and, and that's, that's us. That's our clients adding value back to other clients and to the overall New Constructs brand and value proposition. And again, I'm grateful for that. I think it's awesome. The place for speculation, my friend. Is there a place for speculation? I don't know. I don't like to believe there is. I don't believe in speculation. Sometimes that means I lose, lose a lot. And Kyle can, you know, I'm not going to share too many of my personal portfolio management stories. Kyle knows the worst one. But yeah, my risk averseness has certainly cost me and my family in a big way. But, you know, long term, I care more about doing what's right. And it's always been about, I think, making new constructs the most honest and truest and and best research provider it can be. And if that means I just put my money on the sidelines, that's where it goes. Because at the end of the day, I want people to know they can trust what we do no matter what. And so that means I'm not a big speculator, Robert. But that doesn't mean every— you know, you— it doesn't mean you, you, you know, you shouldn't. I don't give out advice, you know, on that front. I just don't— I don't think it's a great place. And I think in particular with this stock we're talking about, You know, yeah, this shift in, in, in strategy, I think, to get into, into Bitcoin— I think that's what we're talking about here. Yeah, it's a big red flag, my friend. Big red flag. Big red flag. You know, a lot of those Chinese stocks, when we added them to coverage, looked really attractive too, and we saw what was going on there. So, you know, you know, as Warren Buffett likes to say, 10 years of intelligent capital allocation can be ruined with one bad acquisition. So, you know, one bad move.

I think that will about finish it. We had a nice post here just from John on some metrics he looks at, you know, forward sales with some data on some of the companies he used here or was looking at here. And then believe that is it for stuff we haven't talked about before.

Quick shout out to John though. He's posting some super cool stuff on options. That's another thing that I think is really cool and I've Honestly, one of my really smart buddies from the Wall Street days is like, Trainer, you got to get in options, man. You use stuff on options. I'm like, man, I'm so focused on what we do. And it's honestly, it's hard work making sure our models and database, all this stuff works. An incredibly complicated machine. And our models are incredibly complicated to deal with all the different accounting variations and rules. But John posts some really cool— he's a super smart client. Uh, you guys, if you haven't read what he writes, you need to write— you need, you need to. Sorry to interrupt.

Nope, all good. I'm going to jump back to the slides here because we actually— it's a bit from our recent training, sort of using the new construct system to find hidden gems. We were talking about AI stocks, so we had some slides here. Not going to be giving away any AI stocks, but we can talk about, you know, sort of what goes into what we're looking at in the system and what it all means for, you know, how to use the ratings and everything. And one of the things we found in that training was AI semiconductor stocks, you know, the amount of them that is attractive is declining pretty quickly. 6 months ago, there was 35 tech stocks that rated as attractive or better. I think this was as of last week, there's 23. So we're seeing a decline in the number of, you know, high-quality, good risk-reward stocks in the sector, which makes finding good companies even harder. And that's where, you know, digging into the footnotes, digging in to get the actual profitability of the companies is very important because if you just looked at the overall sector, you see, you know, super expensive valuation in technology, great profits, but you know, you're not getting those profits at a good price. And so digging in at a lower level than that and just, you know, going beyond the surface, you can still find a few hidden gems, but they're definitely getting harder to find in the tech sector and especially the AI sector. So we mentioned so much capital's flowing into it. People are jumping into any AI name possible. If it even, you know, some of it's reminiscent of memes, you know, they even mention certain keywords. It's like, oh, AI's in the, you know, filing 40+ times and the stock, you know, jumps because they said AI. And so, yeah.

Remember like when they did that with blockchain too, right? When they like, people put block in the name, they changed their company name to block or put blockchain ticker or whatever. And same thing. Yeah. I mean, the big part of the message here is that that, you know, you need to act fast if you still want to find something that's good in AI because it's not. And we really highly, you know, recommend against taking lots of risk in expensive stocks. That's not a, that's not a good idea. That's dangerous. And we actually talked about the enormous amount of work that we did to find the one, you know, I think we're down to one in the semiconductor software space on our screener. We're down to one attractive stock. And Kyle did a ton of great work on that, and we published a long idea on that recently. We're going to reveal it to clients, uh, on next Wednesday the 24th, I believe, at 5 PM Eastern. We got a special webinar for folks who are clients and who signed up for our most recent promotion, and we're going to talk about that stock. And I think it's— this is amazing work, amazing work to, to be able to sift through the entire market. When I say the entire market, we're covering around 3,300, 3,400 stocks. Those are the most actively traded stocks. And, you know, except for maybe a few exceptions, it's every stock that matters, right? The stocks, you know, we don't get these really small stocks because, I mean, you— they're so small and they're— I don't know, they're just not as consequential. But yeah, yeah, we, we pulled one out, man. We pulled— we found one. We did a ton of diligence on it. And our long idea reports are incredible pieces of work. Some of the biggest, most sophisticated investors in the world pay attention to those, and we help them make billions with these suggestions in this research. And, you know, it all starts with good data. We can't sort through 3,300, 3,400 stocks with any real precision and intelligence if the data on each one of those stocks isn't reliable. You're screening stocks on bad data. Well, What good does that do you? Doesn't, because you're getting a list of stocks that you may be good, maybe not. Who knows? Because that metric you're using isn't reliable. So the screening tools that you know are available to our pro and institutional clients, you know that's part of why that's a professional level subscription, because it's incredibly valuable. And we're spending you know two million bucks or we're creating value two million bucks per quarter. Just to update that with the new filings, you know, to deal with that data every quarter. So extremely valuable, extremely valuable. Kyle, we're coming to the end here. I want to make sure— we got a question about the ETF.

Yep.

We're still hoping that's coming out soon. You know, we're not necessarily supposed to give out the date exactly, but it's on track to come out soon, and we're hoping in a very short amount of time we'll be Talking about that with the ticker and everything on the New York Stock Exchange. So that's cool. We're really close. We're really close. You know, as you know, the prospectus was filed on May 1st. So the effective date or when it could go live was July 15th. So we're really close. We're really close. And we're excited for that to come out there. And I think it's going to make it even harder for people to find good stocks because, you know, there's there's now ways for large amounts of people to put. money into those stocks through that ETF, and it's going to make them move even faster, move up faster and get expensive faster. All right, we're at 30 minutes.

The next one of these will be August 9th. And if you have put the replay of this in our community for everyone to take a look at, we'll put the slides up there as well. Thank you guys for being here, and we'll see you next week or next month.

One last quick plug. If you want to find out that AI stock, sign up and be a subscriber. Um, and you can join that, and that, that's going to be available to the, uh, people that signed up for the last promo, Kyle. And, and who else? Pro and above?

Yes, correct.

So yeah, you want access to that pick, you want access to our long ideas, model portfolios. Um, $1,000 a month, what we believe to be an incredibly good price for research that is worth millions of dollars every quarter just to maintain, not to mention the database of data going back to 1998 for companies that have been around that long, the ability to screen on that. Anyway, I don't want to toot my own horn too much because you guys give me feedback that we do that a little bit too much. But you get the idea. Incredible value proposition. $1,000 a month is an incredible deal. It won't stay that low forever.