New Constructs' Friday Webinar: Intelligent Capital Allocation 16 June 2023

Thanks everybody for joining us for our weekly Friday webinar, Intelligent Capital Allocation, with David Trainer, Kyle Guskey II, and myself, Tamara Pesek. We have a great special guest today that we're very excited to welcome, Morgan Slade from CloudQuant. I'm going to be getting through the announcements as quickly as possible so that we can get on to our special guest, which we're very excited about. But first, disclosures and disclaimers. Let's see, I did want to point out that we do have a couple of new playlists on YouTube. We are making the Friday webinars available on YouTube now in a playlist. So if you would like to go back through the catalog, that's getting a little bit easier. We're also pulling out some excerpts of David from his many interviews. Trainings as well as some of his comments during our Friday webinars into a short playlist for Calling Value in 60 Seconds. We know folks are busy, so we are trying to edit that down as much as possible. The agenda, as always, is to focus on your questions, so please don't hesitate to ask questions throughout. Please use the Q&A tool in Zoom. If you are a member of the Society of Intelligent Investors, our online community, you can always get replays there. This will be posted by the end of day today there and also out on YouTube. We have a ton of research that came out this week. We have all of the links in the agenda. There are several pieces of free research that I wanted to point out, as well as a great amount of research for our members as well. The call on green clothing, the eyewear retailer, Much Further to Fall. And then EV Got Saved from Zombie Status. These are all fascinating reads. Thank you, Kyle, and your team for doing all that amazing work. Without further ado, I'm going to hand this over to David to introduce our special guest.

Morgan, good to see you. Thanks for being with us. I met Morgan a couple of years ago and we formed a partnership around how to use the New Constructs data, especially the core earnings data, to generate novel alpha. Many of you are aware there's a 70 or 80-page Harvard Business School and MIT Sloan paper about how to do just that. But not all of us have the time or inclination to read those 70 or 80 pages. It's written in a more academic way. What Morgan and his team have brought to the table is a much shorter, very succinct paper that shows exactly how you use our data to generate alpha. And Morgan and his team break this down in, in more ways than I can count. They're experts at what they do. They've got a large portfolio of very unique alternative datasets that generate unique alpha. Morgan is, is well known also for speaking around alternative data in general, inflation statistics. They got some unique data that's helping identify trends in CPI better than other people. You may have seen him on Forbes or CNBC talking about that. He's a big deal in CloudQuant. He's a big deal in the alternative data space. We love being a partner with him. We're happy to have him here today to Talk about how to get idiosyncratic alpha out of our data.

David, thank you for the intro. We've been working with David for a number of years and we've had a lot of success using his datasets to identify interesting investment opportunities. So we'll go over some of those today. Welcome to this presentation. We're excited to join Kyle and David and the society here to go over some of our research results, a little bit about CloudQuant. We're a data research and technology provider. We work with lots of different datasets for institutional investors. We try and identify how to use them to make money, how to find edge. Our background is in the quantitative investing space. I've been a portfolio manager for a number of large hedge funds for many years, and we have experience over both short and long-term time horizons. We work with data products to try and find strategies and investment approaches that work. We also spend a lot of time helping people get their hands on the data so that they can actually start doing their own research with it. That's a lot about what CloudQuant does. We've been working with David and his team. This is a look of how you would get the data from us. We have a simple API where you can pull it in, or you can use an Excel spreadsheet. But the most important thing we do is we make sure that the timestamp we give you is the earliest you could have had the new constructs data so that when you're looking at historical scenarios, you can be very realistic about knowing that you could achieve that in real life going forward. That's one of the things that we do. Now, we have analyzed the turnaround time for David's team. They actually process this stuff so quickly. It's actually incredible. A few years ago, they were doing it on average around 2 days. And look at them now, they're turning it around in 24 hours. That's just an incredible amount of— accounting work that has to be done that none of us could do at that scale. So very impressive, David. This is a real value to the community and to any institutional investor as well. There's very few people in the world who can actually do this. That's a very unique capability that New Constructs has. If you look at some of the fields inside of it, we're really looking at these, these one-time items, these things that companies do to to monkey with their earnings to try and meet estimates, because we know that if they miss, they're going to be penalized very severely. And if they just barely meet or beat, then they get a free pass and they get to go to the next quarter. There's a huge incentive to try and do that. So we've got nice detailed descriptions of exactly what all these fields mean. And then we also have created an earnings distortion index where if it's at 100, we know that on average the company's in the Russell 3000 are overstating earnings. And when it's at zero on average, they're understating earnings. So they actually just went through a period, and this is all using new constructs data that we created. This went through a period where they were actually understating their earnings in 2022 Q4 and, and '23 Q1, but they look like they're starting to get back to the middle of the road here. And so it'll be interesting to see how that plays out over the next few months. And of course, if you want any of these proprietary indexes and things like that, you can go to CloudQuant and email us at sales@cloudquant.com. We'll give you that information later. That's the dataset that you can get from CloudQuant as well. You can get the, the Earnings Distortion Index series if you want. That's another dataset that we have connected. We're gonna look at some scenarios where we look at Companies that have at least $100 million in market cap and they trade $1 million a day at least. We want to look at things that aren't penny stocks. We want them to have a stock price over 4. And so with that, we look at kind of a Russell 3000 index universe, but we pull out anything that doesn't fit. And then our trading frequency for all these research results that I'll share with you, They're gonna be trading on the open the day after we receive the data. These are all tradable things and we're executing on them the official market open price. So if you enter in market on open, that's the price you'd get. Other than that, we're doing long and short portfolios. So we have for every dollar long, we have $1 short. And we're trying to do that to show that this is not just getting long the market. This is actually adding value. We're really looking at is outperforming the market. Let's dive into it. We did a number of experiments, which I'll skip over here. What we're really trying to do is start out with something simple and build up to something more complex that might be what you would use if you're trying to come up with ideas to run your own portfolio or manage your own portfolio. So if we start with just the total earnings distortion, we actually do get a little bit of performance here. Up and to the right is what we want. For the all-cap, we've got a little bit here. But we're going to try and improve that by changing a few simple things. And then the small caps seem to have a little bit more edge. So this is looking at, at the top 10% going long and the bottom 10% going short. Taking the difference between those returns is what generates this curve. All right, so it's known as a long-short portfolio, and the small cap outperforms. If we say, well, let's be a little more specific. and granular on the small-cap names. Let's only trade the top 2% and bottom 2% of the score that we came up with. So CloudQuant did create a signal. We did come up with a signal based on new constructs underlying data, and these are the backtests from that signal. So we're looking at the top 2%, bottom 2%. We see that we get even higher performance when we look at the outliers, the companies that are really building up or really drawing down their, their reserves and their earnings. Cushion that they're, that they're either building up or drawing down. These are people who are doing extraordinary things to their earnings. What if we look at sectors where we don't really think this type of analysis will work? And so what if we eliminate those? Well, we can improve the results even a little bit more. That's the limited sectors in blue. Let's look at what if we lag our signals by 25 days? So this is a true test of like, is there something fundamental here? We lag the signals that we came up with from new constructs data 25 days, and that is the bottom one on the legend here. And you see how the performance is equivalent to if I traded it almost immediately after earnings came out. That's a really impressive thing, and it tells us that there's a huge amount of capacity for large investors, but there's also a lot of time for individual investors to do their research and think about these signals before they put the trades on. And so you can really add additional information above and beyond just what we came up with, with our signals to the analysis as a mosaic to come up with a better answer. And finally, we looked at some other things like biotechs and, and I think those were the main results across the board. You see this up into the right pattern that tells us that this is a very robust, very simple approach. with a single factor. A lot of people are looking at multiple factors at once. They're combining things together. In this particular case, we didn't even need to do that. We just took new constructs, did our magic, turned it into a signal. We ended up with something that seems pretty rational and to make sense. Let's look at recent performance. This thing's been on fire lately. I mean, look at this, this year, the market has bottomed. It started to rally. We did have a couple of Rough patches in '21, '22 in the small-cap area, which we know why that was. Everybody was selling small caps. Money was flowing out of that sector. There was pretty much no way to make money trading those things. Now that we've hit bottom, we're back to business as usual. We're seeing really great performance across the board. On the left, we have the very simple approach, and then further to the right, we did more and more detailed filtering. We're getting returns up to 70% in 2023. That's truly impressive and unique and definitely worth a look. We did do some more backtesting. This is just a very simple backtest using an equal weight performance or portfolio. We generate these standard reports with our backtesting platform. So that's another look of another strategy we came up with. Is this unique? We do a, a risk analysis. We try and decompose the returns to see if they're explained by any other factor we could invest in, in the market. And this result here is really telling us that there is nothing that we really could buy to replicate these returns. Nothing available that's tradable that we are aware of that you could use to actually get exposure to this kind of return. So if you want these kind of returns, you would have to buy the data. And that's basically what it boils down to. So here's a couple of takeaways. To recap, reported earnings is equal to the core earnings, which should be things that are repeatable. Those are the things that the company should be actually growing. What they report, well, that's the core earnings minus the total earnings distortion. We've got a great dataset here that's helping us look at and focus with our eyes on the prize and the core earnings. We've got coverage for the top 2,500 equities, 10 years of history. It's all point in time. And it's now being processed within 24 hours of the earnings report. We did show it was most effective in small caps, which is good because there's not a lot of information out there on small caps. It's really hard to get good research for small caps cuz nobody covers 'em. It's not as much investment banking going on. And, and then extreme quantiles have the highest edge. We showed that if you get further and further towards the tail of the distribution, of the scores that we came up with, that we can get much, much higher edge. It had a really tiny exposure to the traditional risk factors in smart beta ETFs. We really couldn't find much of a weighting on those, which shows that this is very novel and should be part of your decision-making process. Most effective in consumer staples, materials, industrials, and consumer discretionary. And we even showed that you could wait around for 25 days before you even put the trade on and still get the same results as if you traded right after the earnings reports come out. That's a summary of what we found. If you want more, we have a much longer report and our researchers would even visit with about our experience with it. If you just wanna reach out.

Thank you, Morgan. I know there's several questions for us to start working through. We'll go ahead and start teeing those up. But before we get to those, I was hoping that you could talk to us a little bit about who your customers are. Who are the consumers of this data?

We spend a lot of time working with firms who work with individual investors as a provider of data and data warehousing technology and research. The other large part of our business is working with tier 1 hedge funds and banks and researchers. We work with the largest firms in the world. We help provide data to them. We also help provide bespoke data and special signals that we develop in-house that we license to individual and institutional investors.

And what do you mean by alternate data?

Market data, which is also something that we have on the platform, is coming from the public equity markets. And fundamental data is Part of what New Constructs is using here, but they're doing a lot of enrichment to it to add, turn it into something that's much more useful. But fundamental data is essentially the financials for the company and any of the regulatory filings that they make about things that they're doing or special situations that they're engaged in. The fundamental datasets tend to be about financials and about earnings and about Things you need to know on the corporate level about the company. Market data's about how it trades in the market. Everything else is alternative data. It's things like if I've got weather data and I'm interested in agricultural stocks, maybe I have some farming REITs that I'm invested in and I wanna understand how they're likely to do this year based on the drought or something. Then you might look at a weather dataset to see what kind of exposure they have to some of these extreme weather events. Those are the types of datasets that, that we look at to try and explain both commodity and, and equity prices.

And for me, I think alternative data is weird data, non-traditional, outside of market data, stock prices and technical analysis stuff. And then fundamental data is the traditional legacy financial data that everybody has and has had forever. And that's been the standard. And we, I think, would qualify— Morgan, correct me if I'm wrong here— but New Constructs qualifies as alternative data, even though we're focused on fundamentals, because we're bringing a new and distinct version of fundamental data to the marketplace that Morgan is demonstrating provides an idiosyncratic alpha. And idiosyncratic means like you're not getting it from anywhere else. So technically it's alternative because it's not part of that mainstream traditional data set.

Yeah, correct.

Is there an order that we should be taking the questions in, Joe, of a preference?

No, I think go with what we have here in the chat and whatever else comes up.

Excellent. Shall we start from the top?

Using a long-short approach allows you to essentially have a measurement of your returns, excluding the systematic return from the market. Markets can go up and down every year. We're not as concerned about that. We're in this for the long run. We want to understand if year over year we are adding value both on the long and short side. Because one of the things you could do, for instance, if you know you're doing that, you could, for instance, buy these stocks and sell the S&P index and make money, right? So we haven't done that as a benchmark. What we do is more like what a hedge fund would do, which is we have things we're willing to buy and we have things we're willing to sell. And net-net, we want to make sure that we're making money both on the long and short side. Now, the short side, you're typically not going to make money in a normal market where it's going up, and on average the market does. But when the market goes down, you'll make maybe most of your money on the short side and lose a bunch of money, hopefully a little bit less. And in this case, it was on the long side. And so we look at long-short as being a way to be indifferent to the direction of the underlying market, and it's a better way of doing the analysis over the long run. Some of the sectors that don't work well, I think David probably knows more about that than I. Off the top of my head, I think financials didn't particularly stand out as being great. They tend to be difficult. We also did call out some, we'll go back here. We excluded biotech because that's a wild animal. If you're familiar with that space, you really have to use some alternative datasets to keep an eye on what's going on there. Really looking at pharmacy databases and FDA databases to really understand the chances of, you know, this phase 1 trial had a p-value of 0.01 and it's just barely above the threshold. So what's the likelihood of getting to phase 2 and things like that? So that's a really whole different animal. And it makes perfect sense that it would be Not sufficient to just have new constructs to be able to understand that. Yeah.

The sector stuff is really more about excluding companies where fundamentals just don't matter at all. And biotech is a great example. As Morgan was saying, it's all about whether or not it's going to, they're going to hire the right lobbyist to help them get through the FDA, whether the FDA is going to approve the drug. The current fundamentals give you no sense of what the future fundamentals might be like. And other places where that can be the case is exploration and production, small cap. oil and gas companies where they're digging for black gold. Similar sort of risk-reward trade-off. And then on the financials, the issue there isn't so much that it isn't relevant, is that there's just not a lot of earnings distortion in financials. I think the financial sector doesn't need much footnotes shenanigans because most people don't understand their income statement and balance sheet anyway. That was my experience when I was on Wall Street. That's a little bit of an overview on The sector front. The idea is we want to focus this strategy on companies and sectors where existing fundamentals are somewhat representative of the underlying business. And when that's not the case, it doesn't make sense to even try to apply this methodology to those kinds of stocks.

No, I agree. I agree completely. So, and I think that I see another question about which of these sectors is most effective and does that change over time? Not really. It doesn't really. Sectors will wax and wane based on institutional flow in and out of them. It's obviously a lot easier to make money for everyone when there's money flowing into a market, and it's a lot harder for everyone when there's money flowing out. So overall investment flows in and out of sectors will impact the, whether it's a target-rich environment or not. And why is that? Well, that's because these flows create inefficiencies. They, cause market impact. They move prices of stocks where they maybe shouldn't be relative to their peers. And it takes a little bit of time for the world to move those things back in line. And so there's a whole slew of hedge funds and other people who spend a lot of time using AI and other tools like we do. We don't trade, but they use the same tools we do. And they're looking for these mispricings and trying to get things back in line with each other. It's obviously a lot easier for any investor to look at a sector with money flowing into it. But the sectors that work for fundamental analysis with the New Constructs data are very consistent. And that's why we feel comfortable saying we know which ones to avoid and which ones to keep in the portfolio. And that's not going to change.

We just have 2, I'm hoping, quick questions that we can go through briefly. Do you still have some time to stay with us, Morgan?

I do.

Oh, awesome. Thank you. First, are you allowed to tell us who's using your method?

Uh, no, that's all confidential. We work with people to help find value in these datasets. So we can certainly work with people who have an interest in the dataset and we'll help you get it across the finish line.

Morgan did say tier 1 hedge funds, and then a lot of bigger firms that also have self-directed or individual client bases as well. I can say from experience here, these hedge funds and quant funds that are clients of Morgan's and clients of ours, They'll chop your head off if you tell anybody they're a client. They don't want anybody piggybacking on their diligence.

We're under NDAs with, with everyone. And we really can't name any names at all. That's good for everybody. It protects information, protects investors, their underlying investors as well.

And then last, can you explain the 2% spread?

Oh, that was just being much more selective, if you will, rather than saying, let's take the top 10 and bottom 10. What we found is Is it even a stronger signal if you go further towards the edge of the distribution? Think of it as a bell-shaped curve and we're going further to the edge of the curve on the top and bottom and, or left and right if you want to think about it that way. And so we were just really getting closer and closer to the edge of it. And when we did that, we found that there was even more opportunity there. And then I do see a question about the signal lag. I can address that real quick. We just paused making any investment decisions for 25 days. We got the information we needed. We knew we were going to go long or short, but we waited 25 days to actually execute the trade. And we showed that you could wait that long and still get the same great results, which gives you plenty of time to incorporate other information from other sources to even be more selective or more informed.

Morgan, that's always fascinated me. And can you go a little deeper on what does that mean? Does that mean that This footnotes data, nobody's even looking at it, or it's not being impounded in the market for at least 25 days?

Yeah, that's what it means. It means there might be some short-term funds that are looking at this and maybe they're executing their trades and getting in and out quickly, but there seems to be a big gap and opportunity in the market because somewhere in between a day or 2 and 25 days, it's going back to where it started and you're getting another bite at the apple, which is really unusual in the world. If you look at the Santa Fe Institute, they did a lot of stock market simulations back in the '80s and early '90s. They tried to create things that look like a regular market, but you use computers. It's called computational econometrics. One of the things they found is that when you, when you have strategies that work When you have agents that are doing things that are profitable, if they're underallocated, they make good returns. When more people start allocating more and more risk or more and more assets to that idea, and you get more and more people like that, like them, you end up actually destroying those returns. This is a case where that hasn't happened. The reason I can tell you this is because I can see the Sharpe ratio. I'm not going to get into the equations, but the Sortino ratio here is higher than the Sharpe ratio. That tells me my winners when I trade with the new constructs data are bigger than my losers. And that's very unusual unless you have a circumstance where nobody's paying attention to it and you don't have too many people chasing. It's not crowded. So you have a great opportunity here to get into something that's not crowded from our perspective quantitatively. And you have the ability to Come up with your own strategy, come up with your own ideas without feeling like you're the last person to the party.

And that's a big deal with the quants cuz they almost want you to say, you're only gonna sell it to us. Well, I mean, if you're gonna take me away from the entire market, well then you gotta pay me as much as I'd make if I went to the entire market. And they typically back off that, but they make a big deal about that, Morgan, right? That's, that's a big—

That's a huge part of our business is, is we find interesting things and then immediately one of the larger Institutional investors will say, okay, I want that exclusively, right?

So yeah, just for them.

I do want to make sure that we have time to get through just a few more things. I know we're headed out on time. Any last comments, Morgan or David, before I push us along?

Thank you, Morgan. That was great. It was awesome. And I think it's important for everybody to understand the data business. And I think what Morgan is doing is awesome work at CloudQuant. They've got a level of transparency and detail that I've not seen in any of the other partners or folks we work with. This is good for society. It's good for the markets. It's good for the integrity of the capital markets for people to package this stuff up and help people take advantage of increasing number of insights to make the market more efficient.

100%. Thanks, David, and everybody else.

Thank you, Morgan. All right, David, I'm going to hand it back over to you to talk about what's been going on this week.

Yeah. So Bloomberg's still making progress. Met with them this week in our regular meeting, and we are in the process of talking to issuers right now about licensing the index. So they finalized the methodology and we are looking to get on a big platform, which is sort of the big ticket. And what needs to be done in order for all of us to be able to put money into this index? We gotta find an issuer, and issuers include firms like BlackRock and iShares and First Trust and Invesco and smaller ones like PACER. And once that's done, then we've got a 75-day waiting period with the SEC, and then we can all start to put money into the True Earnings Index, which we think would be cool. And it's based on somewhat similar methodologies, not as sophisticated as what Morgan does, It's going to be long only, and that's a different animal. I will say that the long-short portfolio, those that actually ever really make money over time are really rare. It's a super high bar to generate positive returns over time when long-short. That's a different animal. This index, much more conservative. That's why it's an index. It's passive. We'll be long only, and we're hoping that there'll be a ticker and people can start putting money into that. In December, maybe a little bit earlier, but December we think is a pretty good timeframe. To the moon. All right. So for a little while ago, we were talking about Nvidia and it was going to the moon. And yesterday we had another stock that we were warning folks about that went to the moon. What's going on here? I mean, is the Fed losing control? I don't know if the Fed ever had control. I want to spend a second here on who benefits from the market going up. First of all, most everybody benefits. We all want the market to go up in the near term. Wall Street's able to sell a lot more stock when the market is healthy. Do we think Kava would've gone public or would've gone up 100% on the day it launched if the markets weren't buoyant and more optimistic and headed up?

No.

Do you see IPOs being issued and when markets are down? No, you don't. There's a lot of incentive, not just from Wall Street, but politically. Do you think anybody who's in office now wants to be on record as the person in office when we finally got inflation under control and rates had to go up? Maybe the inflation part under control, yes, but the pain that we'll have to feel when we take out all this loose money could cause markets to crash, and nobody wants that. This is a game of kick the can down the road. So let me take a second here and share my screen on just how overvalued things are. So we, we did some reverse DCF analysis, our favorite thing to do, on Kava. What we're showing here is they got to do about 20% growth for over 15 years. Let's just say 20% in 15 years to justify that price yesterday, while also improving the return on invested capital spread from -11 to positive 11. That positive 11 actually means that they're getting to— in the optimistic scenario, if we roll to the DCF review and look at return on invested capital, it means they're getting in year 16 to 24%. So super fast growth, 20% compounded annually effectively for 16 years. You can see that in '19, it's just— it's 20 to '19 drop-off there. with a 23.9% return on invested capital. So let's just put that into context. This company's going to grow super fast. Few companies in the history of the world have grown at 20% for more than a decade. That almost never happens regardless of profits. And then to also improve the return on invested capital at the same time is effectively impossible. And it's a joke that that's what's baked into the stock price. To get a sense of how high return on invested capital is, I used our dynamic data screener here and I went through and did a quick analysis on the restaurant sector. And so you can see that 23.9 for Cava to achieve is going to put them higher than everyone except for these sort of super small different types of businesses like a Domino's Pizza or Wingstop or Yum Brands. And Yum Brands, that's Taco Bell, isn't that right?

Kyle.

So there are a few firms that do that. It's a different product, but it's not— firms like McDonald's aren't even close to 23.9, and that's one of the best restaurant businesses. Starbucks, not even close. Chipotle, the one that people are comparing Kava to, not even close to 23.9. So the idea that, that Cava has— is priced to grow at 20% compounded annually for 15 years while improving its return on capital to a level not achieved by most restaurant companies. Kind of ridiculous. And I think this market in general is just getting more and more ridiculous, and it's a mania, and we strongly advise investors to stay away. I think we had some, some questions on Cava too. in our discussions, or do we just make a comment here? Oh, this is what Cody was pointing out. Yeah, earlier in the week that— well, he did this internally, but when it debuted, I think at $22, or that was the expected range, that was a higher price-to-sales revenue that Chipotle's ever had in the history of its valuation. So being more expensive than Chipotle has ever been at 22, much less 42 or 40. I think that speaks a lot to just how wacky things are with Cava. Then I wanted to spend a second on a couple other good posts we had this week. Number one, we had to say goodbye to Hunter. He's still with us. He's going to come on and do something, I think, for credit ratings in the, in the near future, but he's moving back to New Jersey, so he's going to be fully remote. He's been partially remote for a while. He's going to be fully remote going forward. And the $750 million club. This is a great post. I thought this was super cool because what we're seeing here is someone combining technical research with fundamental research. I think there's a great idea because most of you, especially our self-directed clients, you have great access, whether it's Schwab or TD or Interactive Brokers or E-Trade or Public or A lot of these firms have got great technical analysis, and you can do these kinds of screens very easily. The problem is that technical analysis by itself is, in my opinion, very dangerous, right? Because it pays no attention to the underlying fundamentals. And we've talked a lot about why fundamentals matter. And you can even see in Morgan's work, when you take out sectors that don't have fundamentals, that don't report fundamentals that reflect the future fundamentals like biotech, Nothing really works, right? So what we're seeing here is the application of technical analysis combined with fundamental. This chart is showing the list of stocks that met a cool technical screen that Jerry— sorry, I couldn't remember the name there— Jerry put together. And then he takes this list and he runs our attractive stocks list through this screen to get the best of the best. I think it's a great idea, right? If you really like technical research and it's worked well for you, then add our research as a layer to get the best of both worlds, the best of fundamental, the best of technical. That's a great way to go. I just want to give a hat tip to Jerry for that idea and taking advantage of our research and combining it with technical. We think it's a great idea.

We just have a few more minutes left, so I was hoping to hand it over to Kyle for the last bit here. For stock picks.

We just got 2 to feature again this week. Allbirds being the big moving down, down 5% in the past 5 days, 39% year to date, and actually 90, I think 93% when I put this together since we warned before their IPO. So we can see that IPOs can be very dangerous. Talking about that Kava situation there, that initial pop can be great for the early investors, but if you're jumping in at that high, it could be a long ride down. As we've seen Allbirds back in November 2021, that's when a lot of the big IPOs were happening. I think we were putting out, feels like an IPO report a week on, hey, this is another overvalued, unprofitable company. And so to see it moving down big this year and just big overall since the IPO is a sign of when rationality hits, rational thought, so to speak, hits. It could be pretty dangerous for portfolio.

Yeah, this Kava situation is just a redo of Allbirds, of Beyond Meat, of all these guys. And so many of them, some of them didn't open at that super high price. They opened at their closer to their IPO price, but they still skyrocketed way up. And that was, that just proved to be a disaster, an absolute disaster. I mean, 90, 95 plus percent of these stocks going down. And I think that's what we're going to see with Kava. If you've read any of our research, and there are free reports out there, and it's been covered pretty widely in the news, we are unequivocal about how dangerous that stock is. And we strongly caution investors to avoid that.

So people who are jumping in on that IPO, are they hoping to get that quick bump and then sell immediately? Or do you think people are actually thinking, oh, this is going to be good long term?

I think most of them, it's FOMO. It speaks to what Morgan talked about in terms of fund flows. So many institutions and quant firms and individuals are just looking for what's moving. It's like my friends that were some of the original day traders told me that they just like to jump on a trend and sell before it turns. Back then, their ideal trade took just a matter of seconds. And I think with fund flows, like what Morgan's talking about, an entire sector, People want to create those movements. And that's what some people have gotten in trouble for. The SAC Capital folks effectively were a little too good at leading investors or people into creating some momentum. False. They ride it for a little while and they jump off. And the thing that I want individual investors to understand about FOMO is that the institutional folks that are trading in the same way have a huge advantage on you. They know what's going to happen before it happens. They know when the momentum pushing that stock up is going to stop because they see order flow in that individual stock. They can tell when the ratio of buy to sell orders gets a lot lower because they're seeing it happen before it hits the market. So if you are a FOMO momentum investor, understand that you're at a big competitive disadvantage to the institutional investors. It's a dangerous strategy because again, You're investing purely based on momentum, not on underlying fundamentals, and that can be dangerous.

We'll go ahead one last slide, Kyle, and then we'll, we'll wrap it up.

Yeah. On the positive side, we've got Intel up 13% over the past week while the market was up 3%, and it's been actually up 34% year to date, more than doubling the market. That's a nice long stock performance there.

Yeah, we're full of good long ideas too. We're not just about warning folks. We've got a lot of good stuff. The one that came out this week is extremely interesting. And in fact, I need to make a note and encircle to thank one of our contributors there for giving us the idea to look into that one. That was super interesting, really cool, great opportunity in this crazy market. Not everyone can just be in cash or feel comfortable being in cash. We can give you places where you can invest that are safe, even in a market that has mania in certain parts of it.

Thank you, Morgan. Again, we really appreciate you taking the time to join us. Thanks to the audience. Thank you for your questions. And we'll see you guys soon.

Have a great weekend.

Thanks, everyone.