New Constructs' Live Podcast: Intelligent Capital Allocation - 12 January 2024

Thank you so much for joining us today. This is New Constructs Live Podcast, Intelligent Capital Allocation. Today is Friday, January 12th. You have the usual suspects with you here today, myself, Hakan, Kyle, and of course, David Trainer. Please review our disclosures and disclaimers. And our agenda for today, I did want to make sure— I know that this is something that we talked about in December, but not everybody was around in December. Intelligent Capital Allocation is moving to monthly in 2024. All of those should be up on your calendar for at least the first half of the year. If you have any questions about registering for those, or if they drop off your calendar for some reason, you can always email me. At support. One of the reasons that we are doing intelligent capital allocation a little bit less in 2024 is that we want to make sure that we're really able to focus on extensive amounts of training this year. We just put out Stock Tracker 50 in November. We want to make sure that we have time to ensure that we have office hours for the Stock Tracker 50 folks so that they can call in and ask whatever questions they want. Those are going to go up on the schedule shortly, and you have probably already seen the private trainings that David has been doing for our professional and institutional folks. Those will continue. If you have feedback as a professional or institutional member about the topics that you want to make sure that we are hitting this year, don't hesitate to let me know. I'm building out that calendar as we speak, and we very much want to make this year the year that you are getting every bit of value that you possibly can out of the new constructs tools. As usual, you can get replays and slides on Society. And if you are not a member of Society, please do consider joining. That is where we are going to be pushing a lot of the information that you can get and lots of interaction with the analysts, unless it's filing season, of course, and then they are Busy guys. Latest and forthcoming research. We haven't been together in a minute, so there is a lot here. There's a couple weeks, the last week of December, that we never got to discuss, so you might pop out to the research portion of the website and check out what happened in late December if you haven't lately. First week in January, safest dividend yields, dividend growth stocks, Most Dangerous, Most Attractive all came out from Model Portfolios, as well as parsing stats for the 3rd quarter of '23, which is always an amazing bit of numbers to look through. This current week that we're on, a great Danger Zone podcast as usual. Lovely to see those back happening in 2024. I love those. And earnings beat and miss came out as well. Model Model portfolio performance came out for long and short and long-short. So those are really interesting reads to review, as well as a couple of model portfolios. The Exec Comp aligned with ROIC came out, and then the featured stock for most attractive, most dangerous came out. And of course, another fantastic long idea from Italo that the team has been working hard on. part of the ongoing energy theme. So you'll want to check that out for sure.

Yeah, I was just going to jump in. Tam's got it noted there, but the earnings miss and beat are really timely as earnings season kicked off this morning, pretty much with the banks. And we've got the stocks most likely to miss that's actually available to everyone. And then the stocks most likely to beat is available to pro and higher members. And both of those look at 5 stocks in the S&P 500 with overstated earnings on the miss side and understated earnings on the beat side, and look at some of our earnings distortion score metrics and say, look at who's going to be more likely to miss and beat upcoming earnings. So that's something to check out in the next coming days as we move into earnings season. And then also on the short pick mentioned there, we put out 2 reports about existing focus list stocks. Available to Pro and higher members on one that performed really well last year as a short, one that performed really badly as a short, and why they still are both overvalued and could fall a lot further. So definitely check those out.

Now, I wanted to ask you a question about earnings distortion. That is one of the more special things that New Constructs does. Could you say a few words more about the earnings distortion work?

Yeah, so it is an additional layer to our ratings. We've got our overall risk-reward rating, which is more the long-term, takes into earnings valuation. Now, earnings distortion score, which is more short-term, it looks at the earnings distortion with a specific company, whether their earnings are over or understated, and gives a quick and easy rating on the likelihood to beat the next quarter's earnings. We've got, you know, strong miss, strong beat, and levels in between there. So it's a much more short-term Rating on earnings.

That's very cool work. Anything else on the research, guys, before I move on?

I think that's it.

Okay, cool. We can always go back to it. I wanted to mention Stock Tracker 50. I know I already talked about it. If you are new around here and you would like to get to know New Constructs a little bit more, in addition to joining Society, you might consider also getting a Stock Tracker 50 membership. This is our most economical membership. It's a great way to get to know the ratings. You can get a month to month membership. We have no long-term contracts that lock you in. If you have any questions about the best way for you to get to know New Constructs, again, don't hesitate to let me know, support@newconstructs.com. I'm here to facilitate that for you. I'm going to hop out to society next because, as you all know, we drive our agenda from society. And so it is looking at society that helps us understand what you guys care about and what you guys want us to be talking about. Before I dive into the particulars, I did want to point out this new professional and institutional members-only area. If you are a professional member or an institutional member, we will be posting training here. This is a great spot for you to be able to discuss ratings in deep detail, If you want to have conversations about proprietary new constructs, ratings, or data or research, this is a great place to be able to dive deep in there. And again, our analysts will be there to have those deeper discussions with you. We're really excited about this space and being able to partner more closely with our professional and institutional members this year. Before I hand it over to David to talk more about what's been going on in society, I wanted to point out 2 interesting AI pieces that came out lately. One is the All In podcast. I'm not sure if you guys are aware of the All In podcast. It's well respected and they have amazing guests on. And I just wanted to play this 30-second clip. It's really fast because they basically talk about what New Constructs does. And it's just fascinating to see folks out there talking about this.

Today, there's not enough research coverage. There's not enough analysts. There's no organized way to keep companies held accountable. And so if you have a bunch more companies that go public without that infrastructure, it's very difficult. Seems like there's an opportunity somewhere in there. You know, the big opportunity that I, that I, that I think should happen is that a bunch of these banks should actually use these AI tools, Jason, to be crawling all these 8-Ks and 10-Ks and all these filings and generate statistical measurements of all of these public companies that they make free and available to everybody. These things should be a bunch of dashboards and charts that are just out there for anybody to subscribe to so that you can just do the initial work of having to parse through all of these filings, have it done by an AI, by an agent from a bank who can train it properly. I think that would be an immense value to all shareholders.

And I think one of the keywords there is train it properly because you can't just have any language model out there look at financial data and get it right. Just ask David. David knows. I did want to play this quick clip that is David responding to this exact issue. Again, this one's really brief. And then David, I would love to get your input on what we're What's going on in this space?

It gets better. You know, you just got better tools. But to Mark's point, at the end of the day, it's really all about the data and the quality of training data you can give the AI. You know, it's not any different from the— from, you know, before AI, we just called them models. And the old saying about models was garbage in, garbage out. And so it's really the companies that have the best training data are gonna be the ones that make— can make the most of the large language models. Those models themselves are not necessarily that much of that much unique value. At the end of the day, it's the quality of the inputs. It's the detail and accuracy of the training data that's going to distinguish one AI model from another.

Yeah, and definitely the potential.

So David, I'm going to hand it over to you now to talk about society. But before you jump in, could you talk a little bit more about It's a brave new world out there for AI, but it also seems like it's same old, same old.

Yeah. And we're, we're seeing a lot. The, the Chamath thing, the Schwab thing. We're seeing big lawsuits with the New York Times against ChatGPT. And that's, that's what that Schwab interview begins with. And nobody knows what's going on with AI. It's a big unknown. We see movies about how it takes over Skynet and Terminator is gonna kill everybody and we're gonna, be sent back to the Dark Ages or, or fear for our lives and be exterminated as a race altogether. And then we've got stories about how AI can't answer very simple questions about lots of things, including SEC filings. So there's a huge gamut of, of good AI and bad AI. There was a, a great CNBC article we wrote on this topic recently around the challenges with, with AI. And it was around this, this, this interview. But there's, there's a new firm out there, which is really cool called Patronus AI. And they actually created this thing called Finance Bay, which is designed really to ferret out which AI works and which AI doesn't. Because one of the biggest things we gotta figure out these days is Which AIs can we rely on? Not all AI is good. So how do we tell the difference between good AI and bad AI? And that's really what we bought. We, that's what our AI is sort of designed to do for stocks. And at the end of the day, when we're reading all these headlines from the study that have been featured in Fortune about how ChatGPT and other AI chatbots couldn't understand SEC filings, they failed about 70% of the time. Here's from CNBC: ChatGPT and other AI models can't analyze an SEC filing. SEC filings are so complicated even AI is baffled. These are all things we've known for a long time, and this is why 20 years ago I created a technology to really meticulously collect and classify data from the filings according to our proprietary ontology and technology, because I, I knew, having built these models and been on Wall Street for a long time, that there's no way to do this work without somebody with expertise going in and very discreetly making the distinctions between accounting line items that look the same but mean different things, accounting line items that look differently but mean the same thing. There is simply no substitute for someone doing that work. And you got to do it at a huge amount of scale and lots and lots of filings. If you ever wanna be able to have enough data to inform an AI machine learning, a natural language processing tool, a large language model, all these things, whatever you wanna call it, they're all looking to do the same thing. And that is to do the work for the humans. But these machines can't figure out what to do unless an expert tells it what to do. We haven't seen code write itself. You know, we can see machines replicate certain types of code that have been written before on simple things. But that's not code writing itself. There's this thing, what I like to call the virtuoso effect, which is this idea that there's certain types of human creativity, expertise, and capabilities, output, whatever you wanna call it, that machines are, are maybe never going to replicate. For sure, an AI can go read a million articles on the internet or a million stories or watch a million movies and come up with a script. It can take parts of, of previously written scripts or movies or books or whatever and create a new book, something we haven't seen before. But it's only different in that it's a unique combination of stuff that's already been out there. And that's what this suit with the New York Times and ChatGPT is all about. It's like, you're just plagiarizing us. And you've heard a lot of the experts out there call ChatGPT a big plagiarism machine. This is all my long-winded way of saying the, the, the, the, the real path to AI adding value is built on lots and lots of human work, experts informing the AI as to what to look at and what, and, and what to interpret and how to make it work. There is no substitute for that. There's nothing that's gonna allow the machine to figure it out on its own. Any of you who've done any kind of coding or ever done any kind of programming or engineering know very well that the machines don't figure anything out. They just do what they're told. And that's, that's, that's what makes New Construct special, and that we've been telling the machine what to do with filings for 20 years, and we've built a huge database of training data. It's all about the training data, and we have proof that it works. Proof that the data itself is better from Harvard Business School and MIT Sloan in that paper that got published in the Journal of Financial Economics. Proof that our models are better from Ernst Young. And proof that our stock ratings are better from Harvard Business School. You've heard me talk about that many, many times before. I thought it was good to circle it all back and, and bring it all back to why does it matter? What does AI mean? And how does, how does new constructs really been a cutting-edge AI for a long time? So back to Circle. And then any questions about AI or anything like that, please feel free to jump in here. Would love to entertain what you all have to say. Otherwise, I'm gonna jump into some of the cool stuff we've talked about on Circle this week.

Thank you, David, for mentioning that. Please do, folks, use that Q&A tool down at the bottom of the Zoom screen to ask questions. We absolutely want to get to your questions. So, so don't hesitate to pop in there and let us know questions or comments.

So I'm not going to go in any particular order, but I will start with one because it's— I mean, to me, this is just fun. Hertz selling one-third of its electric vehicle fleet. I mean, that's one of the most bearish things I've seen supporting our Tesla thesis for like 5 years now. And the fact they're actually going to Use the proceeds to buy some gas-powered cars. Whoa. I mean, that's a major shift. That's saying the gas-powered car is better. And so look, we all know that there's some really, really nice Teslas. There's some really nice Lucids. I mean, you've seen one of those on the road. Those are super cool looking. There's some really nice Mercedes EVs. There's some really nice, all kinds of electric vehicles. Most of those are out of reach for the mass market. What this thing with Hertz really points to is how the mass market is probably not interested or ready in a fancy EV. I've got some friends who travel a lot for their kids' sports, and they told me some nightmare stories about how they were given a Tesla one time because they just weren't in the practice of like having to get things charged. And it was difficult to learn how to drive, but it was a real— it was really tough because a lot of times you're going from hotels and this or that, and you got to be at a certain place at a certain time. All of a sudden your battery runs low and you don't know where to get a charge in some new city, that's a problem. That's a real problem. And, and the amount of time it takes, you can't just stop and fill it up anyway. Really major signal here that the writing is on the wall for Tesla in the same way we've said it's been on the wall for, for, for, I don't know, Kyle, how many years? 5 years?

I think it's been longer than that, but first Tesla piece might have been 2014, 2015.

Good grief, that's a long time, guys.

Yeah, and, and pretty much everything we've said has come true, including the fact that the stock can, can remain disconnected from economic reality for a long time.

I think this Hertz news is a reminder of all the roadblocks and the problems the EV space has faced, even if it's not for every car. The self-driving issues That's just another reminder of what could come and makes me really wonder if the market could actually grow at the rates that they're forecasting right now. 16%, 18% compounded annually for the next decade seems even less likely with every news, every negative news that comes out just like this.

Yeah, I wanted to take a quick second to answer a question about 2024 investing themes. And do we cover ETFs and mutual funds or only stocks? Yes, absolutely. We cover ETFs and mutual funds. You can look, type in a ticker, or you can even go to it and you get it. You get the same kind of data. You get a PDF report that's really good and detailed on where the stocks in that whole, in that portfolio are allocated. And we've got a screener for ETFs and mutual funds. In fact, probably one of our most powerful tools is our screening. And these 2, the stock screener and the ETF and mutual fund screener, work the same way except one's for stocks and one's for ETFs and mutual funds. But as an example, if you wanted to go in and say, listen, I want to know the best ETFs and mutual funds in technology, go. There's 4. These are the ones we would suggest you, you look at closely. These are the only ones that get a very attractive rating. And if you want to understand why it's very attractive, well, here's our rating. And by the way, this is what— when you combine all the stocks in that mutual fund, this is what they look like. So if we were to aggregate the financial statements of all the companies in the Topaz Technology Fund, This is what the risk-reward rating would look like. Then we give the fund a score on asset allocation. We ding funds that have a bunch of money in cash because we don't think you should be paying fees for someone to keep your money in cash. And then we also grade people on total annual cost, which takes into account all the costs of being in the fund. We have some of that cost. We have cost details in here. I thought we did.

There's a tab above that first chart, right below the table.

Oh yeah, boom.

There you go.

Yeah, all costs. So front-end load, expense ratio, back-end load, redemption fee, transaction costs. Transaction costs are important because all those transaction costs get passed directly to fund holders. So we're the only firm that I know of that has ever taken into account all elements of cost to give you a true, what we call, total annual cost. And, and it's just part of the New Constructs way of giving people access to the whole truth.

Uh, and so anyways, I know this is part of our professional membership. Would you also show the model portfolios for ETFs?

Yeah, great.

And mutual funds, because that's a great way to access similar data, because the professional membership for some folks might not be something that is within their budget. But these model portfolios are incredibly well priced.

Yeah, absolutely. So we do publish some, a lot of great research on ETFs and mutual funds. And one of those is every quarter, and we're due to do this again pretty soon. We will give you ratings on updated ratings and the best and worst on all sectors. And what this gives you is a list of In the consumer cyclical sector, the best and worst, the top 5 ETFs, the worst ETFs, and the top 5 mutual funds and the worst mutual funds. And these can be found here on this page. You can go in and get fund model portfolios, and you can go and sign up for the best and worst by sector and the best and worst by style. Choose which one. And these are really inexpensive at $9.99 a quarter. They're updated quarterly, which is super cool. Great point, Tam. Glad you reminded me to take a look at that.

The ETF and mutual fund model portfolios are some of the coolest things that New Constructs does, in my opinion.

Yeah, pardon me for bragging here, but I do think the ETF and mutual fund research is, is the best in the marketplace. Our stock ratings work really well, but if you really want to go in and say, I want the best of large-cap growth, go. There's one. We're, we're very discerning at New Constructs. We're not going to tell you something that's good just because we need to sell it to you. And to the extent that you believe that our stock ratings are as good as Harvard Business School says they are, well then you got to think that our, our ETF and mutual fund ratings got to be really good too. Because the ETF and mutual fund ratings are effectively just an aggregation, a consolidation of all the individual stock ratings. That's impressive. I think it's super impressive. I really love how we show folks the comparison between the allocations and the rating history compared to the fund performance details on the funds. Some pretty cool stuff there. But really getting a real sense of how well the fund is allocating compared to its benchmark to good and bad stocks. And you'll see here, the reason this fund gets an attractive, very attractive rating is that it just does a lot better job of allocating to very attractive, attractive, neutral, and a lot less to unattractive and very unattractive. That's the key. And I think this is, this is an issue with our data provider. I don't think the fund ever went to zero. It's probably been really flat the whole time. The data on ETFs and mutual funds is a little bit tougher to get, but the underlying company data around the holdings remains best in the business and always will be as long as we're in the business.

We had another comment from Charles saying that even the best ETFs and mutual funds sometimes have neutral or not attractive stocks inside them, and he really preferred to DIY his ETF or mutual fund. And not to get this into another feature training, but there is that feature in the portfolio mechanism. So if you go into portfolios, you can pull together a DIY ETF based on the things that you want in your portfolio.

Would it be a bad idea to show how to do that right now?

I think it's, yeah, we can do it quick. I think that's a great idea.

So yeah, let's say we want very attractive stocks in technology and we want, we focus more on software. Go. And then we can say, okay, there's these stocks and we're going to create the January 12th show portfolio. And we could replicate that exercise across multiple sectors. We could do healthcare and we want to go with pharmaceuticals. Very attractive. Add those to the January 12th portfolio. And we could keep doing this for as long as we wanted. And, and, but the really cool thing to do exactly what Charles was saying is now once we have this portfolio, we can add to it. Let's say what I really want, Microsoft. It's just one of my favorites. And I really like some of these names that New Constructs has come up in the renew and rebuild thesis. Anyway, keep doing that. And then the— but the real, the real money capability here, the real takeaway is this do-it-yourself ETF to do exactly what Mr. Thompson suggests. And that's where you can come in and say, okay, I'm going to create a portfolio based on this, and you can customize it based on how much money you have. So you want to put $5,000 in there. This will automatically create your order count. Or your share count for the order. And you can weight these not based on market cap or equal weighting or the legacy ways of weighting things. You can weight it based on return on invested capital. And now you got a portfolio that's built around return on invested capital, and you can, you can see how your portfolio compares to the benchmark. A lot of benchmarks, or anyone you want to look at against large-cap stocks or small-cap stocks. Either way, this is a much better looking portfolio. Microsoft's the only one that's got the unattractive rating, but you get the sense this one looks really good. We take Microsoft out, it's all green. Look at that, beautiful. And then you click this button and you get an order form. You can in turn, if you wanted— we're not advising anyone to do this, we don't give advice, we're just a research firm— but this would be something you could use to create an order form and whatever trading app you have, or send to your Sent to your broker. And by the way, these portfolios could also include, if you wanted, a little bit of an ETF. Let's say you wanted to put a sector ETF in there as well, and you wanted to create that. You wanted to do it based on ROIC. We have an ROIC for the XLE. It gets a big weight, nice weighting. And so your order form would now also include an ETF. Anyway, to Charles' point, no reason to pay anybody to bundle stocks for you. All right.

Thank you, David. There is another question before we move off ETFs. Could you talk about how to interpret the gap numbers for ETFs? The example is DGRO as very attractive, but the gap is 42. I know this happens in stocks as well where you end up with a very attractive, but there's some oddness, Yeah.

So one of the things to keep in mind is that the ETF and mutual fund ratings methodology is a little bit different because if we just rated the ETFs and mutual funds purely on the constituents of the stocks, what we see is almost all of them are neutral. There's this huge averaging effect. And so you get almost nothing in very attractive and A lot more and very unattractive, but you have, you don't have a very even dispersion. So when we created the ETF and mutual fund methodology, we decided that we wanted it to basically go in and do something we didn't do with stocks. But we break up the universe based on top 10 is very attractive. We rate them all, we rate them all, we rank them all, and only the top 10 get to be very attractive. The next 20 is attractive, the next 40 is neutral, et cetera, et cetera. So you're going to get sometimes some ETFs and mutual funds whose constituent ratings here on the stock side would, would, would not be good enough to actually merit a very attractive rating. And that's the case for DGRO. If these— if a stock had these criteria ratings, it would not get a very attractive. It would be attractive or maybe even neutral, not sure. So we— so that's, that's one of the reasons there's a difference here. And it is possible for a stock rating to be attractive or somewhat, even if the market implied gap is, is, is unattractive. And what, what you're going to see there is, is probably a situation where the company's current NOPAT is possibly really high on a really high trend. And so our algorithm that in our default scenario that's projecting The estimating the future margins is a little bit lower than where the company's current margin is. And so with that lower, more conservative margin, it just takes longer to grow the business enough to create the profits required to justify the stock price. And that typically is when we see margins really, really steep because our algorithm's going to take a 3-year average. Now we weight the average based on more recent data being more important over that 3-year timeframe. But still, a super high increase could create an average that's a little bit below or a good bit below, and therefore the market implied gap looks a little less sanguine than the other stats. But that's a great question. There's another question from Anil about the— could we put all those model portfolios into one product? The answer there, Tam, is we just showed that a second ago, right? There is a product on the site now where you can just get— and that's the link we put into the chat. So I guess you sent that to Aneel probably in the reply, right?

Yeah.

So you can buy those by the one, but if you should also consider the professional membership, I mean, that's one of the huge benefits of the professional membership is just readily access to all of that research that you can DIY the model portfolios that you prefer as well.

Yeah, one question we got was around do we fine-tune the research for clients? We do not.

We do not.

That is not part of what we do. We publish certain research. We give people the information. You can certainly go into our website and choose the data you want, but we do not provide any customized or tailored advice. That is not at all what we do. That's something that advisors do or Consultants, we recommend that you talk to somebody like that to get that kind of information. So let's talk about the BTF, the Bitcoin ETF. Yeah, good luck. I don't, I don't really know what to think about this. You know, Bitcoin has been a really interesting phenomenon around the last ten years, and it's, it's certainly been a really interesting journey. Robbie brings up a really interesting detail around how. The total gold market capitalization was around $1 to $2 trillion before the gold ETF was approved, and then, and then it went to $16 trillion. I thought we had some good, good comments here about how that was also aligned with zero interest rate policy, so that might have been part of it. But really interesting, some great posts here. Love the conversation. This stuff's fun to talk about. And then Nvidia, I've never seen anything like it. Yeah, of course we agree with you. Julio, this is yeah irrational exuberance at its best. And we've done some great reverse DCF case studies in this show on Nvidia, and I recommend everybody take a look at those. Those are on the YouTube channel if you want to see those reverse DCF case studies. Toast. So this was a really cool post by Panaki, who on a regular basis. Provide some really thoughtful comments and ideas and posts in general. And look, this is a point-of-sale technology and hardware that is more popular in restaurants. And I don't know about you, but every time I go to a restaurant and I have a waiter or waitress who is typing my order into a handheld device that's gonna go straight to the kitchen, I love that. I love it. I think they're more likely to get it right. I love the fact that I don't have to worry about them forgetting to put my order in. The fact that it's straight to the kitchen. I love the fact that it's probably more likely to be accurate. I think it's great. I, I've even enjoyed using those, those kinds of points of those tech pieces of technology at the restaurant without a waiter or waitress or with both. It's great. The waiters and waitresses can be important middle people. And, and help out, but you don't always need it. A lot of times slows things down for your faster casual kind of restaurants. I mean, these days you just go in and you place your order, you walk in, it's like at the grocery store except in reverse, right? It's all sort of fast food oriented. This is an important technology and, and we're seeing it around more and more. I think the one thing I would be, be, be mindful of is that we see a lot of these. We saw it first with, with, with Block or Square. And Kyle and I have actually talked about some of these stocks around whether or not they're potentially overvalued because we think that there's multiple technologies— Square, Clover, Toast. And the real question here is whether or not it's going to be commoditized pretty soon. I don't know. I don't know how special that technology is.

Yeah, Toast has been interesting because it, from the research and reporting side, it pops up in a lot of danger zone screens, but with the overhang of does it get commoditized? Does it get acquired? It's not something that we can really nail down a catalyst on it. And so it looks bad right now. There's a chance they get that mass market share, improved profits, but that's, you're betting on a lot there in a market where I think on Toast's own website, they list 25 different POS systems that they're competing with. Obviously they think they're the best, but when there's that many out there, it creates lots of, you know, price wars and race to zero almost.

Yeah. Yeah. And if Block with all that market cap's got a lot of capital put to work, put to work. Here's another great post, Cole Bush from last week, How to Consistently Beat the Market. And I don't know if you guys have heard of Unusual Whales. Super cool. So is Quiver Quantitative. They track congressional trading activity. And if you're not familiar with this, you should be, because this is a really pretty fascinating service and tool. And they're on a regular basis highlighting the trades that congressional members are making based on what, what really looks a lot like front-running. I think there's no question there's a 60 Minutes special on how often the congresspeople are acting on inside information based on the information they get from security meetings, from committee meetings. It was so bad that after 60 Minutes did the exposé, they made it illegal to do what they did. And then 6 months later, they snuck it back in after the headlines had subsided. All we can do is support firms like Unusual Whales and Quiver Quantitative who are bringing transparency into the markets and And pay attention.

I mean, I theoretically knew that this was happening, but reading through the Unusual Whales, it's really specific. Shocking.

Yeah, it's very shocking. It's a pretty fun follow on Twitter, both Quiver Quantitative and, and Unusual Whales, because they're usually pointing out very specific trades and look, the performance of these folks is Been really impressive.

You don't want the folks that are up at the top of the list, but maybe you also don't want the folks that are down at the bottom of the list either.

Yeah. The bottom line is that they get inside information and they trade on it, and that's not cool. Okay. And we did another great long idea this week. I know Tam mentioned that, but the, the long idea, danger zone ideas are deep dives. I can't say enough about Honestly, the quality of that work, and I, and I guess to steal some thunder here, it pays off, which is why we're number one ranked here every month for the last coming up on 3 years for stock picking. We're doing the work. And I, one of the other really cool things about these long ideas and danger zone ideas is they are also case studies, miniature case studies on how to get the most out of our research, how to use our tools. So PRO members that get access to that, every time they read one of those reports, not only are they getting some of the best research in the world, but they're also getting a little tutorial on how to replicate that, how to do this kind of work on their own and find even more ideas.

Agreed. Agreed. All right, folks, we're going to wrap it up there. Thank you so much for joining us. We will see you again in February. Hope 2024 treats you incredibly well. And we look forward to seeing you out on Society very shortly. Have a good weekend. Thank you.