Ryan Pannell, Kaiju

At the time. And that was a low to high mean reversion strategy. So that's a geeky way of saying a dip buying strategy, right? It's looking for artificially oversold stocks. And we turned that into an ETF last December, launched it on the New York Stock Exchange, ticker is DIP, D-I-P. And that came down to the floor, one of only six ETFs on the New York Stock Exchange floor a couple of months ago. Citadel helped us bring it down. So that was a big day for us. And it's been great so far. Uh, it's, it's nice to watch something that you've spent a long time building, uh, successfully operate. Um, I'm sure as like on the private fund side, there's a, there's a lot of

Secret sauce there and a lot of undisclosed performance outside, depending on your offering memorandum, depending on what you're allowed to share or not share. So it's nice to have a, an ETF that's publicly accessible. Performance is publicly viewable. I think we're some somewhere around 12%, 12%, plus 12% in the last 90 days. So that's been good for us. And, uh, we're just looking forward to trying to make these type of AI curated and directed, uh, trading systems more accessible because today, what you've really seen these in high-end hedge funds, which your average investor is never going to get access to. So this is a way to sort of bring it to a broader investment base and hopefully do some good with it. Yeah. So it's interesting. Um, you being the sixth fund of the floor, we were

Actually the fifth. Um, so I was actually looking at your, given the nature of what I, I, you guys do, I was looking at your, premium discounts and it looks like it's really done wonders for you guys in terms of keeping adherence to NAV pretty, pretty strong. And that's the experience we've got. And it seems like you've got the same experience as well. Yeah, that's for sure. nothing against being, uh, being, um, up on Arca, that, that was a good place for us to start. And the nicey guys were super assistive in getting us up there. We were really grateful to, uh, GTS, who is our LMM when we, when we launched helping us with that, but with something that's,

Has a, has a lower AUM and is thankfully trading daily. But, when you're just getting started, there's not a ton of volume there. And when you get spreads that are eight, 10, 12 cents and retail traders coming in with market orders, they can get some disadvantageous fills. And, our concern was that they may not understand why they were getting disadvantageous fills. they get filled up at the offer or especially five minutes from open. It's a big blowout and spread. They get, uh, an erroneous match and they're like, your, your ETF is 26 bucks and they get filled at 32.05. And you're like, Oh my God, this poor guy.

Like, can we, can we find out who it was? Can we do anything? Um, and, uh, and, and really coming down to the floor with Citadel's, tighten that up to like two, three cents, for the, for the trading day. So we can be confident that the investor is going to get, a more appropriate fill relative to NAV. Yeah. And that's what they were trying to accomplish. And they did a good job at accomplishing that. We had a similar experience with, uh, an advisor putting market orders in and closing auction, um, over the counter. So where do you think they were going to get filled at? And so, you feel bad for, um, them and then you feel bad for, your actual investors who are trying to get a clean close, but I digress.

It's good that you're down there. I think we're going to see a lot more of that, but I always like to ask too, before we get too deep into it, what other hobbies you have when you're not working? What are some of the things you like to do before you and I were on the call? You, you mentioned this was an old ski room of yours. You a skier, a snowboarder? I do snowboard. Yes. I'm a terrible skier. Uh, it was, I, I, I started as a skateboarder and then a surfer. So snowboarding for me was the, the, the logical progression still. Um, this is the right now I'm on the West coast of Canada in Whistler, usually here for the summer. And at Christmas, normally I'm based out of Geneva

And, uh, here in Whistler, it's maybe a 60, 40 skier snowboarder split in the Alps. It's more like 90, 10. So I still have like those pariah moments where you end up on a, on a run that definitely was not designed for a snowboarder, but, um, yeah, so there, I do that in the winter. Um, I'm a, I'm a big ocean conservationist and, uh, heavily involved in, in, in philanthropic initiatives to do with ocean conservancy. So I'm a free diver, master scuba diver, um, sailor love I'm happiest, on, in or under the water.

Yeah. So that's, that's kind of my thing. And I added, I added helicopter piloting last year, but I'm not very good at that. So yeah, let's maybe just keep practicing that on simulator until you get a little better at it. You know? Yeah. I like to say, like if it's zombie apocalypse, like I'm your guy, I can definitely get us out of there roughly. But if you, if you wanted to, if you wanted to grab the family and go for a little cruise over the Alps, I, I, there, there are much better choices, helicopter pilots than me. That's great. So let's talk about the firm before we kind of get into process and product. Um, do you guys offer any other services, um,

To clients other than just the ETF? Is there any other advisory services you guys, uh, work on or things that you do on the RIA side? No. Uh, so we just, we just create and then launch, uh, AI curated and directed ETFs. And we have several more in the pipe. Um, dip was just our, our most accessible test. Um, and on the private fund side, we do manage, um, four funds through, uh, Kaiju capital management. Kaiju ETF advisors is the, is the SEC registered RIA that deals with the ETFs and, and Kaiju capital management is our, is our, uh, BVI based, uh, approved manager, which deals with private funds. So we do have, uh, private funds. They're all fully subscribed at this time. We don't

Really have a much of a plan to add an additional fund. We have a new one coming out, but it started its life fully subscribed. So we'll see what happens with that. We talked previously about maybe offering some consultative services out to other managers interested in getting into artificial intelligence. But to be perfectly honest, I really don't, I don't see how that would work given our, our, our, our capacity right now. We're just, we're, we're at or above capacity all the time. And there's, there's, there's private fund initiatives in the pipe, there's ETFs in the pipe. Um, and that, that sort of takes up all our time. I wish, I wish I could say that there were other opportunities, but currently none available. Well, that's, that that's, there, there are a lot of

Opportunities already there and in the pipe. And so while we, while we talk about AI, um, and you're right, there aren't a whole lot of offerings in the space. I know we're going to see, like we saw with, with, uh, crypto and Bitcoin, everybody trying to come out with an AI product, but you've been in it for a very long time before we kind of get in the investment management side of AI. Cause I'm, I am interested in hearing some of your opinions there. Um, you and I talked to a lot of advisors and just wanted to get kind of your opinion on where you think, um, disruption is going to, how it's going to affect their business, right. And the advice

And recommendation business, or if at all, um, I think it's important to have a person there, but it's hard to ignore, um, what's coming down, uh, in terms of AI's capabilities and how it might affect that community, uh, rather than just ours. Yeah. I, I, I don't think it represents quite the same level of doom and gloom that, that others have posited recently. Like you said, there needs, I think broadly there needs to be a person there, right? Like, like people don't articulate their, um, risk parameters, their investment objectives, um, very well to a machine.

Like, if, if it was going to be automated, it would start with some sort of intensive, um, AI calibrated questionnaire that you would fill out. Right. But there needs to be some level of challenge. I think as any, um, RIA would tell you, or any FA would tell you like in meeting with a client and understanding what their objectives are, what they say, what they need, what they can tolerate, um, what they express, they can tolerate and what they actually can are often very different things. And that's a, that's a process that where, where the RIA or the FA's experience really comes into play. you need to just, because somebody says, well, this is what I've gotten. I, I don't, I want high risk and high return and I don't care if I lose it all.

There needs to be a deep, deeper dive into the impact of that decision, right? They, they might say they don't care whether they lose it all, but okay. In addition to this, what's your capital reserve? What kind of debt are you carrying currently? Like you might, you might not even be in a position to make this investment to start with, but nobody's told you that. So AI can't do any of that. It's terrible at context. It doesn't understand nuance. It's really great at, at smashing numbers. Um, so, so I don't see it. I don't see it sort of taking that business away. I see it being a tool that RIAs can use, um, to get to answers more quickly. I think what you're going to see,

Unfortunately, I think robo advisors are going to make a big comeback. And I think there'll be AI robo advisors this time. And I don't think that those are going to work out substantially better than they did the first time around for the, for exactly the same reasons, right? You just, you can't just presume that everyone is equal. And so I'll just give you a basket of crap for you to buy and you go buy it. But I do think that you'll see tools appear that are assistive tools for professional managers so that an FA or an RIA sitting down with a client, understanding all of their needs, what their assets are, risk tolerance, et cetera, will be able to use a tool to dial in a series of potential packages that that investor might consider. It's, well,

We could go this route, here are the pros and cons. We go this route, here are the pros and cons. So that human is still there to use the tool just as I, I think you'll see doctors using AI tools in a diagnostic capacity, but the doctor will still be there to gather the information. And then that will result in some increased output. I do think, yeah, if you have a crummy FA or RIA that's hanging in there by the bottom end of the food chain, bottom of the food chain there, I think that AI portfolio generating systems might wipe those out. But I think that's a very, very small cohort within the broader RIA space.

Yeah, no, I think you and I share, again, very similar opinion there. I just, we hear a lot from the advisor community and some of them are worried. Some of them think they're going away and I just don't see it. But you and I can geek out for probably hours about system building. I've, been obsessed with how to build, mechanical portfolio strategies for a very long time. You have as well. So this, this is more to maybe more to be a fun question than a serious question. Do you remember like the first system you built and, what problems did you have to solve to get to where you are now to have, such successful running systems that you're

Able to kind of run, multi-private funds as well as a handful of ETFs? Yeah. I, I, you have those sort of memories of different types of systems you've built for sure. I was part of the team that built the first 16 core computer system, 96 gigs of RAM. We built our own daughter boards. We built our own riser cards. It was running windows 2000 server 64 bit. And we had to build it in a filing cabinet because there were no towers appropriately sized to, to take it. So it was in a filing cabinet and drives were in subsequent drawers. But I think, you were probably aiming more towards the, the trading systems. And yeah, in 2019, we built a very, very early,

Less sophisticated iteration of the system that we run now and, rigorously tested it throughout the year at the end of the year. it was just crushing returns five straight months in a row. And, looking back in hindsight, I just, we all feel like idiots. Like we were high-fiving each other. That's it. We conquered the market. We, we solved it. It's done. the performance was so outrageous that we weren't mentioning it to anyone because we just thought that they would, they, they would not have, we wouldn't believe it. So we were sitting there going, how are we going to articulate this without looking like we're gambling or we're lying? Like one of the two things, this was like serious conversations. And then

The sixth month, this system, um, could not identify like deep sector rotation that occurred. And it drove like a bus full of my money off a cliff. Like in one month, it wiped out almost all of the profit of the previous five in one go, and we took it offline, obviously immediately. Some of the guys that, that worked on it, I, I like to say it was like, they went off to their bedrooms and they wouldn't come out for a week. Like you can get them on Slack and they weren't responding to emails. Everyone was just sort of crushed by the fact that this thing that we thought was so sophisticated had this, this capacity for fault. I, I always say

Today, I'm so grateful for that because it wasn't catastrophic. We were way up. And then this thing basically took us back to normal, um, which is better than if it wiped us out. And it really paved the road to where we have got to now. Like we were in hindsight, the guardrails that we'd placed were way too far away from where they should have been. We were really optimizing for profit, gross return. And that's almost black box, right? When you're using a black box system, that's what you're going to get. You're going to get a 7,000% 10 year return, but you'll have two or three periods where you'll be down 70 to 90%. Right. You can't actually, unless you're just some, billionaire that wants to mess around and has money to throw at these things. Yeah,

Fine. You can run a system like that. But if you're a responsible manager, you cannot put client money on a system like that, no matter what the 10 year yield is or your disclosures. And so, we added layers of risk mitigation. That was really the beginning of, of us building the arc, which is our AI risk containment system. It's effectively an AI off ramp, which handles all of that. And now like we don't come anywhere close to that outcome, but I don't think I will ever forget that iteration of the system. we gave it a name. It was TJ and, and TJ basically got drunk and drove off, off a cliff. So it's great. It's funny.

Now it was not, it was not funny then. Yeah, I'm sure it's not funny, but it's a, it's an invaluable learning experience, right? Like you need to have those, um, because you go to market too soon with something and, and it goes, it goes haywire at the wrong time. So that's a valuable lesson at the, at the right time. Yeah. And humility as well, I think, you know? Oh yeah, absolutely. So just one more question on AI, then I want to talk about dip extensively. Um, you, I've read that you guys apply, machine learning, uh, to your AI systems. Um, and I know you have guardrails in place. You, you just kind of iterated that, but at what point, uh, do you think as the machine learns, uh, does,

Do the investment system decisions start to come a little bit like unrecognizable or it doesn't sometimes make sense to you, or does that happen now? Like sometimes it might buy something and you say, man, that doesn't make sense. Um, or it wasn't kind of the original design. Do you foresee that happening or do you have like really strict guardrails in place to keep it really focused on certain defined opportunities? Yes. It's, it's somewhere in between the two. So, true black box systems, their investment decisions cannot be explained because they don't follow a, a linear path like we do in making investment decisions. I always, the way I try to explain it is, the, the way that AI makes decisions,

It is this exactly the same as we do when we decide that we like a piece of art or we find somebody attractive or we instantly like, uh, uh, some music, right? So you, you walk into a room, it doesn't matter. You walk into a gallery and you see a piece of art on the wall. You like it instantly. You think, God, I love that painting. Or you walk into a room and you see somebody and you think, Oh man, they're attractive. If I stopped you in the moment and I said, really, why? It would be hard for you to articulate that, right? You, you'd sort of back out of it and you'd say, well, I don't know, maybe, I don't know the blues or the reds, or if it's a person, or I like their

Eyes or you like, you're not sure your brain knew it instantly. You didn't need to do any further calculation. So we do make decisions that way. We just don't make investing decisions that way. We go through this linear process quantitatively. Okay. The criteria that I had flagged were met. Now I'm going to evaluate further. If you're a fundamentalist, you're reading reports, doing fundamental forward-looking analysis, et cetera, to come to the yes, no buy, sell, hold decision that we make. So AI doesn't do that. And black boxes, the most outside. So you can't explain any of that. With us, we have an investment ideology that we teach the machine. And it doesn't, on the private fund side, ETF side, it doesn't matter. It's always the same. There's a trading strategy,

Which we start off with is developed by humans. We've used it before. It's made us money. We teach the machine. Then there's this refinement period that we go through, where within the guardrails that we place, so you cannot use this more than this in terms of capital. You cannot accept more than this in terms of aggregate risk. You cannot, if it's an equity, long-only equity strategy, you can't short. You can't access the options market. So we put these controls in place, and we tell it what it's trying to do with what we give it. Using these, do that. And then we ask it for optimization output. And it'll come back and say, okay, I can achieve that doing this, but I can achieve it better if I slightly alter it in this way. So I'll just, an arbitrary example,

Let's say that your strategy was iron condors. And let's say you said, okay, I want you to sell one standard deviation iron condors in something that has an IVR of 30. All right. So there you go. It goes out and it does that. And then you say, how can you make more money selling spreads above and below the market? So it must contain both sides. It has to be market neutral. And the machine comes back and says, well, I'd like to skew it in this way. I'd like to add long units over here. And I'd like to sell 1.2 standard deviations instead of one. Okay, great. Much better. So we're still on the same page with respect to the investment ideology, but there's a layer of refinement. Okay, super. So

We go ahead with that. And then where we're using the machine learning layer, and just as an aside, all true AI has a machine learning layer, if it doesn't have that, it's not really artificial intelligence. It's just a repeating quantitative process, right? The ability to self evolve is what makes it artificial intelligence. So what it's allowed to do in our strategies is modify the weighting of the criteria that it is using to flag its investment decisions. And it can select from a larger basket of criteria. So let's say we give it 50 criteria that it's going to examine to make an investment management decision in an underlying. But it really only thinks that 21 of those are informative. And it weights those 21. It's like, these three, man, if I see these, that's a guarantee.

If I see these, it's positive. If I see these, it's a little less positive, but still good. Anything down here, I can't find a correlation with winners or losers. So I'm going to ignore that. But it constantly reevaluates that basket of criteria. So over time, as conditions change, market participants get added, some become more consequential, some become less consequential, it adjusts within that basket. It can't suddenly say, I'd love to just short shit all day long. I think that's what I'm going to do today, right? And you're like, whoa, whoa, whoa, whoa, whoa. Unless there are no guardrails, ours can't do that. So it can evolve within that space. In terms of like, are we going to see it get to the point where it just fundamentally changes the training strategy?

No, because it doesn't even know that it has the ability to do that, right? That's, that's, that's where you get into science fiction at that point. That's super interesting and well thought out and super well designed. And I'm glad we had an opportunity to talk about that because I know so many people are fascinated by it. And we all know it's coming. I just hope that there's, there's thought and backtest and humility behind some of the new AI issues out there. And we don't see people get burned and chasing performances, that investors love to do is strategy chase. And then all of a sudden there's a problem, but let's talk about dip. Okay. It's really one of the only pure AI ETF plays in the market today.

So why don't you take, the opportunity to really just talk about the, the high level investment strategy and what it's trying to accomplish and what it's trying to invest, really trying to get exposure in. Sure. So when we were considering which ETF we wanted to launch first, we had, we had a, a few different choices, bleeding edge performance, but more cavitation in the P and L a broader base of assets that it could trade a bigger universe that is, or dip, which is what we decided to trade and, or decided to build. And what, what dip does is it looks for artificially oversold conditions in largely S&P 500 components. So we wanted to keep the universe to

Something that was largely seen as safe and stable, right? So there's no OTCBB. There's no pinks in this. Um, there's no short, it's long only it's equity it's large cap. So you are not going to get like a, a plus 80% year out of this by design, right? It's designed with, with safety in mind. That was our primary sort of goal. And it started with the universe. And then it started with the waiting within the universe. So broad diversification, it can't just pile it all into tech. And, you have a accused type of collapse when, semis blow up or technology sells off because there's lawsuits or antitrust or whatever in the offing for a bunch of the, the, the big nine companies that, that drive the weight of that ETF. Um, and then obviously the long only

Component. And then we were looking for something that investors, a strategy that investors seem to like, like investors, like buy the dip. It it's, it's an acronym BTD, or if you prefer BTFD, we did try to get that as a ticker and we're just flat told no, but, uh, I guess they didn't have the same sense of humor. Um, uh, it's, it's something that, that makes sense on a fundamental level. A stock sells off. If you're bold, it represents a buying opportunity. Most capable companies over the longterm or the longterm trend upwards. So what, why buy at the all time high? Why pay the premium when it pulls back? There's your buying opportunity by the dip. The problem is that most investors can't identify what we would consider an authentic dip. And so authentic dip, I would say

Is an artificially oversold condition. So what the hell does that mean? Okay. So let's, let's use this analogy that's been working for me recently. You've got a basketball sitting, uh, in a swimming pool and that represents the stock's price, uh, fair market value at any given time as agreed by the dominant market participants in the stock. Okay. So it can go up and it can go down. If you add more water to the pool, if you take water out of the pool, up, it goes down, it goes, but it's still sitting on the surface, which means that it is still, um, the fair market value for that, for that stock. Now, if I take my hand and I put it on the basketball and I shove it underwater, I've

Artificially depressed the stock, right? It, that basketball is underwater, but it's only underwater for as long as I hold my hand there. The moment I take my hand away, it's going to pop back to the surface of the water. Now that does work on the other side as well. I can pick it up out of the water. It's going to be above the surface of the water. So long as I hold it there, I'm not going to hold it there forever. Eventually my hand's going to get tired. I'm going to drop it back down to the surface level of stock. Now wrinkle being as I've pushed it down, as I've lifted it up, someone could also be draining water from the pool or adding the water to the pool. So when I let go, it just stays at that

Level. But by and large, that action of pushing the ball underwater leaves a footprint and that's what we're looking for. So how does that happen with a stock? So that's, one of the common ways that it happens is an HFT taking advantage of liquidity pockets and voids in the intraday trading of the stock. And that's, that's not like limited to like small cap, low float, like Chipotle is enormous and it's a liquid as hell intraday. ISRG is a monster company and it's a liquid intraday. Mally, a liquid intraday, right? So you have these moments where spreads blow out and the HFT will take a couple of feeler order pokes at the bid and see whether or not the market immediately drops down. And then it just unleashes that order flow, which like, who can compete with that? You know,

It's $10 million, 60 seconds or something like that just flows in stock collapses through, through the bid levels, eats all the way through that, that side of depth of book until it hits, um, an institutional, uh, buy zone, and something like FMR is just going to soak up an unlimited number of, of, uh, of, uh, sell orders, right? So like, like that's like, uh, a freight train hitting a mountain and the HFT will shut off at that point and it'll cover. So it's, it's, it's shorted, it's sold down, it's covered, but now it's hit the institutional buy zone and now the stock is going to pop back up. Now the institutional buy zone is a zone. It, the institution's not just going to keep buying it forever. So once it enters into this bracket

In between these two points, they'll be buying when it exceeds the top threshold of the bracket, it's going to shut off. So you're going to get that little bounce that, that, that dip that pops up and then maybe it continues. Maybe it doesn't. That's not for us to say. So we're looking for that condition and buying the stock there versus your average investor who maybe sees, like a five, six, 7% pullback and it's like, Oh, Hey, it's a dip. But no, that might be the mean reversion right there. It was over speculated. It was overbought. It's now coming back down to fair market value. You are not going to get a dip there. There is another reason for that happening.

And so, investors keep coming back to buy the dip because they've had intermittent success with it, right? They've, sometimes they identify authentic dips and they're like, sweet. Thanks for the 11 point pop there. Other times it's like, God damn, I buy this thing like every time and it's, it's, it's screwed me over and it's not popping back up. Yeah. Because it wasn't a dip. Right. So that's why we took the sort of tried and true investor fan favorite and said, okay, AI is really good at identifying authentic dips. If you like the concept of this strategy, then let us do it for you and just park whatever you were going to spend on buying the dip in this ETF and focus on whatever else you want to do with your day. Yeah. I love it. I think it's,

It's extremely interesting. Um, and again, love how it's designed, but I see a mix of index funds as well as some regular, uh, equities in there. Can you talk about that like diversification mix and, and are, are those a constant kind of holding in there? They're also at a much larger weight, at least right now. Um, but can you talk about kind of how the holdings, uh, I know the equity holdings are going to change, but what's the reason for the index funds in there? And I did notice at least as of today, the holdings in those index funds, the weights are a little bit higher. Right. So that actually ties into, um, a problem, uh, that's inherent to some AI systems, right? So it's easy for science, it's not easy, but it's appealing to build the profit generating mechanism,

Right? So you're like, you're looking for this and here's what you're going to do. When you see this pattern, you're going to buy it. So what happens when the criteria, the minimum criteria that the AI needs to be successful aren't met, right? Like in our case, what happens when there aren't enough dips? There are always some, even in downward trending markets, because you have, obviously inversely correlated stocks that you, that will then come out of their dips, as you get a downshift, but what happens when there aren't enough? And this was sort of a core question of ours when we started building dip was like, we sort of were able to chunk the candidates into like five tiers. We had like tier one,

Tier two, three, four, five, and tier one and tier two were solid. Generally, we found them to be consistently profitable, high degree of signal correlation. Okay. So they're awesome. So we want to trade tier one and tier two whenever we see these candidates. Three, four, five were still profitable. They were still good candidates. But what we realized throughout our testing was that it didn't add more P and L. If you use three, four, and five, it just used more money. Yeah. And so we did not think that that was, okay. So that was, that was one way we could go because the core question is how are you dealing with excess capital, right? Okay. So you've got, I don't know, a billion dollars, but you've only found $600 million worth of dips. What do you do

With this? Okay. You hold it in cash. That's, that's a lousy solution, right? Your investor's not happy there. Your investor's not paying the fees. So you can just sit there and act as an expensive bank account. Number two, you could trade tiers three, four, and five. So you could use the capital. There's a case, Hey, we're using all the money. Yeah. But we knew that they weren't going to get the performance out of that. And from an ethical perspective, we just, we don't, that's not how we manage money, right? If, if, if we don't use it, we'd rather you took it back with an ETF. We can't do that. So what else could we do? So we sort of embarked on a investigation of using

A weighted blend of index ETFs to soak up the extra capital. And what we discovered was twofold. One over the longterm in our tests, the weighted blend of index ETFs generally outperformed any single one of those, not in the short term, right? I want to just be clear on that. Like if Q's is running, right? Like the bulls and we're holding a weighted blend of let's say Q's, IWM, diamond spiders, it's not going to outperform the Q's. The Q's are running, right? It'll get some of that performance, but not all of it. But in the longer term, when you look at the averages, we found the weighted blend of index ETFs to be beneficial. So that was one in that it's a responsible, instantly diversified, giant cap investment that's going to do you well.

Number two, because those represent, broad technology, the largest of the large cap, the US markets in air quotes, and then small cap, IWM, because it was representing all of that, having one foot in, in those funds at periods, either where there's increased uncertainty, or where there weren't enough dip allowed us to pivot a lot faster. So suddenly there's an explosion in tech, and we're holding some Q's, boom, the dip gets the benefit of that, because the discrete underlying holdings are not always going to be made up of those same components, right? So we saw that in the regional banking collapse. So coming out of the regional banking collapse was the explosion in AI.

But it was really like nine stocks that lifted the entire stock market, and we had people asking us, well, why weren't you holding those nine stocks? Because none of them were in dips, before they took off. And it's not a Momo algorithm, right? It's not like, hey, I'm going to wear them, I'm going to trade off, take the dip hat off. I'm going Momo, I'll just pile it into this. Like who would want that as an investment manager anyway? So that's sort of, that's why you'll see sometimes you're going to see this weighted blend of index ETFs. And then other times you'll see none, it will hold none of those. If it, if it can fill all of its allocation with tier one and tier two dips, those are more profitable, it'll take those

Every time. If it can't, rather than not use your money, it's going to give you the performance of this weighted blend of index ETFs instead. Love it. So briefly, I know you had mentioned there is some risk component, a risk mitigation component in this as well. Is there any, let's say, I know it's dip buying. So I'm probably, I probably am answering my question in my head. Is there ever kind of looking at the other side where it might actually raise cash on purpose to try and get a little bit more defensive? Or is it, is it always going to be fully invested in equities? No, yeah. It's, it's, it's very predatory and it's always fully invested in, in equities. There's it, it actually isn't aware that, high capital reserve is an option

For it. It's mandated, it's programmed to use all of its capital, which is why you're always going to see at the end, cash on hand is like near zero. Got it. Right. It's, we program in the ability to hold cash on hand to safeguard against global collapse. Right. And that's, and that's something that is insisted by, partners and largely regulators as well, to some extent, like, our custodian is US bank and they wanted to know, okay, fine. So there's a nuclear warheads detonated in Washington. What does this thing do? obviously I think the markets are closed. I think we have bigger things to worry about. I think it's, canned food in your basement and the preppers are all going to say,

I told you so, but fine. We've programmed in that if our regime classification and change detection engines come up against global anomaly in terms of their analysis, like, we run these engines at the discrete stock level. We run them sector and industry, we run them broad market. So there's, there's hundreds of classification and change detection engines. And it's not unusual for a bunch of the discrete underlying change detection or regime classification engines to come back with an, I don't know, right? If it sees something that it's never seen before in Disney or in Netflix or something, it's going to report, I don't know. But the global engine is bull bear neutral.

In the time, in times of some sort of incredible collapse, a majority of those engines will report, I don't know. And then it's going to hold cash, right? It's not going to say, well, I'm sure there's dip opportunities out there. It recognizes this is not normal. And it's so far outside of my risk envelope that I'm going to default to this panic cash position. But we don't outside of that expect it will ever do that. Got it. Got it. So as we wrap up here, if you're sitting down, I'm assuming given the complexity, some of the complexity of the product, I'm assuming you're mostly talking to advisors and you have a great ticker. So I'm sure there's some retail out there buying it as well.

But where are you, if you're sitting down with an advisor, kind of recommending they find a place for this in their existing model portfolio? Is it a supplement to, large cap equity exposure? Is it an alt sleeve? Like, where do you, where do you, would you recommend this with this sitting? We usually recommend that it's complimentary to whatever their broad market tracker is going to be. So, we're not trying to be or replace something like SPY, right? There is always going to be the interest in allocating some part of your portfolio to something that effectively, it doesn't matter whether it's SPY or IVV or whatever, it was something that effectively tracks the air quotes broad market, right? And then, so you have your beta correlation,

Like right there, and then you're going to layer your alpha opportunities on top of that. But at the end of the day, responsible allocation, conservative investment will contain some sort of beta tracking allocation like that. And what we're saying is, okay, so we don't want you to replace that with us until maybe you get to a point where there's enough of a history there that you're confident doing so at this stage. But consider us instead of some portion of that. So if you've got a hundred mil that you've dumped into SPY, IVV, whatever it is, maybe there's 10, maybe 10% of it you want to drop into dip. We use the same components. So you're getting the same security, or I should say commensurate security, not the same. But we're trying to opt into the upside and

Step out of the downside in and out, in and out, in and out of that allocation. And we're using this groundbreaking technology to do it. So if that investment manager wanted to access that type of system anywhere else, they sure as hell wouldn't be paying 125 basis points, right? That door opens at two and 20. And the funds that run it effectively are usually like 3.5 and a sliding scale up to 40, right? Because they're generating these 40, 50, 60% returns. You look at Renaissance technologies, Medallion has been closed forever. But if Medallion was open to the public, it's five and 45.

That's where you get that type of technology. So this is a way to get it liquid with very low risk for a percent and a quarter, right? So that's what we're sort of pushing there is consider it for adding a little bit of additional alpha using the same components that you know and trust already. And as there gets to be more confidence, if we're able to prove ourselves to your satisfaction, maybe that allocation skews, it starts at 1% or 3% or 5%, and it ends up being 20. But we don't think it's ever going to be 100, right? It's not going to replace, it's not designed to do that. Right. Well, Ryan, this has been very educational, also very fun for me. And I wish you all the best of

Luck. But before I let you go, where can people learn more about you and your product dip? If you want to learn more about dip, you can go to the website, which is dipetf.com. If you want to learn more about Kaiju, the global ecosystem, that's kaiju.ai, Kaiju Capital Management is at kcm.ai, and the RIA is at kaijuetfadvisors.com. I really should probably bring those all under one umbrella. Now I'm afraid they're a bit spread out. Well, Ryan, again, thank you so much for your time. This has been awesome. And I wish you all the best going forward. Thank you, Brad. It's always great to talk to you.