Animal Spirits Podcast - Talk Your Book: Building Portfolios with AI

Episode Date: January 15, 2024

On this episode, Michael Batnick and Ben Carlson are joined again by Chris Shuba, Founder and CEO of Helios Quantitative Research to discuss: the experience of a diversified investor in 2023, utilizing AI to build portfolios for advisors, Helios' latest trading service offering, and much more! Find complete show notes on our blogs... Ben Carlson’s A Wealth of Common Sense Michael Batnick’s The Irrelevant Investor Feel free to shoot us an email at [e-mail] with any feedback, questions, recommendations, or ideas for future topics of conversation.   Check out the latest in financial blogger fashion at The Compound shop: https://www.idontshop.com Past performance is not indicative of future results. The material discussed has been provided for informational purposes only and is not intended as legal or investment advice or a recommendation of any particular security or strategy. The investment strategy and themes discussed herein may be unsuitable for investors depending on their specific investment objectives and financial situation. Information obtained from third-party sources is believed to be reliable though its accuracy is not guaranteed.   Investing involves the risk of loss. This podcast is for informational purposes only and should not be or regarded as personalized investment advice or relied upon for investment decisions. Michael Batnick and Ben Carlson are employees of Ritholtz Wealth Management and may maintain positions in the securities discussed in this video. All opinions expressed by them are solely their own opinion and do not reflect the opinion of Ritholtz Wealth Management. Wealthcast Media, an affiliate of Ritholtz Wealth Management, receives payment from various entities for advertisements in affiliated podcasts, blogs and emails. Inclusion of such advertisements does not constitute or imply endorsement, sponsorship or recommendation thereof, or any affiliation therewith, by the Content Creator or by Ritholtz Wealth Management or any of its employees. For additional advertisement disclaimers see here https://ritholtzwealth.com/advertising-disclaimers. Investments in securities involve the risk of loss. Any mention of a particular security and related performance data is not a recommendation to buy or sell that security. The information provided on this website (including any information that may be accessed through this website) is not directed at any investor or category of investors and is provided solely as general information. Obviously nothing on this channel should be considered as personalized financial advice or a solicitation to buy or sell any securities. See our disclosures here: https://ritholtzwealth.com/podcast-youtube-disclosures/ Learn more about your ad choices. Visit megaphone.fm/adchoices

Today's Animal Spirits Talk Your Book is brought to you by Helios. For advisors, go to heliosdriven.com to learn how Helios works with advisors to differentiate their firms, build portfolios, all these different solutions. We're going to talk about some of them today. That's heliosdriven.com. Welcome to Animal Spirits, a show about markets, life, and investing. Join Michael Batnik and Ben Carlson as they talk about what they're reading, writing, and watching. All opinions expressed by Michael and Ben are solely their own opinion and do not reflect the opinion of

Ridholt's wealth management. This podcast is for informational purposes only and should not be relied upon for any investment decisions. Clients of Ridholt's wealth management may maintain positions in the securities discussed in this podcast. Welcome to Animal Spirits with Michael and Ben. Michael, you and I have had a few talks with firms that want to use AI to help advisors make their businesses more efficient. And a lot of them, it sounds really interesting, especially when we bring up to the advisors that we work with. My only concern, concern is, what if you hit your wagon to the wrong horse? Is that the saying goes? That's the saying. And it just ends up being Microsoft or Google come in and smashes them

and they're not as relevant. So that's the only concern. I guess it's early enough days where you can play around to things and mix and match, though. That's my only AI worry. Most of the early companies that we've seen are like sort of behind the scene advisor assess type tools. The conversation that we have with Helios today, they're leveraging AI into their investment processes. Right. So, I mean, there's no doubt that this is where the puck is going. And, you know, a million questions on what it's going to look like. We don't know.

And I guess if there's, whatever, a dozen different corporations that make an AI assistant, you're just going to have to figure which one works best for you. But, yeah, you're right. That's where we're heading. So we've talked to Chris Schuba from Helios before. They work as they call an insource. CIO, is that right? InSource CIO for advisors. And we were just talking about this, who was I talking with? Was it you or someone else at one of the conferences? But basically, in the past, you would assume

an advisor is a person who invests your money and manages your portfolio. But most advisors these days, especially I'd say most RIAs, are coming from the angle of financial planning. And they handle a lot of other stuff, creating the financial plan and taxes and insurance and estate planning, all this stuff. And they don't necessarily have the expertise or have like an investment management group of people or person on staff. And so the idea of outsourcing it makes sense for a lot of these firms. So we talked to Chris about that, while they're using AI in their practice, which I thought was interesting conversation and some of the other stuff they're working on. So here's our chat with Chris Shuba from Helios.

We're joined once again on the show, repeat guest, Chris Schuba. Chris is the founder and CEO of Helios. Chris, welcome back. Thanks for having me. This is awesome. You all work with directly with advisors. That's a space that we're obviously interested in a lot of our audience is interested in. Maybe we can start before we get into some nitty-gritty in detail about the business and what's going on with you guys. Where do you see the advisor landscape?

How did 2023 look for you and the advisors you work with? Yeah, I think, you know, the advisors we support as an insource CIO are advisors that, you know, want to have a world-class asset management experience. They want to be different and unique as far as what they do in managing money. they want to deploy kind of a model ecosystem and, you know, do everything they can to mathematically increase their odds of achieving the financial plan. So we have a very certain type of advisor that we tend to support. And it was a very mixed bag for us last year in terms of just how advisors felt. They felt great having a process. They liked the results of having a process that's runs through the total portfolio design. But narrow rallies are always frustrating because as an industry

we're taught be diversified, you know, and let that do the compounding throughout time. And when you have a narrow rally, especially one that doesn't then lead to a broader rally, no, investors don't feel rewarded for being diversified. And so when I've seen advisor satisfaction surveys in the past, I've seen a correlation between narrow rallies and the least satisfied investors. And so, yeah, it's just, I don't have a diversify across Apple, Microsoft, and Google, and you're good. What are you talking about?

Yeah. You know what? I'm wrong. I don't even know what I'm talking about. I agree that that is a challenge that we've, you know, we keep getting questions and the chorus grows lottery every year. Why do I hold international? Why do I hold small caps? Why do I hold quality or value or any other strategy besides big tech or the S&P 500? And there's a lot of people who are just saying, I'm going to throw my hands up and this is all I'm going to invest in, which history shows, you know, having a concentrated position like that can work really well for you when it works. And when it doesn't work, then the regret sets in. But that, that whole mindset seems to be building more and more every year.

Because we had the reprise in 2022 where that strategy, technology, fell hard and the S&P, underperformed a lot of different things. And then in 2023, it came roaring back and people immediately forgot 2022. Right. Well, it just comes down to what's the most important thing. You know, if you run into someone who the most important thing is to look at a sheet and see the highest or close to highest returns, then that's going to be their mentality. If the mentality, you know, is, I want to maximize the odds of achieving my client's financial plan by taking the least amount of risk doing it. That's a different mentality. And, you know, every company in the space, Filios included, is going to pick our team. We're going to line up around the advisors that see things

the way that we do. And if I talk to people who are returns at all cost people, they never indicate that they're being as successful as they want to be. What do you mean by that? Well, it's always about client growth. It's always about, you know, winning and losing clients, being right or being wrong. You know, are you where you want to be at your practice? No, I'm not where I want to be. It seems like the advisors that are much more process oriented tend to acquire practices, achieve their goals. They just seem to be more grounded. Just my anecdotal view of the world, but it's just personality types and, you know, no company can serve them all. The results oriented fashion, like if it works, you feel great, but there's so much

more stress involved in that situation and so many more opportunities to be wrong. And I think that's a big thing advisors are trying to do is, is minimize mistakes. And the first rule is just don't screw this up, right? Especially for people coming to you who already have some wealth. You don't need to make it any harder than has to be. So I definitely agree. Let's also not forget 2022. These stock, a lot of these stocks in video was down 70%. Amazon and Google were cut in half. Facebook was down 60 plus percent. So it's, it's very easy to forget that these stocks like every, I mean, you know, they're not, they're not completely immune. to the ups and downs in the market?

No, not at all. And the other thing that I'll mention is that, you know, the word that I like to use, Ben, for what you were saying, is consistency. Like what, in my opinion, kind of the holy grail of a great investment strategy is it consistently does what you expect it to do over and over and over again. And I know today we're going to talk about AI, machine learning, neural networks, which, you know, clearly we've made a lot of investments in. And that's the only thing I talk about here is the opportunity,

that comes with advancements in technology is not a crystal ball. It's just more consistency through greater data analytics. And that's what I'm, you know, if I'm looking at 2024, you know, I'm really using this. And there's going to be a name to this at some point. I'm not going to be the guy that names it. But I look at 2024 as being the year that we flipped from quant 1.0 to Quant 2.0. It started really in 20203, but 2024, if you're looking for a dividing line, you know, that's the difference to me right there. So, Chris, I'm excited to get to hang with you in a couple of weeks out in the T3 Conference in Vegas. And I suspect one of the key themes there, as you mentioned, is going to be AI. And Ben and I have gotten a look at some of these

companies. And I think it's, we're still, obviously, we're very early. I don't know where this is going to shake out. If this is going to be something that every service provider, we currently use integrates this, whether that's, you know, the name brand, the name brands and then like places like Google and Zoom, what are they going to do for us? Or are there going to be companies that pop up that are that are not on our radar right now? How do you see all of this shaking out? Do you think that, as I mentioned, like companies that we work with, companies like Helios are going to integrate this or are there going to be new companies on the scene or everything in between? What's your thoughts? Oh, all of it. I mean, this is, you know, something we've all

talked about in circles forever about the great increase in productivity that we need as a nation, you know, because we don't have the birth rates, right? So how do you grow the productivity curve? Well, you have to become more productive through technology. And this is it, right? So every company in some way, shape, or form is going to benefit. Even if you're a company that does, you know, packing boxes and you want to be smarter about supply chain, but even if you're just using software packages like Salesforce that are deploying AI, you're going to get benefits. So every company is going to naturally get a certain amount of. benefit. There will be companies that will become the headwater of this new type of technology and

tools. New ones are certainly going to pop up. That's the amazing best part about it. Yeah, everything in between. I think it's just a question of what you're going to use it for. It's not just a singular tool. It's a spectrum of imagination, which is what makes AI so hard to get people's minds around if they're not familiar with it. With Helios, are you going to be rolling out and incorporating AI into your process? Like, are advisors going to even know that they're leveraging AI or is this going to be more in the background that you guys are using to deploy your resources? So both. So we've already rolled out AI-driven solutions for our advisors that they have access to now. We will use that type of technology in front so that advisors can use

that in their modeling. We will also be deploying it through the systems and technology we build behind the scenes to make them scalable and more efficient and so on and so forth. So it's culture here at Helios now. And we really are utilizing. kind of the machine learning and neural net side of it. We will use things like generative AI, such as chat GPT for, you know, client service or content generation, but we're not hooking up, you know, investment algorithms to things that can hallucinate or be creative when we don't want it to. So there's lots of different ways that you can put guardrails around this world, but 100%, we've already rolled out solutions for our advisors in the AI space.

And what are those solutions look like? Is it, are you working at? helping make their processes more efficient. What is it? Because a lot of our advisors have just seen simple things like, listen, after I have a communication with a client, the AI is going to summarize the call and put it all into an email for me and I can kind of check it and look at and just simple things like that. Is that what we're talking about?

Are you looking more in depth? So when we talk about how we might use, you know, chat GPT or other generative AI, those are the things that we're talking about doing it with. Those are fairly easy to do. The stuff that we've done is actually in the, you know, what investment decisions should an advisor make if they're following a certain process. So the one that we've rolled out right now is is a equity major asset class rotation strategy. So we took seven asset classes, you know, large cap, blend, mid cap blend, small cap blend, large cap growth, large cap value,

international and emerging market. So pretty simple basket, right? It's designed to be a baseline line, easy to understand, easy optical way for an advisor to have a broadly diversified portfolio that doesn't scare their clients off, right? So that's the first one we rolled out. And what that does is it kind of solves, it's our attempt to solve this consistency problem. So if you think about Quant 1.0, Quant 1.0 in my book is anything you can do in Excel or with systems like Excel. Anybody can pop some data in with, you know, investment knowledge and create some quantitative answers, which is the vast majority of quant right now. The problem with that approach, which we've always known about, it's always been a problem, has been it's entirely dependent on long run correlations.

And what you're essentially betting on is that I'm going to take a combination of data, analyze it in a certain way, and use that to make investment decisions, and it will average out to be better than what I otherwise would do. That's essentially the bet. The hard part about it is, is that you have to design that. Maybe you designed it five or six years ago. And as the world evolves and changes and so on and so forth, it doesn't necessarily change with it. And so, you know, you get into the COVID area here and you have all these breakdowns, like, you know, now stocks and bonds are positively correlated, or the interest rates and price of housing is now completely upside down relative to long run.

And you might be using that in a linear algorithm to create an investment decision. The main point of when you think about leaping from that linear, mathematic, long run correlation dependent way of managing money is you end up with a scenario where in 2023 and 2024, a lot of those correlations broke down. A lot of advisors are very, very unhappy with our active management over the last couple of years. And it's strictly because of that long run correlation breakdown. So where does AI fit in? Well, that's what I say. So where AI represents an opportunity to add a lot of consistency to this is by taking in vast amounts of right now data through either a machine learning framework or neural net and understanding if there is a

massive departure between those long run characteristics you're dependent on and softening those with the right now knowledge. And when we've built out these algorithms and run them through both of those types of methodologies, frankly, the results are just stupid. I mean, it's stuff I never thought I'd see in my career. So we've spent way more time double checking, triple checking, beating the heck out of our own math. We're about to release a evolved version of that. It's already done. We're just going to release it in a couple weeks where we juice the domestic side with sectors and the international

side with individual countries. And the results are so stunning that I can't, I can't, we have to keep going back and reviewing the code. There's got to be a problem here. But really, what it comes down to is that we no longer have a strict reliance on something we designed eight years ago always holding true, we can now blend the right now in the long run at the same time to create more consistent decisions. And that's just the name of the game. How difficult is it, because I know your clients are the advisors, the advisors' clients are obviously the end clients. How difficult is it for you to explain and then for the advisor to explain what the strategy actually does if it's relying on this new technology? It's a lot harder now. So back in the day, when we were in, you know, purely in the

Kwant Mondodo space, it was very easy to say, well, we take these 50 data points and we do this with it. Right. Right. And this is how it works. In the AI space, you know, you start getting into language people don't understand. We do a good job of it. We always break things down into three steps. We do this. We do this and we do this. And clearly with AI, you're glossing over a lot more than you would with, you know, a quant 1.0 type of way looking at the world. But we figured out a way to do it. It's not easy. But what we've also learned is that much more about setting client expectations isn't necessarily about telling them in words what to expect. It's about showing them in graphics behavior patterns. So we use something we call the Confidence Circle, which helps our

advisors understand how a model is going to behave or has historically behaved and then use that to measure what it's doing right now. And that goes a lot further with a client than needing just purely words. So if you have a small amount of words, in easy staff, and you back it up with some graphics, we found that clients not only get it, but they're much happier through good times and bad, and they tell their friends, which is the referral, you know, golden goose that we want. My biggest worry about the AI models when it comes to asset allocation or tactical investing is pretty much like a back test.

Like you could find the world's greatest back test, but once everyone else knows about it, what if it doesn't work as well anymore? So how much do you worry about once everyone has the AI tools, how useful is it, and how much of the AI tools that you're building are really, okay, this big part of the model is out here, and it's kind of like this amorphous blob, and we put our inputs in it, and that's where the differentiation is. So how do you think about that in terms of, like, it feels like the amount of data that came through really kind of made back testing not work as well, because people could all see it at the same time? There's always that, right? So I worry about lots of things.

It's my job to worry, basically, and that's absolutely one of them. So the good part about AI is is that there's two pieces to it that are unique. The first one is, you know, unlike something like a trend algorithm, which I would put into like 1.0, it learns, so to speak, it gets new information on the front end and that drops information on the back end. So it kind of forgets, right? In a machine learning world, it never forgets anything. So as it makes decisions and it sees those results, it incorporates those results in.

That's a hedge a little bit against the world changing and how does it evolve with it, right? The second piece of it is design really matters. So with most quant 1.0 types of things, there's such a limited number of data sets. And there's only so many mathematic tools you can do in a 1.0 world where you had a much narrower set of outcomes that you could possibly get. In the AI world, like if I build a neural network, which we're in the middle of building one right now or strengthening one right now, I should say, you know, I could build my own neural network and ask it one question, you would build your own neural network and get totally different

answers out of it because design matters. And so there's far more potential outcomes in the world of AI than there was, in my opinion, in previous AI because the possibilities are so much wider. I would imagine, given all of this information that you're leveraging, that the portfolio has more turnover. So I know we've spoken in the past about how advisors are using the models and the platform. Can you talk about how the models might change and what execution looks like now versus what it did before? Absolutely. So at Helios, everything is customized. So we have model designs or ways that we configure models that are very low turnover for taxable accounts. We have models that can trade more frequently and or when they do trade have higher amounts

of turnover. And that's why an ecosystem is important. When we deliver a model set or what we call an ecosystem to an advisor, they're going to have different models that play. different roles within different spots in the portfolio. So the classic reason for tone over concern is taxes so that, you know, we can solve around that. If you're an advisor who just doesn't believe in a lot of turnover, well, that's also easy for us to blend our various mathematics so that it doesn't do that. The problem is always going to come down to there is a seems to be, with very, very few exceptions, an inextricable relationship between going for excess return and trading. If you want to go for excess return, you got to trade. And so, but everybody's a little bit

different. So that's why customization is the way it is. We have advisors of ours that, you know, have seen all the AI that we're rolling out and everything that we're building. And they still want a Quant 1.0 modern portfolio theory way of working in the world. And we will support that 100%. Right. But for the advisors that want to go up the, up the chain to more of a, you know, AI world, we have that too. So we're going to support all of Quant 1.0. We're going to support. all of Quantuatu. We don't have to lose anything that we've done previously. We're just widening out the number of things people can get from us. So, but when you blast out these trade recommendations, is it still up to the advisor to execute these trades? Good question.

Not anymore. So one of the things that we've just rolled out, and I think this is the first interview that we're actually saying this on. So I don't know, is that a scoop? Is that what it's called? You get a scoop. I'm like woe here. So. The historically speaking, yes, we've been a flat fee. We build all the models. We do all the work, you know, all the ICIO work. But at the end of the day, we would send the trades to you. You would approve them and execute them in your system. For RIAs and some independence, we are now rolling out all of our normal capability. So all the things that we do is an insource CIO. But once those models are built, instead of charging a flat fee to the advisor and having the advisor trade, we will have the

ability to operate a little bit more like an SMA. So the model gets built to the advisor's specifications. It gets allocated to the advisor's design. And then when any trading happens, we'll go ahead and automatically execute the trading and charge a fee to the client like an SMA would. But what makes the EOS is a bit different is we're much lower cost than the average SMA would be. And at the same point in time, normally when an advisor gets a SMA, they only get that for their client. At Helios, they get the entire model capability, but they also get everything that Helios does behind the scenes for their practice. So it's a really, really attractive offering, but a little bit different than what we've done in the past. So how much does this change

the interaction operationally with firms? Is there a lot more handholding working with you? Like, how does that shift work? Because obviously, a lot of advisors, I think, would probably love to have another firm take care of more operational stuff for them. So how does that change your relationship? It really, frankly, is up to the advisor. So because the way we built our systems and technology around, you know, selecting the individual holdings that would go in the model and all the investment committee work around that is largely automated, the understanding of whether or not the model is doing what it's supposed to do is calculated and presented to the advisor at all times. So once it's up and running, it should be completely clockwork. So it really doesn't add any more operational work than our standard relationship with an advisor. The one thing I would say is that at Helios, because we do so much, we handle all the various

components and, you know, of what it takes to really run an asset management capability in a practice. A lot of times we become aspirational for advisors. They look at the way we talk, well, like when we talk about how we analyze, you know, mutual funds and ETFs and create the confidence rating, they've never done something. like that before, right? So now they get it. Or when we talk about what an investment committee process would look like, they might not have done one like that. So in many ways, they hear about what we support and they're like, I want that for my practice. So in some cases, we're actually adding a bit to practices, not necessarily taking away, but it strengthens them

all together. Our relationships with advisors are super unique. And that's what makes it fun for us. So when advisors are leveraging Helios, who is typically the point person on that? Is there somebody at the RA that is the ops person that's, that's liaisoning between the advisor and you all. Is it the advisors interacting directly? Is it the investment committee? Talk to us about the process. When you're onboarding a firm, just what does it look like? Sure. Absolutely. So it's always great for us to have a key point of person within the practice that we can turn into the Helios expert, that when we send, you know, our system will notify this person or anybody that they want when there's a trade pending and for that to be reviewed. So having a person on point

is great for us. We love it when they have kind of their own in-house CIO already that loves investments, that we can take a lot of heavy lifting off that person's plate, and they can focus more on the things that add value to the practice with boots on the ground. But if we don't have that in-house CIO, then generally there will be an advisor on the team that will play the point person. And sometimes it's a staff person for larger practices, but most of the time it's going to be that, that CIO that we get to know really well and enable that person to kind of unlock themselves within the practice. So oftentimes people tend to see us as a threat a little bit.

If they're the CIO, like, oh, Helios is going to come in and do all this work that I'm getting paid for. That's never really true. Normally it's, it's really expanding what they really want to do. And we take all the work they don't want to do off their plate. So in terms of helping advisors communicate with the strategies to their clients, I think I remember one of the previous times we spoke that you all will help with with marketing or communication. Am I making that up or do you do that? Yeah. So we produce a ton of content. And content is the name of the game for all communications, whether it's marketing, whether it's talking to your existing clients, doesn't matter what it is. So our advisors will constantly either take our copy and use that within marketing or within a social media context. I'll take our graphics and do the same thing. So we become this warehouse, if you will, of thought leadership and graphics that an advisor can repurpose into whatever marketing or client communications they want. Some things we pre-build

for them, like we'll write a short economic commentary every couple of weeks that they can email out as is. Other things they might want to cobble together. But one of the things on my list that's not really on the docket yet, but I have this idea in mind of a way that we can take all of our content and our technology and chop it into pieces. And then an advisor can just like, hit a button and auto-aggregate something out of our pieces so they don't have to do it. Is that another AI tool you're thinking of there? That's blockchain. I don't know.

I don't think that's blockchain, but it's, uh, I don't know. It's something I want to do. Speaking of blockchain, has, have any of your advisors asked you about the capabilities of like incorporating a Bitcoin ETF into their portfolios if it should receive approval? All the time. I mean, it comes up. And we do produce a lot of analytics that are basically, technical analytics on, you know, a large number of the cryptocurrencies. And, you know, certain advisors

are going to bend that way. It's, you know, we don't currently add crypto as an available asset class for our algorithms to choose from. I don't know if we ever will. But our advisors can, yeah, I mean, it depends on where, I mean, just the stability of it, you know. But our technology allows for sleeving. So all the time, advisors come to us and say, Chris, you know, we want you guys to build your model, but I always want 10% of my model to be in this, or I want 2% to be in this. So our system is built around the ability for an advisor to control their sleeves. And so if they say, hey, I want 3% in Bitcoin, just slides right in and the model builds itself around that sleeve.

So let me ask you this. If an advisor, so I know you guys work with a lot of advisors, have a lot of different models solutions. When they work with you, are they able to create customizable solutions that are their own and where they could say, hey, listen, we want to do this, this, this, and what would look like if we did this? Like how much customization is available for advisors on the platform? So it's right now the way it works in our technology as it's built is that it's a bit of a walled garden. So we have all these ingredients in the kitchen. And together we work with the

advisor to say, hey, what's the store you want to tell your clients? What's your philosophy? And then we make those dishes for them, right? And they're taste testing them and saying, you know, more sugar, you know, more salt, whatever it is. But we come to a team conclusion on what their model ecosystem looks like. But inherently, they're limited to the ingredients we've given them, right? And are the ingredients funds only, or are their individual securities as well? Well, what I was talking about was the actual mathematics there. So, you know, what are the decision-making characteristics of the model itself? When it comes into the holdings, the holdings primarily are preferences using mutual funds and ETFs and you can use whatever you want.

Individual stocks, of course. In fact, we're rolling out a lot of upgrades. We'll probably come on the show later on this year and talk about what we're doing with individual stocks, which is really cool. But all that's up to them on what holding sets are viable. We don't make them go one way or the other. But what I was alluding to more was the decision-making side of it. For larger opportunities, we will create custom algorithms for them. So there's been practices that have come to us that have said, hey, we have somebody who's retiring. This guy has a very specific way of analyzing stocks. It's rules-based. Can you guys build an algorithm that effectively replicates his brain. Yes. We'll take the rules. We'll build it out. And if it's a valuable

enough relationship for us where we're willing to do that one-off, we'll go ahead and do that. But the vast majority of advisors we come across, they don't have strong opinions on how they want money managed. They want a consistent, statistically relevant way of achieving the financial plan. So they tend to default to us quite a bit on what do we think they would need, which is more for pulling out of them what they want. So it sounds like your clients are mostly people that are more planning oriented and they want a world-class investment solution that they can't necessarily deliver on their own. Yeah, I would say we serve every type of advisor you can name. But yes, the majority of advisors that we support, they want to focus on what they do best, which is usually

managing the relationships and the planning side. Anything else that it's not their highest and best use they want to outsource. And that's where we land. I saw there's somebody on Twitter that I I can't remember his name, but he talks a lot about the model portfolio industry. And the compound annual growth rate is astonishing. I think it's in, I think it's like 15 to 20 percent because to your point, advisors, the best use of their time is not necessarily doing those things. It's outsourcing it or insourcing it or whatever you call it to people like, to people like Helios.

Yeah, I mean, it's indisputable. I mean, the hard part is that, you know, a lot of it, not a lot, but a certain percentage of advisors get into being an advisor because they, love investing. And it's hard for them to give up this piece. And that's why partnerships are important. When somebody comes to Helios, we're asking them to use us as, you know, as fully as possible, but at minimal as the baseline. We're going to give you factual answers, statistically relevant decisions. If you want to massage that a little bit based upon the way that you think, great, we're built for it. Our technology is designed to let you tilt and add sleeves and things of

that nature. But you're right. The most successful, most profitable, fastest growing practices are the ones to focus on their highest and best use, and we tend to identify really well with those teams. I assume that means the majority of your clients have, even though they're using a similar decision-making process and similar tools, their portfolios could look completely different. 100%. Yeah. I mean, there's no two advisors we support that have the same identical models. I mean, that's the purpose of technology is to allow for that expression, but again, to stay within the rules that we know are statistically relevant. So you can follow our process through good times in bad and have confidence in it the whole way through. It's hard to do when times are bad.

There's no such thing as a perfect model. And that's why actually the most important thing isn't the models we give the advisor. Those are the sexy things. What matters is we teach them how to combine those models in a way that create higher levels of mathematic diversity so that the portfolio achieves the goals we need it to. Our models themselves will take turns being heroes and goats. But we want the portfolio to do its job over time. That's the deal. Chris, for advisors that want to learn more about how you work with their firms, how do they find you? Heliosdriven.com is the best way to do it or shoot me an email one way or the other. But, you know, we're not hard to find if you look up Helios Quantitative Research or Heliosdriven.com.

All right. It's Chris Shoeb, everybody. Appreciate the time, Chris. Thanks for having me again. All right. Thank you, Chris. Remember that's heliosdriven.com to learn more. Thank you.