Full Transcript
On designforwealth.com, there are several high-distribution ETFs that are featured. This particular chart shows a few of them compared to the S&P 500 in green. You can see OVL and OVS are very similar with respect to share price appreciation. What this chart does not show is the fact that OVL and OVS were both paying annualized distributions around 10%, paid out monthly as cash. And so when you factor in those high distributions into the total cumulative return with share price appreciation, you find that OVL and OVS are outperforming the S&P 500. Let's find out a little bit more about that. This is designforwealth.com. We're gonna have a very special guest who represents a firm that is prominently featured in all of our analytics. And before we start any meeting, just in the engineering world, we like to have a purpose and an agenda. So I just wanna start with the purpose is to introduce readers and viewers to Liquid Strategies and, in that introduction, help them as they're making an assessment on ETF issuers and the management because that is a qualitative judgment. Not all issuers are created equal. And so as we have to add some subjectivity, it's nice to actually hear from the issuers themselves. And so we wanted to give that forum to help people make more informed decisions as they're designing their portfolios. So that's the purpose in a nutshell. The agenda will be introductions. You can introduce yourself, your firm, and then I'll introduce Design for Wealth and some of the data we have that led us to your firm for inclusion and our portfolio design.
And then discussion about some common misconceptions, really what I would call AI conclusions that are erroneous. There's three failure-mode types that I'd love to discuss with you just given your long history and expertise, and I field questions on them all the time. So it'd be great to hear if there's some overlap in your response to some of these kinds of questions. And then just a couple of questions and answers at the end. So without—
Yeah.
—further ado, I would love for you to introduce yourself and your firm and, just for scale, it'd be great in the introduction if you could include the size of your assets under management.
Yeah, absolutely. All right, Noah. Thanks for having me. This is really interesting to be with you here. So yeah, Liquid Strategies was founded back in 2013. So I'm one of the co-founders and the chief investment officer of the firm. And we started with a very specific idea that really defines who we are today, just this basic idea: how can we take someone's existing portfolio, give them more income without inhibiting their returns? And it sounds like a very simple concept, but when you look at the options market, everyone was doing it the opposite way. Yeah, they're generating income, but doing it in a way that was capping appreciation. So at the end of the day, the wealth for the clients they were working with actually was less over time, even though they're generating this cash flow. So for us, it was just a simple: let's help someone generate extra income, but do it in a way where they still get full participation, for good and bad, into whatever assets that they own. And so we started running the same put-spread selling strategy that we have today on top of separate accounts, and we were completely agnostic to what was in these separate accounts because it was the same core strategy regardless of what the clients had. And so we started running this income strategy.
Back in 2013. And, you know, if you look at it today over the full time frame, it's averaged almost 300 basis points annualized in income. That income that's being generated sits on top of whatever returns you're already getting in those portfolios. So it's been very additive over the long term to the total return of the portfolio. So more total return gives you more income, it gives you more principal, it gives you more of kind of everything that you want. In 2019, we ended up having a group of advisors who had client accounts that weren't big enough for doing that strategy in an SMA. So they came to us and said, hey, can you run this in an ETF wrapper? And so that's when we launched the Overlay Shares back in 2019 and basically gave advisors the ability to access our overlay on top of whatever beta that they chose. So we have three equity ones. We have OVL, which is large-cap equity, OVS small-cap, and OVF foreign equity. And then on the fixed-income side we have three: OVB core bond, OVM muni bond, and OVT short-duration bond. So an advisor could just come to us and say, look, I want to do a true kind of 60/40 portfolio. I want to mix some large caps, some small, this, that. They can just mix and package together whatever they want, get exposure not only to our overlay but to that exact underlying mix. And, you know, that's primarily what we do. We've kind of branched into some other related strategies, but we're now approaching $2.1 billion in assets under management.
And so we've just grown this through really educating the marketplace because there is a lot of misinformation out there around option strategies. And it's taken a lot to kind of rewire some people's thinking in terms of different ways to do this in a way that is differentiated from the way most people out there are doing it today. So that's kind of who we are in a nutshell.
And obviously, with your assets under management, incredible. I mean, that's a market signal that you guys are doing something right. In my introduction on designforwealth.com, I'll kind of show what you guys are doing right, and it'll segue into that introduction because I'd like to get a little more into your differentiation because it's very odd. As we go through all of the quantitative analysis, there are different types of high-distribution ETFs, different strategies, and they are almost all predominantly covered calls and sacrificing—
Yeah.
—a whole amount of upside. And it's just interesting that you guys are outliers when your approach is so successful. And so I'll share the screen. To introduce ourselves, we just want to apply engineering principles to high-distribution ETF portfolios. And if I had to put that in layman's terms, it would be: can we just do this with some rationality? We don't want to be in crypto. We don't want somebody promising 36% distributions. Can we just take a rational engineering trade-off-analysis approach where we put some principles in place for the design, and that can serve as some kind of scope that limits us from making bad decisions? And I see a lot of bad decisions in this space, specifically with NAV destruction. So obviously that's where the whole sector gets a really bad name, because there are some bad actors where people can point to some high-distribution ETFs where the NAV has crashed. And so how do we make sure that we're designing a portfolio where we're taking all the relevant variables together to have the least risk possible, with the highest cumulative return, with consistent income, while having NAV appreciation? And so that's a lot. And oftentimes those are in conflict. So if we're talking what's your terminal wealth at the end, if you're trying to take distributions every month, that's a conflict. You're obviously not going to have as high a terminal wealth at the end. So we kind of look at it holistically and—
—put some design principles in place. We're not gonna go through the level of granularity on the site, but the very first real quantitative analysis we get into is really interesting to me. And I think it speaks to your differentiation. I would love to hear your perspective on that differentiation and why people aren't mimicking what you are doing. We have three models that we run. We run an actual— a lot of high-distribution ETF funds are relatively new. And so it's not fair to do an apples-to-apples cumulative return when some of them entered the market at dramatically different times than you guys.
Absolutely.
So what we needed to do is say, what's the most common term we could use from a duration period? And we found the sweet spot was February 1, 2024. We could get a lot of the high-quality big names people know, a lot of people trust, and we can run it. True apples to apples, dollar to dollar: what actually happened? Not hypothetical, not simulated. What actually happened? And so when we ran that analysis, obviously we wanted to put in, if we're looking at terminal wealth without distribution, just a broad index fund, which is obviously what AI is gonna say. Hey, you want to maximize your terminal wealth at the end of your life, then you need to be in a broad index fund and just pay yourself out of it as needed by selling. Then obviously there's some tax implications and some other things there. So we kind of wanted to mix in: okay, what are alternatives? And so what's the alternative? What's the opportunity cost? If we're gonna own OVL or, let's say, JEPQ, what is going to be the opportunity cost for that? And for me, my whole life, I've just been a Charlie Munger, Warren Buffett kind of intrinsic-value guy. I am not going to beat the market. I have no interest in beating the market. I would like to own the S&P 500 and check on it in 30 years and be done with it. And so that's kind of the benchmark that everything's gonna get measured against because that's my true alternative investment. And so, let's just say hypothetically, if somebody's in a position to max out their IRAs, their 401(k)s, and they have excess money to invest—
—at the end of the month, are they gonna buy a rental property for an income stream? Are they going to buy the S&P 500? Are they going to buy high-distribution ETFs? And I've got a neighbor who owns some rental properties and an Airbnb. And after hearing those kind of nightmare stories and seeing the pictures—
Yeah.
—and seeing the video of the SWAT team breaking down the metal gate, and search warrant inventories, I said, you know what, I don't think that 14% that you're clearing is probably gonna be worth all the headache in the end. And then you come and see something like OVL that's very competitive to some of these alternative investments that people are making. And so as we look at it from an alternative perspective, we've got on this chart the opportunity cost in percentage points against the S&P 500. And we just use VOO as the stand-in for that. And so it's not really fair to compare something like OVS to the S&P 500. And it's not—
Correct.
—and definitely not fair to compare OVF to the S&P 500. Even though if you ask AI, if you look at the big banks and institution forecasts for the next 20 years, they all say U.S. big names can't keep this explosive growth, they're going to cool off, foreign and small-cap are going to outperform. So these are the kinds of things that when AI is trying to give advice and do its analysis, it's drawing on that kind of data set. And so when you do want to compare OVF to something, yeah, it might have underperformed the S&P 500 in the last ten years, but who's to say that the next ten years it's gonna be the same? And so we really want to do an apples-to-apples, fair comparison. So what's the best thing to compare OVF to but the international—
Yeah.
—the international equity benchmark? And same with OVS when you look at the S&P SmallCap 600. We kind of look at what would be a fair comparison from an opportunity-cost perspective. This column, opportunity-cost comparison versus VOO in percentage points, you can see all of the rows in yellow are high-distribution ETFs. And so OVL, GPIQ, OVF, those are the top performers in total cumulative return. What's really interesting is, okay, those do well against the S&P 500 if I have to pick an investment, but the opportunity cost on this column, where it's versus the designated benchmark, there's only three green with respect to high-distribution ETFs. And they happen to be OVS, OVF, and OVL. You guys—
How about that.
—have consistently, you're outperforming your benchmarks. And even when your benchmark is the S&P 500, which Buffett and everybody will be the first person to say you can't really do that over the long term. But you guys have been around for quite a while, over half a decade, and you guys are consistently doing something that your competitors can't. So I'd like our listeners and our readers to better understand what this differentiation is. How are you getting these green squares on this chart?
Yeah, boy, a lot to unpack there. And it is really cool to see this chart you pulled together and see our three funds on there. That makes me very proud of what we do. And so when you think about option writing, it's done primarily to generate income. And this goes back all the way to the beginning of the firm. We knew that there's two ways to generate income writing options. You can sell above-market covered calls on the S&P 500 or whatever, and you collect that income, but then once again the stocks may go up or the market may go up, and you may lose appreciation, which is going to cause you to lag both the S&P 500 and whatever your underlying benchmark is. So that's choice number one. Choice number two is we could sell the other option, which is selling below-market put options. And this is why we're still unique. We're starting to see some copycats now, as you can imagine. But this is why we're still fairly unique in the marketplace, because investors for a long time weren't even that comfortable with covered-call writing. They didn't understand it. They thought all options were bad. Now everyone loves covered calls, but people still get spooked by selling puts because they hear nightmares about, what if you sell the put and the market—
—goes to zero? And, you know, there's just all this stuff that's out there that keeps people from really taking all of that seriously. But it is important to think about risk. And so the way that we run our put-selling program, we do everything through spreads. And what that means is for every put that we sell, we own one at the same time to give us kind of disaster protection, is the best way to think about it. So our marginal risk at any point is no more than about three percent. So, you know, we're never at risk of some massive overnight underperformance relative to the market because our risk controls are so tight. And they can be because we're not trying to make eight, nine, ten percent by selling downside puts. We're just trying to beat the market. So anything that we make, even if it's 100 basis points above the benchmark net of fees, that's going to really move the needle because you just think of reinvesting dividends over the long term, that compounding effect is really going to move the needle. So if we can generate a couple hundred basis points, you're really starting to add some value. So that's the big difference. Do we collect income by selling above-market calls or generate similar income by selling below-market puts? But by selling below-market puts, we want the market to go up.
Like, we want it to go up as much as it can. I don't want to be in a position where I'm kind of rooting for the market to not go up because I've sold these covered calls and I don't want to look bad because they've left appreciation on the table. We don't worry about that. The only concern with us is that by selling this kind of incremental risk on the downside, if the market falls, our investors are going to lose more than their underlying benchmark. But for anybody who understands what we do, that's the cost of doing business. And that is why this opportunity exists. Think about it like we're effectively selling below-market insurance to investors that are out there. Well, no one would buy that if the market didn't ever fall. So we need those occasional drops in the market where our strategy loses money because that's what keeps the opportunity alive and also kind of resets the whole thing. It's kind of like a full-circle situation, where more and more people get into the strategy, then the market falls quickly and a lot of people exit it. But for us, that just creates this nice ramp. So if you think about 2018, we lost about 5% or so in our overlay. But then from that point on, we had a great few years until 2022. We then lost some money again. But then since then—so we go through these cycles. So as long as investors—
—are comfortable with occasionally losing more than the market in exchange for long-term outperformance, we could potentially be a really good fit. But for investors who are highly sensitive to risk, I would argue, well, first of all, maybe they shouldn't be in large-cap equities to start with. I mean, with OVL, if you're not comfortable with the risk, maybe large-cap equities aren't the right investment for you. And we're not adding so much additional risk to it that anybody who's investing with us is spooked by the additional risk that we're bringing. But that is the trade-off. So the trade-off is the risk of losing more in the down market, but over the long term, you can see the results from covered-call writers: the risk of giving up appreciation over the long term. That is a real risk, and that's not a risk that you can ever kind of get back. Once you lose that appreciation, you can't just hold on to it and get it back. Whereas if the market falls, you kind of have time on your side that eventually it can recover for you. But once you lose that appreciation selling covered calls, you've gotta dig your way out of that hole somehow.
Yeah, it reminds me, I've been on a board of directors for a very historic academic research institution, and there's a large endowment, and the large big-name bank that manages the endowment comes every year and they'll have their PowerPoint and have great reasons why they're slightly underperforming the S&P 500. And what they'll do is they'll throw a bar chart up and they'll show, over the last hundred years looking at the market, here's how many times the market went up five percent in a year. Pretty big bar. Here's how many times it went up ten. Here's how many times it went up twenty. And then when you go negative, how many times it went negative five, which is pretty small, negative—
Yeah.
—ten, very small, negative twenty, very small. So when you just look at it visually, the law of averages makes it really clear. It reminds me of your strategy to where, you know what, there's going to be those down years. There's gonna be some of that downside risk, which you guys do a pretty good job of mitigating by covering the bottom end, but there's gonna be so many more times where the market's going sideways or slightly up. And so if you just kind of look at that average, yeah, that trade-off, as long as you're a long-term investor, seems very rational and straightforward.
Yeah, and here's another key consideration, Noah, this whole idea of NAV erosion. And so a lot of strategies that are out there that are paying these higher yields, their total returns are lower than their yields. And so what that means, if they make 8% and they're paying out 12%, you're gonna have four percent in NAV erosion. So next time you go to generate that income, you're doing it off of a lower principal base. So over time, yeah, you're getting the same percentage income, but in dollars it could be going down. If you start off with a million dollars, but it's going down by forty thousand dollars a year, that's gonna drop your income. Even though it's still 12% a year, 12% of $960,000 is less than 12% of a million. So NAV erosion is very real, and investors should be very thoughtful about what yield is being paid to me and is there enough return to support that yield that's being generated to avoid me having NAV erosion. So going back to your introduction, if your goal is, hey, I've got this nest egg and I just want to live off of this with the income that I can generate, well, once again, the total return that you're getting off those assets better equal or exceed the income you're taking off if you don't want to dip into the principal. It's just kind of simple math.
Yeah, and when we get to the AI failure modes where it can give some erroneous conclusions, we'll get into the ROC, which is the hot-button topic. One thing I'll mention on what you just said: my wife will hear me talk about Liquid Strategies OVL quite a bit. One of her initial questions is, yeah, it sounds great, but what happens when the market crashes? And I think that's another interesting differentiation from you guys, is you guys have really experienced three true bear periods. There's data where your results can be judged from that. So what kind of downside risk is there? And like you had mentioned, you guys have already experienced some pretty big market downturns. And so one of the questions that is interesting in doing the analysis, where AI misses it quite a bit, is you guys actually changed your distribution strategy on OVL fairly recently. So if we're judging what happened five years ago in the fund, that wasn't the same distribution rate. I'll let you explain that transition and, more specifically, since you made the transition to a higher distribution rather than putting premiums that you're making back into the NAV, have you noticed the sustainable results that you were hoping?
Yeah. And so first and foremost, while our distribution policy changed, the strategy itself didn't change at all. It's the exact same strategy. So no changes. That's a very, very important point. So all we did was put our distribution policy in line with the rest of the market, doing that in response to the demand for income that's out there. So for covered-call funds, the income that they distribute is the income that's generated from the call options when they sell them. So it has nothing to do with the final outcome of those call options. It's how much am I collecting from selling those call options? That's what I'm going to distribute as income. And so then the differential between that income and the total return comes between, well, how much did the market go above that call strike and then the income that I collected? How much did I give up for that income? And so all we did is we adjusted our policy to better match the actual income that we're generating when we sell those put options. So just like everyone else, regardless of the final outcome, we're just taking, hey, whatever income we generate, we're just going to pass that through. And, to use your words, instead of reinvesting all of that back into the funds, we're just going to distribute that out and let investors make the choice whether or not they want to put the capital back in. And we were very comfortable doing that.
Because we just believe over the long term we're gonna have the total return that's going to be able to support that sort of yield without having the big NAV erosion. If we didn't think that was gonna be the outcome, we wouldn't have made that policy change.
All right. Any other areas of differentiation that you wanna mention before we move on to some of the critiques?
No, I mean, it's primarily around the strategy itself, just selling put spreads versus covered calls. But I will say probably the thing that we have a reputation for in the market is being highly sensitive to risk. And so we're very, very mindful of risk. We'd rather miss opportunities and take down risk in certain types of environments than just kind of cross your fingers and hope things work out. And the pandemic's a really good example. During that period, to say there's uncertainty is like the understatement of the year. There was no way to properly price options at that time. What volatility assumptions are you going to use when everything, the economy, is shutting down, right? So there just comes a point in certain environments like that—and I've been doing this since 1997—where you're better off just stepping away until the market normalizes again. When I say normalize, that doesn't mean it goes back to being business as usual, but at least when the options market starts to properly price risk back into these options. And so, you know, we're out for weeks waiting for the market to normalize. But you know what? Yeah, we may have missed some opportunities there, but the name of the game for us is just kind of slow and steady and really having a mind toward preserving capital to the extent that we can.
And when we get into our framework, we'll talk about the ROC and NAV erosion next, but one of the things that I'm seeing as a market trend in this high-distribution ETF space is trying to say a fixed number. We are marketing this fund with an actual—I'll just throw them under the bus—15. Here's our lineup of funds, and they're gonna be 15 forever. And for me, I would much rather have a widely variable, yeah, we're gonna go down to seven if there's COVID and everything is priced wrong and we have a huge problem, rather than you do true non-sustainable ROC. I don't want ROC to help your marketing number that you're putting out there. And so in your worst-case scenario with your current policy where you really are doing a very high distribution, what kind of range do you think the low end would be in one of those really difficult and worst-case-scenario environments?
In terms of the potential NAV erosion?
No, in terms of the actual distribution that you would be paying.
So you may not like this based on what you just said, but we have chosen to fix ours, and there's a reason to fix it. And it really ties into NAV erosion. Well, it ties into a couple things. One, investors are clamoring for predictability. They would just like to know what their portfolio is going to pay out regardless of what's happening in the market. But the other part is sustaining the fund in a way where we don't have NAV erosion. And let me tell you what I mean by that. So if we tied it only to the option premiums that we actually collect—first of all, the ten and a half is very representative of what we actually do collect over the long term. So we didn't just put our finger up in the air and come up with that number. It is representative of what we expect over the long term. But if we set it too high, that to me is where it starts to get problematic. If we let it float, well, you have to let it float both ways. And I was concerned that there could be some environments where volatility is very high. So there's a direct correlation between volatility and the amount of premium that we can collect. So if vol goes up significantly, that income that we collect may go from 10.5% to 30%.
But at the same time, those positions may be less profitable too, because there's more uncertainty in the market and more risk in the market. So we didn't want to be in the position where we're paying out so much of that, knowing that we may not have the total return supporting that payout. And as a matter of fact, we know pretty much for certain we wouldn't in that environment have the total return to support it. So we're just highly sensitive to putting investors in a situation where we're making distributions that can't be supported by the returns of the strategy. So it's one of these things: we had to make a choice. Do you let it float and take that risk of having it be too high? Or do you set what I think is a reasonable rate based on historical norms and go with that? And we chose the latter.
Yeah, and I mean this is for educational purposes, so it's not that I would object to that. In fact, one of the key elements of designforwealth.com that's really important, especially for training AI to be able to assess these things, is the use cases. And that's where it gets a little dicey. So if your use case is, yeah, I'm paying my mortgage with this, then you do need that predictability and therefore—
Yeah.
—your strategy would make perfect sense for that particular objective. All right, so speaking of some of those issues, we'll get into what I call the AI failure modes. So one thing will happen: we'll have Design for Wealth, we'll have three models. We'll have the actual that we looked at, what actually happened since February 1, 2024, apples-to-apples comparison. Then what we'll do is say what would have happened had we had data for the last 15 years, which is a little more difficult because obviously you're reconstructing strategies that didn't exist fifteen years ago. And then we have Model 3, which is a true Monte Carlo looking at different probabilistic outcomes, factoring in the randomness of the universe and seeing what we think future return would look like. As we go through, we set this up very transparently. You can download the spreadsheets with all the underlying data, case study published, full disclosure. I own OVL, OVF, OVS, so I am speaking as an owner and putting my actual account on there to look: here's what the distributions actually look like. This is a real thing.
Mm-hmm.
AI really struggles in what I would call three failure modes. One is a semantic failure where it sees ROC and it automatically means NAV destruction, unsustainable ROC. And it'll just flag everything saying, well, they're destroying your capital. Don't buy a high-distribution ETF. And I have to feed it representative samples like OVL. OVL is like the premier re-education for frontier models these days. Okay, well then how do you explain OVL? Because that's classified as ROC and this truly is an issuer making a tax-efficiency decision for investors, to where they can structure the fund where they can classify it as ROC and therefore you can defer the taxes via your cost basis, so you can take that money and compound it more efficiently. And it is a great benefit. In fact, after doing the analysis I've exited out of several funds because the ROC wasn't high enough, and so there is a tax drag. And so AI doesn't get that. The very first assessment, it's just gonna automatically flag it. And the second one would be a heuristic failure mode. And that is where it says there's a low asset under management for the fund, which is something like OVS, where it's obviously not as big, and AI will automatically flag it. Well, therefore there's a liquidity risk. And your bid-ask is gonna be higher spread, and I'm looking at it, it's like—
Yeah.
—AI, you realize the bid-ask, we're talking about like one penny, right? Like, this is so trivial, it shouldn't even be a weighted variable in this. But to see the red flags on an AI assessment that people send me, well, look at this. And so—
Yeah.
—that's a true heuristic failure on the AI. I mean, it really should be able to recognize from a weighted perspective what variables really should be included in these kinds of conclusions. And then the third would be what I would call an objective failure, like an objective-function failure, where it will look at a portfolio and it'll say Portfolio A produces the highest terminal wealth on your deathbed. Portfolio B is two percent less terminal wealth. Therefore Portfolio A is better. Stay away from Portfolio B. Well, an objective function is: I have utility for that money over the next 30 years. Like, I'm objectively benefiting. I don't want to die with the highest terminal wealth. I want to live with the highest rewarding life. Whether that is being able to take extra vacations with my family, whether that is giving to charities that I care deeply about while right now my money can be impactful, there are a lot of use cases. Whether it's somebody transitioning from a W-2 job to being self-employed and needing some stability and income while they make the transition, there's a lot of utility use cases that AI just completely disregards and is so fixated on this final terminal wealth on your deathbed. And so it'll be a huge red flag. And so we'll just start with each one of those failures. The semantic failure is probably the most interesting and hot-button item, and that's return of capital.
Yeah.
We have a whole treatment on Design for Wealth on return of capital. What is sustainable return of capital and what is not sustainable? And the real problem is as you guys, as ETF issuers, are issuing, all right, here's the percent ROC. There's no unit for someone like me to know if that was sustainable or not. It's like, okay, was that actual premium-based income that you brought in—
Yeah.
—or is this really truly return of capital for an unsustainable fund that someone has created? And so I'll let you speak—
Yeah.
—to that ROC dilemma. And it's hard to blame AI because there is no unit to be able to differentiate when something just says ROC, whether it's sustainable or not.
Yeah, well let me start by kind of the extreme example in the other direction of not using ROC, almost to the point where somebody's bragging about not using ROC. Okay. So there's at least one major fund—and I won't name any funds, I don't want to get in trouble—but there's at least one major covered-call-writing fund that takes great pride in all of their income being characterized as ordinary income. And it just baffles me. So at the very least, all of these option contracts should benefit from 60/40 treatment because of their Section 1256 nature, which means they should get at the very least 60% long-term, 40% short-term if you're doing it on the S&P 500 index. So the idea of saying, no, we don't want to do that, we're gonna convert it all to ordinary income so that you have a higher SEC yield, to me is just mind-boggling. So I think that's a good extreme example of why ROC should be considered because you certainly don't want to be in a situation where you're paying as much ordinary income as possible. Okay. Now, as far as when you don't want ROC, and I'll just take a very simple example: if you had a fund out there that just held the S&P 500, they didn't do anything, there are no options involved, but they come out and they say, we're gonna give you a 10% distribution or a 15% distribution. Okay, they're just saying, I'm just gonna give you your money back, and there's nothing in the chassis there that's supporting any income generation at all.
You could do that for any security out there. Just say, hey, look, I'm gonna set this up to give you 10% of your money back every year, 20%. Hey, why not make it 50% and just have this eye-popping yield? But it's really me just giving you half your money back every year. That's obviously a horrible use of return of capital. But if you look at it from a balanced perspective and you have an opportunity where a manager can validate that there is income supporting that level of income being paid out, then it just becomes the fundamental choice: if I'm gonna get 10% income, do I prefer for that to be ordinary income, Section 1256 treatment—which isn't horrible, it's still pretty good too—or would I rather have it go toward lowering my basis and basically having that tax impact go down the road and be treated the same as my shares that I own? I'll take choice C. That's what I would want to choose. And I think that's where the world's changing a little, starting to rethink that. So I think what AI's picking up on is the old red flag of, well, if somebody's paying out a return of capital, that means they're not generating income. And that very well may be true. So I would just caution investors to do their due diligence. I wouldn't just say any return of capital is great. I would say strategies that have return of capital with income that supports it and avoid NAV erosion, that's worth considering.
And you just hit the nail on the head at the end. So from a metric perspective, you gave the extremes on both sides. But then there's this middle ground where there are people doing covered calls, they're doing put overlays, they're doing these income-generating strategies. But to an average investor, do you know if they're generating enough money for the distribution they're giving you? They may truly be giving you ROC, not just an offset of the income they were bringing in. And to me it seems like the only metric I can use for that is long-term NAV. If there's NAV erosion, that's where that would show up. And would you say that's pretty much the only tool?
Absolutely. Yeah. That is the holy grail of identifying kind of the real yield that's out there versus the not. It is NAV erosion. It's really not that much more complicated.
So the next failure mode is the heuristic failure mode: this fund has low assets under management, therefore too big of a spread on bid-ask and liquidity—
Yeah, yeah.
—issues. And so I see something like OVS as a great example. It's like, AI, are you insane for flagging OVS—
Yeah.
—as this kind of liquidity risk? Looking at the issuer quality behind it, looking at—I'm not looking to day trade OVS. And so what are your thoughts on when the size of assets under management on a newer fund actually is a red flag, a variable that should be higher weighted? Just because even before Liquid Strategies, your options background is very impressive, what you've done in your career.
Yeah, it totally depends on what the fund invests in. Okay, so for us, let's just stick with OVS as an example. There's two investments in there. We're invested in IJR as the underlying, which has plenty of liquidity. And then we're invested in S&P 500 index options, which trade over a trillion dollars of notional value every day. So either one of these pieces, whether I manage 50 million in it, 100 million, or a billion in it, I can get in and out of those positions with ease, generally speaking, in just a normal market. Obviously, there's always going to be exceptions. But in a normal market, those are positions that are very easy to get in and out of. So those are the type of funds that should not get penalized for what they're invested in. Now, if our small-cap portfolio was a bunch of individual stocks that we're choosing, many of which are very, very, very thin, okay, it's a little bit of a different conversation. So it really just depends on what you're investing in. But if you look across our entire suite, it's all the same thing. We're in one or two—in the case of OVF, one or two—underlying ETFs that are very liquid and as low-cost as we can get them, plus this S&P 500 index option. So, you know, I understand the old concern is I don't want to be more than—
—X percent of a fund or this or that, but that came from a time or a place where there was a mismatch between how much somebody had in versus how much they didn't want to be the last one holding the bag. But that doesn't happen when you're dealing with a strategy that's extremely liquid, kind of at its core. Now, with that said, when you're dealing with smaller funds, there's still a risk. You want to trade it the right way still, which to me is you always want to work with limit orders, for example. And if you put in a buy of ten thousand shares at the market in any ETF, not just ours, there is risk that you're gonna really move it in a way that can hurt your clients if you're an advisor that's out there. So I would still just caution investors to be mindful of how they're getting their orders executed, but there's plenty of liquidity there for the taking as long as the underlying is liquid like ours is.
Yeah, and it's interesting when AI would audit something like Design for Wealth and it'll see some of those lower assets under management. The obvious inference it can make is this isn't something where there's gonna be a lot of limit sales on it. This is something where we're really emphasizing the accumulation DRIP period. So the only real risk is like the buying from that bid-ask. And so it's interesting that that always seems to get flagged. And I just had somebody last week send me a frontier model flagging this. I'm like, you need to tell it this.
Well, you know what? And from an asset manager's perspective, it's really frustrating because, you know, we think OVF and OVS are really good solutions that we really believe in. But if you have to overcome this, well, you have to have at least X hundreds of millions—well, how do you get there? It has to start off small. There are some companies, I guess, that can overnight bring in a couple hundred million and that's not a problem. But for most companies, normal firms or boutique firms like us, it takes time to get scale. And how can you get scale if everyone thought that way?
Yeah, and that's part of when we talk about the purpose of this conversation, is people being able to have some trust in the issuer, basically to be able to hear: okay, AI says this, but this is the actual reality from the issuer. And I think you make very good points on that particular subject.
Yeah, and it's great to have a platform where you can kind of get this information out because it's hard. It's hard to have the ability to get out there and kind of help people understand what's underneath the hood a little.
And then probably the biggest failure mode is that objective-function failure with AI, to where we get to that—and I already mentioned it—that terminal wealth. So AI is gonna just look at portfolios and say terminal wealth. And it's a really philosophical conversation about—you can really see the contrast between artificial intelligence and the human experience. And so as AI is optimizing terminal wealth, it's really failing to see that utility. And you really have to have extensive dialogue to say, okay, AI, I have a finite amount of time on this earth, and I want—
Yeah.
—to maximize every single minute of that. Would it be—
Yeah.
—better to die with a four percent higher cumulative return? Or to take distributions monthly and maximize utility. And so it can get it, but it is not its initial conclusion when it comes to high-distribution ETFs. And so—
Yeah.
—that's a real struggle. And so I'm curious to hear your perspective on that.
Well, that's a highly philosophical question. And I would just say it's extremely unique to every person that's out there. Everyone's gonna have a slightly different answer to this question. You're gonna have some people—and I probably am kind of in this camp—of why wait until I die to pass on whatever wealth I may have to my kids when that may be 30, 40 years down the road? And I would love to actually observe my gifting to them making a difference in their lives. That is something that I would take great pride in and would really enjoy. Then you have the people who are just like, hey, I don't want to have a dollar left over when I die. I just want my wife and I to have the best time possible for the time we're on this earth. All right, that's type number two. And you have type number three who wants to maximize their legacy, whether it's leaving it for their kids or leaving it to nonprofits or both, whatever they want to do. These are all just so uniquely different types of people and priorities. If you're a single person, you're not gonna care at all about terminal wealth. If you're just all alone, you don't have family, you're not gonna care about that at all.
But if you're very closely knit with your family and all this, your viewpoints are gonna be very different. So that's an area where I would just tell AI to kind of stay in its lane. You know, that's probably not a good lane for it to get into, like the emotional part of all of this.
It actually does process it pretty well once it's rebutted properly, and it would—
Yeah.
—pick Portfolio B. When all the information or use case is presented, it can get to, if I were a human being, I would pick B. And so it's interesting to see it make that thinking evolution toward the initial giant red flags that people text me: hey, look at this, you're not maximizing terminal wealth. And it's like, okay, well that's actually not the objective.
So I think where we are with AI, it's really interesting because you have some people who take the initial answer as, all right, that's what AI says, then that must be right or pretty close to right. That's one failure of AI, people just taking it as kind of the gospel. And I think the other failure—and I think it's getting better with this—is it's very much a pleaser. It really wants to give you the answer you want. And so it's almost a little garbage in, garbage out. It's like, okay, if you keep telling it you don't want to have money at the end, it's gonna relent and give you what you want, right? And so hopefully at some point it's going to evolve in a way where it has a little bit more of a backbone. Draw the line in the sand a little bit. Like, hey, I hear you, but here are the reasons why I don't think that's a good idea. We're just not there yet. But I think it'd be interesting when, A, people don't just take it as gospel, and then B, where there's not this desire to please and change the answers all the time.
Yeah, and there's a really straightforward use case for ETFs that you guys offer and potentially some of the others, although covered calls do cap upside. For people that want to have early retirement before they can access their IRAs or 401(k)s penalty-free, there are several years, potentially even a decade, where this is a use case where you really can have monthly income for your living expenses as a stopgap until you can reach your IRAs and your 401(k)s. And that one helps AI a lot. Yeah, well, of course. This is a perfect strategy for that. And so it really—
Yeah, good point.
—does come down to just making sure the right data is going into it to make sure that, yeah, you do understand what the objective function is. And so if you just upload something into some frontier model and say, all right, look at this portfolio, it's just gonna put back some very broad assumptions about assets under management size and about what the actual objective function is. It's just gonna make some generalizations that really are doing a disservice to a lot of confused people that don't understand ROC—
Yeah, absolutely.
—which is very good in some contexts as long as it's sustainable.
Yeah.
All right, and so since we're very short on time, just got a couple questions. And one that I'm really curious about is just from a supply-and-demand perspective. I still can't understand why there aren't more people copying your methodology. And obviously, and we didn't get into it, but from my research on you guys, you guys are very dynamic, closing out positions, you're really adjusting well. I mean, if you guys were boxers, it'd be a knockout every time. You really adapt, you adjust, and just to minimize risk—you guys are really, really good at minimizing risk, which I can appreciate. But I'm curious, as people start to copy you, there's going to be a lot of people. So people are going to start selling the same puts that you're selling, and they're going to try to buy the same puts that you guys are buying. And so as that supply of people trying to sell that same put increases, is there a risk that's gonna have a real impact on the premium you guys are able to make?
No, because if you look at the options market, there's what's called option skew, which basically means all of the above-market calls in the S&P 500 tend to be cheaper than below-market puts because there's the natural order flow. People are selling calls and buying downside puts. So the only time that's ever going to become an issue is if that demand from buyers for that downside protection dissipates and the supply of it continues to grow. And there's just no sign of any slowing of that buying frenzy that's on the downside. And I made a comment earlier: sometimes you start to see the buying slow because if we're in a bull market, people get tired of paying for downside protection. They just get fatigued paying those premiums. And so that could be a scenario where there can be a temporary, yeah, more and more people are coming into it. But at some point, when the market resets and you have vol go up quite a bit, all the buyers in puts come back in. And then they'll stay for a while and then they'll kind of slowly work. It's just this cycle that I've seen since 1997. So I'm not concerned at all about other players coming into the market. Yeah, there's gonna be a lot of difference in terms of, are some people doing shorter term versus longer term? So I don't think anybody's gonna go in and do exactly what we're doing because—
—I could change all of our positions today and they won't know about it until tomorrow. So, you know, they could find themselves in a situation where they're just chasing their own tail, so to speak. So yeah, I'm not worried about that either from an efficiency perspective or from kind of a market-share perspective. If people want to copy us, I mean that's like the sincerest form of flattery, right?
Yeah, so it was just one, like I said, one of those questions on, if there's too much, is it gonna impact—
Yeah.
—the premium? But from what I've seen and even on AI assessment, it's like, nope, this is pretty much—I would think there's some kind of finite demand, but it's—
And we do watch that risk premium. So that really comes down to risk premium. How much are we getting paid for these options versus how much are they really worth based on volatility in the market? And there hasn't been any compression in that time premium or in that risk premium. So it is something we're mindful of.
And then one of the other closing questions, I still don't have an answer and I would love to hear your expert opinion. Why, if covered calls give up so much upside, and you guys have proven for a long time that put overlays are a superior strategy in this kind of market, why are they still doing it? Why is the whole sector so dominated by that, I mean, for lack of a better word, empirically inferior strategy?
My opinion is it's a very, very easy sell. When you get in front of an investor and say, look, Mr. Jones, I can generate 10% a year in option premium for you, and you still get to keep some upside potential in the market. And if you just stop there, the client's like: sign me up. I can keep some appreciation and get 5%, 10% in income. That's just fantastic, right? But if they knew over time that, well, that year that you made 15% total, the market was actually up 40, so you actually left 25% on the table, that's the part of the story they're not getting. In my opinion, if they really knew that part—they know going into it they're capping their upside, but I don't think that there's a real effective auditing that's going on of, well, at the end of the day, what did this covered-call writing cost me? But it's actually very easy to do, right? Because if you own the S&P 500 and you're selling covered calls against it, I can tell you exactly what it costs you: what's the underperformance relative to the S&P 500? That's what it costs you. But it's so easy, it's very easy to talk to people about this, and we're very much in a copycat business. And so once you see ideas take off, whether it's buffered strategies or any of these other things, once someone sees something take off, everyone comes out and starts to want to do it. And we're kind of starting to see that with us now. So even though we've been doing it for seven years in the funds, longer than that—
—in SMAs, we're just now, because we're starting to get attention, the right information is starting to get out there. You're just now starting to see other people come in and do this. But that's just the nature of how this business is. So I wouldn't be surprised if in three to five years this kind of approach ends up being as big as covered-call writing. I wouldn't be surprised.
Interesting. To your point on the opportunity cost, yeah, you made fifteen percent, but actually whatever index you're comparing yourself against, whether it's Nasdaq or S&P 500, something like chips, when you look at some of those sectors and you look at some of these funds—and it's in Model 1 on designforwealth.com—you actually see underperformances of thirty percent. I mean, if you would have just bought that broad index that's the exact same underlying securities that this high-distribution ETF strategy is, you left thirty percent on the table.
And the way that I think about that is, well, give me the extra thirty percent and I'll pay myself whatever income I want to pay myself. I'll pay my—
Or—
—extra 30% that I just made. I'll just take that out as income. You know, how about that? How about that as an income strategy? But, you know, I love to see the information in a forum like this. I love to see this information start to get out there. And I think people are starting to see it. But this has been a strategy that's been in vogue. It's been used for a long time, but it got really popular six or seven years ago. And sometimes it's hard to retrain the way people are thinking about this. But I see it slowly happening.
Yeah, I'll just say this site formerly known as Twitter, man, there are definitely some people that could benefit from understanding opportunity costs. You just see some of these things that are hype where, literally, had you bought the underlying index that whatever it's trying to represent, you would have just done a lot better. And obviously thirty's an extreme, but ten isn't. I mean, to be off by ten, fifteen is very, very common. And so that's why it's so fascinating. You guys are the only three green blocks on Model 1 on performance relative to whatever the underlying benchmark is.
Well listen, I thank you for being aware of that and appreciating that. And I appreciate everyone who's kind of tuning into this that sees the value of what we do. Like I said, we take great pride in this. We've been doing it a long time and really take a lot of pride in it.
One last item. So at the bottom, this is all quantitative analysis. Like I've mentioned in this conversation, the three models, all quantitative, actual case study, quantitative, and then we get to our qualitative. And so our issuer and management confidence assessment: Liquid Strategies with a Very High rating. And so this is just that due diligence. You guys have obviously had the track record, you've got the team with the experience, and Model 1 doesn't lie. You guys obviously have the results. As people become educated, especially on social media, it's really good to look at some of these different ETF issuers, and the ultimate subjectivity is: do I trust this issuer? Because your NAV erosion is going to be a lagging indicator. So you're gonna find out you lost a bunch—
Very true.
—of NAV pretty late in the game. And so it's better to be making those upstream decisions. And so that's where I definitely encourage everybody to just try to do your due diligence on ETF issuers and really try to look at basic variables like organization-wide investment experience, institutional resources, scale, management, operational depth, and the product and platform execution. And so obviously you guys are off the charts on product and platform execution. Not to say that you're lacking in any other category, but man, you guys are really standing out in any rational assessment there. So I'll just end with that. Kudos to you. Definitely we'll continue to watch you.
Thanks, Noah, that's great.