Raymond Micaletti, MOOD ETF
How Institutions Beat Retail
Raymond Micaletti studied engineering at Notre Dame and Princeton, where his focus on probabilistic engineering mechanics turned out to be a straight line into finance. The math used to analyze buildings in earthquakes and airplanes in turbulence is the same math used to price options. He spent the early part of his career in systematic long-short equity and global macro before pivoting to tactical asset allocation around 2014. That pivot led him to develop the relative sentiment indicator that powers MOOD, his ETF built on how institutions position themselves relative to retail traders.
On this episode of Behind the Ticker, Ray sits down with Brad to walk through the origin story of relative sentiment, how the MOOD ETF actually makes allocation decisions week to week, and why a former engineer from Princeton ended up breeding longhorns in Puerto Rico while running a one-of-a-kind tactical fund.
Discovering Relative Sentiment
The core idea behind MOOD came from a Lehman Brothers quant equity pamphlet that had been sitting on Micaletti's desk for a decade. The pamphlet contained an indicator tracking how institutions were positioned relative to retail traders. He implemented it, tested it, and found it was okay but not great. He didn't let it go. He added new information, smoothed out the signal, and something clicked.
The real proof came in two market selloffs. In August 2015, the market dropped and every indicator in Micaletti's suite went bearish except relative sentiment. The market then rallied 10% in a straight line. Six months later, same pattern: 10% selloff, everything bearish except relative sentiment, and the market took off and didn't look back. He discovered that whenever momentum was negative but institutions were buying the dip, the market tended to produce its best annual returns.
He wrote a paper on the findings and sent it to Wes Gray at Alpha Architect, a PhD from Chicago who studied under a Nobel Prize winner. Gray challenged him to control for additional variables. The results held up. Gray's assessment: "This is interesting. It's not momentum, it's not trend, it's not value. It's its own thing. You should launch it as an ETF."
How MOOD Actually Works
MOOD runs five distinct models, each capturing a different dimension of relative sentiment across different asset classes. Micaletti runs these models once a week on Wednesdays. Three models use weekly data, one uses monthly data, and the fifth uses daily data that he smooths by taking a multi-day average.
The models produce a target equity allocation. If that target is within 10 percentage points of the current allocation, he doesn't trade. So if the fund is 50% equities and the target comes in at 56%, it stays at 50%. If it moves to 65%, outside the band, the portfolio adjusts. This keeps turnover in check and avoids trading on noise that won't meaningfully move the needle over short time horizons.
The equity side of the portfolio is purely rules-based. The non-equity side involves some art alongside the science. For non-equity positions, Micaletti looks at three things: whether the signal is bullish (rules-based gate), the expected duration of the signal, and the historical performance metrics like Sharpe ratio and max drawdown. He's working on formalizing this into a full optimization, but for now there's a qualitative overlay on the non-equity sleeve.
Building the Business Without Being a Salesman
Micaletti is refreshingly honest about his temperament. "My hobby is to decipher the market," he told Brad. "I'm one of those plant guys that just wants to be kind of left alone. I'm not a sales guy." When he asked Wes Gray how to pitch the fund, Gray told him he wouldn't be able to sell it for the first three years. Micaletti's response: "That's fine by me."
The fund's performance did the talking. People found MOOD on their own and reached out. He now works with a media company that has a network of tactically-oriented RIAs and gets warm introductions. His comfort zone is one-on-one conversations, not pitching to large groups. And he's deliberately not looking for a step function in AUM. "If you go through a period of mediocre performance and you have all these disappointed people," he said, the steady, organic growth he's seeing is ideal.
Key Takeaways
- MOOD is built on a relative sentiment indicator that tracks institutional positioning versus retail traders. When institutions buy the dip while momentum is negative, markets have historically produced their best forward returns.
- The fund runs five models weekly, with a 10-percentage-point dead band around the target equity allocation to control turnover.
- Wes Gray at Alpha Architect vetted the research, challenged the controls, and ultimately said the signal was distinct from known factors like momentum, trend, and value.
- The equity sleeve is fully rules-based; the non-equity sleeve incorporates signal strength, duration, and historical performance with a qualitative overlay.
- Micaletti moved to Puerto Rico, breeds longhorns, and freely admits he's not a natural salesman. The fund grew organically through performance and word of mouth for its first three years.
Listen to the full conversation on Spotify, Apple Podcasts, or YouTube.
Full Transcript
6,430 wordsMachine transcribed from Brad Roth's conversation with Raymond Micaletti, MOOD ETF, with speakers identified automatically. Timestamps link to that moment on YouTube. Lightly cleaned, otherwise unedited.
Welcome to Behind the Ticker, the podcast where we go beyond the symbol and into the strategy. I'm Brad Roth, founder and chief investment officer at Thor Funds. And in each episode, I sit down with ETF managers, CIOs, and industry leaders to break down how these funds are actually built, how they behave in real markets, and how advisors use them in real portfolios. Most people just see a ticker symbol, but we know much more goes on behind the ticker.
Hey, Ray, welcome to the show. Thank you, Brad. It's great to be here.
So before we get started, why don't you take just a little bit of time, tell everybody a bit about your background. I saw you were a civil engineer at Notre Dame, then you went into engineering at Princeton, and now you're a founder and I would assume chief investment officer over at Relative Sentiment Technologies.
That is correct. So yes, I studied engineering in grad school, and people often think it's a big leap going from the grad school, the engineering I was doing, to Wall Street. But it's actually an easy transition because in grad school, my focus was on what was called probabilistic engineering mechanics. So it was essentially looking at engineering systems that were subject to random vibrations. So if you can think of like a building in an earthquake or an airplane flying through turbulence, and the math that's used to analyze those systems is the same math that they use the price options. So it was kind of like an easy segue into Wall Street from there. In the early part of my career, I was doing systematic long-short equity and then global macro. And then around 2014,
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I switched over to tactical asset allocation. I didn't really know much about it at the time. So I did a lot of Google searches, came up with papers that I was reading and put together some value, momentum, sentiment strategies. But I thought I needed a few other strategies to kind of round out the suite. And I had this pamphlet on my desk from 10 years earlier from Lehman Brothers when they were still in existence. It was a quant equity presentation. Flipped through it, and I saw this indicator. And it was looking at how institutions were positioned in the market relative to retail traders. And I thought, well, I like that concept. There's smart money, there's dumb money. I often felt like I was dumb money. So I really was interested in this indicator. I implemented it, tested it out,
And it was okay, but not great. But I really loved the concept. So I didn't let it go. I added some new information, smoothed it out. And all of a sudden, it was pretty interesting. So I threw it into the mix. Again, didn't think it was anything special. But then in August, this was late 2014 when I stumbled upon it. And then in August of 2015, the market had sold off. All of my indicators were bearish, except for this one relative sentiment indicator. And the market rallied 10% in a straight line. And I thought, oh, that's interesting. Fast forward another six months, market sold off 10% again. Everything was bearish in my indicator suite, except for relative sentiment. Market took off and didn't look back. And I was like, darn, it's not cool being on the wrong side of that. So I went and I looked,
And it turned out that anytime momentum was negative, but institutions were buying the dip, market tended to have their best annualized returns. So when I learned that, I just started focusing on relative sentiment. I wrote a paper on it, sent that paper to Wes Gray at Alpha Architect. And he has a PhD from Chicago studying under a Nobel Prize winner. He has sort of the pedigree to kind of critique what I had done. And I knew that I was making bold claims in the paper about market timing, factor timing. And he looked at it and he said, okay, well, you controlled for a lot of things, but did you control for X, Y, and Z? And these were some things that were sort of outside of the
Scope of what I had controlled for. I said, no. He goes, well, control for those. So I controlled for them. The results held up. He said, wow, this is interesting. It's not momentum trend. It's not value. It's its own thing. You should launch it as an ETF. That was about 2018. I was working at an RIA. They had sort of different interests. So I kind of put that on the back burner, but then COVID came and I was looking for a new opportunity. And Wes revisited the idea of launching this as an ETF. He said he had people lined up to invest in the company and in the ETF and let's do it. So that's how it happened. And that was four years ago. So a little bit
Securitist. We're going to talk a lot about Mood today. I'm fascinated by your process and can't wait to hear more. It's funny when you and I first met a couple of weeks ago, I was just joking with you. I've got 19 inches of snow. Wes actually also outside of talking you into starting an ETF, talked you into moving to Puerto Rico. So as we were talking, I'm a little bit jealous. So I like to ask all of our guests, what do they like to do for fun? You and I can geek out on technical indicators and quantitative indicators all afternoon, but what do you like to do for fun when
You're not behind a desk clicking buttons? Well, see, that's the thing. If you ask my wife, she'll say, I don't know how to have fun and that I never leave the house. Because to be honest with you, what I really love is like puzzles and brain teasers and what bigger puzzle or brain teaser than the market, right? So I love trying to decipher what's happening in the market, testing my ideas against reality, which is basically your P&L. So I really, so I view my work as a hobby. But outside of that, we have a five-year-old son that keeps us busy. I try to hit the gym. I try to practice the piano. I try to, every week, Wes and I meet at the golf range. We
Have little competitions so you can get the balls closest to the practice pins. That's basically it. So it's work, family, piano, golf, gym. That's basically it.
Love it. So let's talk about, the mood ETF at a very high level. First of all, I love the ticker. I think it's, it's perfect for what you're doing, but at a very high level, what is kind of the investment methodology behind mood and really what is it trying to accomplish?
Okay. So it's a very high level. It's a multi-asset go anywhere, tactical asset allocation ETF that seeks capital appreciation. It tries to do this by attempting to align its allocations with how the so-called smart money is positioned relative to how the not so smart, or I don't like to say dumb money, but the dumb money. You can say it. I feel like it's a curse word.
I'm on Twitter too, Ray. I see a lot of the dumb money. Go ahead.
Um, and then the reason is, is because, there's a lot of empirical research, um, that shows that institutions tend to have better outcomes in the financial markets than retail traders. And you might ask, well, why is that? And I believe there are several structural reasons why institutions tend to outperform. One is that they just have better information networks. When I was at hedge funds, they would have, ex-fed governors come in every week and just discuss what their friends at the fed were going to do. Senators would come into the office. Uh, they would back senators for, political positions. Um, so you just have better information. They have lobbyists, they have, fed governors on speed dial. They
Talk to corporate CEOs, better information. So that's one thing. Uh, secondly, they have just deeper pools of human capital every year. They're hiring new MBAs, new CFAs, new PhDs. They have the best data, the best technology. How does someone in their basement compete with that? And then lastly, and I think this could be the most significant advantage that they have is that where do retail traders get their ideas? They tend to get their ideas from CNBC, Fox business, Bloomberg, or institutional research reports. So you essentially, you have the defense calling the offenses plays for it. And so if the defense knows what play the offense retail is going to run, they can front run that, take advantage of it. They probably already front run it when they're talking about it publicly.
So you have all these structural advantages that institutions have. And if you can align yourself with how they're positioned relative to how the not so smart money's position, you might be able to participate in those better outcomes. So that's sort of high level, what it seeks to do and be happy to discuss, in more detail, how it goes about doing that. Sure. So when you're, when you're
Actually quantifying this and, and deriving a signal, like what actual data sources are you pulling from? If you can share that and, how do you quantify, that institutional versus retail positioning? Is it just flows or kind of how are you, how are you quantifying all this and
Deriving signal from it? Sure. There's two primary data sources. There's actual positions that separate classes of investors have. So their actual positioning, that's one source. And the other source is survey data where the surveys are posed to institutions and private investors individually separately. So that you have an institutional sentiment index and a retail sentiment index. And then I just use that and compare them to each other. On the positioning side, it comes from the commitments of traders report. A lot of people are very familiar with the commitments of traders report. I see people talk about it all the time on Twitter. Personally, I don't think that people are looking at it in a way that you can maximize the information content in it. I think that, you
Know, a lot of people look at it, just the raw contracts and, oh, the most short ever. Well, yeah. Okay. Well, the money supply has increased and, GDP has increased. So shouldn't you be normalizing that in some way? Shouldn't it always increase through time? And that's what we do see. So, if you look at the normalized percentage of open interest and how it, and then if you look at the separate investor classes, how they're currently positioned relative to how they're typically positioned and then how those compare to each other, that tends to give you a fairly strong signal for various instruments, not only equities, but commodities, currencies, fixed income, et cetera. So I look at actual positioning. And then I also look at separate surveys posed to
Institutions and retail separately. Well, you walked me right into my next question, because you alluded to this earlier, which was, this just isn't in equities, right? It's multi-asset class, it's ETFs, or I'm sorry, equities, fixed income, dollar, precious metals, commodities. So can you kind of walk me through how you've built these different models and how they can really work together in a portfolio to provide the best result?
Sure. So when I started, I was looking solely at equities and I had a few models and I would come up with an equity allocation from those models. And then whatever was left over was just put into bonds. And this was in the 2014 to say 2018 period. And that was a secular environment where equities and bonds had negative correlations. So if you weren't in equities and you were in bonds, you were kind of diversified. And if you had less equities, you'd want more bonds because bonds would probably do well if equities weren't going to do well. So that was, I didn't really give a second thought to developing relative sentiment models for non-equity assets until we launched this ETF and we launched it in a secular environment where equities and bonds had positive correlations. And so now it's like,
Okay, well, how do you get a diversifying aspect to your non-equity portfolio? And from there, it was just a matter of just doing research on the various positioning indicators that I mentioned, primarily how the separate asset class, investor classes are positioned in the futures and options market. And just looking at which ones were predictive for each of those asset classes and testing it and make sure it was statistically significant and then going from there. So when we launched, I was still using relative sentiment primarily only for equities, but in the several years since, as you mentioned, we have relative sentiment for the dollar, for various bonds, nominal inflation protected, corporate government, precious metals, currencies, et cetera. And it's a never-ending thing, right? So continually looking for new models and strategies, within the commodities
Complex, different currencies, et cetera. So I think you just answered this, but are you creating
Different relative sentiment indicators or a model for each different asset class because they act so different? Or is there kind of, certain data or what I would call maybe like a master sentiment indicator that you're deriving all of your allocations from, or do they all kind of work
Independently? it's a little bit of both. So for the equity side, I have five separate relative sentiment indicators and I average those. So each one ranges between zero and 100% and each one represents a desired tactical allocation to equities. And so I just, I average across those to get my portfolio's overall equity allocation. And if you look at those, one, two, three, four. So three or four of them are what I would call cross asset relative sentiment in the sense that they look at relative sentiment in asset or asset class A to predict equities and where asset class A is not equities. It could be how people are positioned in currencies. It could be how they're positioned in natural gas. It could be how they're positioned in the long duration bond. There was a
Paper written in 2000. It was in the journal of finance. It talked about predicting futures returns. And one of the big takeaways from that paper is that if you want to predict, say, equity returns, you have to look at instruments that are highly correlated to equities. And in that case, it was like long duration bonds and other things where how they're positioned in equities, complemented by how they're positioned in long duration bonds or along the yield curve, was what would actually give you more predictive power? And that's pretty much what I do. One of my models looks at how investors are positioned directly in equities. So the S&P, the Nasdaq, the Russell 2000, aggregate that information, but also added to that how they're positioned in long duration bonds. And in addition to that,
How are they positioned along the yield curve? Are they more relatively bullish the 10-year or the 30-year? Where are they more bullish or more bearish? All of that information as a composite is one of my indicators. So that's direct relative sentiment plus some cross-asset. Then I have purely cross-asset strategies. And then I have some survey-based strategies as well.
Sure. Now, just out of curiosity, because some signal processes are built this way and some aren't, do you find that when you get a certain spread between, we'll call it the smart money and the dumb money again, in terms of sentiment, that that signal is more telling, it's stronger? Is there any statistical validity to the spread between retail and institution that you're really going to be confident in that signal? And if so, yes, do you work on, is that how you're kind of deriving asset allocation? So if you find a better opportunity, you might overweight something that's showing a relative spread between that?
The answer is no. And I'll explain. So it turns out that these indicators are really, they're kind of simple, but they're very robust in the sense that you mentioned, okay, if there's a big spread, does that mean it's more predictive? What actually is predictive is just, okay, how are institutions typically positioned? How is retail typically positioned? How do they relate to those? So like take a Z score of their current positioning relative to their typical positioning, because the Z score subtracts off their average positioning. Compare those to each other and then compare that to what that comparison tends to be. If it's above that, it's bullish. If it's below that, it's bearish.
And it's like, I don't want to oversell it. It's not a holy grail or anything, but it's so simple in its concept. It's like, there's no advanced math. It's just greater than or less than. It's basically, if it's above that median, boom, you want to be long. If it's below that median, you want to be flat or short. And, but so where the interesting stuff comes in is that I use various different combinations of the underlying predictors and different parameters. So now I have like an array of these, binary one, zero indicators. And then I look at what proportion of those are signaling. And if that is a larger proportion, then it gets more weight. If it's above a critical mass, I want to be long. If it's below that critical mass, I don't want to be in.
And if it's way above that critical mass, then I really want to be in. So yes, that's sort of the weight of the evidence approach where it does have a signal strength based on the number of nearby strategy parameter cases that are all lined up in the same direction. But on an individual basis, I'm not really looking at the strength of how far apart the positioning is currently. It's more just, is it a plus or a minus? Yeah. And then, then looking at plus or minus across many different variations of that same strategy, essentially with different parameter cases.
Makes a ton of sense to me. Thanks for, yeah, thanks for getting into it. Cause I, it's, you've got, um, an array of different decisions trees that you've got to put together in order to get to, your final allocation, which I read it correctly. This is a weekly rebalance. So you're looking at all of these indicators and everything and rebalance the fund. If it needs to weekly or you're, you can wait until there's drift. Can you just explain your
Rebalance process? Sure. Uh, so I run the models the once a week on Wednesday, the data is primarily weekly for three of the models monthly for one of the models and daily for the fifth model. But I kind of smooth out. I take a multi-day average of that daily to kind of slow it down a little bit. Um, so I run those models once a week and I average them to get my desired equity allocation. Now, if that desired target equity allocation is within 10 percentage points of where we currently are in terms of our equity allocation, I would just leave it like that. So if we're currently 50% equities and the target is 56 this week, I'll leave it at 50. If it goes down to 42, I'm going to leave
It at 50. If it goes up to 65, well, I'm going to move it because 65 is outside that 10 percentage point range. Um, just to, keep turnover in check. And also, if it's anything less than 10 percentage points on the equity side, it doesn't necessarily move the needle over short time horizon. So, um, no need to make that, uh, trade. So, so is everything you do from an allocation
Standpoint and waiting standpoint or rebalance standpoint, you got a checklist of rules or is there any art than finesse that you guys are putting into the portfolio or you just strictly created a checklist of rules of which you're going to follow and, and it's, it's a fully
Rule-based rules-based process. So when we started, it was fully rules-based and we had the, the equity, um, uh, side of the portfolio was entirely rules-based and I had non-relative sentiment strategies, as I mentioned, when I launched for the non-equity portion and those are rules-based as well. But as I develop new equity, uh, or new non-equity relative sentiment strategies, um, we, it's not purely science on the non-equity side. The equity side is still purely science on the neck non-equity side. I look at three things. One is the signal bullish. If it is, it could be in the portfolio. If it's not bullish, it can't be. So that's rules-based, but then I look at the length of the signal. So these signals are created in such a way that you can get an idea of what the minimum length,
The time is for the signal. And that if, if you have a longer signal, I would want to give more weight to that, everything else being equal. And then I also look at the historical performance of those signals, the sharp ratio, the compound annualized growth rate, the maximum drawdown, the stronger the signal is historically, I'd also want to give more weight to that. Um, so that I'm working on throwing that into an optimization to make it purely science. But at the moment, it's, sort of art in a way of how I kind of just look at it and say, okay, this is a stronger signal. It's going to be in longer. I'm going to give more weight to that than I'm going to give
To this signal. And when I look at the signals, the signals are essentially the dollar, uh, long duration bonds, precious metals, and commodities. And of those, the best signals historically have been precious metals. Um, and then I would say commodities and then long duration bonds. And then the dollar in terms of, their sharp ratio and return potential. So when I have those to choose from, my checklist is, okay, I'm going to give more to precious metals. And then if commodities are in, I'll give them the next amount and then long duration bonds and then the dollar. So that's sort of a thought process. It's not purely rules-based on the non-equity side now, since we added these new models, but I'm working on making it rules-based.
Yeah, that's great. And just, I should have probably asked this earlier, you're investing across all these different asset classes. How are you actually making those, uh, getting that exposure? Is it a fund of funds or how, like, what is the holdings look like if someone were to, go to the website and take a look at your holdings page? Like what's in it?
It is a fund of ETFs. So we try to use the, the most liquid lowest cost ETFs to represent the various exposures, um, for broad U S equity exposure. We'd want to have BTI or SPI, um, small caps. we look at, some Vanguard small cap ETF or IWM, QQQ, et cetera. Um, same thing on, on the bond side. And certainly for the commodities, there's some disparity. So like the most liquid commodities, say for example, gold and silver GLD and SLV, they also have fairly high management fees. So we go for commodity ETFs and, and precious metals ETFs that have the lower management fees, but still have reasonable amount of AUM and pretty small bid-off spreads.
I, I always like to keep the, I tell everybody I like to keep these episodes evergreen, but I'd be remiss to ask you had mentioned that your relative strength signals in commodities are pretty strong. And we're seeing something I think is fairly unprecedented in recent future, which is, gold and silver going nuts. So, um, I'm sure that it's helping your portfolio somewhat
As in the most recent time period here. Absolutely. So we benefited greatly from precious metals in 2025. Our model for precious metals, uh, turned bullish the last week of 2024. So December 29th, I believe we entered into it. And I think I actually made a tweet like, it's happening. Uh, we're, we're getting into precious metals. Uh, and, and one thing that's kind of notable is that you look historically, those signals tend to last seven weeks, 12 weeks, maybe you'll get a four month, precious metal signal. This signal with the exception of a two week period around liberation day, when it turned bearish, just briefly, but got right back in, it's been bullish since the end of December, 2024, into the end of January, 2026. So as you can imagine, given what they've done and given our
Constant near constant exposure to it, yes, that's, that's helped, um, our performance, uh, immensely. And interestingly, uh, if you look at the positioning on, so a lot of people are writing silver's obituary and, uh, it's going vertical and I would never advise anyone to jump into anything vertical. And a lot of people are saying it's driven by speculative mania, but if you look at the positioning, that's not what it's saying. What it's saying is that retail and speculators are getting skittish and they're adding to their short positions. And the commercial traders, the market makers and the producers are buying silver contracts and they're covering shorts. So they're getting relatively more bullish while the so-called dumb money is getting relatively more bearish. And so we're still in precious metals and, uh, you know,
I'm not going to say that they're definitely going up, but if they continue to go up, it wouldn't
Surprise me giving how positioning hits. Yeah, that's great. And, um, it's funny, I'm getting a lot of, I'm getting a lot of questions from advisors that I talked to, like is now the time to get silver back. And it was so hated for so long. And, now everybody's trying to get their, their hands back on it. But, um, one of the where, one of the places I wanted to go is just in general, when you talk to advisors about tactical strategies, their number one and number two thing that they always say is, Oh, it gets whipsawed in choppy markets and it's late to get in or late to get out. how does your process kind of, nothing ever takes it completely out. Um, we know that no signals perfect, but how do you try to
Combat that choppiness, those whipsaws? How do you try to, you use the word a couple of times, smoothing things out. So, how do you avoid, getting whipsawed like a lot of
Tactical strategies have a tendency to do? It's a great question. So I think the nature of relative sentiment. So I think in the context of, of advisors being wary of tactical strategies because of a whipsaw potential, it's probably because they're looking at momentum or trend following to do the tactical adjustments. And so, yes, something can, a price can fall below moving average. You get out, it can rise back above it. Oh, you got to get in. Then it goes back below. So if you're doing it purely based on price, you can be whipsawed fairly easily. Relative sentiment is based on positioning, which doesn't change nearly as quickly. And on top of that, we're averaging across five different indicators. And so they tend to be fairly smoothly varying in the week to week
Change. Um, yeah, you can go from having, uh, say 80% equities this week and now all of a a sudden you want only 40% next week, but it's not all that often. But what happens is if you go from 80 to 40, you're not going to go back to 80 the next week, you'll probably be somewhere between 40 and 50 the next week, which we wouldn't change because of our 10% window rule. But, um, so I just think the nature of relative sentiment kind of wards off that type of whipsaw that you see in other tactical strategies, because it's not based on price, but where relative sentiment, uh, where that could come hurt you is there's been several times historically, uh, in 2009 at the market bottom,
The fourth quarter of 2018 into 2019. And then most recently in April of 2025, where the smart money was appropriately extremely bearish and the markets were complying with what, how the smart money was positioned. But apparently it didn't, uh, occur to the smart money that, Oh, the policymakers can come in and change things on a dime. So whenever they changed the gap rules in March of 2009, smart money was super bearish. It took them four weeks to get back bullish. That was the market bottom. You missed out on four weeks, but those four weeks were very meaningful as it rebounded. And then, um, in 2018, Powell said, we're nowhere near neutral. Market sells off 20%. Smart money was super bearish there. So they missed that drawdown. But then in January 19, after Trump humiliated Powell, Powell's like, well, I'm
Just kidding. We're not going to raise rates. And the market took off. It took two, three weeks for relative sentiment to get back bullish. So, and the same thing happened when Trump paused tariffs in April of this year, um, it took it four weeks to get back onto the bullish side. So that's where being kind of a little bit less price sensitive can hurt you. Yeah. Makes sense. So million dollar
Question, you got an advisor in front of you, who's got a diversified portfolio, who is mood designed for and how should they look at it? Right. Is this a core holding? Is it, it's not, it's not really a hedge in, in my opinion, in, in the, the practical sense, but how would you sit down and advise an advisor, uh, to use mood inside of a portfolio? It could be used in, in several different
Ways. So I think if you are someone who likes passive investing, um, but wants to be a little bit more tactical, but aren't sure quite how to be tactical or, it's a tough decision, something's falling in a free fall. Do you want to add to it? You might be a little scared. Um, and same thing, if, something's rising, you might want to hold onto it when you maybe should be selling. So this kind of takes away that, uh, responsibility of having to do the decision-making yourself. It doesn't under the hood in a tax efficient way. So you don't have to worry about the tax consequences of being tactical. So you can add it to a core portfolio
As a satellite. Um, so that's one way to look at it. Another way to look at it is if you're a factor investor and you let value and momentum and quality trend following and what have you, uh, relative sentiment could fit into a factor portfolio. In fact, um, I should talk about maybe not, uh, with this question, but there's a lot to be said about trend following and relative sentiment in a portfolio together. And then like another way to think of relative sentiment is just as an alternative.
If you have an alternative sleeve, it could fit into that alternative sleeve. Um, I guess I'll
Double back and talk about the trend following thing because I brought it up, but if I'm sitting down with someone and I tend to ask them, do you like trend following? Because if you like trend following, then pairing trend following and relative sentiment could possibly make your life a lot easier. And the reason is, is they're very complimentary to one another. If you think about what trend following does, if something goes up, it's buying it or adding to it. If something goes down, it's reducing it or exiting from it. So it's buying high and it's selling low. Now relative sentiment tends to do the opposite. And the reason it tends to do the opposite is because institutions tend to be on the other side of retail. And what does retail do? Retail buys high and
Sells low. So you see a relative sentiment buying low and selling high. So what you find is, is that if you put load of sentiment and trend following together in a portfolio, it has thinner tails of the distribution and it has a lower tracking error to a perfect foresight strategy, where if you had a crystal ball and you could see which of those two strategies was going to outperform, um, at the beginning of every month and switching into it. If you knew that perfect foresight strategy, a 50, 50 combination of the two strategies, trend following and relative sentiment would have a lower tracking error to it than either of them standalone. So it's one of those things where you'll never be euphoric because you're never going to have the absolute best return,
But you're not going to be despondent because you're not going to have the absolute worst return. It's kind of like even keel, lower tails of distribution, less stress, less regret. Um, so yeah, so I think, it pairs very well with trend following around draw downs. It outperforms trend following and strong up trends trend following outperforms relative sentiment. So
They're very complimentary. Yeah, no, it's, it's, um, Ray, I, I, you and I talked a couple, when we talked a couple of weeks ago, you were kind of like, Hey, we're, we wanted to get our track record in this thing. I actually found out about your fund from an advisor that I worked together with and, the performance and the risk adjusted returns have been great. So now that you've kind of got these couple of years under your belt, you're starting to take this to market. Like, how are you thinking about distribution and, and going to start making some noise in the space? Cause like I said, the fund's done great. And I think the concept is awesome. And I think it fits well in an investor's portfolio. So how are you thinking, you know,
As a, we'll call you a boutique asset manager around distribution and starting to get some flows in here.
Well, so that's a great question. Um, the first three years did no marketing. I asked Wes, I said, how do we, pitch this? And first of all, I have to say that, you asked me on my hobbies and I'm telling you, my hobby is to kind of decipher the market. So I'm like one of those plunk guys that just wants to be kind of left alone. I'm not a sales guy. I don't like going out and, talking to people and having to sell something. It's just not how I'm built constitutionally. Um, so he said, Ray, you're not gonna be able to sell it for the first three years. So just sit back and relax. I'm like, Oh, that's good. That's fine by me.
I don't want to be out there selling this. we're, I'm grateful for the performance and it's, it's done well. And in the process, people have found us and have reached out to us. So again, I haven't had to do a lot of direct selling, but I do have a relationship with, uh, with a media company and they have a very vast network of RIAs, especially RIAs that are tactically oriented. And, um, through them, I get warm introductions to these advisors. And so, I'm having one-on-one meetings, several times a month with, uh, with people and, uh, it's been very good so far. They're very receptive and it's, that's sort of how I, that's really in my wheelhouse as far as marketing goes, like one-on-one conversations as opposed to, you
Know, pitching to a large group or blasting things out there. And to be honest with you, well, I love the flows and the AUM increasing. I wouldn't want it to take a huge step function from where it is now to some huge number. And then, uh, it may go through a period of mediocre performance and you have all these disappointed people. I'd want it to just kind of grow steadily and slowly and kind of the way it's happening now is, is, has been ideal. So
That's sort of how we see it. Well, Ray, I, again, I love it. I really appreciate you spending some time with me today. I always find these conversations and these types of strategies very fascinating, but before I let you go, where can people find out more about your company and where can people learn more about the mood ETF? I'm going to make you do a little selling here. Sorry.
No problem, Brad. Uh, so we have a, an RIA website, which is just www.relativesentiment.com. And, uh, that's the RIA and you can sign up for our free weekly newsletter where I talk about how the positioning is changing and what that likely means from the different asset classes. Um, and then we also have our ETF website, which you can get to from the RIA website, but that website is www.relativesentimentetfs.com. And, uh, you can find me on Twitter at, uh, RELSENT Tech, R-E-L-S-E-N-T-T-E-C, or just Google my name and it'll come up. Um, but no, thank you, Brad. I had a great time. Uh, appreciate the invite.
Yeah, sure. Right. Thanks again. Thanks for spending some time with me today. Thank you.
Thank you.
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