[00:00:26]Mike Philbrick: All right. Welcome to ReSolve Riffs, and we have with us today Aahan Menon from Prometheus Macro. He is everywhere and anywhere on Substack and Twitter and whatnot, and he has decided that he is gonna give away the macro research and keep the portfolio edge. Aahan, what is going on with that buddy? Tell us more. What made you come to that decision?

[00:00:56]Aahan Menon: Yeah. Well, first off, great to be on. Great to see you guys. It is my first time chatting with Richard, so, hey. Good me finally, when I have been listening to you guys on Riffs all the time, so I am glad we are finally chatting.

Well, when it comes to making the macro research free, I think there is a slogan that nicely kind of captures it all, which is, pay for portfolios, do not pay for content, right? And the content is in air quotes, right? And the idea over there is super simple. It is basically that most long-term fundamental macro research is almost entirely useless for making any types of portfolio decisions. You guys know this better than anyone, right? You have tested everything under the sun.

Most long-term growth forecasts, inflation forecasts, all that stuff does not actually move the needle in terms of improving risk adjusted returns, and I think it is really important to recognize that, because most people that buy investment research, if they are not doing it just because it is super entertaining, it is quite dry if you actually think about it. The reason people are super interested in all this stuff is because they wanna gain some type of edge. They wanna gain some type of portfolio improvement and make their investment strategies better somehow.

And I think as somebody who is designing model portfolios and systematic research, it does not make sense to charge investors for something that is not accretive to their portfolios. So I just do not think that you should have to pay for something that is not gonna improve your performance in any measurable way. And so at Prometheus, all basic high level fundamental economic research is now 100% free. And, yeah, that is basically the whole idea there.

[00:02:48]Mike Philbrick: And so if you want the narrative, where do they sign up for that? That is on your Substack.

[00:02:54]Aahan Menon: So Prometheus-macro.com. You wanna know anything about the economy, what is happening in growth, what is happening in inflation, you know, like what are the odds for this upcoming Consumer Price Index (CPI), we have done stuff like that. You know, all of that type of stuff you should not have to pay for.

In my view, it is 2025. You might have had to pay for that in the eighties when it was tough to get data and test stuff and all that. Today’s day and age, all that stuff should be free, and that is why we made it free.

[00:03:21]Richard Laterman: So to get to…

[00:03:22]Mike Philbrick: I think you are saying what everyone, or many people have been afraid to say, and you are just stating it as it is, and factually and as humans, we do love narrative though, to be fair. Anyway, go ahead, Richard. What were you gonna say?

[00:03:37]Richard Laterman: Yeah, no. I am just trying to understand a little bit within your framework, how are you defining long term economic variables, and what is the timeframe that you actually think is relevant, because I remember over the years having come up, I mean listening to different commentators, and doing some research, six months on the, three-six months on the short end, maybe 18-24 months would probably be the most that markets are looking forward. It seems like in this day and age with so much disruption is probably even shorter than that.

I am trying to understand what do you define as long-term economic variables? What timeframes do you think lend themselves better to predictive power? And perhaps does that change depending on the variables that you are looking at?

[00:04:23]Aahan Menon: Yeah. I think, you know, what we wanted to do, we, so we actually wrote a note, kind of documenting a lot of the stuff. And there is a very, very common kind of saying in discretionary macro, which is like, you know, it is very hard to predict the next couple of days, next couple weeks, next couple months, but it is much easier to forecast the next 6-18 months, right? Like, there is this thing that everyone seems to say, and I have been hearing my entire career.

And actually when I started Prometheus, I went out and I tested this. And it is not even close to true. So typically people are talking about growth and inflation, you know, though, that is the big macro thing. And if you look at changing your asset allocation on a daily basis, based on a one-year forward, 100% accurate growth and inflation forecast, you will not outperform your beta, right? And there is a little nuance that you need to do to illustrate this, but I think that the nuance actually goes to show what is actually important.

So I think the biggest edge that anybody can ever have in the world is being able to predict the one-day forward return, right? Like, if you find someone or you guys find something, hit me up.

But what we did was we basically said, Hey, like we cannot give an investor that edge, right? Like, we cannot say that they can predict the one-day forward to return, but everything from the day after tomorrow until the next year, you know, with perfect precision, you know, whether Gross Domestic Product (GDP) is gonna be up or down, whether the S&P 500 is gonna be up or down, whether reserve balances are gonna be up or down, whether inflation is gonna be up or down, and you adjust your exposure to stocks, bonds, commodities, Bitcoin, what have you, right? We tried everything, and nothing durably outperforms its underlying beta.

And so, you know, the thing that I think that highlights is the most important thing, is the trading horizon that you are trading, right in front of you. And if you are not getting that right, you are just kind of giving yourself a little bit of comfort that there is more time until your forecast pans out.

And I do not think that is something that, I think that is something that we all intuitively, we would like to think, right? That oh yeah, if I know what growth is gonna be over the next year, my equities call is gonna be amazing. But when you actually roll up your sleeves and you try it out and say, hey, like I am the best predictor in the world, I can predict everything, it just does not seem to pan out.

[00:07:07]Richard Laterman: You touched on something really interesting there, which you talked about trading cadence and then the frequency of data and sort of how far into the future that data is looking in order to be informative or predictive in some way, shape, or form to asset allocation, portfolio making decisions.

The average investor is probably trading, I mean, if they are doing it right and they are not over trading and they are not messing too much with their portfolio on a daily basis, they are may be trading, trading once a month, probably closer to once a quarter. Most of them are probably trading somewhere between once or twice a year. If you are considering those types of trading frequencies and portfolio rebalancing frequencies, what is the horizon of data that you think is most suited for those decisions?

[00:07:57]Aahan Menon: I mean, I do not, I would say that the first litmus test for me is always gonna be whether the highest frequency, best implementation can get better, right? And so, if I can, every single day of the year, know exactly where growth is gonna be a year from now, and somehow I am still not getting better, you know, we can create a bunch of back tests because you know what, we can create a bunch of back tests that look better, right? So we can say, oh yeah, we rebalance only once a month, right? And maybe that because that includes more of the forecast horizon, it gets a little bit better.

But the thing is whether that is a function of just luck, or it is actual skill and a lot of sample, we do not really know. And so I think that when I look at that, yeah, you could probably, like, if you were the perfect forecaster, which, you know, that is a big asterisk in front of that, right, like you are the perfect forecaster, maybe if you had a holding period of a month, and you only rebalanced on certain calendar days, you might do better. But first you would have to achieve this impossible target of being the perfect forecaster.

And then there is also the question of your sample size on testing, it kind of decreases a lot. You are a lot less certain about the verifiability of the results. And so I think, I think when you, I think that there are so many more low hanging fruit than trying to do the crystal ball thing, and you know, try to figure out where stuff is gonna be a year from now. You know, and there is a spectrum of stuff, right? There is the simple stuff that, like the stuff that you guys preach, when it comes to diversification, right? That is the easiest thing you can do. No crystal ball needed.

Just some mechanical understanding, a little bit of understanding of what risk parity is, and you can vastly improve your performance. Understanding some basic trend following, hey, like no crystal ball needed, you can dramatically improve your performance.

And, you know, and then you start getting into more esoteric kind of things, right? Like, you know, there are all kinds of mean reversion strategies, the carry strategies, they are like all these, there is a universal stuff depending on how sophisticated you want to be.

But I think if you assume that there is this ability of using growth and inflation to predict asset markets, and spending all your time and effort and, you know, spending money on research providers to help you figure it out and not knowing that even in its best form, it probably will not make you better. I think you are kind of doing yourself a disservice there, you know?

[00:10:31]Richard Laterman: Yeah.

[00:10:32]Mike Philbrick: The sacred cows are falling one at a time. Alright, so, maybe take us through what does work, how do you bridge, you know, the macro view to the tradable portfolio. Why do not you maybe walk us through the data, the modeling signal, position sizing, risk controls. How do you actually take the data and information you are receiving from that field and then actually translate that into a portfolio that does add value?

[00:11:06]Aahan Menon: So I think there is, I think there is a lot of stuff to be done in my world. Basically, what we try to do is we want to construct daily and weekly strategies, right? Like, we think the faster you can go, the closer you are to finding a little bit of predictability, right? And I think people really need to understand what predictability is, right? You are talking about hit rates of like 52-53% and stuff like that. Like, that is what predictability is.

But if you can do that every single day, over the course of a year, five years, you start to get something that looks very interesting and very attractive. Maybe people that are not familiar with the space do not realize that Medallion, which is like the greatest hedge fund on the planet, probably has something like a 51% hit rate on its traits, right? If that, but the thing is the sample over which they are deploying that is just absolutely tremendous, right?

And so when you get into predictability, I do not want to give anyone the impression that, oh, do not look at long-term growth, but if you look at one-day prediction, you know, you will suddenly have a 70% hit rate and you will be the greatest investor on the planet. It is not like that.

What you need is a lot of bets, and for that you need to trade fairly often, and you need them over a diverse set of things, which brings your aggregated risk down, and you get something really nice. And so that is really what we endeavor to do.

In terms of our own particular style of doing that, Prometheus, basically the way we see markets is that there are three big forces and those are the only things that matter, at least to me. They, other people can have their focus and emphasis, but the way we do things at Prometheus is that every T-plus-one exposure that you have is going to be a function of either carry, trend, or reversion.

As far as I am concerned and the work we do, is that all of your trades you put on is gonna be an expression of one of those three things, whether you are trading vol or you are trading the S&P 500. And so what we wanna do at Prometheus is we want to have a dynamic but balanced exposure to those factors.

Now, I think the balance part makes sense, right? Because you just do not wanna, you know, go all in betting on any one factor, and then have a lost decade, right? Like, you can have that in trend, you can have that in reversion.

What we do is we basically say that, okay, we want to balance, but what you, we also wanna do is over time, we want to tilt from one factor to the other, right? And the way we get to the tilting part is really the secret sauce, right? Like that is what is proprietary to our business.

But what we try to do is we try to say, hey, like what determines whether you are gonna tilt from a carry to a reversion, to a trend factor, is going to be some kind of macroeconomic circumstance, and so that is what we try to build all of our strategies around.

And, you know, I would be lying to you if I can, I would, I am saying that you can just take that template and create one set of rules and apply to every market. Like, that is not how it works.

How it works is, you know, taking that understanding and applying it to each individual market because, you know, bonds trend in a very different way from the way commodities tend to trend, and commodities are very, very different in that they have a preponderance of trend relative to equities, right? So maybe the term structure and commodities is more mean reverting than the equities, which are outright mean reverting.

And so like, it is all these little nuances and sort of like adding them up and putting them together, but with that overarching view of like, hey, we like, we believe that carry, trend, and reversion define all forward returns. We wanna have a balance with dynamic exposure to those things.

[00:15:00]Richard Laterman: Yeah, what you are saying resonates a lot with us, particularly the way you started describing edges, anywhere between 51-54, maybe 55% on the high end, and probably those edges are varying over time.

That is very much how we have explained a lot of our strategies, and we have used this analogy in the past, and some people like it, some people do not, because you are kind of equating or creating an analogy between investing and gambling, but it is really the casino edge, right? The idea that the casino industry is built on a razor thin, half a percent edge, right? The house has something about 50.5 edge, and the player has a 49.5 edge or something along those lines.

But the issue is, the benefit is the ensembles, right, and on, you have so many slot machines and so many poker tables and jack, blackjack and then craps and so on and so forth. So you create those edges, and over time the law of large numbers manifest, and you are able to harvest that edge over time and compound it to create. And in our world, you have multiple strategies, multiple asset classes, and then the ability to trade at different frequencies and so on and so forth.

So that resonates a lot are when you are thinking about those three main variables, trend, carry, mean reversion. Are you, do you incorporate any other kind of fundamental data, or is that data manifesting within those three key features, if you will. Like for instance, liquidity, or the rate of change of inflation, the rate of change of growth, because I know often people think of the variable itself, but it really is the rate of change in the direction of rate of change, right, the delta that really matters over time. It is the marginal allocation of dollars, and where the variable is shifting towards that really makes a difference.

[00:16:52]Aahan Menon: Yeah. So great question because when I say this, it often lends itself to the idea that we only do price-based stuff and the, do not get me wrong, that like you can do an amazing amount which is price-based stuff, like an amazing amount, right? But that is not my, necessarily my core expertise, right?

Like when you are, when you go into price-based only world, you need a core expertise that is much more in line with what you guys do. You guys are much more sophisticated quants than I. I happen to be someone who is well, worse with the quantitative techniques, but like, I am primarily a macro guy. Like, I am a full macro guy.

And so, we try to blend fundamental data to come up with things that fit in those buckets, right? So like a good example is, you could use price-based trend, but you could also use earnings momentum as an indicator. You could use business cycle indicators, you know, AQR has a paper called Macro Momentum, right?
Like those basic ideas can be expressed both using fundamental data and using price-based data. And what we found is that the fundamental data is rarely superior to the price-based data, but it is diversifying and adds more additional signal. And so that is what we try to do. We try to say, hey, these are the concepts, right? Like it is reversion, carry, trend.

What can we use that fits in these buckets? Oh, you know, bond yields are deviating from a Fisher Rule or whatever, right? And we say, oh, like that, that might be a good reversion signal. Or we look at, hey, like in equity space, we are looking at price-based momentum, but maybe we can look at earnings momentum, and that might be able to improve our signal a little bit. And so anything that is on the table, we will take it, and we kind of put it into those buckets.

[00:18:51]Mike Philbrick: How does, go ahead, keep going Rich.

[00:18:53]Richard Laterman: No, I was just going to like, just as a follow up, do you incorporate liquidity as a variable, as a macro variable?

[00:19:04]Aahan Menon: So, I have some qualms and with liquidity just generally as a concept because I think that it is, I think cons actually, not as a concept, but like as the way people are using it or thinking about it perhaps, right? I think that first thing, like when I think about liquidity, it is just basically how much cash or liquid assets is there in the system, which can potentiate further risk taking, right? That is an amazing concept, and if you can capture that well in some sort of programmatic way, you will do well.

But the thing is that the ways people go about in terms of trying to get a signal is like super subpar, right? Like they are looking at things like reserve balances or like some mixing up of the Fed’s balance sheet to get something. And one, those things do not change often enough for you to have any signal. Two, the changes in those things are not related to asset markets in any measurable way whatsoever, right? So I think that the concept is great, but like, you know, looking at just the Fed’s reserve balances and or some version of that, is not good.

What I think actually makes sense is to recognize that the Fed’s reserve balances has effects in a lot of places, right? Has effects on SOFA spreads. Like, that is the thing everyone is talking about right now. It has effects on commercial paper spreads, right? It has effects on longer term corporate credit spreads. It has effects on the move, it has effects on the term structure.

All of those things can be added up, right? Like all of those things, you get daily data for all of those things, those can be turned into very nice financial conditions measures, which actually allow you to trade across assets.

But, you know, in terms of what the performance that they generate, like once you strip out beta, right, you are talking about something like if you really, really mine hard, you might get a 0.6 Sharpe ratio, and it is really, like a, it is a really, a financial conditions trend index, so is, it is gonna be fairly correlated to existing trend measures. So, you know, it is not the thing that people make it out to be. Is it useful? Yes. There are certain places where it is, where it can be really useful. So you know, you can use it to come up with fair value measures of curve steepness.

And if you are a bond guy and you really like, that is your world, you might make out like a bandit doing that. But beyond that to just say like, I have a view on assets based on liquidity, when you try to do that quantitatively, it is like, if I really data mine the shit out of my back test, I might get a 0.6 Sharpe ratio. So realistically, I am talking about a 0.3. That is not the thing you should spend all your time and believe in that much, but you know, otherwise, I think like conceptually, it is really good.

[00:22:02]Rodrigo Gordillo: Sorry to interrupt, but I did want to take a quick second to remind listeners that while we do absolutely love providing our audience with world class guests and weekly investment insights, we wanted to remind you that we actually do our best work outside of this podcast, and we try to do this by providing cutting edge, globally diversified, and systematic investment strategies that are designed to be broadly non-correlated to traditional equity and bond portfolios.

So we actually manage private and public funds as well as bespoke separately managed accounts for investors that seek the potential to smooth out portfolio returns in the long run.

So if you do want to see that theory that we have been talking about put into practice, please do go ahead and check us out at www.investresolve.com. Now back to the podcast.

[00:22:44]Mike Philbrick: And you, well you mentioned earlier about tilting some of those, those, well tilting those three factors based on, I think it was, you know, sort of the growth and inflation and liquidity sort of.

[00:23:01]Aahan Menon: Mm-hmm.

[00:23:01]Mike Philbrick: Overall view. And so I was wondering how you come to that view in the sort of the meta, for the underlying carry, trend and reversion models. Like what are the things that go into that?

[00:23:19]Aahan Menon: Yeah. So that is really the sauce, to be honest.

[00:23:22]Mike Philbrick: So I am asking for the secret stuff. Okay.

[00:23:24]Aahan Menon: Yeah, the secret sauce that on…

[00:23:28]Richard Laterman: Typical Mike,

[00:23:29]Mike Philbrick: No, absolutely not. But yeah, no, but…

[00:23:32]Aahan Menon: But I think I give you a…

[00:23:33]Mike Philbrick: Well, also illustrate an example and share, you know, how those growth and inflation liquidity dynamics kind of work together. You do not have to sort of give the sauce to share some insight. I think.

[00:23:45]Richard Laterman: Yeah, and perhaps do, would you shift completely out of one and into another, or would you just kind of dial it a little bit in favor of this, but you will still keep the other signals at…

[00:23:57]Aahan Menon: So the, I mean like broad strokes, the way we keep it is there are signals, live, for all of these things all the time. What ends up dominating is the thing that ends up getting the most signal, right? So it is never like we switch off our trend, it is just that trend does not have much signal. Reversion has a huge amount of signal. And so for the foreseeable future, we will be reversion style, you know, reversion style return stream.

But as that dynamic kind of shifts, we will start to have more trend or more carry or something like that. And so it is like, we will never just go completely on or off one. It depends on where we are getting the most amount of the opportunity set.

So to give a really good illustrative example, I think something that I noticed in 2023, in 2020, 2022, in 2023 after the hiking cycle in the U.S., was just something I happened to notice day to day while trading, which was that, you know, we had these trend signals, and these trend signals just kept getting messed up. Like they kept, you know, we used relatively short-term measures of trends. So like something like six months, three months or less, right? And so they just kept getting tripped up every day.

Like, we put on a trend-based signal, like we increase exposure, we get completely smoked the next day. And so I said, hey, can we check this out real quick? Like, what is going on here? And what we noticed is basically the term structure, because we had never been in a hiking cycle like that before, right? The term structure of interest rates – every time there was even slightly bad economic data, would begin to mean revert and price in cuts dramatically.

And so what you have had since basically 2022 until present is the most short-term mean reversion bonds, like we have seen. So, you know, short-term mean reversion in bonds this year has put up a 1.9 sharpe ratio, right? And the reason for that is because you are in a place where the growth and inflation mandate do not give you clarity, right?

You have, I should say the unemployment and inflation mandate do not give you clarity. So unemployment data and employment data has broadly been softening. There are issues around NFP, and potentially issues around the population adjustments, which suggests that employment growth is actually a lot weaker than the official numbers. And the Fed knows this, everyone knows this, right? And so everyone is basically haircut it. We have estimates of like what the employment growth trend actually is, and we think it might be actually negative.

And so you have that on one side of the mandate, and on the other side, you are now in the fifth year running of not being a target. So every time you get weaker data, it is like boom, let us price a recession immediately, because you know, you expect a ton of cuts, but then you slowly continue to have nominal growth data, which continues to surprise the other way.

And so as a result, the term structure, which is really just like SOFA plus a little bit of term premium, honestly, like the term premium is not even that a big good deal. It is basically like SOFA pricing just continues to mean revert really dramatically. And so as a result, like what you would wanna do is you wanna have measures around that.

You know, you wanna have measures around the dispersion would be in the growth and inflation mandate, and that is what really feeds whether you want to be in diversion or not, if that makes…

[00:27:36]Mike Philbrick: Yeah. And so yeah, the future always holds what the past is yet to reveal, right? It is always amazing to me how that, how true that always is. So it is not really sort of the typical macroeconomic growth, inflation, liquidity factors that you are overlaying as the meta on your carry, trend, reversion framework. But it is more the actual models and their functioning themselves and their ability to be effective that you are managing with the tilting.

[00:28:13]Aahan Menon: Yeah, exactly. The models have to all like the, there is, I think that this is something that macro guys like, you know, I started my career at a macro hedge fund and you know, one thing that like discretionary macro style investing always leads to is one view expressed a lot of, across a lot of things. But if that view is wrong, you are screwed, right? So, like,  I have this big view about like, we are in a reflation, you know, run it hard and all that stuff.

All of my expressions, even if I do 30 of them right, they all hinge on that macro view being right. And like, I, you know, initially, you know, when I started building all this stuff, I tried to do that a lot, and I just found that you could not push performance. But what we found makes a lot more sense is to say, hey, how does each individual asset work? Let me try and create something for each individual asset, add them up, and you get some type of macro view out of that, and that seems to work better.

You know, it has, it is naturally way more diversified. You have more, you have so much more signal, and the aggregate macro view you get too, seems to be a lot more high quality, even though it shifts a lot. I think the only downside that it, it is a…

[00:29:32]Mike Philbrick: Price before narrative

[00:29:34]Richard Laterman: Yeah, and then narrative feeds price, and then there is this reflexive symbiotic relationship where one feeds the other until such time as you have an inflection or a paradigm shift of some sort.

Did does this framework lend itself to traditional asset classes across the board equally? I guess it can vary a little bit here and there, some asset classes may, it may have a higher predictive power for some asset classes versus others at different, at varying moments in time. But have you tested this out in digital assets? Are you looking at Bitcoin, Ether and any other of these tokens? Do do these apply? Do these rules apply?

[00:30:16]Aahan Menon: You know, the thing, so I, so it is super interesting with the crypto universe, right? Because I know you guys have gotten involved. I think like, when it comes to applying this stuff, I have seen one, I have seen more and more systematic macro style, or carry, trend type styles go into the space and they are killing it, right? But for me, I am really boring as a person, and the way I kind of imagine it is, it is kind of like being one of the first quants to trade trend, in like the seventies through the nineties.

Like, you might just absolutely kill it. You will be a legend, but the amount of alpha decay you will probably go through will be terrifying. And I personally do not have the stomach for that, and I do not necessarily wanna put my business through that just yet.

And so I think that when we look at things like cross-sectional carry and a bunch of these cryptos and stuff like that, they put up crazy numbers. You know, even basic trend factors seem to put up crazy numbers. But like, I do not think that you can continue to expect that, and so, you know, it is just not something I feel, I feel like the space is gonna mature a lot more and a lot of the, you don’t want, even if you, I do not think that you can factor in the sheer amount of alpha decay that you are gonna have, even if you put in a factor for the amount of alpha decay. And so like, that is the reason we have been kind of careful about getting involved.

[00:31:51]Richard Laterman: Steered clear from the crypto space. Okay, that makes sense. And so I guess you were looking at traditional stock, bond, as well as currencies and commodities. Is that the asset…

[00:32:04]Aahan Menon: So we are doing, so we do U.S., we do all the major sectors, so the 11 sectors, and then we do global equities. We do global fixed Income, so 10 country, eight country bond futures. And then we do the sovereigns. And then we do the industrial complex and we do energy.

[00:32:30]Richard Laterman: So you mean metals, energy? No, agri?

[00:32:33]Aahan Menon: No.

[00:32:34]Richard Laterman: Gold, silver, platinum, poly. Yeah.

[00:32:36]Aahan Menon: Gold and silver is involved. We do, so we do have, so we have a sub-portfolio that is called our Crisis Protection Program, and what that is really meant to do is it is like it is a countercyclical program. So it basically looks for value in TIPS and gold.

And they are really kind of like, I think I have heard you guys use this, putting the sugar in the medicine for us to be able to have long-vol exposure. And so we have paired the gold and the TIPS with our long-vol exposure. The gold and TIPS are not meant to be super high edge or anything like that. They are just meant to be something that allows you to carry this long-vol exposure well.

[00:33:25]Richard Laterman: And you are trading vol through VIX Futures?

[00:33:29]Aahan Menon: Yeah.

[00:33:30]Richard Laterman: And…

[00:33:31]Aahan Menon: VIX Futures, and then we have a retail product, which they, we use the VIX Exchange Traded Funds (ETFs).

[00:33:37]Richard Laterman: And you are using that same three feature set of carry, trend and mean reversion? And within each one of those, do you have different sub-strategy, different implementations of trend, different implementations of carry and so on?

[00:33:52]Aahan Menon: Yes, yes. Well, when it comes to the Crisis Protection Program, like a primary objective other than the VIX, where we, the VIX, we are applying all three of those concepts, but when it comes to TIPS and gold, we are really just trying to do reversion and carry, right? Like, we just want to have a counter cyclical exposure. So when expected returns are basically good, we wanna be able to hold a little bit more of, you know, the TIPS and the gold. And that allows us to basically carry the VIX positively.

[00:34:27]Richard Laterman: That makes sense. And how are you sizing.

[00:34:30]Mike Philbrick: Okay, good.

[00:34:31]Richard Laterman: Sorry. Yeah, just the one follow-up.

[00:34:33]Mike Philbrick: Keep going. Yeah, keep…

[00:34:34]Richard Laterman: How are you sizing those positions? Are you basing them on volatility sizing? How is the framework?

[00:34:43]Aahan Menon: So all of our signals basically live in like expected Sharpe ratio. So we do the vol sizing, but you know, just from the push from clients, you know, many years ago, it is like, it is very counterintuitive to have a full position on when your signals are really small.

And so what we found typically is if you do this blend of reversion carry, trend, you need to be careful because I know who I’m talking to. But you do improve the relationship between the magnitude of expected return and signal. So it’s not like it’s a straight line or something, but what you do get is you do get a little bit of improvement, because you usually, when you get a trend signal, like the larger of the trend signal, the expected return starts to fall off as you get really, really further out.

But when you start implementing the carry and the reversion, and you start to get a slightly more linear, so the higher the signal. And so all our signals across all our strategies basically live in expected operational space.

[00:35:51]Mike Philbrick: Interesting. Yeah. And there’s a, there’s almost, in that pocket there’s actually a special use case that you’re designing to, that’s complementary to the rest of the portfolio. So that’s a very interesting way to think about that.

[00:36:08]Aahan Menon: Yeah. I mean the Crisis, sorry, sorry to cut you off, but like, the Crisis Program is super interesting because it’s not actually meant to be like a high edge we’re timing everything under the sun kind of program. But for what? But because of the correlation characteristics, it just seems to fit in with everything you throw it into.

So you put it on top of stocks, it does really well. You put it with the commodities, it seems to do really well. You put it with bonds, it seems to do really well. So it’s the, it’s the most bang for our buck program, but it’s not supposed to be the most high alpha program, which is really funny.

[00:36:41]Mike Philbrick: Amazing. I just, I wanted to come back to what we were talking about earlier, which was this idea that through these, the myriad of signals that you were getting, then you would get sort of a story. The macro narrative would bubble from that, but you also mentioned that it changes a lot and we didn’t get a chance to pull on that thread.

And I’d like to pull on that thread a little bit because recently you had a note that went from max-long equities to basically negative beta, which I think is indicative of what you’re actually saying right now, is that the, boy-oh-boy, does it ever shift quickly! And probably that relates to what you were talking about earlier and being able to trade a little bit more, being able to adapt your positions a little bit more.

And then the headline narrative, which was these long-term global macro thematic notes really are not going to improve portfolio performance, but maybe let’s just dig into that. Let’s pull on that thread a little bit and you know, you’ve had a flip recently. How is that, how is that working out?
Have, has it flipped back, and that type of thing?

[00:37:52]Richard Laterman: Maybe he can share what precipitated the flip, as a bit of a teaser.

[00:37:56]Aahan Menon: Yeah. Yeah, yeah. Happy to. So we have a common friend, Bob Elliot. He said something to me, or no, he said something in a tweet a long time ago, and I do not think he ever thought that it was that important, but I thought it was really important, and it stuck with me for many years, and I keep reminding him about it, but he tweeted that there is no award in markets for consistency of narrative because there is no award for that. And that’s really something I try to hold really true. Like, I try to come to the table.

So what I’m trying to do on a day-to-day basis is basically, we get all of these signals. A lot of it is fundamentally informed. I want to try and piece together what the signals are telling you, and try to get the big muscle movements and trend to you in a digestible way, right? Like, that’s what we’re doing when we write, you know, we’re sorting through everything.

A lot of times I am super late to writing about the thing, right? This happened, I cannot even tell you how many times where, you know, we’ve had an exposure on, it starts to work, it starts to work for a few months. I’m like, oh yeah, this is a theme. I write about it, and it’s done in the next week.

But I think that what it really boils down to is, you have all of these signals, and these signals are meant to be predictive of asset markets, right? And asset markets are discounting machines, and the discounting changes way faster than the underlying conditions, right? Like, the expectations for growth whips all around every single day. The actual growth does not change at all.

And so, when you’re running a process like this, what you’re really getting is, you’re getting the information on like, is expected growth underpriced, overpriced every single day? And that can shift. And so that’s just something that you have to become comfortable with.

And I, it took some doing, because, you always, in traditional macro circles, you’re always trying to have this consistent narrative, and then kind of position around that narrative. And I just, what I continued to come around to is that listen, like we’re not trying to predict the macro narrative. We we’re trying to predict the markets, and the predictions change every day, and that’s just what it is.

And so I think that what, but what we do try to do is that like, we, asset prices by and large do move in large cross-asset trends, right? Like, you know, when equities rally a lot and commodities are rallying a lot, you can pretty much bet that bonds are also selling off.

And the economies do tend to move in a slow fashion. And markets, you know, for whatever overreaction, under reaction phenomena, take your choice, right? They tend to trend. And so what we wanna try to do is we wanna try and say, hey, these are the moves that are being made.

Aside from the really tactical opportunities, so aside from something that’s like a one-day mean reversion move, or one-day breakout signal or something like that, what are kind of the themes under the hood that are evolving, or coming to the front? And I think that recently was a super interesting example, right? On this year, our equity signals, so our U.S. equity and global equity signals showcased some of the strongest signal strength that we’ve ever seen, even compared to our back tests, right?
And how that manifests is basically 100% of our max notional in both programs, which is just absolutely harrying for me to look at every day, right, because all the positions are the same.

They’re correlated. The signals are moving in the same way. You’re like, oh man, I might as well just stop all of this and open a long only equity shop.

[00:41:51]Mike Philbrick: Vanguard. Here I…

[00:41:52]Aahan Menon: Um, yeah, exactly. And so fortunately, because of the mix of carry, trend, reversion, that did lead to also good forward returns when we had those high signals. But what started to happen over the last couple of months is we started to have a shift down across all our signals, and I started to notice that.

And so when our aggregate risk started to come down, I said, hey, something’s going on. You know, we need to peel back. And so we start doing the work to see, we have a whole bunch of stuff that’s been systematized, like now for years. I’m not always on top of every piece.

So what we started to, one of the first things that actually started to bubble to the top was our index level view. So our index level views went from max-bullish to like, let’s be a little bit more conservative, to getting a little bit short, right? And what really drove that is that we have these fair value models for what consensus earnings expectations should look like.

And what we do is we take a bunch of fundamental macro data and we basically try to reconstruct something that looks a lot like analyst consensus. And what we found is that if there are major gaps between those two things, you basically have an opportunity to trade.

And so what we started to see is that as we went into earning season, these tech numbers just came in super hard, super hard, and everything else sucked, right? And so as a result, we started to have these macro indications start to get, get our gross exposure down a little bit at the index level.

And we also, after a little bit of waiting, being early or being wrong, we basically started to get our price-based signals also started to confirm that a little bit. And then that started to kind of, so that started at the S&P 500, where honestly like that’s the place where, if we have any expertise, it would be there. But, it started to kind of spread out a little bit to our global signals. And what we started to see is that, hey, like if you look at a bunch of local FX equity trend globally, they’re not doing that well. Like, China’s not doing that well anymore. India’s not doing that well anymore.

And you start to look at the earnings momentum in all of those countries as well. You’ve actually started to see over the last couple of months that earnings momentum has actually started turn negative. And so you put all of that together into one kind of view, is you went from a place where risks were, you know, the expectations around Liberation Day were basically like, hey, the world is over. We’re gonna have a recession like tomorrow, to okay, like we’re in an exuberant kind of environment where if you look across earnings aggregates, both globally and within the U.S. equity market, internal, the only thing really floating all of it up is this tech component, right?

And so, if you have any type of macro tracking, you basically say, yeah, the check component is there, but it can’t be everything. And so, we started to get a little bit more negative. We got a little bit of price comp information. We got, basically net negative beta for a couple weeks, and over the last couple of sessions, we basically come back to a more neutral place.

I now, you know, to synthesize that and kind of put it into like, what do I think of the world? I, it’s not that the, that we’re going into recession or the world is gonna end or whatever, but I think that it’s just a recognition that hey, we’re in an increasingly lopsided expansion both globally and in the U.S. and so there are two different ways to play those sets of bets.

I don’t think it makes sense to just go out and short tech indexes. Like, that’s probably not a good idea. But a really interesting way in a market neutral fashion is possibly to go long the tech indexes and short the most cyclical parts of the economy. Like, that’s a rule that’s been one of the best plays of the year and continues to be.

An alternative way is just to say, hey, maybe I just wanna lower my exposure and have more balance. So instead of actually just doing the S&P 500 index, why don’t I grab the individual sectors, find the ones which have good earnings momentum, good fundamental momentum, and also are not so egregiously valued. So there are multiple different ways to do it, but I think it’s just like a time for more caution based on what we’re seeing.

[00:46:28]Mike Philbrick: Yeah, there, there’s certainly, um, you have a market that’s dominated by those very large tech names, and as you point out, or as Bob Elliot points out, the market is a discounting mechanism and is trying to discount a lot of things that have maybe have no precedent, right?

What’s, what’s the impact of Artificial Intelligence (AI)? What’s the cost of the CapEx boom? How quickly is it gonna roll out? And so fundamentally, there’s things happening, as you say, that are like the big ship, but trying to discount that, you can see why the markets would be moving around a lot in and having fits and starts of well, what is that going to be, because it’s so unknowable and somewhat unprecedented in where we are today.

[00:47:20]Aahan Menon: Yeah.

[00:47:20]Richard Laterman: You what, what you’re saying makes a lot of sense, Mike. And it’s exactly what I was thinking because you’re describing a very quantitative process, right, Aahan? And you’re speaking our language, is that that’s precisely how we attack the problem. That that’s how we’ve thought through the problem for many years.

But in a world of, you know, paradigm shift is the word that always comes to mind, like things a lot. The word unprecedented seems to be thrown around a lot these days, but it truly does encapsulate a lot of the feelings that we see with disruption in technology, but also the move away from the unipolar moment of the U.S., this more fragmented geopolitical environment that we’re in, the trade war, all these things.

How often are you tweaking your models? Are you bringing any discretion to your decision making? How are you attacking this conundrum, this issue of trying to model an environment that perhaps the last few decades are not representative of the environment.

[00:48:22]Aahan Menon: Yeah, I mean, I think when it comes to tweaking, I am always open. Like, I’m always open to tweaking things. But because we have so many strategies now, I have a lot more leeway to be patient with things, probably more consistent with the way that I should be, right? Like, I think the less breadth you have, the more you wanna tweak things to optimize because you’re having a problem, and the second you start having more breadth, candidly, I’m very open about all these things. Like, we have some strategies that are negative one Sharpe ratio this year. It’s just absolutely horrible.

[00:48:58]Mike Philbrick: Of course you would. I mean, this may sound strange to people. Yes, that is something that…

[00:49:05]Aahan Menon: We’re just…

[00:49:07]Mike Philbrick: And last year, negative one Sharpe ratio strategy might have been two.

[00:49:12]Richard Laterman: Yeah, diversification means always having to say your sorry about something, whether it’s a line item in a portfolio or within a very diversified program. Any one of those sub-strategies across a number of dozens and dozens of markets.

[00:49:26]Mike Philbrick: Doesn’t invalidate that thing that whatever you want to, whatever you’re gonna articulate, whether it was a strategy of an asset on a strategy, on an asset, whatever it was, it does not invalidate it on a one-year basis to have a particularly challenging Sharpe ratio.
Anyway, back over to you.

[00:49:46]Aahan Menon: Yeah. So I mean, there are certain things, right? Like where if we feel like we got tooled up in something that, you know, like we understood something about a certain fundamental where we’re just like, hey, this is just better. Like, it’s not that this wasn’t working or that, you know, anything like that, but this is just better, right?
So, you know, there’s certain things that we did in say like energy, trend stuff, right, where we basically said, hey, we were looking at basic time, but there are a bunch of signals that we spent a lot of time kind of looking at, at the energy space and we said, hey, there are a few signals that are just like way, way better, way more sound fundamental reasoning. We were open to integrating those and including those into the programs.

I think the place where I start becoming concerned is when you see something that’s really, really dramatically different from anything you back tested, right? Like completely different, and then you have structural concern. You know that something about the market structure has changed very dramatically.

And so if there’s that type of thing, then we’re much more hands-on and hey, do we just need to sunset this program entirely? Has something shifted? Do we need to change it? But you know, I  would say that maybe three years ago I was very quick to change things. But you know, as we added more and more breadth, I’ve become much more patient with changing things.

But that being said, I’m always, the clients that we work with are a mix of fast money and institutions, and so they’re always looking for, hey, what’s working? You know, like, that’s just the truth of the business. So you have to be ready to say, hey, like, this is not working. Is there a reason it’s not working, and, you know, can I fix that? And so I’m always open, but there needs to be a good enough…

[00:51:41]Mike Philbrick: Yeah. I think to, to provide some context, context to that more, and maybe make it, simplify it a bit. If you’re someone and you’re operating with five systems, well yeah, you’re gonna tweak it, and those tweaks are actually monumental because you’re tweaking one 20th of your system load.

If you have a thousand strategies, I mean, to some extent, tweak away.

I mean, you’re one thousandth, you can be patient, you can take a more patient view of it as well, because it is only one 1000th of the information that you’re drawing. And so it’s just not as urgent to try to fix something or do something. You have a lot more patience there when you’ve got a suite of a thousand versus a suite of five, and I think that’s the point you were making earlier, and I just wanna sort of emphasize that when you think about that.

And I think the other thing that you mentioned was that something structurally is changing, right? Something that we ask ourselves is like, what do we know that the model doesn’t know? The model has a certain purview of facts that it is data that it’s gathering, and is it something, is there something that it can’t know? And so that’s that again, that’s, that’s in the purview of the portfolio manager to actually think that through and obviously document that.

You know, if you’re going to put a strategy on, in the penalty box or on the sidelines for some reason, you’re gonna document that review. And then is that a permanent situation or is that a situation that changes? So an easy example is when the Euro and the Swiss Franc were pegged, right? So, and then the peg broke and it was a 20 standard deviation event.

Well, you know that the model doesn’t know that .And so those, that’s a simple example of one of those things where, well, do you need to trade both of those items, because they’re the same anyway. Probably not. But that’s where the portfolio manager with their insight and experience and expertise across the models will intervene and quite rightly so.

So, you know, quant is not about closing your eyes and doing quant. It’s about monitoring and and understanding how your models interact and understanding where their blind spots are, and actually intervening when it’s appropriate to intervene.

[00:54:07]Richard Laterman: That’s a really good example, Mike. Any currency that’s pegged, there’s probably a lot of mean reversion signals that are working really well because they’re trading within a certain band, but then all of a sudden the peg breaks, in that you have a breakout, and all of a sudden the never-trending market begins to trend. So is it a malfunction of the market? Is it a malfunction of the systems?
Which one is Yeah, exactly. So, what comes to mind is when we put an entire market in the Peleton box, Japanese Government Bonds (JGBs), right? So, yield curve control.

And so when you were, Aahan describing a moment ago when a market is now functioning or the structure of a market seems to be unhealthy in any way, shape or form.

And then, on the narrative side of things, yield curve control comes to mind, right? The idea that you start to have a gravitational pull that this very large state actor in this case influencing prices and price discovery in the markets.

And then all of a sudden, JGBs went for several years, where not at a lot of trading was happening in that market. And so do we stop trading a market for a period of time when you start to see that microstructure of that market behaving in an unhealthy way, right? And I think the answer would be yes to you.

[00:55:22]Aahan Menon: Mm-hmm. Well, the JGB circumstance for us, because of the way we have, so the way we look at it is basically like, when we’re trading bonds globally, what we’re trying to do is we’re trying to get carry for the least amount of monetary policy risk possible. That is the way we do it. And we do that cross-sectionally across the globe.

And basically, what you had in JGBs for a while, which is like, no carry, no monetary policy risk, nothing to really do for a while. And so, we want trading during that period. So, I can’t speak to that period very well, but I can say that this particular year has worked really well for that kind of approach for us, because I think that the term structure of the JGB code was actually lying to you most of the time, when monetary policy risk was actually really, really significant.

And so, but I think conceptually what you’re outlining is 100% right. There are so many things like, I think I heard Andy Constance say this about Ray Dalio, where he said that basically, what you’re looking at when you systematize something is a pixelated version of reality, right, and I think that’s a hundred that, that’s on the notes, right?

You basically have a bunch of parameters that you feed in, but there are a million parameters that you can discretionarily kind of understand, that the model has no idea about, and sometimes you just have to intervene and be like, hey, I think that these three factors explain X percentage of the returns. But you know what, maybe they’re not important, relative to this ongoing development and you just have to step in and you have to make adjustments.

I have an interesting example to add on that end myself, as well, where we actually, I think one thing that tripped up a lot of macro guys, this economic cycle, typical business cycle analysis, right? So what was really popular in most macro communities was using something that looked like the Conference Board Leading Economic Index.

For those that are unfamiliar, that’s basically just 10 economic indicators aggregated up, after adjusting for volatility, into one index. Historically, that index has been really, really good at predicting recessions, right? But in this index, basically started to point to recession in 2022 and is still pointing to recession until today.

The reason that we think that that index stopped working as well is because, let’s be clear, that index was designed in like 1980, okay? The economy was a little bit different from the way it is today. In particular, there’s been a very, very big shift from having a manufacturing and industrial economy to having a much more tech and services oriented economy, right?
And so what we did was we said, does that framework of Leading Economic Indexes not work at all anymore. And what we found is that if you add measures that are more consistent with the composition of the economy, which basically take into account intellectual property investment today, you improve your signal in modern date. And you also get something that’s meaningfully different from what’s being predicted right now by that signal.

And so we, you know, we started doing that work, I would say like a year or two ago. We made the move to say, hey, we did a presentation for our clients and all that stuff that, hey, like the business cycle has changed. You can’t just bet on housing and industrial production. That’s not where the economy is anymore.

And you have to make adjustments to your leading economic indicator style, trend models, using this kind of understanding. So that was a shift we made.

It took a lot of time to make, but you know, those are the types of things, because if you’re just stuck with the old program and just, you know, we’re religious about it, you’ve been short or leaning short equities and long bonds for like, three years.

[00:59:49]Richard Laterman: Yeah, the map is not the territory. I think that’s the mental model to be used here and markets are ever shifting. I mean, even our own understanding of reality requires updating. Like Newtonian physics lasted until a certain era, and then Einstein with relativity. And then we’re probably coming into a new paradigm for physics as well.

So, but in markets it’s even more so because these variables are shifting quite a bit over time, and growth and inflation and liquidity dynamics change quite a bit. And there’s reflexivity to use sources, concepts that really, I think, describes a lot of the fact that these things, the way that we’re measuring, and the way that we’re observing these things will shift our own understanding over time. And the way that markets will interact with these variables will impact the variables themselves.

[01:00:38]Aahan Menon: Yeah, yeah. A hundred percent. A hundred percent. I think a good example of the fact of that is the fact that everyone’s talking about liquidity, right? Like a lot of liquidity. There’s liquidity, that and stuff like the Fed’s actions in liquidity basically stopped like a year or two ago, right? Like about a year ago they basically stopped doing anything very meaningful and most of the handoff was actually to the private sector.

And where is a lot of that private sector liquidity coming from? It’s actually coming from a bunch of tech companies that have excess cash balances that store them with financial institutions and in money market funds. And that actually creates the potential for leverage.

And so, you know, what you have to recognize there is that, hey, like the Fed isn’t that important anymore, or probably matters is the private sector impulse. Like, how do I attract the private sector impulse? Do I have any measures? And you know, trying to improve that understanding and turn it into something which can generate signal in markets.

[01:01:39]Mike Philbrick: Well, amazing. We’ve been at it for about an hour. Any, Richard, Aahan, any kind of hanging threads that you guys wanna dig into a little bit more?

[01:01:47]Richard Laterman: I was gonna ask Aahan, if there’s anything that is flying under the radar of the market and investors right now that you’re picking up through your framework, through your models, things that you’re looking into that you think perhaps are being underappreciated at this point.

[01:02:04]Aahan Menon: Yeah, I think that the biggest thing that I see is a very, very large and unusual divergence between output and nominal growth relative to labor, right? And we’re basically having a divergence like we’ve never seen probably in modern history, where what you have today is a labor market as measured by total employment growth, which is heading south, potentially contracting and maybe even potentially contracting meaningfully while output and spending are just continuing on, like nothing’s happened. And that’s not to say that there’s a big crash coming tomorrow, or something like that.

But I think that it’s super important to recognize that the center of economic growth, if you were to go and say there’s one variable I want to use to do a GDP out-cost, and I can pick only one, as somebody who’s done every version of a out cost possible, I would tell you just pick the employment numbers. They’re great.

[01:03:11]Richard Laterman: Particularly non-farm payroll, would that be the…

[01:03:15]Aahan Menon: Non-farm payroll is good. There’s some revision risk in non-farm payrolls. So you would use the establishment, sorry, the household survey instead. So those are total employment numbers. So here’s the thing that’s going on with those numbers. Basically the unemployment rate and the participation rate are unrevised numbers.

So they’re great, but what is revised every single year in January, only in January, and it’s, they leave it in the time series without changing at all, which is kind of funny, but useful at the same time, is the total population numbers. And so what happened this January was they dramatically, I forget how big the number is, so I’m not gonna quote a number, but it basically took employment growth trend from neutral to meaningfully positive.

And so what’s typically happened when you have that kind of revision is the next, the subsequent year is a down revision. So, you know, when you basically account for the participation rate and the unemployment rate, and you basically say that, hey, the overall population is probably growing at 1%, 1.4%, you basically get an employment number, which is probably close to contracting, if not contracting already.

And so that measure, the aggregate employment number, is the driving factor for GDP growth over time, like it is the most explanatory variable for GDP growth over time. But today we have this really weird circumstance where GDP growth seems to be completely fine, but employment is falling off a cliff. The culprit is likely to be immigration in the United States.

So we don’t, like I was saying, the population numbers are kind of shoddy through the year. Like, they’re not great. There’s the, there’s a lot of the, they’re not very reliable on a month to month basis. But what we do see is that the participation rate is just falling off a cliff. And the reason, you know, some people say that this is the boomers exiting the labor force and all that. I think that’s definitely part of the equation.

But the speed at which it has begun to fall off is indicative to me, which is supported by the data of labor market re-composition. And what that is, is basically that foreign workers have much higher participation rates than U.S. workers. And so as you have this immigration unwind, participation is falling off a cliff.

And when we get to January, we might see a meaningful down revision in the pace of population growth, which means that the labor market’s a lot weaker. Now the question I think that investors need to wrestle with is like, these two series are gonna mean revert, right? Like, it’s gonna be one of two things. Either employment is gonna get a lot better, or output is gonna come down to meet that employment. Or maybe you have a mix of those two.

But the, you know, the destiny for GDP growth over time is the pace of employment growth. And I think that’s the biggest question investors need to wrestle with. I’m not saying I have a clear answer, but I think that that’s something that’s just flying under the radar for most people.

[01:06:30]Richard Laterman: Great. I think that’s a good place to put a pin on this conversation. Aahan, great chatting with you, so much insight packed into an hour conversation. Thank you so much for joining us today.

[01:06:46]Aahan Menon: Always, such a pleasure guys. Thanks for having me on.

[01:06:48]Richard Laterman: Great weekend all.

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