Episode 238

Aahan Menon: Systematic Macro in a Shifting Economy: Signals Over Stories

Mike and Richard are joined by Aahan Menon of Prometheus Macro for a discussion on systematic macro investing. Aahan begins by challenging the utility of long-term macro forecasts, arguing they are largely ineffective for improving portfolio performance and advocating for shorter trading horizons. He then details his investment framework, which involves dynamically tilting portfolio exposure between carry, trend, and mean reversion based on evolving macroeconomic circumstances. The conversation also explores a curious and significant divergence currently observed between labor market data and broader economic output.

Topics Discussed

• The philosophy of providing macro research for free while charging for portfolio implementation

• A critique of long-term macro forecasting's ineffectiveness for improving portfolio returns

• An investment framework based on the three core factors of carry, trend, and mean reversion

• Dynamically tilting between core factors based on evolving macroeconomic conditions and signal strength

• Integrating fundamental data as a diversifying signal within the carry, trend, and reversion framework

• Aggregating bottom-up signals from individual assets to form a macro view, rather than imposing a top-down narrative

• The use of a crisis protection program combining long volatility with positive carry assets like TIPS and gold

• Skepticism towards common liquidity measures and a preference for financial conditions indices

• The importance of adapting models to structural economic shifts, such as the move to a services-based economy

• An underappreciated divergence between strong economic output and a weakening labor marke

Transcript
Aahan Menon:

I started my career at a macro hedge fund, and you know,

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one thing that like discretionary

macro style investing always

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leads to is one view expressed a

lot of, across a lot of things.

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But if that view is wrong, you're screwed.

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Mike Philbrick: All right.

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Welcome to ReSolve Riffs.

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And we have with us today Aahan,

Menon from Prometheus Macro.

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He is, everywhere and anywhere on Substack

and Twitter and whatnot, and he's decided

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that he's gonna give away the macro

research and keep the portfolio edge.

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Aahan, what's going on with that buddy?

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Te, tell us more.

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What made you come to that decision?

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Aahan Menon: Yeah.

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well first off, great to be on.

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Great to see you guys.

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it's my first time ch chatting

with Richard, so, hey.

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good me finally when I've been listening

to you guys on Riffs all the time,

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so I'm glad we're finally chatting.

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Um, well, when it comes to, you know,

making the macro research free, I think

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there's a slogan that ni nicely kind

of captures it all, which is pay for

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portfolios, don't pay for content.

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Right?

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And the content is in air quotes, right?

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And the, the idea over

there is super simple.

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It's basically that most long-term

fundamental macro research is

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almost entirely useless for making

any types of portfolio decisions.

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You guys know this better

than anyone, right?

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You, you've tested

everything under the sun.

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Most long-term growth forecast, inflation

forecast, all that stuff doesn't

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actually move the needle in terms

of improving risk adjusted returns.

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And I think it's really important

to recognize that because most

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people that buy investment research.

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If they're not doing it, just 'cause

it's super entertaining, right?

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It's, it's quite dry if you

actually think about it.

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The reason people are, are super

interested in all this stuff is because

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they wanna gain some type of edge.

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They wanna gain some type of

portfolio improvement and make their

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investment strategies better somehow.

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And I think as somebody who, you know,

is designing model portfolios and

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systematic research, it doesn't make sense

to charge investors for something that

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is not a creative to their portfolios.

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So I just don't think that you

should have to pay for something

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that's not gonna improve your

performance in any measurable way.

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And so at Prometheus, all basic

high level fundamental economic

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research is now 100% free.

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And, yeah, that's basically

the, the whole idea there.

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Mike Philbrick: And so if you want the

narrative, where do they sign up for that?

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That's on your substack.

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And, you,

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Aahan Menon: So Prometheus

macro prometheus macro.com.

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You wanna know anything about the

economy, what's happening in growth,

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what's happening in inflation?

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You know, like what are the

odds for this upcoming CPI?

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We've done stuff like that.

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You know, all of that type of stuff

you shouldn't have to pay for.

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In my view, it's 2025.

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You might have had to pay for that

in the eighties when it was tough to

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get data and test stuff and all that.

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Today's day and age, all that stuff should

be free, and that's why we made it free.

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Richard Laterman: So to get to

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Mike Philbrick: I think, I think you're,

I think you're saying what everyone

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or many people have been afraid to

say and you're just stating it as it

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is and factually and as humans, we

do love narrative though, to be fair.

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anyway, go ahead, Richard.

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What were you gonna say?

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Richard Laterman: Yeah, no, I, I'm

just trying to understand a little

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bit, within your framework, how are you

defining long term economic variables

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and, and what is the timeframe that

you actually think is relevant?

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Because I remember over the years having

come up, I mean listening to different,

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commentators and, and, and doing some

research six months on the three to

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six months on the short end, maybe

18 to 24 months would probably be the

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most that markets are looking forward.

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It seems like in this day and

age with so much disruption is

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probably even shorter than that.

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I'm trying to understand what do you

defines long-term economic variables?

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what, what's the timeframe for that and,

and how, what timeframes do you think lent

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themselves, better to predictive power?

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And perhaps does that change depending

on the variables that you're looking at?

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Aahan Menon: Yeah.

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Yeah.

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I think, you know, what we wanted to do,

we, so we, we actually wrote a, a note

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kind of documenting a lot of the stuff.

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and, there's a very, very common kind

of saying in, you know, discretionary

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macro, which is like, you know, it's

very hard to predict the next couple

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of days, next couple weeks, next couple

months, but it's much easier to forecast

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the next six to 18 months, right?

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Like, there's this thing that

everyone seems to say, and I've

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been hearing my entire career.

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And actually when I started Prometheus,

I went out and I tested this.

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and it's not even close to true.

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So, typically people are talking

about growth and inflation.

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You know, though, that's

the big macro thing.

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And if you look at changing your

asset allocation on a daily basis

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based on a one year forward, 100%

accurate growth and inflation forecast,

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you will not outperform your beta.

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Right?

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And there is a little nuance that you

need to do to, to illustrate this, but

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I think that the nuance actually goes

to show what's actually important.

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So I think the biggest edge that

anybody can ever have in the

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world is being able to predict

the one day forward return, right?

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Like, if you find someone or you

guys find something, hit me up.

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Um, but, what we did was we

basically said, Hey, like we can't

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give an investor that edge, right?

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Like, we can't say that they can predict

the one day forward to return, but

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everything from the day after tomorrow

until the next year, you know, with

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perfect precision, you know, whether

GDP is gonna be up or down, you know,

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whether the s and p 500 is gonna be

up or down, you know, whether reserve

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balances are gonna be up or down,

you know, whether inflation is gonna

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be up or down, and you adjust your,

your, your exposure to stocks, bonds,

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commodities, Bitcoin, what have you.

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Right?

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We tried everything and

nothing durably outperforms.

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Its underlying beta.

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And so, you know, the, the, the, the thing

that I think that highlights is the most

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important thing is the trading horizon

that you're trading right in front of you.

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And if you're not getting that right,

you're just kind of, you're giving

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yourself a little bit comfort that there's

more time until your forecast pans out.

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And I don't think that's something

that, you know, I think that's something

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that, you know, we all intuitively

we would like to think, right?

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That Oh yeah.

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If I know what growth is gonna be

over the next year, my equities

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call is gonna be amazing.

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but when you actually roll up your

sleeves and you try it out and say,

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Hey, like I'm the best predictor in

the world, i can predict everything.

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It just doesn't seem to pan out.

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Richard Laterman: You touched on something

really interesting there, which, You

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talked about trading cadence and then

the, frequency of data and sort of how far

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into the future that data is looking in

order to be informative or, or predictive

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in some way, shape or form, to asset

allocation portfolio making decisions.

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The average investor is probably trading.

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I mean, if, if they're doing it right

and, and they're not over trading and,

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and they're not, messing too much with

their portfolio on a daily basis, they're

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may be trading, trading once a month,

probably closer to once a quarter.

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Most of them are probably trading

somewhere between once or twice a year.

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If you're, if you're considering those

types of, trading frequencies and, and

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portfolio, uh, rebalancing frequencies,

what is the, horizon of data that you

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think is most suited for those decisions?

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Aahan Menon: I mean, I don't, I I

would say that the, the first litmus

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test for me is always gonna be

whether the highest frequency, best

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implementation can get better, right?

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And so, if I can every single day of the

year know exactly where growth is gonna

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be a year from now, and somehow I'm still

not getting better, the, the, you know, we

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can create a bunch of back tests because

you know what, we can create a bunch

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of back tests that look better, right?

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So we can say, oh yeah, we, we

rebalance only once a month.

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Right?

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And maybe that because that includes

more of the forecast horizon,

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it gets a little bit better.

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But the thing is whether that's a,

that that's a function of just luck.

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Or it's actual skill and a lot

of sample, we don't really know.

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And so I think that when I look at

that, yeah, you could probably, like,

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if you were the perfect forecaster,

which, you know, that's a, there's a

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big asterisk in front of that, right?

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Like you are the perfect forecaster.

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Maybe if you had a holding period of a

month and you only rebalanced on certain

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calendar days, you might do better.

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But first you'd have to achieve

this impossible target of

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being the perfect forecaster.

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And then there's also the, the question

of, you know, your, your sample size

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on testing, it kind of decreases a lot.

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You're a lot less certain about, you know,

the, the verifiability of the results.

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And so I think, I think that there are

so many more low hanging fruit then

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trying to do the crystal ball thing

and you know, try to figure out where

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stuff is gonna be a year from now.

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You know, and there's a

spectrum of stuff, right?

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There's the simple stuff that, like

the stuff that you guys preach when

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it comes to diversification, right?

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That is the easiest thing you can do.

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No crystal ball needed.

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Just some mechanical understanding,

a little bit of understanding of

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what risk parity is, and you can

vastly improve your performance.

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Understanding some basic trend following,

hey, like no crystal ball needed, you can

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dramatically improve your performance.

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And, you know, the, and then

you start getting into more

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esoteric kind of things, right?

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Like, you know, there are all kinds of

mean reversion strategies, the carry

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strategies, they're like all these,

there's a universal stuff depending

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on how sophisticated you want to be.

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But I think if you, if you assume

that, you know, there's this ability

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of using growth and inflation to, to

predict asset markets and spending

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all your time and effort and, you

know, spending money on research

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providers to help you figure it out.

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And not knowing that even in its best

form, it probably won't make you better.

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I think you're kind of doing yourself

a disservice there, you know?

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Richard Laterman: Yeah.

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Mike Philbrick: The sacred

cows are falling one at a time.

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Alright, so, so maybe, maybe take

us through what does work, how do

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you bridge, you know, the macro

view to the tradable portfolio.

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why don't you maybe walk us through

the data, the modeling signal,

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position sizing, risk controls.

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How do you actually take the data and

information you're receiving from that

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field and then actually translate that

into a portfolio that does add value?

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Aahan Menon: so I think there's, I

think there's a, that, that there's

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a lot of stuff to be done, in, in

my world, basically, you know, what

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we try to do is we want to construct

daily and weekly strategies, right?

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Like we, we think the, the faster

you can go, the closer you are

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to finding, you know, a little

bit of predictability, right?

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Like.

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And I think people really need to

understand what predictability is, right?

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You're talking about like hit rates

of like 52 to 53% and stuff like that.

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Like that is what predictability is.

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But if you can do that every single

day, over the course of a year,

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you know, five years, you start

to get something that looks very

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interesting and very attractive.

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Like maybe people that aren't familiar

with the space don't realize that,

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you know, Medallion, which is like

the greatest hedge fund on the

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planet, probably has something like

a 51% hit rate on its traits, right?

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If that, but the thing is the, the

sample over which they're deploying that

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is just absolutely tremendous, right?

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And so when you, when you get into

predictability, I don't want to

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give anyone the impression that,

oh, don't look at long-term growth.

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But if you look at one day prediction,

you know, you'll suddenly have

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a 70% hit rate and you'll be the

greatest investor on the planet.

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It's not like that.

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What you need is, you know,

what you need is a lot of bets.

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And for that you need to trade

fairly often, and you need them over

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a di diverse set of things, which

brings your aggregated risk down

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and you get something really nice.

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And so like that's really

what we endeavor to do.

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In terms of our own particular style

of doing that, Prometheus, basically

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the way we see markets is that there

are three big forces and those are the

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only things that matter, at least to me.

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They, you know, other people can have

their focus and emphasis, but the way

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we do things at Prometheus is that

every t plus one exposure that you

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have is going to be a function of

either carry, trend, or reversion.

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As, as far as I'm concerned and the work

we do, is that all of your trades you put

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on is gonna be an expression of one of

those three things, whether you're trading

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vol or you're trading the s and p 500.

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And so what we wanna do at Prometheus

is we want to have a dynamic but

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balanced exposure to those factors.

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Now, I think the balance

part makes sense, right?

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Because you just don't wanna, you know,

go all in betting on any one factor

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and then have a lost decade, right?

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Like, you can have that in trend,

you can have that in reversion.

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What what we do is we basically say that,

okay, we want to balance, but what you,

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we also wanna do is over time we want to

tilt from one factor to the other, right?

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And the way we get to the tilting part

is really the secret sauce, right?

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Like that's, that's what's

proprietary to our business.

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But what we try to do is we try to say,

Hey, like what determines whether you're

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gonna tilt from a carrier to a reversion,

to a trend factor is going to be some

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kind of macroeconomic circumstance.

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And so that's what we try to try to

build all of our strategies around.

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And, you know, I would be lying to you if

I can, I would, I'm saying that you can

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just take that template and create one

set of rules and apply to every market.

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Like that's not how it works.

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how it works is, you know, taking that

understanding and applying it to each

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individual market because, you know,

bonds trend in a very different way

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from the way commodities tend to trend.

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You know, and, you know, commodities

are very, very different in that they

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have a, they have a preponderance of

trend relative to like equities, right?

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So maybe, you know, the term structure

and commodities is more mean reverting

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than, you know, the, the equities

which are outright mean reverting.

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And so like, it's all these little

nuances and sort of like adding them

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up and putting them together, but with

that overarching view of like, hey, we,

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like, we believe that carry trend and

reversion define all forward returns.

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We wanna have a balance with

dynamic exposure to those things.

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Richard Laterman: Yeah, what you're saying

resonates a lot, with us, particularly

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the way you started describing edges,

anywhere between 51 to 54, maybe

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55% on the high end, and probably

those edges are varying over time.

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that's very much how we have explained

a lot of our strategies and, and we've

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used this analogy in the past, and

some people like it, some people don't

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because you're, you're, you're kind of

equating or, or, creating an analogy

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between investing in gambling, but

it is really the casino edge, right?

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The idea that the, the casino

industry is ba is built on a razor

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thin, half a percent edge, right?

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The house has something about 50.5

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edge, and the player has a 49.5

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edge or something along those lines.

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But the, the, the issue is the, the,

the benefit is the ensembles, right?

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An on, you have so many slot machines

and so many poker tables and blackjack

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and then, craps and so on and so forth.

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So you create those edges and over time

the law of large numbers manifest and

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you're able to harvest that edge over

time and compound it, to, to, to create.

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And in, in our world, you have multiple

strategies, multiple asset classes, and

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then the ability to trade at different

frequencies and so on and so forth.

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So, so that resonates a lot are when

you're thinking about those three main

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variables, trend carry, mean, reversion,

are you in, do you incorporate any

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other kind of fundamental data or

are, is that data, manifesting within

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those three key features, if you will.

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Like for instance, liquidity or

the rate of change of inflation,

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the rate of change of growth.

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'cause I know often people think of the,

the variable itself, but it really is

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the rate of change in the direct, the

direction of rate of change rights, the

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delta that really matters over time.

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It's the marginal allocation of, dollars,

and where the variable is shifting

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towards that really makes a difference.

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Aahan Menon: Yeah.

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so great question because, I,

when I, when I say this, it often

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lends itself to the idea that we

only do price-based stuff and the.

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Don't get me wrong, that like you can do

an amazing amount, which is price-based

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stuff, like an amazing amount, right?

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But that's not my, necessarily

my core expertise, right?

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Like when you are, when you go into

price-based only world, like you

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need a core expertise that's much

more in line with what you guys do.

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You guys are much more

sophisticated quants than I, right?

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Like I happen to be someone who

is well worse with the quantit,

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the quantitative techniques, but

like, I am primarily a macro guy.

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Like I'm a full macro guy.

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And so, what we do is we, we we try to

blend, fundamental data to come up with

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things that fit in those buckets, right?

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So like a good example is you could use

price-based trend, but you could also use

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earnings momentum as a, as an indicator.

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You could use business cycle indicators,

I think the, the, you know, AQR has

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a paper called Macro Momentum, right?

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Like those basic ideas can, can be

expressed both using fundamental

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data and using price-based data.

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And what we found is that, you

know, the, the fundamental data is

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rarely superior to the price-based

data, but it is diversifying

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and adds more additional signal.

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And so that's what we try to do.

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We try to say, Hey, like

these are the concepts, right?

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Like it's reversion, carry, trend.

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What can we use that

fits in these buckets?

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Oh, like, you know, bond yields

are deviating from, you know, a

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Fisher rule or whatever, right?

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And we say, oh, like that, that

might be a good reversion signal.

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Or we look at, hey, like in equity

space we're looking at price-based

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momentum, but maybe we can look at

earnings momentum and that might be

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able to improve our signal a little bit.

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And so anything that's on

the table, we'll take it.

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And we kind of put it

into those, those buckets.

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Mike Philbrick: How does,

go ahead, keep going Rich.

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Richard Laterman: No, I, I was just

going to like, just as a follow up,

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do you incorporate liquidity, in

as a variable, as a macro variable?

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Aahan Menon: so I, I have some qualms and

with liquidity just generally as a, as a

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concept because I think actually, not as

a concept, but like as the way people are

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using it or thinking about it perhaps.

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Right.

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I think that first thing, like when

I think about liquidity, it's just

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basically how much, cash or liquid

assets is there in the system, which

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can potentiate further risk taking.

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Right?

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That is an amazing concept, and

if you can capture that well

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in some sort of programmatic

way, you know, you'll do well.

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But the thing is that the, the ways

people go about, in terms of trying to

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get a signal is like super subpar, right?

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Like they're looking at things like

reserve balances or like some mixing up of

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the Fed's balance sheet to get something.

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And one, those things don't change

often enough for you to have any signal.

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Two, the, the changes in those things

are not related to asset markets in

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any measurable way whatsoever, right?

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So I think that the concept is great,

but like, you know, looking at just

343

:

the Fed's reserve balances and or, or

some version of that is like not good.

344

:

what, what I think actually

makes sense is to recognize that

345

:

the Fed's reserve balances has

effects in a lot of places, right?

346

:

Has effects on sofa spreads.

347

:

Like that's the thing everyone's

talking about right now.

348

:

It has effects on commercial

paper spreads, right?

349

:

It has effects on like longer

term corporate credit spreads.

350

:

It has effects on the move, it

has effects on the term structure.

351

:

All of those things

can be added up, right?

352

:

Like all of those things, you get daily

data for all of those things, those

353

:

can be turned into very nice financial

conditions measures, which actually

354

:

allow you to trade across assets.

355

:

but you know, in terms of what the

performance that they generate, like once

356

:

you strip out beta, right, you're talking

about something like if you really,

357

:

really mine hard, you might get a 0.6

358

:

sharpe ratio.

359

:

You know, and it's, and it's really

like a fi it's a really a, it's a

360

:

financial conditions trend index,

you know, so is, it's gonna be fairly

361

:

correlated to existing trend measures.

362

:

So, you know, it's not the thing

that people make it out to be.

363

:

Is it useful?

364

:

Yes.

365

:

There are certain places where

it's, where it can be really useful.

366

:

So you know, you can use it

to come up with fair value

367

:

measures of curve steepness.

368

:

Okay.

369

:

And if you are, if you're a bond,

if you're a bond guy and you really

370

:

like, that's your world, you might

make out like a bandit doing that.

371

:

But beyond that to just say like, I have

a view on assets based on liquidity.

372

:

When you try to do that quantitatively,

it's like, if I really data mine the shit

373

:

out of my back test, I might get a 0.6

374

:

sharpe ratio.

375

:

So realistically, I'm talking about a 0.3.

376

:

That's not the thing you should spend

all your time and believe in that

377

:

much, but you know, otherwise, I think

like conceptually, it's really good.

378

:

Mike Philbrick: And you, well you

mentioned earlier about, tilting some

379

:

of those, well tilting those three

factors based on, I think it was, you

380

:

know, sort of the growth and inflation

and liquidity sort of, Overall view.

381

:

And so I was wondering how you

come to that view in the, in the

382

:

sort of the meta for the underlying

carry trend and reversion models.

383

:

Like what, what are the

things that go into that?

384

:

Aahan Menon: Yeah.

385

:

So that's really the sauce, to be honest.

386

:

Mike Philbrick: so I'm

asking for the secret stuff.

387

:

Okay.

388

:

Aahan Menon: yeah, the

secret sauce that on

389

:

Richard Laterman: Typical Mike,

390

:

Mike Philbrick: not.

391

:

No, absolutely not.

392

:

But yeah, no, but

393

:

Aahan Menon: But I think I give you a

394

:

Mike Philbrick: yeah.

395

:

Well, well also illustrate an example

and, and share, you know, how those

396

:

growth and inflation liquidity

dynamics kind of work together.

397

:

You don't, you don't have to sort of

give the sauce to share some insight.

398

:

I think.

399

:

Richard Laterman: Yeah.

400

:

And, and perhaps do, would you shift

completely out of one and into another,

401

:

or would you just kind of dial it a li

a a little bit in favor of this, but

402

:

you'll still keep the other signals at?

403

:

Aahan Menon: So the, I mean like

broad strokes, the way we keep it is

404

:

there's, there are signals live for

all of these things all the time.

405

:

What ends up dominating is the thing that

ends up getting the most signal, right?

406

:

So it's never like we switch

off our trend, it's just that

407

:

trend doesn't have much signal.

408

:

Reversion has a huge amount of signal.

409

:

And so for, you know, the next

foresee for the foreseeable future,

410

:

we'll be reversion style, you know,

rev reversion style return stream.

411

:

But as that dynamic kind of shifts,

we'll start to have, you know, more trend

412

:

or more carry or something like that.

413

:

And so it's like, we'll never

just go completely on or off one.

414

:

It depends on where we're getting the

most amount of the opportunity set.

415

:

So to, to give a really good, like

illustrative example, I think something

416

:

that I noticed, in 2023, right?

417

:

Like in, in, in 2020, 2022, in 2023

after the, the hiking cycle in,

418

:

in the US was just, something I.

419

:

Happened to notice day to day while

trading, which was that, you know, we, we

420

:

had these trend signals and these trend

signals just kept getting messed up.

421

:

Like they kept, you know, we used

relatively short term measures of trends.

422

:

So like something like six months,

three months or less, right?

423

:

and so they just kept

getting tripped up every day.

424

:

Like we put on a trend based signal,

like we increase exposure, we get

425

:

completely smoked the next day.

426

:

And so, you know, I, I said, Hey,

like, can we check this out real quick?

427

:

Like, what's, what's going on here?

428

:

And what we noticed is basically

the, the term structure, because,

429

:

because we had never been in a

hiking cycle like that before, right?

430

:

The term structure of interest rates,

every time there was even slightly bad

431

:

economic data would begin to mean revert

and price and cuts like dramatically.

432

:

And so what, what you've had

since basically:

433

:

is the most short term mean

reversion bonds, like we've seen.

434

:

So, you know, short term mean reversion

in bonds this year has put up a 1.9

435

:

sharpe ratio, right?

436

:

And the, the, the reason for that

is because you are in a place where

437

:

the growth and inflation mandate

don't give you clarity, right?

438

:

you have, I should say the

unemployment and inflation

439

:

mandate don't give you clarity.

440

:

So unemployment data and employment

data has broadly been softening.

441

:

There are issues around NFP and

potentially issues around, uh, the

442

:

population adjustments, which suggests

that employment growth is actually a

443

:

lot weaker than the official numbers.

444

:

And the Fed knows this,

everyone knows this, right?

445

:

And so everyone's basically haircut it.

446

:

We have estimates of like what the

employment growth trend actually is, and

447

:

we think it might be actually negative.

448

:

And so, you have that on one side

of the mandate and on the other

449

:

side, you, you're now in the fifth

year running of not being a target.

450

:

So every time you get weaker data,

it's like, boom, let's price a

451

:

recession immediately, because you

know, you expect a ton of cuts, but

452

:

then you slowly continue to have

nominal growth data, which continues

453

:

to surprise, surprise the other way.

454

:

And so as a result, the term structure,

which is really just like sofa

455

:

plus a little bit of term premium,

honestly, like the term premium

456

:

isn't even that a big good deal.

457

:

It's, it's basically like sofa

pricing just continues to mean,

458

:

revert really dramatically.

459

:

And so as a result, like what

you would wanna do is you wanna

460

:

have measures around that.

461

:

You know, you wanna have measures around

the dispersion would be in the growth

462

:

and inflation mandate, and that's what

really feeds whether you want to be

463

:

in diversion or not, if that makes

464

:

Mike Philbrick: Yeah.

465

:

And so yeah, the future always holds

what the past is yet to reveal, right?

466

:

It's, it's always amazing to me,

how that, how true that always is.

467

:

So it's not really, sort of the typical

macroeconomic growth, inflation liquidity

468

:

factors that you're overlaying as the meta

on your carry, trend reversion framework.

469

:

But it's more the actual models and

their functioning themselves and

470

:

their ability to be effective that

you're managing with the tilting.

471

:

Aahan Menon: Yeah, exactly.

472

:

Exactly.

473

:

The, the, the models have to all like

the, there's, I, I think that this is

474

:

something that macro guys like, you know,

I started my career at a macro hedge fund.

475

:

and you know, one thing that like

discretionary macro style investing

476

:

always leads to is one view expressed

a lot of, across a lot of things.

477

:

But if that view is wrong, you're screwed.

478

:

Right?

479

:

So, like, you know, I have this big view

about like, we're in a reflation, you

480

:

know, run it hard and all that stuff.

481

:

All of my expressions, even if I

do 30 of them right, they all hinge

482

:

on that macro view being right.

483

:

And like, I, Initially, you know,

when I started building all this

484

:

stuff, like I tried to do that a

lot and I just found that like you

485

:

couldn't push performance, you know?

486

:

But, and what we found makes a lot

more sense is to say, Hey, like how

487

:

does each individual asset work?

488

:

Let me try and create something for

each individual asset, add them up.

489

:

And you get some type of

macro view out of that.

490

:

And that seems to work better.

491

:

it has, it is naturally

way more diversified.

492

:

You have more, you have so much more

signal and, and the aggregate macro view

493

:

you get too, seems to be a lot more high

quality, even though it shifts a lot.

494

:

I think the only downside that it, it's a

495

:

Mike Philbrick: Price before narrative

496

:

Richard Laterman: Yeah.

497

:

And then narrative feeds price, and

then there's this reflexive symbiotic

498

:

relationship where one feeds the other

until such time as you have an inflection

499

:

or a paradigm shift of some sort.

500

:

Did does this framework lend itself

to traditional asset classes?

501

:

across the board equally?

502

:

I guess it, it can vary a little bit here

and there, some asset classes, uh, may,

503

:

it may have a higher predictive power

for some asset classes versus others at

504

:

different, at varying moments in time.

505

:

But have you, have you tested

this out in digital assets?

506

:

Are you looking at, at Bitcoin, ether

and, and any other, of these tokens?

507

:

do do these apply?

508

:

Do these rules apply?

509

:

Aahan Menon: You know, the thing, so

I, so it's super interesting with,

510

:

with the crypto universe, right?

511

:

Because, I know you guys

have gotten involved.

512

:

I, I think like when it comes to

applying this stuff, I have seen, one

513

:

I've seen like more and more, you know,

systematic macro style or, you know,

514

:

carry trend type styles go into the

space and they're, they're killing it.

515

:

Right?

516

:

but for me, like I'm really boring as

a person and the way I kind of imagine

517

:

it is, it's kind of like being one of

the first quants to trade trend in like

518

:

the seventies through the nineties.

519

:

Like, you might just absolutely kill it.

520

:

You'll be a legend, but the amount

of alpha decay you'll probably

521

:

go through will be terrifying.

522

:

And I personally don't have the stomach

for that, and I don't necessarily wanna

523

:

put my business through that just yet.

524

:

And so I think that, you know, when

we look at things like, you know,

525

:

cross-sectional carry and a bunch

of, these cryptos and stuff like

526

:

that, they put up crazy numbers.

527

:

You know, you know, even basic trend

factors seem to put up crazy numbers.

528

:

But like, I, I don't think that

you can continue to expect that.

529

:

And so, it's just not something I feel,

I feel like the space is gonna mature

530

:

a lot more and a lot of the, you know,

you, you don't want, even if you.

531

:

I don't think that you can factor in

the sheer amount of alpha decay that

532

:

you're gonna have, even if you put in

a factor for the amount of alpha decay.

533

:

And so like, that's the reason

we've been kind of, you know,

534

:

careful about getting involved

535

:

Richard Laterman: steered

clear from the crypto space.

536

:

Okay, that makes sense.

537

:

And so I guess you were looking

at traditional stock bond, as well

538

:

as currencies and commodities.

539

:

Is that the, the asset?

540

:

Asset

541

:

Aahan Menon: so we're

doing, so we do, U.S.,

542

:

we do all the major sectors, so the 11

sectors, and then we do global equities,

543

:

we do, global fixed income, so, 10

country, eight country, bond futures.

544

:

And then we do the yeah, yeah.

545

:

Sovereigns.

546

:

And then we do, we do the industrial

complex and we do, energy.

547

:

Richard Laterman: So

you mean metals, energy?

548

:

No, agri

549

:

Aahan Menon: No, I

550

:

Richard Laterman: gold,

silver, platinum, poly.

551

:

Yeah.

552

:

Aahan Menon: Gold, gold

and silver is involved.

553

:

We do, so we do have, so we have

a sub portfolio that's called

554

:

our Crisis Protection Program.

555

:

And what that's really meant

to do is it's like, it's like

556

:

it's a countercyclical program.

557

:

So it, it, it actually, it, it basically

looks for value in, in, in tips and gold.

558

:

And they're really kind of the, you

know, I think I've heard you guys

559

:

use this, putting the, the sugar

in the medicine for, for us to be

560

:

able to, to have long vol exposure.

561

:

And so we've, we've paired the gold and

the, the tips with our long vol exposure.

562

:

The gold and tips are not meant to be

super high edge or anything like that.

563

:

they're just meant to be

something that allows you to

564

:

carry this long vol exposure well.

565

:

Richard Laterman: And you're

trading vault through VIX Futures

566

:

Aahan Menon: Yeah.

567

:

Richard Laterman: and.

568

:

Aahan Menon: VIX Futures.

569

:

VIX Futures.

570

:

And then we, we have an, we

have a, a retail product, which

571

:

they, we use the, the VIX ETFs.

572

:

Richard Laterman: And you're using,

that same three, feature set of

573

:

carry trend and mean reversion.

574

:

And are, and, and within each

one of those, do you have

575

:

different sub strategy, different

implementations of trend, different

576

:

implementations of carry and so on.

577

:

Aahan Menon: Yes, yes.

578

:

Well, when it comes to the crisis,

the crisis protection program,

579

:

like a primary objective other than

the VIX, where we, the VIX, we're

580

:

applying all three of those concepts.

581

:

but when it comes to tips and, and

gold, we're really just trying to

582

:

do, reversion and carry, right?

583

:

Like, we just want to have

a counter cyclical exposure.

584

:

So when expected returns are basically

good, we wanna be able to hold a

585

:

little bit more of, you know, the,

the tips and the, and the gold.

586

:

And, uh, that allows us to

basically carry the VIX positively.

587

:

Richard Laterman: That makes sense.

588

:

And how are you sizing.

589

:

those positions?

590

:

Are you, are you basing them

on, on volatility sizing?

591

:

How's the, how's that framework?

592

:

Aahan Menon: So all of our, all

of our signals basically live

593

:

in like expected sharpe ratio.

594

:

So we do the vol sizing, but you

know, just from the push from clients,

595

:

you know, many years ago, it's like,

it's very, very, counterintuitive

596

:

to have a full position on when, you

know, your signals are really small.

597

:

And so what we found typically is if

you do this blend of reversion carry

598

:

trend, you, yeah, I, I need to be careful

because I know who I'm talking to.

599

:

Um, but you know, you do improve, the

relationship between the magnitude

600

:

of expected return and signal.

601

:

So it's not like it's a straight line or

something, but what you do get is you do

602

:

get a little bit of improvement because

you, you know, usually when you get a

603

:

trend signal, like the larger of the trend

signal, the expected return starts to fall

604

:

off as you get really, really further out.

605

:

But when you start you know, implementing

the carry and the reversion and the,

606

:

you start to get a slightly more

linear, so the higher the signal.

607

:

And so all of, all of our signals

across all our strategies basically

608

:

live in expected to operational space.

609

:

Mike Philbrick: Interesting.

610

:

Yeah.

611

:

And there's a, there's almost a,

in that pocket there's actually

612

:

a special use case that you're

designing to, that's complimentary

613

:

to the rest of the portfolio.

614

:

so that, that's a very interesting

way to think about that.

615

:

I

616

:

Aahan Menon: yeah.

617

:

I mean the, the crisis, sorry, sorry to

cut you off, but like, the, the crisis,

618

:

program is super interesting because it's

not actually meant to be like a high edge.

619

:

We're timing everything under

the sun kind of program.

620

:

But for what?

621

:

But because of the correlation

characteristics, it just seems to fit

622

:

in with everything you throw it into.

623

:

So you put it on top of

stocks, it does really well.

624

:

You put it with the commodities,

it seems to do really well.

625

:

You put it with bonds, it

seems to do really well.

626

:

So it, it's the, it's the most bang

for our buck program, but it's not

627

:

supposed to be the most high alpha

program, which is really funny.

628

:

Mike Philbrick: Amazing.

629

:

I just, I wanted to come back to, what we

were talking about earlier, which was the,

630

:

this idea that through these, the, the

myriad of, signals that you were getting,

631

:

then you would get, sort, sort of a story.

632

:

You, the, the macro narrative would

bubble from that, but you also mentioned

633

:

that it changes a lot and we didn't get

a chance to, to pull on that thread.

634

:

And I'd like to pull on that thread a

little bit because, you know, recently

635

:

you had a note that went from, you know,

max long equities to basically negative

636

:

beta, which I think is indicative

of what you're actually saying right

637

:

now is that, you know, the, the boy,

oh boy, does it ever shift quickly?

638

:

And probably that relates to what you were

talking about earlier and being able to

639

:

trade a little bit more, being able to

adapt your positions a little bit more.

640

:

And then the headline narrative, which

was these long, long-term global macro

641

:

thematic, notes really aren't going

to improve portfolio performance.

642

:

but maybe let's just dig into that.

643

:

Let's pull on that thread a

little bit and, you know, you've,

644

:

you've had a flip recently.

645

:

how is that, how is that working out?

646

:

have, has it flipped back

and, and that type of thing.

647

:

Richard Laterman: he can

share what precipitated the

648

:

flip, as a bit of a teaser

649

:

Aahan Menon: Yeah.

650

:

Yeah, yeah.

651

:

Happy to.

652

:

so we, we have a, we have a

common friend, Bob Elliot.

653

:

he said something, to me, or no,

he said something in a tweet.

654

:

A long time ago, and I don't think he ever

thought that it was that important, but

655

:

I thought it was really important and it

stuck with me for many years, and I keep

656

:

reminding him about it, but he, he, he

tweeted that there is, there is no award

657

:

in markets for consistency of narrative

because there is no award for that.

658

:

and, and that's really something

like, I try to hold really true.

659

:

Like I try to come to the table.

660

:

So like, what, what I'm trying to do

on a day-to-day basis is basically

661

:

like we get all of these signals.

662

:

A lot of it is fundamentally informed.

663

:

I want to try and piece together what

the signals are telling you and try

664

:

to get the big muscle movements and

trend to you in a digestible way.

665

:

Right?

666

:

Like that's, that's what we're doing when

we write, you know, we're sorting through.

667

:

Everything.

668

:

A lot of times I am super late

to writing about the thing.

669

:

Right?

670

:

this happened, you know, it, I,

I can't even tell you how many

671

:

times where, you know, we've had

an exposure on, it starts to work,

672

:

it starts to work for a few months.

673

:

I'm like, oh yeah, this is a theme.

674

:

I write about it and it's

done in the next week.

675

:

but, but, but I, I think that, you

know, what, what it really boils down

676

:

to is you have all of these signals,

and these signals are meant to be

677

:

predictive of asset markets, right?

678

:

And asset markets are discounting

machines, and the, the discounting

679

:

changes way faster than the

underlying conditions, right?

680

:

Like the, the, the expectations for

growth whips all around every single day.

681

:

The actual growth doesn't change at all.

682

:

and so, when you're running a

process like this, what you're

683

:

really getting is you're get, you're

getting the information on like.

684

:

Is expected growth, underpriced,

overpriced every single

685

:

day, and that can shift.

686

:

And so that's just something that

you have to become comfortable with.

687

:

and I, I, you know, it, it took some

doing, uh, because, you always, in,

688

:

in traditional macro circles, you're

always trying to have like this

689

:

consistent narrative and then kind

of position around that narrative.

690

:

And I just, you know, what, what

I continued to come around to is

691

:

that listen, like we're not trying

to predict the macro narrative.

692

:

We, we we're, we're trying to predict

the markets, and the predictions change

693

:

every day, and that's just what it is.

694

:

and so I think that what, but what we

do try to do is that like, you know,

695

:

we, asset prices by and large do move

in large cross asset trends, right?

696

:

Like, you know, when equities rally

a lot and commodities are rallying

697

:

a lot, you can pretty much bet

that bonds are also selling off.

698

:

And the economies do tend

to move in a slow fashion.

699

:

And, um, markets, for whatever

overreaction under reaction

700

:

phenomena, take your choice, right?

701

:

They, they tend to trend.

702

:

And so what we wanna try to do is we

wanna try and say, Hey, like, okay,

703

:

these are the moves that are being made.

704

:

Aside from the really tactical

opportunities, so aside from

705

:

something that's like a one day mean

reversion move, or one day breakout

706

:

signal or something like that, what

are kind of the themes under the

707

:

hood that, you know, are evolving

or, you know, coming to the front?

708

:

And I think that recently was a

super interesting example, right?

709

:

On this year, our equity signals, so

our, our, US equity and global equity

710

:

signals showcased some of the strongest

signal strength that we've ever seen,

711

:

even compared to our back tests, right?

712

:

And how that manifests is basically

a hundred percent of our, of our

713

:

max notional in both programs.

714

:

Which is just like absolutely harying

for me to look at every day, right?

715

:

Because all the positions are the same.

716

:

They're correlated.

717

:

The signals are moving in the same way.

718

:

You're like, oh man, like I, I might

as well just stop all of this and

719

:

open along only the equity shop,

720

:

right?

721

:

Um, yeah, exactly.

722

:

And so, you know, we, we, fortunately

because of the mix of carry trend

723

:

reversion, that did lead to also

good, you know, forward returns

724

:

when we had those high signals.

725

:

but what started to happen over the last

couple of months is we started to have

726

:

a shift down across all our signals.

727

:

and I started to notice that.

728

:

And so when, when our aggregate

risk started to come down, I

729

:

said, Hey, something's going on.

730

:

You know, we need to peel back.

731

:

And so we start doing the work to see,

we have a whole bunch of stuff that's

732

:

been systematized, like now for years.

733

:

You know, I'm not always

on top of every piece.

734

:

So what we started to, one of the first

things that actually started a bubble

735

:

to the top was our index level view.

736

:

So our index level views went from,

you know, max bullish to like, let's

737

:

be a little bit more conservative to

getting a little bit short, right?

738

:

And what really drove that is that we

have these, we have these fair value

739

:

models for what consensus earnings

expectations should look like.

740

:

And what we do is we take a bunch of

fundamental macro data and we basically

741

:

try to reconstruct, something that

looks a lot like analyst consensus.

742

:

And what we found is that if there are

major gaps between those two things, you

743

:

basically have an opportunity to trade.

744

:

And so what we started to see is that

as we went into earning season, these

745

:

tech numbers just came in super hard,

super hard, and everything else sucked.

746

:

Right.

747

:

and so as a result, we, we started

to have, you know, these macro

748

:

indications start to get, you know,

get our, get our gross exposure down

749

:

a little bit at the index level.

750

:

And we also, after a little bit of

waiting, you know, being early or

751

:

being wrong, we basically started

to get, our price-based signals also

752

:

started to confirm that a little bit.

753

:

And then that started to kind of,

so that started at the s and p 500,

754

:

where honestly like that's, that's

the place where, you know, we, if we

755

:

have any expertise, it would be there.

756

:

But, you know, it started to

kind of spread out a little

757

:

bit to our global signals.

758

:

And what we started to see is that,

hey, like if you look at a bunch of

759

:

lo local FX equity trend globally,

they're not doing that well.

760

:

Like China's not doing that well anymore.

761

:

India's not doing that well anymore.

762

:

And you start to look at, the,

the earnings momentum in all

763

:

of those countries as well.

764

:

You've actually started to see over

the last couple of months that,

765

:

you know, earnings momentum has

actually started turn negative.

766

:

And so you, you put all of that

together into one kind of view is

767

:

you went from a place where, you

know, risks were, you know, the, the

768

:

expectations around Liberation Day were

basically like, Hey, the world is over.

769

:

You know, like we, we, you know, we're

gonna have a recession like tomorrow to

770

:

okay, like we're in an exuberant kind

of environment where if you look across

771

:

earnings aggregates, both globally and

within the US equity market, internal,

772

:

the only thing really floating all of

it up is this tech component, right?

773

:

And so.

774

:

If you, if you have any type of

macro tracking, you basically

775

:

say, yeah, the check component is

there, but it can't be everything.

776

:

And so, you know, we started to

get a little bit more negative.

777

:

We got a little bit of

price comp information.

778

:

We got, basically net net

negative beta for a couple weeks.

779

:

and over the last couple of

sessions we basically come

780

:

back to a more neutral place.

781

:

I now, you know, to synthesize that

and kind of put it into like, what

782

:

do, what do I think of the world?

783

:

I, it's not that the, you know, that

we're going into recession or the world

784

:

is gonna end or whatever, but I think

that it's just a recognition that hey,

785

:

like, you know, we're in an increasingly

lopsided expansion both globally and in

786

:

the US and so, you know, there are two

different ways to play those sets of bets.

787

:

Like, I don't think it makes sense to

just go out and short tech indexes,

788

:

like that's probably not a good idea.

789

:

But a really interesting way in a

market neutral fashion is possibly to

790

:

go long the tech indexes and short the

most cyclical parts of the economy.

791

:

Like that's a rule that's

been one of the best plays off

792

:

the year and continues to be.

793

:

an alternative way is just to say,

Hey, like, you know, maybe I just wanna

794

:

lower my exposure and have more balance.

795

:

So instead of actually just doing the

s and p 500 index, why don't I grab

796

:

the individual sectors, find the ones

which have good earnings momentum,

797

:

good fundamental momentum, and also are

not so, you know, egregiously valued.

798

:

So there, there are multiple

different ways to do it, but I

799

:

think it's just like a time for more

caution based on what we're seeing.

800

:

Mike Philbrick: Yeah, there,

there's certainly, you have a

801

:

market that's dominated by the, that

very, those very large tech names.

802

:

And, and, and as you point out, or as

Bob Elliot points out, the, the market

803

:

is a discounting mechanism and is trying

to discount a lot of things that have

804

:

maybe have, don't have no precedent.

805

:

Precedent, right?

806

:

What's, what's the impact of AI?

807

:

What's the cost of the CapEx boom?

808

:

How quickly is it gonna roll out?

809

:

And so, fundamentally there's things

happening, as you say, that are like

810

:

the big ship, but trying to discount

that, you can see why the, the markets

811

:

would be moving around a lot in and

having fits and starts of, of, well

812

:

what, what is that going to be?

813

:

'cause it, it's so unknowable and somewhat

unprecedented in where we are today.

814

:

Aahan Menon: Yeah.

815

:

Richard Laterman: you what, what you're

saying makes a lot of sense, Mike.

816

:

And it's exactly what I was thinking

because you're describing a very

817

:

quantitative process, right, Aahan?

818

:

And you're speaking our language

is that, that's precisely

819

:

how we attack the problem.

820

:

That that's how we've thought through

the problem for, for, for many years.

821

:

But in a world of, you know,

paradigm shift is the word

822

:

that always comes to mind.

823

:

Like things a a lot.

824

:

The, the word unprecedented seems to be

thrown around a lot these days, but it,

825

:

it, it truly does encapsulate a lot of

the feelings that we see with disruption

826

:

in technology, but also the, move away

from the unipolar moment of the U.S.

827

:

this more fragmented geopolitical,

uh, environment that we're in,

828

:

the trade war, all these things.

829

:

How, how often are you

tweaking your models?

830

:

Are you bringing any discretion

to your decision making?

831

:

How are you attacking this, this

conundrum, this issue of trying

832

:

to, to model an environment that

perhaps the last few decades are not

833

:

representative of the environment.

834

:

Aahan Menon: Yeah, I mean, I think, when

it comes to tweaking, I am always open.

835

:

Like I'm always open to tweaking things.

836

:

But, because we have so many

strategies now, I have a lot more

837

:

leeway to be patient with things.

838

:

Probably more consistent with

the way that I should be.

839

:

Right?

840

:

Like I think the less breadth you

have, the more you wanna tweak things

841

:

to optimize because you're having

a problem and like the second you

842

:

start having more breadth, you know,

I have, we have, you know, candidly,

843

:

I'm very open about all these things.

844

:

Like we have some strategies that are

negative one sharpe ratio this year.

845

:

It's just absolutely horrible.

846

:

Like it looks

847

:

Mike Philbrick: of course you would.

848

:

I mean, this may sound strange to people.

849

:

Yes.

850

:

That is something that,

851

:

Aahan Menon: bunch.

852

:

We're just,

853

:

Mike Philbrick: and, and last

year negative one sharpe ratio

854

:

strategy might have been two.

855

:

Richard Laterman: diversification

means always having to say your

856

:

sorry about something, whether

it's a line item in a portfolio or

857

:

within a very diversified program.

858

:

Any one of those sub strategies across

a number of dozens and dozens of markets

859

:

Mike Philbrick: It doesn't invalidate

that thing that whatever you want

860

:

to, whatever you're gonna articulate,

whether it was a strategy of an asset

861

:

on a, a strategy, on an asset, whatever

it was, it does not invalidate it

862

:

on a one year basis to have a a, a a

particularly challenging sharpe ratio.

863

:

Anyway, back over to you.

864

:

Aahan Menon: Yeah.

865

:

So I mean, there, there are,

there are certain things, right?

866

:

Like where if we feel like.

867

:

We got tooled up in something that,

you know, like we, we understood

868

:

something about, you know, a certain

fundamental where we're just like,

869

:

hey, like this is just better.

870

:

Like, it's not that this wasn't working

or that, you know, anything like

871

:

that, but this is just better, right?

872

:

So, you know, there's certain

things that we did in say like,

873

:

energy trend stuff, right?

874

:

Where we basically said, Hey, like, we

were looking at basic time, but there

875

:

are a bunch of signals that we, we,

you know, we spent a lot of time kind

876

:

of, looking at, at the energy space and

we said, Hey, there are a few signals

877

:

that are just like way, way better,

way more sound, fundamental reasoning.

878

:

you know, we were open to

integrating those and including

879

:

those into the programs.

880

:

I think the, the place where I start

becoming concerned is like when

881

:

you see something that's really,

really dramatically different from

882

:

anything you back tested, right?

883

:

Like completely different.

884

:

and then you have structural concern.

885

:

Like, you know, that something

about the market structure

886

:

has changed very dramatically.

887

:

and so if there's that type of thing,

then, you know, then we're much more

888

:

like hands-on and, Hey, do we just

need to sunset this program entirely?

889

:

Has something shifted?

890

:

Do we need to change it?

891

:

But, you know, I I would say

that maybe three years ago I

892

:

was very quick to change things.

893

:

but you know, as we added more

and more breadth, I've become much

894

:

more patient with changing things.

895

:

but that being said, like I'm always,

you know, the, my, the, the clients

896

:

that we work with are, are a mix

of fast money and institutions.

897

:

And so they're always looking

for, Hey, what's working?

898

:

You know, like, that's just

the truth of the business.

899

:

So you have to be ready to say,

Hey, like, this is not working.

900

:

Is there a reason it's not working?

901

:

And, you know, can I fix that?

902

:

And so I'm, I'm always open, but

there needs to be a good enough

903

:

Mike Philbrick: Yeah.

904

:

I think to, to provide some

context, context to that more.

905

:

And maybe make it sim, simplify it a bit.

906

:

If you're someone and, and you're

operating with five systems,

907

:

well yeah, you're gonna tweak it.

908

:

And those tweaks are actually

monumental because you're

909

:

tweaking one 20th of your system

910

:

Aahan Menon: Yeah.

911

:

Mike Philbrick: If you have a

thousand strategies, I mean,

912

:

to some extent, tweak away.

913

:

I mean, you, you, you're one, one

thousandths, you can be patient, you can

914

:

take a more, a more patient view of it

well, because it is only one,:

915

:

of the information that you're drawing.

916

:

And so it's just not as urgent to

try to fix something or do something.

917

:

You have a lot more patience

there when you've got a suite of

918

:

a thousand versus a suite of five.

919

:

And I think that's the, the

point you were making earlier.

920

:

And I just wanna, you know, sort of

emphasize that when you think about that.

921

:

and, and I think the other thing

that you mentioned was that something

922

:

structurally is changing, right?

923

:

Something that we ask ourselves

is like, what, what do we know

924

:

that the model doesn't know?

925

:

The model has a certain purview of facts

that it is a, a data that it's gathering.

926

:

And is it something, is there

something that it can't know?

927

:

and so that's that again, that's,

that's in the purview of the portfolio

928

:

manager to actually think that

through and obviously document that.

929

:

You know, if you're going to put, a

strategy on, in, in the penalty box

930

:

or on the sidelines for some reason,

you're gonna document that review.

931

:

And then is that a permanent situation

or is that a situation that changes?

932

:

so an, an easy example is when

the, you know, the, the Euro

933

:

and the Swiss Franc were pegged.

934

:

Right.

935

:

So, so, and then the peg broke and it

was a, a 20 standard deviation event.

936

:

Well, you know that the

model doesn't know that.

937

:

And so those, that's a simple example

of one of those things where, well,

938

:

do you need to trade both of those

items, because they're the same anyway.

939

:

Probably not.

940

:

But that's that's where, the portfolio

manager with their insight and experience

941

:

and expertise across the models will,

will intervene and quite rightly so.

942

:

So, you know, quant is not about

closing your eyes and doing quant.

943

:

It's, it's about monitoring and and

understanding how your models interact

944

:

and understanding where their blind

spots are and actually intervening

945

:

when it's appropriate to intervene.

946

:

And,

947

:

Richard Laterman: That's a really good

948

:

Aahan Menon: Yeah.

949

:

Yeah.

950

:

Richard Laterman: The, any currency that's

pegged, there's probably a lot of mean

951

:

reversion signals that are working really

well because they're trading within a

952

:

certain band, but then all of a sudden

the peg breaks in that, you know, you,

953

:

you have a breakout and all of a sudden

the never trending market begins to trend.

954

:

So is it a malfunction of the market?

955

:

Is it a malfunction of the systems?

956

:

Which one is Yeah, exactly.

957

:

So.

958

:

What comes to mind is when, when we put,

an entire market in the Peleton box, JGBs.

959

:

Right?

960

:

So, so yield curve control.

961

:

And so when you were, Aahan describing

a moment ago when, when a market is

962

:

now functioning or, or, or, or the

structure of a market seems to be

963

:

unhealthy in any way, shape or form.

964

:

And then, you know, on the

narrative side of things, yield

965

:

curve control comes to mind, right?

966

:

The idea that, you know, you start to

have, a, a gravitational pull that,

967

:

that, that this, this very large, state

actor in this case influencing prices

968

:

and price discovery in the markets.

969

:

And then all of a sudden, JGBs went

for several years where not at a lot of

970

:

trading was happening in that market.

971

:

And so do we stop trading a market for

a period of time when you start to see

972

:

that microstructure of that market,

behaving in an unhealthy way, right?

973

:

And I think the answer

would be yes to you.

974

:

Aahan Menon: Mm-hmm.

975

:

Well, the, the JGB, the JGB circumstance

for us, because of the way we, we have,

976

:

so the way we look at it is basically like

when, when we're trading bonds globally,

977

:

like what we're trying to do is we're

trying to get carry for the least amount

978

:

of monetary pol policy risk possible,

like that, that is the way we do it.

979

:

And we do that cross-sectionally

across the globe.

980

:

And, you know, basically what you had

in, in JGBs for a while, which is like.

981

:

No carry, no monetary policy risk,

nothing to really do for a while.

982

:

And so like, you know, we want, we

want trading during that period.

983

:

so, you know, I can't speak to that

period very well, but like I can say

984

:

that, you know, this, this particular

year has worked really well for that

985

:

kind of approach for us, because I think

that the term structure of the, of the

986

:

JGB code was actually lying to you most

of the time when monetary policy risk

987

:

was actually really, really significant.

988

:

and so, you know, I, but I think like

conceptually what you're outlining

989

:

is a hundred percent right, like, you

know, there are so many things like a,

990

:

I think, I heard, Andy Constance say

this about Ray Dalio where he said that,

991

:

you know, basically what you're looking

at when you systematize something is

992

:

a pixelated version of reality, right?

993

:

And, I think that's a hundred

that, that's on the notes.

994

:

Right.

995

:

Like you, you basically have a bunch of

parameters that you feed in, but there

996

:

are a million parameters that you can, you

know, discretionarily kind of understand

997

:

that the model has no idea about.

998

:

And sometimes you just have to intervene

and be like, Hey, like I think that,

999

:

you know, these three factors explain,

you know, X percentage of the returns,

:

00:54:44,088 --> 00:54:47,508

but you know what, like maybe they're

not important relative to this ongoing

:

00:54:47,508 --> 00:54:51,828

development, and you just have to step

in and you have to make adjustments.

:

00:54:51,918 --> 00:54:57,048

We, I have a, I have a, an interesting

example to add on that end myself as well.

:

00:54:57,078 --> 00:55:02,538

Where we, where we actually, I think

one thing that tripped up a lot of

:

00:55:02,538 --> 00:55:08,858

macro guys, this economic cycle, was

typical business cycle analysis, right?

:

00:55:09,098 --> 00:55:13,868

So what was really popular in most

macro communities was using something

:

00:55:13,868 --> 00:55:17,315

that looked like, the conference

board leading economic index.

:

00:55:17,670 --> 00:55:21,240

for those that are unfamiliar, that's

basically just 10 economic indicators

:

00:55:21,240 --> 00:55:25,650

aggregated up after adjusting

for volatility into one index.

:

00:55:26,010 --> 00:55:29,310

Historically, that index has

been really, really good at

:

00:55:29,310 --> 00:55:31,290

predicting re recessions, right?

:

00:55:31,770 --> 00:55:37,800

but in this, this index basically started

to point to recession in:

:

00:55:37,800 --> 00:55:39,660

still pointing to recession till date.

:

00:55:40,500 --> 00:55:45,750

the reason that we think that that

index stopped working as well is

:

00:55:45,750 --> 00:55:49,230

because, let's be clear, like that

index was designed in like:

:

00:55:49,770 --> 00:55:50,040

Okay?

:

00:55:50,400 --> 00:55:54,960

The, the, the, the economy was a little

bit different from the way it is today.

:

00:55:55,620 --> 00:56:02,130

in particular, there's been a very, very

big shift from having a manufacturing and

:

00:56:02,130 --> 00:56:07,680

industrial economy to having a much more

tech and services oriented economy, right?

:

00:56:08,070 --> 00:56:13,305

And so what we did was we said, does

that framework of leading economic

:

00:56:13,305 --> 00:56:14,985

indexes not work at all anymore.

:

00:56:15,795 --> 00:56:21,885

And what we found is that if you add

measures that are more consistent

:

00:56:21,915 --> 00:56:25,155

with the composition of the economy,

which basically take into account

:

00:56:25,425 --> 00:56:29,415

intellectual and property, intellectual

property investment today, you

:

00:56:29,475 --> 00:56:32,595

improve your signal in modern date.

:

00:56:33,285 --> 00:56:36,165

And you also get something

that's meaningfully different

:

00:56:36,585 --> 00:56:40,155

from what, from what's being

predicted right now by that signal.

:

00:56:40,425 --> 00:56:43,065

And so we, you know, we, we

started doing that work, I would

:

00:56:43,065 --> 00:56:44,475

say like a year or two ago.

:

00:56:44,865 --> 00:56:48,925

We made, we made the move to say, Hey,

like, we, we did a presentation for our

:

00:56:48,930 --> 00:56:51,745

clients and all that stuff that, hey,

like the business cycle has changed.

:

00:56:52,315 --> 00:56:55,675

You can't just bet on housing

and industrial production.

:

00:56:56,095 --> 00:56:57,835

That's not where the economy is anymore.

:

00:56:58,195 --> 00:57:02,515

And you have to make adjustments

to your, you know, leading economic

:

00:57:02,515 --> 00:57:07,405

indicator style, trend models using

this, this kind of understanding.

:

00:57:07,405 --> 00:57:08,335

So that was a shift we made.

:

00:57:08,335 --> 00:57:11,335

It took a lot of time to make, but you

know, those are the types of things

:

00:57:11,335 --> 00:57:13,975

because if you're just stuck with the

old program and just, you know, we're

:

00:57:13,975 --> 00:57:19,375

religious about it, you know, you've

been short or leaning short equities

:

00:57:19,375 --> 00:57:21,355

and long bonds for like three years

:

00:57:21,595 --> 00:57:23,395

Richard Laterman: Yeah, the

map is not the territory.

:

00:57:23,515 --> 00:57:26,732

I think that's the, the, the mental

model to be used here and, and it,

:

00:57:27,062 --> 00:57:29,132

it's, markets are ever shifting.

:

00:57:29,132 --> 00:57:32,972

I mean, even our own understanding

of reality requires updating, like

:

00:57:33,302 --> 00:57:37,712

Newtonian physics lasted until a certain

era and then Einstein with relativity.

:

00:57:37,712 --> 00:57:40,322

And then we're probably coming into

a new paradigm for physics as well.

:

00:57:40,352 --> 00:57:43,832

So, but in markets it's even more so

because these variables are shifting.

:

00:57:44,357 --> 00:57:47,777

Quite a bit over time and growth

and inflation and liquidity

:

00:57:47,777 --> 00:57:49,637

dynamics change quite a bit.

:

00:57:49,637 --> 00:57:54,767

And there's reflexivity to use sources,

concept that really, I think, describes

:

00:57:54,767 --> 00:57:59,537

a lot of the fact that these things, the,

the, the way that we're measuring and the

:

00:57:59,537 --> 00:58:03,377

way that we're observing these things will

shift our own understanding over time.

:

00:58:03,377 --> 00:58:06,227

And, and the way that markets will

interact with these variables will,

:

00:58:06,227 --> 00:58:07,847

will impact the variables themselves.

:

00:58:09,092 --> 00:58:09,782

Aahan Menon: Yeah, yeah.

:

00:58:09,822 --> 00:58:10,382

A hundred percent.

:

00:58:10,382 --> 00:58:10,517

A hundred percent.

:

00:58:11,737 --> 00:58:14,642

I think like a good example of the fact

of that is the fact that, you know,

:

00:58:15,262 --> 00:58:17,827

everyone talking about liquidity, right?

:

00:58:17,827 --> 00:58:18,787

Like a lot of liquidity.

:

00:58:18,787 --> 00:58:19,327

There's liquidity.

:

00:58:19,327 --> 00:58:23,407

That and stuff like the fed's actions

in liquidity basically stopped

:

00:58:23,407 --> 00:58:25,327

like a year or two ago, right?

:

00:58:25,327 --> 00:58:28,987

Like about a year ago they basically

stopped doing anything very meaningful

:

00:58:29,227 --> 00:58:32,917

and like most of the handoff was

actually to the private sector.

:

00:58:33,367 --> 00:58:36,667

And where is a lot of that private

sector liquidity coming from?

:

00:58:36,667 --> 00:58:39,127

It's actually coming from a bunch

of tech companies that have excess

:

00:58:39,127 --> 00:58:42,427

cash balances that store them

on, store them with financial

:

00:58:42,427 --> 00:58:44,017

institutions and in money market funds.

:

00:58:44,017 --> 00:58:47,047

And that actually creates the,

the potential for leverage.

:

00:58:47,437 --> 00:58:51,487

And so, you know, what you have to

recognize there is that, hey, like the Fed

:

00:58:51,487 --> 00:58:55,987

isn't that important anymore or probably

matters is the private sector impulse.

:

00:58:55,987 --> 00:58:57,937

Like how do I attract the

private sector impulse?

:

00:58:57,937 --> 00:58:59,077

Do I have any measures?

:

00:58:59,467 --> 00:59:03,157

And you know, trying to improve

that, you know, that, that, that

:

00:59:03,157 --> 00:59:06,067

understanding and turn it into something

which can generate signal in markets.

:

00:59:06,067 --> 00:59:06,217

Yeah.

:

00:59:07,207 --> 00:59:07,777

Mike Philbrick: Well, amazing.

:

00:59:07,777 --> 00:59:09,307

We've been at it for about an hour.

:

00:59:09,397 --> 00:59:14,137

Any, uh, Richard, Aahan, any, any

kind of hanging threads that you guys

:

00:59:14,137 --> 00:59:15,547

wanna dig into a little bit more?

:

00:59:15,817 --> 00:59:18,472

Richard Laterman: I was gonna ask

Aahan, if there's anything that is

:

00:59:18,472 --> 00:59:22,612

flying under the radar of the market

and investors right now that you're

:

00:59:22,612 --> 00:59:25,582

picking up through your framework,

through your models, things that you're

:

00:59:25,582 --> 00:59:29,662

looking into that you think perhaps are

being underappreciated at this point.

:

00:59:30,607 --> 00:59:35,437

Aahan Menon: Yeah, I, I think that

the biggest thing that I see is,

:

00:59:36,307 --> 00:59:42,337

a very, very large and unusual

divergence between output and nominal

:

00:59:42,337 --> 00:59:45,997

growth relative to labor, right?

:

00:59:46,237 --> 00:59:52,567

And we're basically having, you know, a

divergence like we've never seen probably

:

00:59:52,567 --> 00:59:59,982

in modern history, where what you have

today is a labor market as measured by

:

00:59:59,982 --> 01:00:04,542

total employment growth, which is heading

south, potentially contracting and maybe

:

01:00:04,542 --> 01:00:09,582

even potentially contracting meaningfully

while output and spending are just

:

01:00:09,912 --> 01:00:11,892

continuing on, like nothing's happened.

:

01:00:12,612 --> 01:00:15,642

And, that's not to say that, oh,

like there's a big crash coming

:

01:00:15,642 --> 01:00:16,902

tomorrow or something like that.

:

01:00:17,142 --> 01:00:20,622

But I think that it's super important

to recognize that like the center of

:

01:00:20,622 --> 01:00:24,192

economic growth, like if, okay, if

you were to go and say there's one

:

01:00:24,192 --> 01:00:28,092

variable I want to use to do a GDP

out-cost, and I can pick only one.

:

01:00:28,782 --> 01:00:31,872

Like as somebody who's done every version

of a out cost possible, I would tell

:

01:00:31,872 --> 01:00:34,242

you just pick the employment numbers.

:

01:00:34,332 --> 01:00:34,782

They're great.

:

01:00:34,782 --> 01:00:34,842

Um.

:

01:00:35,177 --> 01:00:37,622

Richard Laterman: Particularly

non-farm payroll, would that be the.

:

01:00:38,555 --> 01:00:39,935

Aahan Menon: Non non-farm payroll is good.

:

01:00:39,935 --> 01:00:42,215

There's some revision

risk in non-farm payrolls.

:

01:00:42,245 --> 01:00:46,375

So you would use the, the establishment,

sorry, the household survey instead.

:

01:00:46,405 --> 01:00:48,055

So those are total employment numbers.

:

01:00:48,415 --> 01:00:51,595

So, so here's the, here's the thing

that's going on with those numbers.

:

01:00:51,925 --> 01:00:56,285

Basically the unemployment rate and the

participation rate are unrevised numbers.

:

01:00:56,355 --> 01:01:02,515

So they're great, but what is revised

every single year in January, only in

:

01:01:02,515 --> 01:01:05,155

January, and it's, they like, they,

they leave it in the time series

:

01:01:05,155 --> 01:01:08,425

without changing at all, which is

kind of funny, but useful at the same

:

01:01:08,425 --> 01:01:11,125

time is the total population numbers.

:

01:01:11,995 --> 01:01:17,755

And so what happened this January was

they dramatically, like I, I forget

:

01:01:17,755 --> 01:01:20,515

like how big the number is, so I'm not

gonna quote a number, but it basically

:

01:01:20,515 --> 01:01:26,645

took, employment growth trend from like

neutral to, to meaningfully positive.

:

01:01:27,725 --> 01:01:32,495

And so what's typically happened when you

have that kind of revision is the next,

:

01:01:32,495 --> 01:01:34,685

the subsequent year is a down revision.

:

01:01:35,885 --> 01:01:39,815

So, you know, when you basically

account for the, the participation

:

01:01:39,815 --> 01:01:43,625

rate and the unemployment rate, and

you basically say that, hey, like,

:

01:01:44,585 --> 01:01:48,305

you know, the, the overall population

is probably growing at one, 1.4%.

:

01:01:48,935 --> 01:01:53,025

You basically get, an employment

number, which is probably close to

:

01:01:53,025 --> 01:01:54,645

contracting, if not contracting already.

:

01:01:55,605 --> 01:02:00,615

And so that, that measure, the, the,

the aggregate employment number is

:

01:02:01,185 --> 01:02:05,775

the driving factor for GDP growth over

time, like it is the most explanatory

:

01:02:05,775 --> 01:02:07,035

variable for GDP growth over time.

:

01:02:07,455 --> 01:02:10,935

But today we have this really weird

circumstance where like, GDP growth

:

01:02:10,935 --> 01:02:14,235

seems to be completely fine, but

employment is falling off a cliff.

:

01:02:14,565 --> 01:02:18,995

The culprit is likely to be

immigration in the United States.

:

01:02:19,850 --> 01:02:23,030

so we don't, you know, the, like, like

I was saying, the population numbers

:

01:02:23,030 --> 01:02:24,380

are kind of shoddy through the year.

:

01:02:24,440 --> 01:02:25,370

Like they're not great.

:

01:02:25,615 --> 01:02:29,060

There's the, there's a lot of the,

the, they're not, they're not very

:

01:02:29,060 --> 01:02:30,620

reliable on a month to month basis.

:

01:02:30,980 --> 01:02:33,530

But what we do see is that

the participation rate is

:

01:02:33,530 --> 01:02:34,580

just falling off a cliff.

:

01:02:35,260 --> 01:02:38,950

And the, the reason, you know, some

people say that this is the, the boomers

:

01:02:38,950 --> 01:02:40,870

exiting the, the labor force and all that.

:

01:02:40,870 --> 01:02:42,760

I think that's definitely

part of the equation.

:

01:02:43,180 --> 01:02:46,810

But the, the speed at which has

begun to fall off is indicative

:

01:02:46,810 --> 01:02:51,650

to me, which is supported by the

data of labor market recomposition.

:

01:02:52,190 --> 01:02:57,200

And what, what that is, is basically

that foreign workers have much higher

:

01:02:57,200 --> 01:02:59,600

participation rates than US workers.

:

01:03:00,410 --> 01:03:03,350

And so as you have this

immigration unwind, participation

:

01:03:03,350 --> 01:03:04,640

is falling off a cliff.

:

01:03:04,970 --> 01:03:10,300

And when we get to January, we might see

a meaningful down revision in, in the

:

01:03:10,330 --> 01:03:13,850

pace of, of population growth, which means

that the labor market's a lot weaker.

:

01:03:14,420 --> 01:03:17,540

Now the question I think that, you

know, investors need to wrestle

:

01:03:17,540 --> 01:03:21,540

with is like, these two series

are gonna mean revert, right?

:

01:03:21,540 --> 01:03:23,120

Like, it's gonna be one of two things.

:

01:03:23,150 --> 01:03:28,310

Either employment is gonna get

a lot better, or output is gonna

:

01:03:28,310 --> 01:03:29,960

come down to meet that employment.

:

01:03:30,350 --> 01:03:32,120

Or maybe you have a mix of those two.

:

01:03:32,420 --> 01:03:36,290

But the, you know, the destiny

for GDP growth over time is

:

01:03:36,290 --> 01:03:37,490

the pace of employment growth.

:

01:03:38,060 --> 01:03:40,820

And I think that's the biggest question

investors need to wrestle with.

:

01:03:41,000 --> 01:03:44,490

I'm not saying I have a clear answer, but

I think that that's something that's just

:

01:03:44,490 --> 01:03:45,930

flying under the radar for most people.

:

01:03:47,235 --> 01:03:47,775

Richard Laterman: Great.

:

01:03:48,555 --> 01:03:51,135

I think that's a good place

to, put a pin on conversation.

:

01:03:51,365 --> 01:03:56,348

Aahan, it's great chatting with

you, so much insight packed

:

01:03:56,628 --> 01:03:58,008

into an hour conversation.

:

01:03:58,008 --> 01:03:59,388

Thank you so much for joining us today.

:

01:04:00,100 --> 01:04:01,210

Aahan Menon: Always, such a pleasure guys.

:

01:04:01,210 --> 01:04:02,050

Thanks for having me on.

:

01:04:02,125 --> 01:04:02,365

Richard Laterman: weekend

:

01:04:34,316 --> 01:04:34,367

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