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

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

217

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a di diverse set of things, which

brings your aggregated risk down

218

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and you get something really nice.

219

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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,

236

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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.

245

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Like that's not how it works.

246

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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

248

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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?

251

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So maybe, you know, the term structure

and commodities is more mean reverting

252

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than, you know, the, the equities

which are outright mean reverting.

253

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And so like, it's all these little

nuances and sort of like adding them

254

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up and putting them together, but with

that overarching view of like, hey, we,

255

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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

258

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the way you started describing edges,

anywhere between 51 to 54, maybe

259

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55% on the high end, and probably

those edges are varying over time.

260

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that's very much how we have explained

a lot of our strategies and, and we've

261

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used this analogy in the past, and

some people like it, some people don't

262

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

equating or, or, creating an analogy

263

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between investing in gambling, but

it is really the casino edge, right?

264

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The idea that the, the casino

industry is ba is built on a razor

265

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thin, half a percent edge, right?

266

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The house has something about 50.5

267

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edge, and the player has a 49.5

268

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edge or something along those lines.

269

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

the benefit is the ensembles, right?

270

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An on, you have so many slot machines

and so many poker tables and blackjack

271

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and then, craps and so on and so forth.

272

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So you create those edges and over time

the law of large numbers manifest and

273

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you're able to harvest that edge over

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

274

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And in, in our world, you have multiple

strategies, multiple asset classes, and

275

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then the ability to trade at different

frequencies and so on and so forth.

276

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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

278

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other kind of fundamental data or

are, is that data, manifesting within

279

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those three key features, if you will.

280

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Like for instance, liquidity or

the rate of change of inflation,

281

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the rate of change of growth.

282

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'cause I know often people think of the,

the variable itself, but it really is

283

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the rate of change in the direct, the

direction of rate of change rights, the

284

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delta that really matters over time.

285

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It's the marginal allocation of, dollars,

and where the variable is shifting

286

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towards that really makes a difference.

287

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Aahan Menon: Yeah.

288

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so great question because, I,

when I, when I say this, it often

289

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lends itself to the idea that we

only do price-based stuff and the.

290

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

an amazing amount, which is price-based

291

00:17:32,917 --> 00:17:34,477

stuff, like an amazing amount, right?

292

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But that's not my, necessarily

my core expertise, right?

293

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Like when you are, when you go into

price-based only world, like you

294

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need a core expertise that's much

more in line with what you guys do.

295

00:17:42,637 --> 00:17:45,217

You guys are much more

sophisticated quants than I, right?

296

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Like I happen to be someone who

is well worse with the quantit,

297

00:17:49,232 --> 00:17:53,797

the quantitative techniques, but

like, I am primarily a macro guy.

298

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Like I'm a full macro guy.

299

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

blend, fundamental data to come up with

300

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things that fit in those buckets, right?

301

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So like a good example is you could use

price-based trend, but you could also use

302

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earnings momentum as a, as an indicator.

303

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You could use business cycle indicators,

I think the, the, you know, AQR has

304

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a paper called Macro Momentum, right?

305

00:18:18,187 --> 00:18:24,892

Like those basic ideas can, can be

expressed both using fundamental

306

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data and using price-based data.

307

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And what we found is that, you

know, the, the fundamental data is

308

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rarely superior to the price-based

data, but it is diversifying

309

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and adds more additional signal.

310

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And so that's what we try to do.

311

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We try to say, Hey, like

these are the concepts, right?

312

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Like it's reversion, carry, trend.

313

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What can we use that

fits in these buckets?

314

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Oh, like, you know, bond yields

are deviating from, you know, a

315

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Fisher rule or whatever, right?

316

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And we say, oh, like that, that

might be a good reversion signal.

317

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Or we look at, hey, like in equity

space we're looking at price-based

318

00:19:00,832 --> 00:19:03,502

momentum, but maybe we can look at

earnings momentum and that might be

319

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able to improve our signal a little bit.

320

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And so anything that's on

the table, we'll take it.

321

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And we kind of put it

into those, those buckets.

322

00:19:13,582 --> 00:19:16,072

Mike Philbrick: How does,

go ahead, keep going Rich.

323

00:19:16,162 --> 00:19:19,282

Richard Laterman: No, I, I was just

going to like, just as a follow up,

324

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do you incorporate liquidity, in

as a variable, as a macro variable?

325

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Aahan Menon: so I, I have some qualms and

with liquidity just generally as a, as a

326

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concept because I think actually, not as

a concept, but like as the way people are

327

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using it or thinking about it perhaps.

328

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Right.

329

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I think that first thing, like when

I think about liquidity, it's just

330

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basically how much, cash or liquid

assets is there in the system, which

331

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can potentiate further risk taking.

332

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Right?

333

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That is an amazing concept, and

if you can capture that well

334

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in some sort of programmatic

way, you know, you'll do well.

335

00:19:56,159 --> 00:20:03,479

But the thing is that the, the ways

people go about, in terms of trying to

336

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get a signal is like super subpar, right?

337

00:20:07,649 --> 00:20:11,909

Like they're looking at things like

reserve balances or like some mixing up of

338

00:20:11,909 --> 00:20:13,739

the Fed's balance sheet to get something.

339

00:20:14,099 --> 00:20:17,309

And one, those things don't change

often enough for you to have any signal.

340

00:20:17,579 --> 00:20:20,969

Two, the, the changes in those things

are not related to asset markets in

341

00:20:20,969 --> 00:20:23,519

any measurable way whatsoever, right?

342

00:20:23,729 --> 00:20:28,799

So I think that the concept is great,

but like, you know, looking at just

343

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the Fed's reserve balances and or, or

some version of that is like not good.

344

00:20:34,539 --> 00:20:37,869

what, what I think actually

makes sense is to recognize that

345

00:20:37,869 --> 00:20:42,099

the Fed's reserve balances has

effects in a lot of places, right?

346

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Has effects on sofa spreads.

347

00:20:43,809 --> 00:20:45,309

Like that's the thing everyone's

talking about right now.

348

00:20:45,309 --> 00:20:47,919

It has effects on commercial

paper spreads, right?

349

00:20:47,919 --> 00:20:51,759

It has effects on like longer

term corporate credit spreads.

350

00:20:51,939 --> 00:20:55,449

It has effects on the move, it

has effects on the term structure.

351

00:20:55,824 --> 00:20:58,554

All of those things

can be added up, right?

352

00:20:58,554 --> 00:21:01,734

Like all of those things, you get daily

data for all of those things, those

353

00:21:01,734 --> 00:21:05,094

can be turned into very nice financial

conditions measures, which actually

354

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allow you to trade across assets.

355

00:21:07,274 --> 00:21:10,964

but you know, in terms of what the

performance that they generate, like once

356

00:21:10,964 --> 00:21:15,404

you strip out beta, right, you're talking

about something like if you really,

357

00:21:15,404 --> 00:21:17,624

really mine hard, you might get a 0.6

358

00:21:17,624 --> 00:21:18,314

sharpe ratio.

359

00:21:18,644 --> 00:21:23,444

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

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

360

00:21:23,444 --> 00:21:28,904

financial conditions trend index,

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

361

00:21:28,904 --> 00:21:31,394

correlated to existing trend measures.

362

00:21:31,664 --> 00:21:36,314

So, you know, it's not the thing

that people make it out to be.

363

00:21:36,464 --> 00:21:37,094

Is it useful?

364

00:21:37,094 --> 00:21:37,334

Yes.

365

00:21:37,334 --> 00:21:39,914

There are certain places where

it's, where it can be really useful.

366

00:21:39,914 --> 00:21:43,624

So you know, you can use it

to come up with fair value

367

00:21:43,624 --> 00:21:45,074

measures of curve steepness.

368

00:21:45,584 --> 00:21:45,884

Okay.

369

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

if you're a bond guy and you really

370

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like, that's your world, you might

make out like a bandit doing that.

371

00:21:52,349 --> 00:21:56,309

But beyond that to just say like, I have

a view on assets based on liquidity.

372

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When you try to do that quantitatively,

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

373

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out of my back test, I might get a 0.6

374

00:22:04,439 --> 00:22:05,099

sharpe ratio.

375

00:22:05,099 --> 00:22:07,229

So realistically, I'm talking about a 0.3.

376

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That's not the thing you should spend

all your time and believe in that

377

00:22:10,649 --> 00:22:13,799

much, but you know, otherwise, I think

like conceptually, it's really good.

378

00:22:15,599 --> 00:22:19,284

Mike Philbrick: And you, well you

mentioned earlier about, tilting some

379

00:22:19,284 --> 00:22:23,827

of those, well tilting those three

factors based on, I think it was, you

380

00:22:23,827 --> 00:22:29,544

know, sort of the growth and inflation

and liquidity sort of, Overall view.

381

00:22:29,964 --> 00:22:35,454

And so I was wondering how you

come to that view in the, in the

382

00:22:35,454 --> 00:22:41,814

sort of the meta for the underlying

carry trend and reversion models.

383

00:22:41,814 --> 00:22:43,974

Like what, what are the

things that go into that?

384

00:22:44,879 --> 00:22:45,269

Aahan Menon: Yeah.

385

00:22:45,269 --> 00:22:47,249

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

386

00:22:47,369 --> 00:22:48,959

Mike Philbrick: so I'm

asking for the secret stuff.

387

00:22:48,959 --> 00:22:49,289

Okay.

388

00:22:49,844 --> 00:22:53,504

Aahan Menon: yeah, the

secret sauce that on

389

00:22:53,564 --> 00:22:54,374

Richard Laterman: Typical Mike,

390

00:22:54,419 --> 00:22:54,599

Mike Philbrick: not.

391

00:22:54,599 --> 00:22:55,469

No, absolutely not.

392

00:22:55,469 --> 00:22:57,359

But yeah, no, but

393

00:22:57,614 --> 00:22:58,964

Aahan Menon: But I think I give you a

394

00:22:58,979 --> 00:22:59,219

Mike Philbrick: yeah.

395

00:22:59,219 --> 00:23:03,209

Well, well also illustrate an example

and, and share, you know, how those

396

00:23:03,209 --> 00:23:06,599

growth and inflation liquidity

dynamics kind of work together.

397

00:23:06,599 --> 00:23:10,199

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

give the sauce to share some insight.

398

00:23:10,199 --> 00:23:10,559

I think.

399

00:23:10,754 --> 00:23:10,964

Richard Laterman: Yeah.

400

00:23:10,964 --> 00:23:15,014

And, and perhaps do, would you shift

completely out of one and into another,

401

00:23:15,014 --> 00:23:18,734

or would you just kind of dial it a li

a a little bit in favor of this, but

402

00:23:18,734 --> 00:23:21,464

you'll still keep the other signals at?

403

00:23:22,475 --> 00:23:25,220

Aahan Menon: So the, I mean like

broad strokes, the way we keep it is

404

00:23:25,220 --> 00:23:28,175

there's, there are signals live for

all of these things all the time.

405

00:23:29,330 --> 00:23:33,087

What ends up dominating is the thing that

ends up getting the most signal, right?

406

00:23:33,087 --> 00:23:36,747

So it's never like we switch

off our trend, it's just that

407

00:23:36,747 --> 00:23:38,547

trend doesn't have much signal.

408

00:23:38,787 --> 00:23:40,737

Reversion has a huge amount of signal.

409

00:23:41,007 --> 00:23:44,337

And so for, you know, the next

foresee for the foreseeable future,

410

00:23:44,337 --> 00:23:48,572

we'll be reversion style, you know,

rev reversion style return stream.

411

00:23:49,152 --> 00:23:54,627

But as that dynamic kind of shifts,

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

412

00:23:54,627 --> 00:23:56,367

or more carry or something like that.

413

00:23:56,577 --> 00:24:00,537

And so it's like, we'll never

just go completely on or off one.

414

00:24:00,867 --> 00:24:04,317

It depends on where we're getting the

most amount of the opportunity set.

415

00:24:04,317 --> 00:24:10,042

So to, to give a really good, like

illustrative example, I think something

416

00:24:10,042 --> 00:24:13,212

that I noticed, in 2023, right?

417

00:24:13,212 --> 00:24:18,489

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

after the, the hiking cycle in,

418

00:24:18,549 --> 00:24:21,722

in the US was just, something I.

419

00:24:22,097 --> 00:24:26,688

Happened to notice day to day while

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

420

00:24:26,688 --> 00:24:30,678

had these trend signals and these trend

signals just kept getting messed up.

421

00:24:31,248 --> 00:24:33,828

Like they kept, you know, we used

relatively short term measures of trends.

422

00:24:33,828 --> 00:24:36,558

So like something like six months,

three months or less, right?

423

00:24:36,938 --> 00:24:39,008

and so they just kept

getting tripped up every day.

424

00:24:39,008 --> 00:24:42,548

Like we put on a trend based signal,

like we increase exposure, we get

425

00:24:42,548 --> 00:24:43,958

completely smoked the next day.

426

00:24:44,798 --> 00:24:47,858

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

like, can we check this out real quick?

427

00:24:47,858 --> 00:24:49,328

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

428

00:24:49,718 --> 00:24:55,928

And what we noticed is basically

the, the term structure, because,

429

00:24:55,988 --> 00:24:59,288

because we had never been in a

hiking cycle like that before, right?

430

00:24:59,648 --> 00:25:02,978

The term structure of interest rates,

every time there was even slightly bad

431

00:25:02,978 --> 00:25:08,288

economic data would begin to mean revert

and price and cuts like dramatically.

432

00:25:08,752 --> 00:25:13,192

And so what, what you've had

since basically 2022 till present

433

00:25:13,492 --> 00:25:18,852

is the most short term mean

reversion bonds, like we've seen.

434

00:25:19,672 --> 00:25:23,332

So, you know, short term mean reversion

in bonds this year has put up a 1.9

435

00:25:23,332 --> 00:25:25,312

sharpe ratio, right?

436

00:25:25,372 --> 00:25:32,092

And the, the, the reason for that

is because you are in a place where

437

00:25:32,092 --> 00:25:35,422

the growth and inflation mandate

don't give you clarity, right?

438

00:25:35,775 --> 00:25:38,165

you have, I should say the

unemployment and inflation

439

00:25:38,165 --> 00:25:39,365

mandate don't give you clarity.

440

00:25:39,575 --> 00:25:43,295

So unemployment data and employment

data has broadly been softening.

441

00:25:43,685 --> 00:25:47,975

There are issues around NFP and

potentially issues around, uh, the

442

00:25:47,975 --> 00:25:51,905

population adjustments, which suggests

that employment growth is actually a

443

00:25:51,905 --> 00:25:53,525

lot weaker than the official numbers.

444

00:25:54,305 --> 00:25:57,185

And the Fed knows this,

everyone knows this, right?

445

00:25:57,185 --> 00:25:58,925

And so everyone's basically haircut it.

446

00:25:58,955 --> 00:26:03,065

We have estimates of like what the

employment growth trend actually is, and

447

00:26:03,065 --> 00:26:04,505

we think it might be actually negative.

448

00:26:04,955 --> 00:26:07,935

And so, you have that on one side

of the mandate and on the other

449

00:26:07,935 --> 00:26:11,820

side, you, you're now in the fifth

year running of not being a target.

450

00:26:13,005 --> 00:26:16,335

So every time you get weaker data,

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

451

00:26:16,335 --> 00:26:19,935

recession immediately, because you

know, you expect a ton of cuts, but

452

00:26:19,935 --> 00:26:24,015

then you slowly continue to have

nominal growth data, which continues

453

00:26:24,015 --> 00:26:25,635

to surprise, surprise the other way.

454

00:26:25,845 --> 00:26:29,775

And so as a result, the term structure,

which is really just like sofa

455

00:26:29,775 --> 00:26:32,385

plus a little bit of term premium,

honestly, like the term premium

456

00:26:32,385 --> 00:26:33,645

isn't even that a big good deal.

457

00:26:34,005 --> 00:26:37,155

It's, it's basically like sofa

pricing just continues to mean,

458

00:26:37,155 --> 00:26:38,175

revert really dramatically.

459

00:26:38,745 --> 00:26:42,490

And so as a result, like what

you would wanna do is you wanna

460

00:26:42,525 --> 00:26:44,085

have measures around that.

461

00:26:44,715 --> 00:26:47,295

You know, you wanna have measures around

the dispersion would be in the growth

462

00:26:47,295 --> 00:26:50,535

and inflation mandate, and that's what

really feeds whether you want to be

463

00:26:50,535 --> 00:26:52,845

in diversion or not, if that makes

464

00:26:52,860 --> 00:26:53,070

Mike Philbrick: Yeah.

465

00:26:53,070 --> 00:26:56,730

And so yeah, the future always holds

what the past is yet to reveal, right?

466

00:26:56,730 --> 00:27:00,760

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

how that, how true that always is.

467

00:27:01,000 --> 00:27:08,380

So it's not really, sort of the typical

macroeconomic growth, inflation liquidity

468

00:27:08,380 --> 00:27:16,340

factors that you're overlaying as the meta

on your carry, trend reversion framework.

469

00:27:16,340 --> 00:27:22,460

But it's more the actual models and

their functioning themselves and

470

00:27:22,460 --> 00:27:26,870

their ability to be effective that

you're managing with the tilting.

471

00:27:28,355 --> 00:27:29,255

Aahan Menon: Yeah, exactly.

472

00:27:29,255 --> 00:27:29,795

Exactly.

473

00:27:29,795 --> 00:27:34,895

The, the, the models have to all like

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

474

00:27:34,895 --> 00:27:38,595

something that macro guys like, you know,

I started my career at a macro hedge fund.

475

00:27:39,595 --> 00:27:44,075

and you know, one thing that like

discretionary macro style investing

476

00:27:44,075 --> 00:27:50,225

always leads to is one view expressed

a lot of, across a lot of things.

477

00:27:50,435 --> 00:27:52,865

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

478

00:27:53,825 --> 00:27:54,245

Right?

479

00:27:54,275 --> 00:27:58,955

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

about like, we're in a reflation, you

480

00:27:58,955 --> 00:28:00,785

know, run it hard and all that stuff.

481

00:28:00,845 --> 00:28:05,315

All of my expressions, even if I

do 30 of them right, they all hinge

482

00:28:05,315 --> 00:28:06,545

on that macro view being right.

483

00:28:07,325 --> 00:28:10,653

And like, I, Initially, you know,

when I started building all this

484

00:28:10,653 --> 00:28:13,893

stuff, like I tried to do that a

lot and I just found that like you

485

00:28:13,893 --> 00:28:15,723

couldn't push performance, you know?

486

00:28:16,083 --> 00:28:21,033

But, and what we found makes a lot

more sense is to say, Hey, like how

487

00:28:21,033 --> 00:28:23,283

does each individual asset work?

488

00:28:24,003 --> 00:28:27,933

Let me try and create something for

each individual asset, add them up.

489

00:28:28,503 --> 00:28:30,723

And you get some type of

macro view out of that.

490

00:28:31,533 --> 00:28:33,183

And that seems to work better.

491

00:28:33,347 --> 00:28:35,680

it has, it is naturally

way more diversified.

492

00:28:35,680 --> 00:28:40,120

You have more, you have so much more

signal and, and the aggregate macro view

493

00:28:40,120 --> 00:28:43,480

you get too, seems to be a lot more high

quality, even though it shifts a lot.

494

00:28:43,870 --> 00:28:45,925

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

495

00:28:45,955 --> 00:28:47,245

Mike Philbrick: Price before narrative

496

00:28:47,925 --> 00:28:48,255

Richard Laterman: Yeah.

497

00:28:48,255 --> 00:28:51,915

And then narrative feeds price, and

then there's this reflexive symbiotic

498

00:28:51,915 --> 00:28:56,385

relationship where one feeds the other

until such time as you have an inflection

499

00:28:56,445 --> 00:28:58,665

or a paradigm shift of some sort.

500

00:28:59,115 --> 00:29:04,665

Did does this framework lend itself

to traditional asset classes?

501

00:29:04,715 --> 00:29:06,572

across the board equally?

502

00:29:06,572 --> 00:29:10,052

I guess it, it can vary a little bit here

and there, some asset classes, uh, may,

503

00:29:10,052 --> 00:29:13,352

it may have a higher predictive power

for some asset classes versus others at

504

00:29:13,352 --> 00:29:15,092

different, at varying moments in time.

505

00:29:15,452 --> 00:29:18,452

But have you, have you tested

this out in digital assets?

506

00:29:18,452 --> 00:29:22,872

Are you looking at, at Bitcoin, ether

and, and any other, of these tokens?

507

00:29:23,257 --> 00:29:24,127

do do these apply?

508

00:29:24,127 --> 00:29:24,847

Do these rules apply?

509

00:29:25,857 --> 00:29:29,147

Aahan Menon: You know, the thing, so

I, so it's super interesting with,

510

00:29:29,283 --> 00:29:30,693

with the crypto universe, right?

511

00:29:30,693 --> 00:29:32,973

Because, I know you guys

have gotten involved.

512

00:29:33,077 --> 00:29:37,717

I, I think like when it comes to

applying this stuff, I have seen, one

513

00:29:37,717 --> 00:29:42,957

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

systematic macro style or, you know,

514

00:29:43,047 --> 00:29:48,087

carry trend type styles go into the

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

515

00:29:48,237 --> 00:29:48,657

Right?

516

00:29:49,037 --> 00:29:54,947

but for me, like I'm really boring as

a person and the way I kind of imagine

517

00:29:54,947 --> 00:30:01,037

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

the first quants to trade trend in like

518

00:30:01,037 --> 00:30:02,687

the seventies through the nineties.

519

00:30:03,197 --> 00:30:05,627

Like, you might just absolutely kill it.

520

00:30:05,627 --> 00:30:08,417

You'll be a legend, but the amount

of alpha decay you'll probably

521

00:30:08,417 --> 00:30:10,697

go through will be terrifying.

522

00:30:11,147 --> 00:30:15,137

And I personally don't have the stomach

for that, and I don't necessarily wanna

523

00:30:15,137 --> 00:30:16,457

put my business through that just yet.

524

00:30:16,787 --> 00:30:19,787

And so I think that, you know, when

we look at things like, you know,

525

00:30:19,787 --> 00:30:22,567

cross-sectional carry and a bunch

of, these cryptos and stuff like

526

00:30:22,567 --> 00:30:24,217

that, they put up crazy numbers.

527

00:30:24,547 --> 00:30:27,487

You know, you know, even basic trend

factors seem to put up crazy numbers.

528

00:30:27,817 --> 00:30:31,417

But like, I, I don't think that

you can continue to expect that.

529

00:30:31,927 --> 00:30:36,913

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

I feel like the space is gonna mature

530

00:30:36,913 --> 00:30:40,513

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

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

531

00:30:41,428 --> 00:30:44,308

I don't think that you can factor in

the sheer amount of alpha decay that

532

00:30:44,308 --> 00:30:46,888

you're gonna have, even if you put in

a factor for the amount of alpha decay.

533

00:30:47,338 --> 00:30:50,608

And so like, that's the reason

we've been kind of, you know,

534

00:30:51,388 --> 00:30:52,918

careful about getting involved

535

00:30:52,993 --> 00:30:54,883

Richard Laterman: steered

clear from the crypto space.

536

00:30:54,913 --> 00:30:55,993

Okay, that makes sense.

537

00:30:56,113 --> 00:31:01,003

And so I guess you were looking

at traditional stock bond, as well

538

00:31:01,003 --> 00:31:03,343

as currencies and commodities.

539

00:31:03,373 --> 00:31:04,748

Is that the, the asset?

540

00:31:04,878 --> 00:31:05,228

Asset

541

00:31:05,968 --> 00:31:08,308

Aahan Menon: so we're

doing, so we do, U.S.,

542

00:31:08,308 --> 00:31:13,678

we do all the major sectors, so the 11

sectors, and then we do global equities,

543

00:31:13,708 --> 00:31:18,758

we do, global fixed income, so, 10

country, eight country, bond futures.

544

00:31:19,298 --> 00:31:21,278

And then we do the yeah, yeah.

545

00:31:21,278 --> 00:31:21,758

Sovereigns.

546

00:31:22,058 --> 00:31:26,538

And then we do, we do the industrial

complex and we do, energy.

547

00:31:27,183 --> 00:31:28,713

Richard Laterman: So

you mean metals, energy?

548

00:31:28,713 --> 00:31:29,553

No, agri

549

00:31:30,582 --> 00:31:30,882

Aahan Menon: No, I

550

00:31:30,922 --> 00:31:32,692

Richard Laterman: gold,

silver, platinum, poly.

551

00:31:32,692 --> 00:31:32,892

Yeah.

552

00:31:33,342 --> 00:31:35,292

Aahan Menon: Gold, gold

and silver is involved.

553

00:31:35,292 --> 00:31:40,662

We do, so we do have, so we have

a sub portfolio that's called

554

00:31:40,722 --> 00:31:42,672

our Crisis Protection Program.

555

00:31:43,272 --> 00:31:46,482

And what that's really meant

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

556

00:31:46,512 --> 00:31:48,012

it's a countercyclical program.

557

00:31:48,402 --> 00:31:55,902

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

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

558

00:31:56,322 --> 00:31:59,662

And they're really kind of the, you

know, I think I've heard you guys

559

00:31:59,662 --> 00:32:03,702

use this, putting the, the sugar

in the medicine for, for us to be

560

00:32:03,702 --> 00:32:06,352

able to, to have long vol exposure.

561

00:32:07,132 --> 00:32:12,172

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

the, the tips with our long vol exposure.

562

00:32:12,502 --> 00:32:15,892

The gold and tips are not meant to be

super high edge or anything like that.

563

00:32:16,262 --> 00:32:17,942

they're just meant to be

something that allows you to

564

00:32:17,942 --> 00:32:19,392

carry this long vol exposure well.

565

00:32:20,057 --> 00:32:22,967

Richard Laterman: And you're

trading vault through VIX Futures

566

00:32:23,627 --> 00:32:23,837

Aahan Menon: Yeah.

567

00:32:24,617 --> 00:32:24,857

Richard Laterman: and.

568

00:32:25,187 --> 00:32:25,207

Aahan Menon: VIX Futures.

569

00:32:25,397 --> 00:32:25,937

VIX Futures.

570

00:32:25,937 --> 00:32:28,907

And then we, we have an, we

have a, a retail product, which

571

00:32:28,907 --> 00:32:31,057

they, we use the, the VIX ETFs.

572

00:32:31,207 --> 00:32:34,677

Richard Laterman: And you're using,

that same three, feature set of

573

00:32:34,887 --> 00:32:36,697

carry trend and mean reversion.

574

00:32:37,107 --> 00:32:40,617

And are, and, and within each

one of those, do you have

575

00:32:40,617 --> 00:32:43,467

different sub strategy, different

implementations of trend, different

576

00:32:43,737 --> 00:32:45,447

implementations of carry and so on.

577

00:32:45,612 --> 00:32:47,367

Aahan Menon: Yes, yes.

578

00:32:47,667 --> 00:32:50,217

Well, when it comes to the crisis,

the crisis protection program,

579

00:32:50,247 --> 00:32:54,027

like a primary objective other than

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

580

00:32:54,027 --> 00:32:55,677

applying all three of those concepts.

581

00:32:55,987 --> 00:33:00,037

but when it comes to tips and, and

gold, we're really just trying to

582

00:33:00,037 --> 00:33:02,737

do, reversion and carry, right?

583

00:33:02,737 --> 00:33:04,957

Like, we just want to have

a counter cyclical exposure.

584

00:33:05,167 --> 00:33:09,037

So when expected returns are basically

good, we wanna be able to hold a

585

00:33:09,037 --> 00:33:12,427

little bit more of, you know, the,

the tips and the, and the gold.

586

00:33:12,787 --> 00:33:17,630

And, uh, that allows us to

basically carry the VIX positively.

587

00:33:18,770 --> 00:33:19,550

Richard Laterman: That makes sense.

588

00:33:20,300 --> 00:33:21,290

And how are you sizing.

589

00:33:21,595 --> 00:33:22,495

those positions?

590

00:33:22,495 --> 00:33:25,915

Are you, are you basing them

on, on volatility sizing?

591

00:33:25,945 --> 00:33:27,665

How's the, how's that framework?

592

00:33:28,925 --> 00:33:32,915

Aahan Menon: So all of our, all

of our signals basically live

593

00:33:33,005 --> 00:33:34,655

in like expected sharpe ratio.

594

00:33:35,075 --> 00:33:41,628

So we do the vol sizing, but you

know, just from the push from clients,

595

00:33:41,628 --> 00:33:45,538

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

it's very, very, counterintuitive

596

00:33:45,538 --> 00:33:48,598

to have a full position on when, you

know, your signals are really small.

597

00:33:48,868 --> 00:33:54,838

And so what we found typically is if

you do this blend of reversion carry

598

00:33:54,838 --> 00:33:59,548

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

because I know who I'm talking to.

599

00:33:59,788 --> 00:34:04,818

Um, but you know, you do improve, the

relationship between the magnitude

600

00:34:04,818 --> 00:34:05,988

of expected return and signal.

601

00:34:06,703 --> 00:34:11,083

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

something, but what you do get is you do

602

00:34:11,083 --> 00:34:13,900

get a little bit of improvement because

you, you know, usually when you get a

603

00:34:13,900 --> 00:34:17,260

trend signal, like the larger of the trend

signal, the expected return starts to fall

604

00:34:17,260 --> 00:34:19,090

off as you get really, really further out.

605

00:34:19,389 --> 00:34:23,183

But when you start you know, implementing

the carry and the reversion and the,

606

00:34:23,393 --> 00:34:26,303

you start to get a slightly more

linear, so the higher the signal.

607

00:34:26,483 --> 00:34:30,473

And so all of, all of our signals

across all our strategies basically

608

00:34:30,473 --> 00:34:32,063

live in expected to operational space.

609

00:34:33,077 --> 00:34:33,737

Mike Philbrick: Interesting.

610

00:34:33,827 --> 00:34:33,976

Yeah.

611

00:34:34,637 --> 00:34:37,527

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

in that pocket there's actually

612

00:34:37,527 --> 00:34:41,286

a special use case that you're

designing to, that's complimentary

613

00:34:41,286 --> 00:34:42,607

to the rest of the portfolio.

614

00:34:43,040 --> 00:34:46,250

so that, that's a very interesting

way to think about that.

615

00:34:46,760 --> 00:34:46,969

I

616

00:34:47,120 --> 00:34:47,389

Aahan Menon: yeah.

617

00:34:47,465 --> 00:34:51,103

I mean the, the crisis, sorry, sorry to

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

618

00:34:51,132 --> 00:34:55,362

program is super interesting because it's

not actually meant to be like a high edge.

619

00:34:55,362 --> 00:34:57,613

We're timing everything under

the sun kind of program.

620

00:34:58,033 --> 00:34:58,813

But for what?

621

00:34:58,873 --> 00:35:03,013

But because of the correlation

characteristics, it just seems to fit

622

00:35:03,013 --> 00:35:04,573

in with everything you throw it into.

623

00:35:04,873 --> 00:35:06,763

So you put it on top of

stocks, it does really well.

624

00:35:06,763 --> 00:35:09,103

You put it with the commodities,

it seems to do really well.

625

00:35:09,373 --> 00:35:11,233

You put it with bonds, it

seems to do really well.

626

00:35:11,473 --> 00:35:15,163

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

for our buck program, but it's not

627

00:35:15,163 --> 00:35:18,073

supposed to be the most high alpha

program, which is really funny.

628

00:35:18,793 --> 00:35:19,153

Mike Philbrick: Amazing.

629

00:35:19,423 --> 00:35:24,323

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

were talking about earlier, which was the,

630

00:35:24,323 --> 00:35:29,313

this idea that through these, the, the

myriad of, signals that you were getting,

631

00:35:29,883 --> 00:35:32,350

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

632

00:35:32,350 --> 00:35:36,760

You, the, the macro narrative would

bubble from that, but you also mentioned

633

00:35:37,060 --> 00:35:40,570

that it changes a lot and we didn't get

a chance to, to pull on that thread.

634

00:35:40,570 --> 00:35:43,600

And I'd like to pull on that thread a

little bit because, you know, recently

635

00:35:43,600 --> 00:35:47,350

you had a note that went from, you know,

max long equities to basically negative

636

00:35:47,680 --> 00:35:51,430

beta, which I think is indicative

of what you're actually saying right

637

00:35:51,430 --> 00:35:55,280

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

oh boy, does it ever shift quickly?

638

00:35:55,640 --> 00:35:59,660

And probably that relates to what you were

talking about earlier and being able to

639

00:35:59,660 --> 00:36:02,930

trade a little bit more, being able to

adapt your positions a little bit more.

640

00:36:03,320 --> 00:36:07,550

And then the headline narrative, which

was these long, long-term global macro

641

00:36:08,030 --> 00:36:12,180

thematic, notes really aren't going

to improve portfolio performance.

642

00:36:12,760 --> 00:36:14,320

but maybe let's just dig into that.

643

00:36:14,320 --> 00:36:17,260

Let's pull on that thread a

little bit and, you know, you've,

644

00:36:17,260 --> 00:36:18,850

you've had a flip recently.

645

00:36:19,140 --> 00:36:21,510

how is that, how is that working out?

646

00:36:21,590 --> 00:36:24,575

have, has it flipped back

and, and that type of thing.

647

00:36:25,400 --> 00:36:27,080

Richard Laterman: he can

share what precipitated the

648

00:36:27,080 --> 00:36:28,130

flip, as a bit of a teaser

649

00:36:28,245 --> 00:36:28,535

Aahan Menon: Yeah.

650

00:36:29,955 --> 00:36:30,530

Yeah, yeah.

651

00:36:30,530 --> 00:36:31,190

Happy to.

652

00:36:31,290 --> 00:36:34,410

so we, we have a, we have a

common friend, Bob Elliot.

653

00:36:34,593 --> 00:36:37,723

he said something, to me, or no,

he said something in a tweet.

654

00:36:37,903 --> 00:36:40,813

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

thought that it was that important, but

655

00:36:40,813 --> 00:36:43,513

I thought it was really important and it

stuck with me for many years, and I keep

656

00:36:43,513 --> 00:36:49,243

reminding him about it, but he, he, he

tweeted that there is, there is no award

657

00:36:49,243 --> 00:36:54,523

in markets for consistency of narrative

because there is no award for that.

658

00:36:55,013 --> 00:36:58,073

and, and that's really something

like, I try to hold really true.

659

00:36:58,073 --> 00:37:00,023

Like I try to come to the table.

660

00:37:00,023 --> 00:37:03,473

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

on a day-to-day basis is basically

661

00:37:03,473 --> 00:37:05,123

like we get all of these signals.

662

00:37:05,603 --> 00:37:07,403

A lot of it is fundamentally informed.

663

00:37:07,883 --> 00:37:11,513

I want to try and piece together what

the signals are telling you and try

664

00:37:11,513 --> 00:37:16,343

to get the big muscle movements and

trend to you in a digestible way.

665

00:37:16,583 --> 00:37:16,793

Right?

666

00:37:16,793 --> 00:37:20,123

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

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

667

00:37:20,453 --> 00:37:20,963

Everything.

668

00:37:20,993 --> 00:37:24,503

A lot of times I am super late

to writing about the thing.

669

00:37:24,743 --> 00:37:25,103

Right?

670

00:37:25,413 --> 00:37:28,443

this happened, you know, it, I,

I can't even tell you how many

671

00:37:28,443 --> 00:37:31,683

times where, you know, we've had

an exposure on, it starts to work,

672

00:37:31,743 --> 00:37:33,033

it starts to work for a few months.

673

00:37:33,033 --> 00:37:34,533

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

674

00:37:34,533 --> 00:37:37,053

I write about it and it's

done in the next week.

675

00:37:37,613 --> 00:37:41,843

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

know, what, what it really boils down

676

00:37:41,843 --> 00:37:46,493

to is you have all of these signals,

and these signals are meant to be

677

00:37:46,493 --> 00:37:48,383

predictive of asset markets, right?

678

00:37:49,193 --> 00:37:53,153

And asset markets are discounting

machines, and the, the discounting

679

00:37:53,153 --> 00:37:56,303

changes way faster than the

underlying conditions, right?

680

00:37:56,303 --> 00:38:00,803

Like the, the, the expectations for

growth whips all around every single day.

681

00:38:01,433 --> 00:38:03,263

The actual growth doesn't change at all.

682

00:38:03,873 --> 00:38:06,153

and so, when you're running a

process like this, what you're

683

00:38:06,153 --> 00:38:09,333

really getting is you're get, you're

getting the information on like.

684

00:38:10,203 --> 00:38:12,873

Is expected growth, underpriced,

overpriced every single

685

00:38:12,873 --> 00:38:13,743

day, and that can shift.

686

00:38:14,163 --> 00:38:16,653

And so that's just something that

you have to become comfortable with.

687

00:38:16,713 --> 00:38:22,341

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

doing, uh, because, you always, in,

688

00:38:22,834 --> 00:38:25,444

in traditional macro circles, you're

always trying to have like this

689

00:38:25,444 --> 00:38:29,014

consistent narrative and then kind

of position around that narrative.

690

00:38:29,434 --> 00:38:33,371

And I just, you know, what, what

I continued to come around to is

691

00:38:33,371 --> 00:38:35,891

that listen, like we're not trying

to predict the macro narrative.

692

00:38:35,891 --> 00:38:40,434

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

the markets, and the predictions change

693

00:38:40,434 --> 00:38:41,844

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

694

00:38:42,304 --> 00:38:47,914

and so I think that what, but what we

do try to do is that like, you know,

695

00:38:47,974 --> 00:38:53,734

we, asset prices by and large do move

in large cross asset trends, right?

696

00:38:53,734 --> 00:38:57,244

Like, you know, when equities rally

a lot and commodities are rallying

697

00:38:57,244 --> 00:39:00,154

a lot, you can pretty much bet

that bonds are also selling off.

698

00:39:00,574 --> 00:39:03,964

And the economies do tend

to move in a slow fashion.

699

00:39:04,519 --> 00:39:08,039

And, um, markets, for whatever

overreaction under reaction

700

00:39:08,039 --> 00:39:09,719

phenomena, take your choice, right?

701

00:39:10,019 --> 00:39:11,609

They, they tend to trend.

702

00:39:12,089 --> 00:39:16,019

And so what we wanna try to do is we

wanna try and say, Hey, like, okay,

703

00:39:16,019 --> 00:39:17,699

these are the moves that are being made.

704

00:39:18,449 --> 00:39:22,019

Aside from the really tactical

opportunities, so aside from

705

00:39:22,169 --> 00:39:24,899

something that's like a one day mean

reversion move, or one day breakout

706

00:39:24,899 --> 00:39:28,319

signal or something like that, what

are kind of the themes under the

707

00:39:28,319 --> 00:39:31,949

hood that, you know, are evolving

or, you know, coming to the front?

708

00:39:32,789 --> 00:39:35,999

And I think that recently was a

super interesting example, right?

709

00:39:36,029 --> 00:39:43,239

On this year, our equity signals, so

our, our, US equity and global equity

710

00:39:43,239 --> 00:39:48,819

signals showcased some of the strongest

signal strength that we've ever seen,

711

00:39:48,819 --> 00:39:51,249

even compared to our back tests, right?

712

00:39:51,339 --> 00:39:56,059

And how that manifests is basically

a hundred percent of our, of our

713

00:39:56,059 --> 00:39:58,189

max notional in both programs.

714

00:39:58,609 --> 00:40:02,419

Which is just like absolutely harying

for me to look at every day, right?

715

00:40:02,719 --> 00:40:04,339

Because all the positions are the same.

716

00:40:04,399 --> 00:40:05,239

They're correlated.

717

00:40:05,239 --> 00:40:06,949

The signals are moving in the same way.

718

00:40:07,189 --> 00:40:10,969

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

as well just stop all of this and

719

00:40:10,969 --> 00:40:13,019

open along only the equity shop,

720

00:40:13,304 --> 00:40:13,489

right?

721

00:40:13,909 --> 00:40:15,449

Um, yeah, exactly.

722

00:40:15,449 --> 00:40:19,949

And so, you know, we, we, fortunately

because of the mix of carry trend

723

00:40:19,969 --> 00:40:24,709

reversion, that did lead to also

good, you know, forward returns

724

00:40:24,709 --> 00:40:26,389

when we had those high signals.

725

00:40:26,899 --> 00:40:32,329

but what started to happen over the last

couple of months is we started to have

726

00:40:32,359 --> 00:40:34,459

a shift down across all our signals.

727

00:40:34,969 --> 00:40:36,439

and I started to notice that.

728

00:40:36,499 --> 00:40:39,979

And so when, when our aggregate

risk started to come down, I

729

00:40:39,979 --> 00:40:41,029

said, Hey, something's going on.

730

00:40:41,359 --> 00:40:43,219

You know, we need to peel back.

731

00:40:43,519 --> 00:40:46,849

And so we start doing the work to see,

we have a whole bunch of stuff that's

732

00:40:46,849 --> 00:40:49,219

been systematized, like now for years.

733

00:40:49,609 --> 00:40:51,829

You know, I'm not always

on top of every piece.

734

00:40:52,069 --> 00:40:55,669

So what we started to, one of the first

things that actually started a bubble

735

00:40:55,669 --> 00:40:57,829

to the top was our index level view.

736

00:40:58,219 --> 00:41:02,399

So our index level views went from,

you know, max bullish to like, let's

737

00:41:02,399 --> 00:41:05,699

be a little bit more conservative to

getting a little bit short, right?

738

00:41:05,699 --> 00:41:10,119

And what really drove that is that we

have these, we have these fair value

739

00:41:10,119 --> 00:41:13,449

models for what consensus earnings

expectations should look like.

740

00:41:14,319 --> 00:41:18,909

And what we do is we take a bunch of

fundamental macro data and we basically

741

00:41:18,909 --> 00:41:23,179

try to reconstruct, something that

looks a lot like analyst consensus.

742

00:41:23,599 --> 00:41:27,949

And what we found is that if there are

major gaps between those two things, you

743

00:41:27,949 --> 00:41:30,109

basically have an opportunity to trade.

744

00:41:30,739 --> 00:41:35,449

And so what we started to see is that

as we went into earning season, these

745

00:41:35,449 --> 00:41:39,499

tech numbers just came in super hard,

super hard, and everything else sucked.

746

00:41:40,084 --> 00:41:40,234

Right.

747

00:41:40,754 --> 00:41:44,544

and so as a result, we, we started

to have, you know, these macro

748

00:41:44,544 --> 00:41:49,734

indications start to get, you know,

get our, get our gross exposure down

749

00:41:49,734 --> 00:41:51,144

a little bit at the index level.

750

00:41:51,594 --> 00:41:56,454

And we also, after a little bit of

waiting, you know, being early or

751

00:41:56,994 --> 00:42:00,724

being wrong, we basically started

to get, our price-based signals also

752

00:42:00,724 --> 00:42:01,984

started to confirm that a little bit.

753

00:42:03,004 --> 00:42:06,994

And then that started to kind of,

so that started at the s and p 500,

754

00:42:06,994 --> 00:42:11,734

where honestly like that's, that's

the place where, you know, we, if we

755

00:42:11,734 --> 00:42:13,234

have any expertise, it would be there.

756

00:42:13,354 --> 00:42:16,304

But, you know, it started to

kind of spread out a little

757

00:42:16,304 --> 00:42:17,504

bit to our global signals.

758

00:42:17,534 --> 00:42:20,669

And what we started to see is that,

hey, like if you look at a bunch of

759

00:42:20,669 --> 00:42:24,239

lo local FX equity trend globally,

they're not doing that well.

760

00:42:24,269 --> 00:42:25,919

Like China's not doing that well anymore.

761

00:42:26,229 --> 00:42:27,639

India's not doing that well anymore.

762

00:42:27,789 --> 00:42:30,889

And you start to look at, the,

the earnings momentum in all

763

00:42:30,889 --> 00:42:32,029

of those countries as well.

764

00:42:32,209 --> 00:42:34,729

You've actually started to see over

the last couple of months that,

765

00:42:34,789 --> 00:42:37,189

you know, earnings momentum has

actually started turn negative.

766

00:42:37,489 --> 00:42:42,169

And so you, you put all of that

together into one kind of view is

767

00:42:42,169 --> 00:42:46,849

you went from a place where, you

know, risks were, you know, the, the

768

00:42:46,849 --> 00:42:51,169

expectations around Liberation Day were

basically like, Hey, the world is over.

769

00:42:51,174 --> 00:42:56,509

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

gonna have a recession like tomorrow to

770

00:42:56,569 --> 00:43:01,489

okay, like we're in an exuberant kind

of environment where if you look across

771

00:43:01,489 --> 00:43:06,379

earnings aggregates, both globally and

within the US equity market, internal,

772

00:43:06,799 --> 00:43:11,419

the only thing really floating all of

it up is this tech component, right?

773

00:43:11,869 --> 00:43:12,349

And so.

774

00:43:12,919 --> 00:43:16,099

If you, if you have any type of

macro tracking, you basically

775

00:43:16,099 --> 00:43:18,499

say, yeah, the check component is

there, but it can't be everything.

776

00:43:19,219 --> 00:43:21,259

And so, you know, we started to

get a little bit more negative.

777

00:43:21,259 --> 00:43:23,059

We got a little bit of

price comp information.

778

00:43:23,779 --> 00:43:27,629

We got, basically net net

negative beta for a couple weeks.

779

00:43:27,669 --> 00:43:30,399

and over the last couple of

sessions we basically come

780

00:43:30,399 --> 00:43:31,779

back to a more neutral place.

781

00:43:32,769 --> 00:43:36,519

I now, you know, to synthesize that

and kind of put it into like, what

782

00:43:36,519 --> 00:43:38,049

do, what do I think of the world?

783

00:43:38,139 --> 00:43:42,519

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

we're going into recession or the world

784

00:43:42,519 --> 00:43:46,059

is gonna end or whatever, but I think

that it's just a recognition that hey,

785

00:43:46,059 --> 00:43:52,509

like, you know, we're in an increasingly

lopsided expansion both globally and in

786

00:43:52,509 --> 00:43:56,859

the US and so, you know, there are two

different ways to play those sets of bets.

787

00:43:57,219 --> 00:44:00,699

Like, I don't think it makes sense to

just go out and short tech indexes,

788

00:44:00,699 --> 00:44:01,929

like that's probably not a good idea.

789

00:44:02,199 --> 00:44:05,439

But a really interesting way in a

market neutral fashion is possibly to

790

00:44:05,439 --> 00:44:08,459

go long the tech indexes and short the

most cyclical parts of the economy.

791

00:44:09,109 --> 00:44:11,484

Like that's a rule that's

been one of the best plays off

792

00:44:11,484 --> 00:44:12,594

the year and continues to be.

793

00:44:13,054 --> 00:44:16,504

an alternative way is just to say,

Hey, like, you know, maybe I just wanna

794

00:44:16,504 --> 00:44:18,514

lower my exposure and have more balance.

795

00:44:18,514 --> 00:44:22,774

So instead of actually just doing the

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

796

00:44:22,774 --> 00:44:26,464

the individual sectors, find the ones

which have good earnings momentum,

797

00:44:26,464 --> 00:44:32,464

good fundamental momentum, and also are

not so, you know, egregiously valued.

798

00:44:33,274 --> 00:44:35,254

So there, there are multiple

different ways to do it, but I

799

00:44:35,254 --> 00:44:38,014

think it's just like a time for more

caution based on what we're seeing.

800

00:44:39,049 --> 00:44:40,819

Mike Philbrick: Yeah, there,

there's certainly, you have a

801

00:44:40,819 --> 00:44:44,839

market that's dominated by the, that

very, those very large tech names.

802

00:44:44,839 --> 00:44:49,579

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

Bob Elliot points out, the, the market

803

00:44:49,579 --> 00:44:55,369

is a discounting mechanism and is trying

to discount a lot of things that have

804

00:44:55,639 --> 00:44:58,819

maybe have, don't have no precedent.

805

00:44:58,909 --> 00:45:00,589

Precedent, right?

806

00:45:00,799 --> 00:45:02,719

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

807

00:45:02,719 --> 00:45:04,429

What's the cost of the CapEx boom?

808

00:45:04,429 --> 00:45:05,989

How quickly is it gonna roll out?

809

00:45:05,989 --> 00:45:10,669

And so, fundamentally there's things

happening, as you say, that are like

810

00:45:10,669 --> 00:45:14,959

the big ship, but trying to discount

that, you can see why the, the markets

811

00:45:14,959 --> 00:45:20,539

would be moving around a lot in and

having fits and starts of, of, well

812

00:45:20,539 --> 00:45:21,949

what, what is that going to be?

813

00:45:21,949 --> 00:45:27,859

'cause it, it's so unknowable and somewhat

unprecedented in where we are today.

814

00:45:29,264 --> 00:45:29,554

Aahan Menon: Yeah.

815

00:45:29,689 --> 00:45:32,239

Richard Laterman: you what, what you're

saying makes a lot of sense, Mike.

816

00:45:32,239 --> 00:45:35,479

And it's exactly what I was thinking

because you're describing a very

817

00:45:35,479 --> 00:45:37,199

quantitative process, right, Aahan?

818

00:45:37,219 --> 00:45:39,469

And you're speaking our language

is that, that's precisely

819

00:45:39,469 --> 00:45:40,999

how we attack the problem.

820

00:45:41,089 --> 00:45:44,149

That that's how we've thought through

the problem for, for, for many years.

821

00:45:44,149 --> 00:45:48,604

But in a world of, you know,

paradigm shift is the word

822

00:45:48,604 --> 00:45:49,984

that always comes to mind.

823

00:45:49,984 --> 00:45:51,724

Like things a a lot.

824

00:45:51,724 --> 00:45:55,229

The, the word unprecedented seems to be

thrown around a lot these days, but it,

825

00:45:55,234 --> 00:45:58,864

it, it truly does encapsulate a lot of

the feelings that we see with disruption

826

00:45:58,864 --> 00:46:03,218

in technology, but also the, move away

from the unipolar moment of the U.S.

827

00:46:03,218 --> 00:46:06,338

this more fragmented geopolitical,

uh, environment that we're in,

828

00:46:06,338 --> 00:46:08,618

the trade war, all these things.

829

00:46:08,858 --> 00:46:11,138

How, how often are you

tweaking your models?

830

00:46:11,138 --> 00:46:14,618

Are you bringing any discretion

to your decision making?

831

00:46:14,618 --> 00:46:18,908

How are you attacking this, this

conundrum, this issue of trying

832

00:46:18,908 --> 00:46:26,288

to, to model an environment that

perhaps the last few decades are not

833

00:46:26,288 --> 00:46:28,628

representative of the environment.

834

00:46:29,569 --> 00:46:33,833

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

it comes to tweaking, I am always open.

835

00:46:34,073 --> 00:46:36,083

Like I'm always open to tweaking things.

836

00:46:36,083 --> 00:46:41,633

But, because we have so many

strategies now, I have a lot more

837

00:46:41,633 --> 00:46:43,643

leeway to be patient with things.

838

00:46:43,643 --> 00:46:46,733

Probably more consistent with

the way that I should be.

839

00:46:47,093 --> 00:46:47,423

Right?

840

00:46:47,423 --> 00:46:50,603

Like I think the less breadth you

have, the more you wanna tweak things

841

00:46:50,603 --> 00:46:54,023

to optimize because you're having

a problem and like the second you

842

00:46:54,023 --> 00:46:56,843

start having more breadth, you know,

I have, we have, you know, candidly,

843

00:46:56,843 --> 00:46:58,283

I'm very open about all these things.

844

00:46:58,283 --> 00:47:01,583

Like we have some strategies that are

negative one sharpe ratio this year.

845

00:47:02,008 --> 00:47:03,658

It's just absolutely horrible.

846

00:47:03,658 --> 00:47:04,108

Like it looks

847

00:47:04,348 --> 00:47:05,428

Mike Philbrick: of course you would.

848

00:47:05,428 --> 00:47:07,648

I mean, this may sound strange to people.

849

00:47:08,728 --> 00:47:09,388

Yes.

850

00:47:09,448 --> 00:47:10,498

That is something that,

851

00:47:10,528 --> 00:47:10,828

Aahan Menon: bunch.

852

00:47:11,998 --> 00:47:12,598

We're just,

853

00:47:12,783 --> 00:47:15,928

Mike Philbrick: and, and last

year negative one sharpe ratio

854

00:47:15,928 --> 00:47:17,248

strategy might have been two.

855

00:47:18,118 --> 00:47:20,578

Richard Laterman: diversification

means always having to say your

856

00:47:20,578 --> 00:47:23,878

sorry about something, whether

it's a line item in a portfolio or

857

00:47:23,878 --> 00:47:25,738

within a very diversified program.

858

00:47:25,888 --> 00:47:31,228

Any one of those sub strategies across

a number of dozens and dozens of markets

859

00:47:31,828 --> 00:47:35,548

Mike Philbrick: It doesn't invalidate

that thing that whatever you want

860

00:47:35,548 --> 00:47:39,418

to, whatever you're gonna articulate,

whether it was a strategy of an asset

861

00:47:39,418 --> 00:47:43,378

on a, a strategy, on an asset, whatever

it was, it does not invalidate it

862

00:47:43,438 --> 00:47:48,358

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

particularly challenging sharpe ratio.

863

00:47:48,778 --> 00:47:49,588

Anyway, back over to you.

864

00:47:50,518 --> 00:47:50,938

Aahan Menon: Yeah.

865

00:47:50,938 --> 00:47:53,608

So I mean, there, there are,

there are certain things, right?

866

00:47:53,608 --> 00:47:55,558

Like where if we feel like.

867

00:47:56,323 --> 00:47:59,893

We got tooled up in something that,

you know, like we, we understood

868

00:47:59,893 --> 00:48:02,923

something about, you know, a certain

fundamental where we're just like,

869

00:48:02,923 --> 00:48:04,843

hey, like this is just better.

870

00:48:05,233 --> 00:48:08,383

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

or that, you know, anything like

871

00:48:08,383 --> 00:48:10,633

that, but this is just better, right?

872

00:48:10,633 --> 00:48:13,633

So, you know, there's certain

things that we did in say like,

873

00:48:13,943 --> 00:48:15,263

energy trend stuff, right?

874

00:48:15,263 --> 00:48:19,578

Where we basically said, Hey, like, we

were looking at basic time, but there

875

00:48:19,578 --> 00:48:22,788

are a bunch of signals that we, we,

you know, we spent a lot of time kind

876

00:48:22,788 --> 00:48:26,448

of, looking at, at the energy space and

we said, Hey, there are a few signals

877

00:48:26,448 --> 00:48:30,648

that are just like way, way better,

way more sound, fundamental reasoning.

878

00:48:31,068 --> 00:48:33,498

you know, we were open to

integrating those and including

879

00:48:33,498 --> 00:48:34,488

those into the programs.

880

00:48:34,808 --> 00:48:39,278

I think the, the place where I start

becoming concerned is like when

881

00:48:39,278 --> 00:48:42,248

you see something that's really,

really dramatically different from

882

00:48:42,248 --> 00:48:43,688

anything you back tested, right?

883

00:48:43,688 --> 00:48:44,858

Like completely different.

884

00:48:45,228 --> 00:48:47,238

and then you have structural concern.

885

00:48:48,138 --> 00:48:50,838

Like, you know, that something

about the market structure

886

00:48:50,928 --> 00:48:52,638

has changed very dramatically.

887

00:48:53,158 --> 00:48:56,668

and so if there's that type of thing,

then, you know, then we're much more

888

00:48:56,668 --> 00:49:00,598

like hands-on and, Hey, do we just

need to sunset this program entirely?

889

00:49:00,598 --> 00:49:01,498

Has something shifted?

890

00:49:01,498 --> 00:49:02,368

Do we need to change it?

891

00:49:02,698 --> 00:49:05,828

But, you know, I I would say

that maybe three years ago I

892

00:49:05,828 --> 00:49:07,508

was very quick to change things.

893

00:49:07,838 --> 00:49:11,288

but you know, as we added more

and more breadth, I've become much

894

00:49:11,288 --> 00:49:13,328

more patient with changing things.

895

00:49:13,748 --> 00:49:17,558

but that being said, like I'm always,

you know, the, my, the, the clients

896

00:49:17,558 --> 00:49:21,998

that we work with are, are a mix

of fast money and institutions.

897

00:49:22,298 --> 00:49:24,998

And so they're always looking

for, Hey, what's working?

898

00:49:25,208 --> 00:49:27,278

You know, like, that's just

the truth of the business.

899

00:49:27,608 --> 00:49:30,758

So you have to be ready to say,

Hey, like, this is not working.

900

00:49:30,998 --> 00:49:32,498

Is there a reason it's not working?

901

00:49:32,498 --> 00:49:33,758

And, you know, can I fix that?

902

00:49:33,788 --> 00:49:36,728

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

there needs to be a good enough

903

00:49:36,908 --> 00:49:37,198

Mike Philbrick: Yeah.

904

00:49:37,253 --> 00:49:40,373

I think to, to provide some

context, context to that more.

905

00:49:40,433 --> 00:49:42,983

And maybe make it sim, simplify it a bit.

906

00:49:43,493 --> 00:49:46,853

If you're someone and, and you're

operating with five systems,

907

00:49:47,783 --> 00:49:49,373

well yeah, you're gonna tweak it.

908

00:49:49,373 --> 00:49:51,833

And those tweaks are actually

monumental because you're

909

00:49:51,833 --> 00:49:54,473

tweaking one 20th of your system

910

00:49:54,723 --> 00:49:55,013

Aahan Menon: Yeah.

911

00:49:55,703 --> 00:49:59,213

Mike Philbrick: If you have a

thousand strategies, I mean,

912

00:49:59,423 --> 00:50:00,863

to some extent, tweak away.

913

00:50:01,718 --> 00:50:05,958

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

thousandths, you can be patient, you can

914

00:50:06,048 --> 00:50:10,928

take a more, a more patient view of it

as well, because it is only one, 1000th

915

00:50:10,928 --> 00:50:13,238

of the information that you're drawing.

916

00:50:13,328 --> 00:50:19,328

And so it's just not as urgent to

try to fix something or do something.

917

00:50:19,388 --> 00:50:22,568

You have a lot more patience

there when you've got a suite of

918

00:50:22,568 --> 00:50:24,638

a thousand versus a suite of five.

919

00:50:25,118 --> 00:50:27,368

And I think that's the, the

point you were making earlier.

920

00:50:27,368 --> 00:50:31,478

And I just wanna, you know, sort of

emphasize that when you think about that.

921

00:50:32,098 --> 00:50:34,348

and, and I think the other thing

that you mentioned was that something

922

00:50:34,348 --> 00:50:36,328

structurally is changing, right?

923

00:50:36,868 --> 00:50:39,958

Something that we ask ourselves

is like, what, what do we know

924

00:50:39,958 --> 00:50:41,218

that the model doesn't know?

925

00:50:41,608 --> 00:50:46,708

The model has a certain purview of facts

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

926

00:50:47,278 --> 00:50:49,828

And is it something, is there

something that it can't know?

927

00:50:50,608 --> 00:50:54,688

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

that's in the purview of the portfolio

928

00:50:54,688 --> 00:50:58,798

manager to actually think that

through and obviously document that.

929

00:50:58,828 --> 00:51:02,258

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

strategy on, in, in the penalty box

930

00:51:02,258 --> 00:51:06,608

or on the sidelines for some reason,

you're gonna document that review.

931

00:51:07,178 --> 00:51:10,658

And then is that a permanent situation

or is that a situation that changes?

932

00:51:10,938 --> 00:51:14,208

so an, an easy example is when

the, you know, the, the Euro

933

00:51:14,208 --> 00:51:15,308

and the Swiss Franc were pegged.

934

00:51:16,488 --> 00:51:16,728

Right.

935

00:51:16,728 --> 00:51:20,988

So, so, and then the peg broke and it

was a, a 20 standard deviation event.

936

00:51:21,348 --> 00:51:23,238

Well, you know that the

model doesn't know that.

937

00:51:23,748 --> 00:51:27,138

And so those, that's a simple example

of one of those things where, well,

938

00:51:27,138 --> 00:51:31,098

do you need to trade both of those

items, because they're the same anyway.

939

00:51:31,488 --> 00:51:32,478

Probably not.

940

00:51:32,508 --> 00:51:37,178

But that's that's where, the portfolio

manager with their insight and experience

941

00:51:37,238 --> 00:51:41,858

and expertise across the models will,

will intervene and quite rightly so.

942

00:51:42,428 --> 00:51:45,848

So, you know, quant is not about

closing your eyes and doing quant.

943

00:51:45,848 --> 00:51:51,218

It's, it's about monitoring and and

understanding how your models interact

944

00:51:51,218 --> 00:51:55,330

and understanding where their blind

spots are and actually intervening

945

00:51:55,330 --> 00:51:56,560

when it's appropriate to intervene.

946

00:51:56,560 --> 00:51:56,680

And,

947

00:51:57,430 --> 00:51:58,000

Richard Laterman: That's a really good

948

00:51:58,100 --> 00:51:58,390

Aahan Menon: Yeah.

949

00:51:58,500 --> 00:51:58,790

Yeah.

950

00:51:59,310 --> 00:52:03,330

Richard Laterman: The, any currency that's

pegged, there's probably a lot of mean

951

00:52:03,330 --> 00:52:06,570

reversion signals that are working really

well because they're trading within a

952

00:52:06,570 --> 00:52:10,115

certain band, but then all of a sudden

the peg breaks in that, you know, you,

953

00:52:10,120 --> 00:52:15,240

you have a breakout and all of a sudden

the never trending market begins to trend.

954

00:52:15,240 --> 00:52:17,940

So is it a malfunction of the market?

955

00:52:17,940 --> 00:52:19,560

Is it a malfunction of the systems?

956

00:52:19,740 --> 00:52:22,140

Which one is Yeah, exactly.

957

00:52:22,200 --> 00:52:22,560

So.

958

00:52:23,490 --> 00:52:28,415

What comes to mind is when, when we put,

an entire market in the Peleton box, JGBs.

959

00:52:28,435 --> 00:52:28,705

Right?

960

00:52:28,705 --> 00:52:30,445

So, so yield curve control.

961

00:52:30,445 --> 00:52:34,075

And so when you were, Aahan describing

a moment ago when, when a market is

962

00:52:34,075 --> 00:52:36,865

now functioning or, or, or, or the

structure of a market seems to be

963

00:52:36,865 --> 00:52:38,515

unhealthy in any way, shape or form.

964

00:52:38,515 --> 00:52:41,125

And then, you know, on the

narrative side of things, yield

965

00:52:41,125 --> 00:52:42,505

curve control comes to mind, right?

966

00:52:42,505 --> 00:52:45,943

The idea that, you know, you start to

have, a, a gravitational pull that,

967

00:52:45,943 --> 00:52:50,933

that, that this, this very large, state

actor in this case influencing prices

968

00:52:50,933 --> 00:52:52,223

and price discovery in the markets.

969

00:52:52,223 --> 00:52:55,893

And then all of a sudden, JGBs went

for several years where not at a lot of

970

00:52:55,893 --> 00:52:57,453

trading was happening in that market.

971

00:52:57,453 --> 00:53:01,893

And so do we stop trading a market for

a period of time when you start to see

972

00:53:01,893 --> 00:53:05,543

that microstructure of that market,

behaving in an unhealthy way, right?

973

00:53:05,543 --> 00:53:07,193

And I think the answer

would be yes to you.

974

00:53:08,383 --> 00:53:08,803

Aahan Menon: Mm-hmm.

975

00:53:09,188 --> 00:53:16,718

Well, the, the JGB, the JGB circumstance

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

976

00:53:16,778 --> 00:53:21,968

so the way we look at it is basically like

when, when we're trading bonds globally,

977

00:53:21,968 --> 00:53:25,388

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

trying to get carry for the least amount

978

00:53:25,388 --> 00:53:30,278

of monetary pol policy risk possible,

like that, that is the way we do it.

979

00:53:30,278 --> 00:53:32,318

And we do that cross-sectionally

across the globe.

980

00:53:32,828 --> 00:53:36,807

And, you know, basically what you had

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

981

00:53:37,815 --> 00:53:41,895

No carry, no monetary policy risk,

nothing to really do for a while.

982

00:53:42,135 --> 00:53:45,735

And so like, you know, we want, we

want trading during that period.

983

00:53:45,985 --> 00:53:48,505

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

period very well, but like I can say

984

00:53:48,505 --> 00:53:51,895

that, you know, this, this particular

year has worked really well for that

985

00:53:51,895 --> 00:53:56,555

kind of approach for us, because I think

that the term structure of the, of the

986

00:53:56,555 --> 00:54:00,305

JGB code was actually lying to you most

of the time when monetary policy risk

987

00:54:00,305 --> 00:54:01,895

was actually really, really significant.

988

00:54:02,345 --> 00:54:06,605

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

conceptually what you're outlining

989

00:54:06,605 --> 00:54:09,635

is a hundred percent right, like, you

know, there are so many things like a,

990

00:54:10,955 --> 00:54:15,358

I think, I heard, Andy Constance say

this about Ray Dalio where he said that,

991

00:54:15,358 --> 00:54:19,288

you know, basically what you're looking

at when you systematize something is

992

00:54:19,288 --> 00:54:22,228

a pixelated version of reality, right?

993

00:54:22,768 --> 00:54:25,638

And, I think that's a hundred

that, that's on the notes.

994

00:54:26,028 --> 00:54:26,478

Right.

995

00:54:26,598 --> 00:54:30,348

Like you, you basically have a bunch of

parameters that you feed in, but there

996

00:54:30,348 --> 00:54:34,368

are a million parameters that you can, you

know, discretionarily kind of understand

997

00:54:34,368 --> 00:54:35,838

that the model has no idea about.

998

00:54:36,168 --> 00:54:39,678

And sometimes you just have to intervene

and be like, Hey, like I think that,

999

00:54:39,678 --> 00:54:44,088

you know, these three factors explain,

you know, X percentage of the returns,

1000

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

but you know what, like maybe they're

not important relative to this ongoing

1001

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

development, and you just have to step

in and you have to make adjustments.

1002

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.

1003

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

Where we, where we actually, I think

one thing that tripped up a lot of

1004

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

macro guys, this economic cycle, was

typical business cycle analysis, right?

1005

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

So what was really popular in most

macro communities was using something

1006

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

that looked like, the conference

board leading economic index.

1007

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

for those that are unfamiliar, that's

basically just 10 economic indicators

1008

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

aggregated up after adjusting

for volatility into one index.

1009

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

Historically, that index has

been really, really good at

1010

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

predicting re recessions, right?

1011

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

but in this, this index basically started

to point to recession in 2022 and is

1012

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

still pointing to recession till date.

1013

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

the reason that we think that that

index stopped working as well is

1014

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

because, let's be clear, like that

index was designed in like 1980.

1015

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

Okay?

1016

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

The, the, the, the economy was a little

bit different from the way it is today.

1017

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

in particular, there's been a very, very

big shift from having a manufacturing and

1018

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

industrial economy to having a much more

tech and services oriented economy, right?

1019

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

And so what we did was we said, does

that framework of leading economic

1020

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

indexes not work at all anymore.

1021

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

And what we found is that if you add

measures that are more consistent

1022

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

with the composition of the economy,

which basically take into account

1023

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

intellectual and property, intellectual

property investment today, you

1024

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

improve your signal in modern date.

1025

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

And you also get something

that's meaningfully different

1026

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

from what, from what's being

predicted right now by that signal.

1027

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

And so we, you know, we, we

started doing that work, I would

1028

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

say like a year or two ago.

1029

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

1030

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

clients and all that stuff that, hey,

like the business cycle has changed.

1031

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

You can't just bet on housing

and industrial production.

1032

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

That's not where the economy is anymore.

1033

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

And you have to make adjustments

to your, you know, leading economic

1034

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

indicator style, trend models using

this, this kind of understanding.

1035

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

So that was a shift we made.

1036

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

1037

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

because if you're just stuck with the

old program and just, you know, we're

1038

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

religious about it, you know, you've

been short or leaning short equities

1039

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

and long bonds for like three years

1040

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

Richard Laterman: Yeah, the

map is not the territory.

1041

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

I think that's the, the, the mental

model to be used here and, and it,

1042

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

it's, markets are ever shifting.

1043

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

I mean, even our own understanding

of reality requires updating, like

1044

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

Newtonian physics lasted until a certain

era and then Einstein with relativity.

1045

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

And then we're probably coming into

a new paradigm for physics as well.

1046

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

So, but in markets it's even more so

because these variables are shifting.

1047

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

Quite a bit over time and growth

and inflation and liquidity

1048

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

dynamics change quite a bit.

1049

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

And there's reflexivity to use sources,

concept that really, I think, describes

1050

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

1051

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

way that we're observing these things will

shift our own understanding over time.

1052

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

And, and the way that markets will

interact with these variables will,

1053

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

will impact the variables themselves.

1054

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

Aahan Menon: Yeah, yeah.

1055

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

A hundred percent.

1056

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

A hundred percent.

1057

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,

1058

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

everyone talking about liquidity, right?

1059

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

Like a lot of liquidity.

1060

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

There's liquidity.

1061

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

That and stuff like the fed's actions

in liquidity basically stopped

1062

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

like a year or two ago, right?

1063

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

Like about a year ago they basically

stopped doing anything very meaningful

1064

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

and like most of the handoff was

actually to the private sector.

1065

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

And where is a lot of that private

sector liquidity coming from?

1066

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

It's actually coming from a bunch

of tech companies that have excess

1067

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

cash balances that store them

on, store them with financial

1068

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

institutions and in money market funds.

1069

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

And that actually creates the,

the potential for leverage.

1070

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

And so, you know, what you have to

recognize there is that, hey, like the Fed

1071

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

isn't that important anymore or probably

matters is the private sector impulse.

1072

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

Like how do I attract the

private sector impulse?

1073

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

Do I have any measures?

1074

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

And you know, trying to improve

that, you know, that, that, that

1075

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

understanding and turn it into something

which can generate signal in markets.

1076

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

Yeah.

1077

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

Mike Philbrick: Well, amazing.

1078

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

We've been at it for about an hour.

1079

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

Any, uh, Richard, Aahan, any, any

kind of hanging threads that you guys

1080

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

wanna dig into a little bit more?

1081

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

Richard Laterman: I was gonna ask

Aahan, if there's anything that is

1082

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

flying under the radar of the market

and investors right now that you're

1083

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

picking up through your framework,

through your models, things that you're

1084

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

looking into that you think perhaps are

being underappreciated at this point.

1085

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

Aahan Menon: Yeah, I, I think that

the biggest thing that I see is,

1086

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

a very, very large and unusual

divergence between output and nominal

1087

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

growth relative to labor, right?

1088

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

And we're basically having, you know, a

divergence like we've never seen probably

1089

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

in modern history, where what you have

today is a labor market as measured by

1090

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

total employment growth, which is heading

south, potentially contracting and maybe

1091

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

even potentially contracting meaningfully

while output and spending are just

1092

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

continuing on, like nothing's happened.

1093

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

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

like there's a big crash coming

1094

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

tomorrow or something like that.

1095

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

But I think that it's super important

to recognize that like the center of

1096

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

economic growth, like if, okay, if

you were to go and say there's one

1097

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.

1098

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

Like as somebody who's done every version

of a out cost possible, I would tell

1099

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

you just pick the employment numbers.

1100

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

They're great.

1101

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

Um.

1102

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

Richard Laterman: Particularly

non-farm payroll, would that be the.

1103

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

Aahan Menon: Non non-farm payroll is good.

1104

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

There's some revision

risk in non-farm payrolls.

1105

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

So you would use the, the establishment,

sorry, the household survey instead.

1106

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

So those are total employment numbers.

1107

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

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

that's going on with those numbers.

1108

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

Basically the unemployment rate and the

participation rate are unrevised numbers.

1109

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

So they're great, but what is revised

every single year in January, only in

1110

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

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

they leave it in the time series

1111

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

without changing at all, which is

kind of funny, but useful at the same

1112

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

time is the total population numbers.

1113

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

And so what happened this January was

they dramatically, like I, I forget

1114

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

1115

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

took, employment growth trend from like

neutral to, to meaningfully positive.

1116

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

And so what's typically happened when you

have that kind of revision is the next,

1117

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

the subsequent year is a down revision.

1118

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

So, you know, when you basically

account for the, the participation

1119

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

rate and the unemployment rate, and

you basically say that, hey, like,

1120

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

you know, the, the overall population

is probably growing at one, 1.4%.

1121

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

You basically get, an employment

number, which is probably close to

1122

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

contracting, if not contracting already.

1123

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

And so that, that measure, the, the,

the aggregate employment number is

1124

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

the driving factor for GDP growth over

time, like it is the most explanatory

1125

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

variable for GDP growth over time.

1126

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

But today we have this really weird

circumstance where like, GDP growth

1127

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

seems to be completely fine, but

employment is falling off a cliff.

1128

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

The culprit is likely to be

immigration in the United States.

1129

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

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

I was saying, the population numbers

1130

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

are kind of shoddy through the year.

1131

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

Like they're not great.

1132

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

1133

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

reliable on a month to month basis.

1134

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

But what we do see is that

the participation rate is

1135

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

just falling off a cliff.

1136

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

And the, the reason, you know, some

people say that this is the, the boomers

1137

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

exiting the, the labor force and all that.

1138

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

I think that's definitely

part of the equation.

1139

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

But the, the speed at which has

begun to fall off is indicative

1140

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

to me, which is supported by the

data of labor market recomposition.

1141

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

And what, what that is, is basically

that foreign workers have much higher

1142

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

participation rates than US workers.

1143

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

And so as you have this

immigration unwind, participation

1144

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

is falling off a cliff.

1145

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

And when we get to January, we might see

a meaningful down revision in, in the

1146

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

pace of, of population growth, which means

that the labor market's a lot weaker.

1147

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

Now the question I think that, you

know, investors need to wrestle

1148

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

with is like, these two series

are gonna mean revert, right?

1149

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

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

1150

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

Either employment is gonna get

a lot better, or output is gonna

1151

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

come down to meet that employment.

1152

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

Or maybe you have a mix of those two.

1153

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

But the, you know, the destiny

for GDP growth over time is

1154

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

the pace of employment growth.

1155

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

And I think that's the biggest question

investors need to wrestle with.

1156

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

1157

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

flying under the radar for most people.

1158

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

Richard Laterman: Great.

1159

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

I think that's a good place

to, put a pin on conversation.

1160

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

Aahan, it's great chatting with

you, so much insight packed

1161

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

into an hour conversation.

1162

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

Thank you so much for joining us today.

1163

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

Aahan Menon: Always, such a pleasure guys.

1164

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

Thanks for having me on.

1165

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

Richard Laterman: weekend

1166

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

All.