1:09

Welcome. Welcome everybody. Welcome to

1:12

our, latest webinar covering

1:14

Managed Futures Carry: A Practitioner's Guide.

1:17

before we begin, we're going to give everybody

1:19

a little time to get settled in. Make

1:21

sure that everybody's signing up and, I've

1:23

seen the participant count go up

1:26

quite quickly. So, it looks like

1:28

we may be ready in a couple of minutes.

1:30

How's everybody doing today? This

1:32

whole webinar format is not so good

1:34

for my presentations. You know, I'm struggling

1:37

to picture everybody naked over

1:39

the

1:40

webinar. I

1:42

guess it's only you, Rod, and that's no good for

1:44

me.

1:45

So I wonder, I

1:47

want to make sure I can, yeah, the Q and A.

1:50

How's everybody doing today? Feel free to get into the

1:52

q and a chat box and start

1:55

testing to see whether we can

1:57

get your questions, as we wait for people

1:59

to come by. What we really wanna do in this webinar

2:01

is Adam and, Andrew

2:03

wrote it very technical paper. We're gonna go through

2:05

a lot of stuff, but I'm gonna be, Adam's

2:08

co-pilot here. So I'm gonna try to channel

2:10

everybody that's sitting in their seats right now

2:13

trying to think through whether. You know, they might be asking

2:15

questions throughout. I'll try to be that

2:18

co pilot and ask those questions, but please

2:21

do answer. so do

2:23

ask questions. I'll be reading them and

2:25

I'll see if, if I can help get that

2:27

answer for you guys. Adam,

2:29

are you saying Rod isn't good enough to look at?

2:32

Exactly right. Like if he's going to look at a

2:34

naked person, you'd think at

2:36

this point it'd be his business partners. Feel

2:39

comfortable with it.

2:40

It's just getting old. I

2:42

have to say that I've been looking forward to

2:44

presenting on carry for years.

2:48

This is my favorite, most misunderstood

2:52

strategy. So it's great

2:54

to get it out in the sunlight.

2:56

Is it possible to have a fund

2:59

that is trend and yield no stocks

3:01

and bonds? It certainly is possible.

3:04

we'll be talking less about product today and we'll

3:06

be talking about more conceptually the,

3:08

the carry strategy and

3:10

the carry approach. Just to kind of introduce this because I think

3:13

one of the reasons we were, I'm really pumped

3:15

about this. I know Adam's been dying to

3:17

talk about this for years is that Even

3:20

three years ago, nobody wanted to talk about trend. It seems like

3:22

that, that there's been a big

3:25

uptake on trend. General

3:27

audiences are starting to understand it. They see the

3:29

value in it. And, very

3:31

few people know about carry. So

3:34

lots to talk about. There's, I'm so excited

3:36

about this for many reasons we're going to cover, but,

3:38

yeah, I think there's many applications to this

3:40

concept in the investment

3:42

world.

3:44

All right. We better roll because, we've got

3:46

a lot to cover.

3:48

All right. So you want to pop up that presentation, Adam,

3:50

since you can be doing most of it.

3:52

Okay, let's do it.

3:54

All right. So we have

3:56

a lot to cover today and

3:59

we're going to try to get us through it as much as possible.

4:01

We are covering a white paper that has

4:03

a lot of output and a lot of back tests. And so lots

4:06

of this will be just kind of reiterating

4:09

the concepts through charts. So even

4:11

though there's a lot of slides, we'll try to get through them quickly. but

4:13

if we go to the next slide, Adam, why,

4:16

you know, should you be listening to me as a host? I

4:18

am Adam Butler's business partner.

4:21

Been together with him and Michael Philbrick

4:24

since 2011. I've been in the business for almost 20

4:26

years now, and I'm president of Resolve

4:29

Asset Management, Global. Been

4:32

a-co contributor

4:34

to a lot of white papers and podcasts

4:36

and blog posts on Resolve

4:38

and, you know, writing articles

4:41

as well now on the return stack, website

4:43

and, and so on. So been managing

4:46

quantitative investment strategies for many years.

4:48

And today I'm going to help facilitate the conversation

4:50

with the co authors. If we go to the next slide, Adam. So

4:53

for those who haven't read the white paper, I

4:55

would encourage you to click on the link. Maybe

4:57

Ani can push that through for you guys to

5:00

be able to click on that. If it's not, if you haven't already,

5:02

it'll be a good thing to have side by side

5:04

as we go through this presentation. and the

5:07

white paper was written by Adam Butler.

5:09

Our CIO here

5:11

at Resolve Global and Dr.

5:14

Andrew Butler, our resident PhD

5:16

in Resolve Asset Management Canada. yes,

5:18

they are related. Genius does

5:21

run in the genes. So our two

5:23

propeller heads did a fantastic job at

5:26

creating a comprehensive review

5:28

and framework for different ways of looking at a carry.

5:30

And everything that we're going to present in this presentation,

5:33

all the performance stats will be sourced from

5:35

this white paper. So just,

5:37

click on that. There's also data that you can download.

5:40

So if you can kind of double check everything that we review

5:42

here.

5:44

We should also mention there's an advisor,

5:47

summary of this on

5:49

the InvestResolve blog, which

5:51

is a little bit more accessible. We've cut out some

5:53

of the more, nebulous

5:56

sections of the white paper and

5:58

made it just grounded a little bit more. So,

6:00

feel free to check that out too.

6:02

and Ani, you can put the link to that in

6:04

the chat as well. If we could next

6:07

slide, Adam, for me, you know,

6:09

as always with, any sort

6:11

of work that you do with the investment

6:13

in the investment universe, Adam, the next slide, I

6:15

still don't see it. we have

6:17

to be cognizant of, all of the important

6:19

disclaimers. We're going to be presenting a lot of hypothetical results.

6:22

This is merely a research

6:25

project that tries to shine a light

6:27

on a premium that may add

6:29

value to people's portfolios. There's many ways

6:31

to skin this cat by no means it is

6:34

a, an offer to, buy

6:36

a fund or anything like that. So really it's

6:38

about covering that research. Next slide for me,

6:41

Adam. So please do read those disclaimers

6:43

and recognize that there is risks

6:45

in everything that we do in this space. And

6:48

then I'll tell you quickly what we're

6:50

going to tell you. We're going to talk about

6:52

what is basic carry, how we define

6:55

the three basic definitions of carry. How

6:57

we can practically implement all these

6:59

carry strategies in a portfolio

7:02

and incorporating with other asset classes.

7:04

And then from then on, we'll really go

7:06

through some analytical

7:08

framing, you know, how to put these things

7:11

together to make them work in real life

7:13

with real trading, and then from then on,

7:15

it'll be back test showing performance

7:17

analysis scenario and regime analysis.

7:20

So a couple of case studies on the return stacking side

7:22

and then conclude with some questions. Okay, so

7:26

next slide for me, Adam. Before we do begin,

7:28

let's start with a little poll. Ani,

7:30

would you mind pushing through that poll? I

7:32

want the audience to, I'm just trying

7:34

to get a gauge as to how many people had heard about

7:37

Diversified Carry before

7:39

downloading our white paper or

7:41

being in this, webinar

7:43

or the invite. And also if you

7:45

can answer the second question, which is if you

7:47

had heard a carry, how many of you do you currently

7:50

invest in a carry

7:52

strategy? Give it a couple

7:54

of minutes there. I've never done a poll,

7:56

so I'm just gonna let that

7:58

linger there for a bit. And maybe

8:00

we can come back to it, Adam. but, Ani, you

8:02

let me know when it goes, when

8:04

Yeah, I think Ani closes it out and then it shows

8:06

you the results. So, it's

8:09

probably long enough. It's only two.

8:10

Yeah, Ani, let's go. Two quick questions. Let's see

8:12

if we can find the answers. Okay,

8:16

what do we have? So, have you heard about

8:18

diversified care before? Yes.

8:21

So most people, about two thirds

8:23

have said, yes. One third said, no, that's a good

8:25

ratio. that's probably above average. We have an above

8:27

average intelligent crowd here today.

8:30

And you currently invest in diversified carry strategies

8:33

only around 20%. So that's kind of what we

8:35

would expect. hopefully, you

8:37

know, we certainly do and have been talking about carry

8:39

strategies and have those in our back

8:41

pocket. And if you go to our website, you can find

8:44

some, but, more and more solutions will be coming out

8:46

very soon. All right,

8:48

so this is good to know. Let's

8:51

hand it over to our lead author

8:53

here. Adam, why don't you take it away and tell us what

8:55

Carry is.

8:56

Okay. What

8:58

is Carry? Yeah, so Rodrigo is going to step

9:01

in and ask questions and seek

9:03

clarity if I kind of missed something.

9:05

But let's start with basic definition. Carry

9:08

is what you expect to

9:10

return on an asset if the price doesn't

9:12

change. In the white paper, we use

9:14

an apartment building investment to

9:18

illustrate the concept, right?

9:20

Because you buy an apartment building, typically

9:23

you're going to own it for many years, maybe decades.

9:27

Maybe you're not so concerned

9:29

with what the price will

9:31

be when you sell it in 20 or 30 years.

9:33

In the meantime, it is generating a lot of cash

9:36

flow for you, the rents on the apartments,

9:38

right? Well, going a little

9:40

bit more into public markets, equities

9:44

are expected to deliver cash

9:46

flows in the form of dividends. Even equities

9:48

that don't currently pay dividends are

9:50

priced on the basis that eventually they're going to return

9:53

cash flow to investors. Bonds.

9:57

Almost all of them pay a coupon.

9:59

That is the carry on bonds.

10:02

And we're going to demonstrate that

10:05

in the concept of futures markets,

10:07

commodities, equities, currencies,

10:10

bond futures, all of these

10:12

can be expected to pay or,

10:15

absorb carry at,

10:18

at different points in time for different reasons. So

10:20

let's get into an example. All

10:23

we've done here is. Sort of illustrated

10:26

a simple futures

10:28

market. One futures market that has a,

10:31

let's say it's copper, right? Copper

10:33

is currently trading at 3 dollars and 50 cents in

10:36

a spot market. So if you're going to go buy.

10:38

A ton of copper, you're going to pay

10:41

350 a pound, say, right. And

10:43

then we've got a future on copper

10:46

and you, someone wants to buy the future

10:49

on copper, to take delivery

10:51

in September. And that's the

10:54

point that's sort of out on the right

10:56

here, right? So here's the spot price and

10:58

here's the futures price. And,

11:01

they're paying a little bit less. In

11:03

the future, then the current spot

11:05

price for dynamics

11:07

that currently exist in the, in

11:09

this market, which we'll get into.

11:12

And then over time,

11:14

if the price of copper doesn't move,

11:16

if it stays where it is, we would

11:18

expect the futures price

11:21

to rise to

11:23

eventually hit the

11:25

price of copper. And therefore this

11:27

market where the price of the future

11:30

in the future is lower

11:33

than the spot price or the near term

11:35

futures market contract, we

11:38

expect that to have positive carry as

11:40

the price converges to

11:42

spot over time, right? So

11:44

in this case, we're sort of saying we're

11:46

going to earn a little over 3 dollars as

11:49

this distant future becomes

11:52

less distant over time in terms of

11:54

time as time rolls forward. But

11:57

futures markets can take many

11:59

shapes. So you'll, you'd imagine

12:02

copper futures. You might be able to buy

12:05

copper for delivery and maybe,

12:07

you know, it's May now, maybe you can buy copper for delivery

12:09

in June, in September and

12:11

December, in June

12:14

of 2026. And

12:16

so you can plot the

12:18

price of each of these different futures

12:20

markets. On a chart

12:23

going out through time. And that

12:25

describes the term structure

12:27

of that futures market. So in this case,

12:30

you know, the contracts going out into

12:32

the future are priced

12:35

at successively lower prices.

12:38

And as you go out sort of far enough, they begin to

12:40

rise a little bit. And that's just very

12:42

typical futures markets tend to have

12:44

a curve in them, reflecting the supply

12:47

demand dynamics of the market

12:49

at different points in time.

12:51

Right. And so this has got kind of just

12:53

thinking about putting the two charts that we just went through

12:56

together here. There's many opportunities

12:58

to measure carry, assess

13:00

carry to define what carry is for

13:02

this particular fictitious contract,

13:04

right? And you're going to get, you're going to get a reading

13:06

on that. And carry

13:08

is really about. You're making

13:10

allocation decisions based purely

13:13

on that yield, that futures

13:15

yield, much like if you think about

13:17

in, trying to select securities

13:20

based on some sort of

13:22

a shareholder yield, right? So I know

13:24

that these are done very well. Meb Faber has done

13:27

a lot of work on this, right? You're just measuring

13:29

the type of yield that the company as a whole is

13:32

pushing out and selecting those assets.

13:34

Solely on that, not on price momentum,

13:37

not on value, just purely on that. And

13:41

while much like, shareholder yield

13:43

provides positive expectancy above cash,

13:46

we find the same thing when we

13:48

select based purely on this

13:50

type of futures yield.

13:52

Right. And in this case, this futures

13:54

curve is what we might call backward

13:57

dated, where the few, the prices in the future

13:59

are lower than the nearer

14:01

term or spot price. And

14:04

again, with this future market,

14:06

we would expect this to deliver positive

14:09

carry as these distant futures begin

14:12

to converge on the higher

14:14

spot price over time, all

14:16

things equal, right? Of course, it never

14:18

works out exactly like that. The

14:21

price is, The near term contract and

14:23

spot change over time. The prices of the

14:25

distance contracts change over time,

14:27

but on average, we expect this general

14:30

drift to occur. Just like

14:33

on average, we expect equities to have a positive

14:35

drift, but they go up and down over time.

14:37

And two things on that. Number one, the, on

14:39

this shape of the curve in that case, We're

14:43

backward aided, we're going long those contracts.

14:45

If it's flipped on the opposite side where

14:48

it's upward sloping, it would be the opposite. We'd

14:50

be looking to short those contracts,

14:52

right? Cause those will, they will gravitate towards zero.

14:55

And yes, just to round off what you said,

14:58

shareholder yield, finding a stock that has a strong

15:00

shareholder yield does not guarantee that

15:02

the price plus shareholder yield is

15:05

going to make you a positive return at the end of the year.

15:07

It just tends to be that way over time

15:10

on average. carry,

15:13

it's sort of lived outside the Overton window

15:16

for several decades. There,

15:18

you know, there were lots of managed futures funds

15:20

that did either indirectly

15:22

or directly use carry

15:24

as a signal to inform their portfolios.

15:27

And in fact, when we survey the

15:30

offering documents for a variety

15:32

of funds in the SOC gen,

15:35

CTA index, We find that

15:38

carry is mentioned second only to trend

15:40

in terms of the frequency that it's

15:43

mentioned as a signal that

15:45

it informs the, the

15:47

trades that they make and the portfolios that they hold

15:49

over time. So, you know, it's not as

15:52

esoteric as,

15:54

as many people believe. And it also,

15:56

in a managed futures context, is,

15:59

Considerably different than what

16:02

many old timers might remember as being

16:04

the sort of idea of currency

16:06

carry, which for a while was

16:09

the idea of, shorting

16:12

the currencies in low yielding,

16:15

regions, like the U.S. dollar or

16:17

the Japanese yen and buying emerging

16:20

market currencies that typically have, Higher

16:22

local interest rates, right? So

16:24

that's not the carry that we're that we

16:26

are implementing currency carry in

16:28

broadly that way. But when

16:30

you expand a canvas to include

16:33

equities, bonds, and a wide variety of different

16:35

commodities, the carry strategy

16:37

takes on a very different profile as you'll see.

16:40

So what drives carry? We

16:43

talked about equities and bonds, you know,

16:45

in equities, it's the dividend yield. That

16:47

is reflected in the futures term

16:50

structure for bonds. It's

16:52

the, we're only dealing with government bonds

16:54

in this context. So it's the,

16:56

you know, whether the bond cash

16:58

treasury term structure or

17:01

the guilt term structure. or

17:03

the bund term structure in Europe, for example,

17:06

has a positively sloping yield curve.

17:09

So if the 10 year yield

17:11

is higher than the three month treasury,

17:13

for example, that would, we would sort of

17:15

consider that to be a positive yield curve,

17:18

and it would have positive carry

17:20

in bonds. At the moment,

17:22

you know, we're in a bit of a strange situation where

17:25

near term yields Are actually higher

17:27

than most longer term yields. So

17:30

bonds currently are typically measured

17:32

to have negative carry. but again, that's rather

17:35

unusual over the past 30, 40

17:37

years. In commodities,

17:39

there's a convenience yield, which is sort of

17:42

the convenience that the

17:45

speculators offer to producers

17:48

in order to take on the price risk.

17:51

So producers can sell their production

17:53

forward, have some certainty about

17:55

the price that they're going to get for that production,

17:58

and they can go and raise capital for

18:00

to invest in new projects, that sort

18:02

of thing. And in currencies, it's

18:05

just the difference between the. the

18:08

short term interest rate in the jurisdiction you're borrowing

18:10

in. In our case, we're only

18:12

using U. S. dollar crosses.

18:15

We're always borrowing in the U. S. dollar to

18:17

invest in a foreign currency or

18:19

borrowing in a foreign currency to invest in U.

18:21

S. dollars, depending on which of those has

18:23

a higher interest rate. Now,

18:25

I'm not going to dwell on this. This is just why

18:28

do equities and bonds need

18:30

to have a positive long term

18:33

carry? Well, because in

18:35

order to invest in equities and bonds, You

18:37

need to move from very liquid cash into illiquid

18:40

securities. You don't know what you're going to

18:42

be able to sell those securities at some

18:44

point in the future. If you need liquidity

18:47

quickly, you may have to take

18:49

a hit on the price you would realize

18:51

for those securities. There's also inflation

18:54

uncertainty. We don't know what inflation will

18:56

be like in the future and you need

18:58

to be compensated for locking your money

19:00

up for a long period of time.

19:03

And also you also, you need to defer

19:05

consumption, right? Instead of

19:08

buying something that you want today, you're

19:10

deferring consumption. Putting it in

19:12

savings vehicles or in investments,

19:14

hoping that those investments will grow

19:16

over time, but you need to defer what you want

19:19

to buy today. So that's standard

19:22

stocks and bonds. Commodities,

19:24

just to dig in a little further, it's

19:28

very accretive for

19:30

commodity producers to sell

19:33

their production forward often

19:35

many years into the future. Imagine,

19:38

a mining company wants to develop

19:40

a new copper mine. And.

19:43

By the time you get the environmental permitting,

19:46

all of the engineering spec'd out,

19:49

you do all the assays, et cetera,

19:51

to figure out what kind of

19:53

mine you want to build, it's probably

19:55

10 or 15 years before you get

19:58

to first production. So the

20:00

copper companies will sell a good portion

20:02

of the expected production from those mines forward

20:05

in order to block in

20:07

the economics or a substantial portion of

20:09

the economics on that project. And they have many

20:11

projects going on in many different regions for

20:13

many different metals, et cetera. Energy

20:15

companies are doing the same thing. Grain

20:18

producers are doing the same thing. And

20:20

this is highly accretive because it lowers

20:23

the, variability of

20:25

their earnings over time and gives investors

20:28

certainty and that higher

20:30

certainty for investors lowers

20:32

the cost of capital to

20:34

the producer. They're able to go to the debt

20:36

market and raise money at lower interest

20:38

rates and go to the equity market and

20:40

raise money at higher multiples, lower

20:43

cost of capital, higher ROI

20:46

to the producer over time. So

20:48

it's kind of a win situation. Speculators

20:51

are providing, they're insuring the

20:54

producers against price

20:56

fluctuations and they earn a premium

20:58

on this insurance. And that's why

21:01

we expect this over time. Now, the

21:03

commodity premium can be positive or negative

21:06

depending on the short term dynamics

21:08

in a market. We saw energy

21:11

prices go negative in

21:13

early 2020 during the COVID crash

21:16

when there were all these pipelines

21:18

leading to storage facilities and

21:20

the storage facilities were totally full and

21:23

they would, were selling

21:25

oil at negative prices in order

21:27

to make room for new

21:30

oil coming from pipelines that had to go

21:32

somewhere, right? So there's short term

21:34

supply demand and dynamics that can

21:36

make the term structure for

21:38

commodities positive or negative And

21:40

make it more attractive to be short than long

21:42

or vice versa and that's why in

21:45

carry strategies, sometimes you want to be long

21:47

a market if it has positive

21:49

expected carry short of market,

21:51

if it has negative expected carry, if

21:54

you're short of market that has negative expected

21:56

carry, you're expecting to earn a positive

21:59

return on that.

22:01

Yeah. So I'll just kind of tie this all up

22:03

in a bow here. I think generally speaking, we think that

22:05

carry signals do provide insight into

22:07

future expected returns because

22:10

they should be compensated sources of

22:12

economic risk. So, while these

22:15

things may get crowded at times, right? If too

22:17

many people go into this

22:19

one trade in a particular market, the yen,

22:21

U.S. trade or whatever, it's

22:24

not rational economically

22:26

for a risk premium like carry to get arbitraged

22:28

away. Because As Adam

22:31

kind of alluded to here, there are players,

22:33

there are willing participants

22:35

here that are getting economic

22:37

benefit for hedging their risk and the other

22:39

side for taking that risk, right? So

22:42

it would require for this not to be

22:44

a risk premia, it would require

22:46

parties to be, willing to

22:48

bear risk with zero expected compensation,

22:50

which is not how the economy works, right? So I think

22:53

that's a good basis for this whole carry

22:55

thing.

22:57

Yeah, so carry is more of a classical

22:59

risk premium, then

23:01

it's a lot harder to make that same sort of case

23:04

for trend. There are different reasons why we think trend

23:06

exists. Carry it's a little bit clearer

23:08

that this is a risk based premium. So,

23:11

for the purpose of our experimentation,

23:14

we use this diverse universe of

23:17

different global equity markets, major global

23:20

government bond indices, a variety

23:22

of major currencies and, commodities

23:24

in the energy and metal sectors. We

23:27

didn't include any, grains

23:29

or softs or other more,

23:31

out there, commodity sectors

23:34

for, you know, liquidity reasons,

23:37

et cetera. In practice, there

23:39

are commodities within other sectors

23:41

that are plenty liquid. That,

23:43

you know, could be used in scalable

23:45

carry strategies, but this

23:48

was our experimental universe.

23:49

And just as we go into

23:52

a lot of the analytics here, I think this is a good question

23:54

somebody's asked for us to clear up. So

23:56

the question asked is it considered a carry trade

23:58

when, for example, commodity, a commodity

24:00

pool owns treasuries

24:03

as a form of collateral for the futures contract,

24:05

right? So this is, what he's alluding to is that

24:07

if you X ray a fully

24:09

funded futures strategy or commodity

24:12

strategy, it is anywhere

24:14

between 80 and 90% Treasuries,

24:17

short term treasuries that you're earning yield on. And

24:19

then the remaining

24:21

cash is used as collateral to

24:24

buy and sell those futures contracts.

24:26

And so I guess I'll get my 2 cents

24:28

out Adam and then maybe you can correct me, but it's

24:31

important to note that what we're going to be presenting

24:34

here is excess

24:36

returns. So we are not including

24:38

the carry on cash

24:41

that would exist if we

24:43

were to port this strategy over

24:45

to a traditional fund. So this is, am

24:48

I right in saying this is excess returns, Adam? And

24:50

so what excess returns mean is the returns above

24:52

cash. it's what you would get, if

24:54

you were to just run the strategy

24:57

without any cash yield whatsoever.

24:59

So it's not the, to answer your question, this is

25:01

the yield you get on the treasuries in

25:03

a pool is not considered

25:05

carry in the traditional sense, in the sense that we're going to be

25:07

talking about here. Now, can

25:09

we make a case for it being

25:12

carry if you're fully funding a

25:14

fund that uses carry? Yeah,

25:16

I guess you could say that it is a carry, but

25:19

it has no specific, quantitative

25:21

strategy. It's just, it just happens to

25:23

be along for the ride rather than it being an

25:25

explicit bet on carry.

25:28

Yeah, that's a really good point. So

25:30

how do you build carry strategies? There's actually a few different

25:32

ways to do it. One way is using

25:35

time spreads or calendar spreads where

25:38

you want to be totally neutral

25:40

exposure to a market.

25:43

And you will just, for example, go

25:45

long the near term contract and

25:48

short the far contract, and then

25:50

just take the ride of the

25:52

short term contract. the longer

25:54

term contract converging to the

25:56

shorter term contract over time. That's

25:58

one, way to do it. Another

26:01

is cross sectional carry.

26:03

We're going to go through that in quite a bit of detail,

26:06

which is typically implemented at the sector

26:08

level. So for example, if you've

26:10

got eight different equity markets, you're

26:13

running a cross sectional equity

26:15

carry strategy. You're going to be long for

26:18

equity markets and short for

26:20

equity markets all the time to

26:22

main, maintain that sector neutrality.

26:24

And same for long and short energy

26:27

markets, long and short bond markets, et

26:29

cetera, right? So that's a cross

26:31

sectional or sector neutral strategy.

26:34

And then the third way is a time series

26:37

carry strategy, which is

26:39

the one that we're gonna spend the most time on here

26:41

today, where you're allowing

26:44

the portfolio to get,

26:47

you know, a little bit more crowded. In

26:49

the, on the long side, if

26:52

most of the markets in a

26:54

sector or most of the markets

26:56

in the portfolio have positive

26:58

expected carry and you're allowing,

27:00

you know, get more short in

27:03

on a net basis. If more of those markets,

27:06

have negative expected carry than positive expected

27:08

carry, right? So again,

27:10

time series, strategies allow

27:13

for sector exposure

27:15

to drift higher

27:18

or lower into negative territory over

27:20

time in response

27:22

to how the underlying markets

27:25

are, you know, expressing positive

27:28

or negative carry. Whereas

27:30

a sector neutral strategy. All

27:33

the markets in the sector

27:35

could have positive expected carry, but

27:38

you're still enforcing the constraint that half

27:40

of them need to be held short in

27:42

order to eliminate any sector

27:44

exposure. Okay. And we'll see how that impacts

27:47

strategy performance over time. So

27:50

within these, cross sectional

27:53

or time series strategies, we

27:55

also divide it up in terms of measuring

27:57

carry on an absolute basis. Which

28:00

is what we've been talking about so far is

28:03

the term structure of

28:05

the futures for a market positive

28:08

sloping, implying negative

28:10

carry or negative sloping,

28:12

implying positive carry. so

28:14

that's sort of absolute carry and have

28:16

a more traditional way of people thinking about it, but

28:19

there's also a relative value carry. So

28:22

if we look at the very long term average

28:25

of the term structure, so for example,

28:28

gold is usually in, has

28:30

a positively sloping term structure, slightly

28:32

positively sloping term structure. So

28:36

the carry for gold in an absolute sense,

28:38

usually implies negative, slight negative

28:41

carry. But if

28:43

instead you look at the long term average

28:46

term structure and you measure

28:48

carry relative to that long term average,

28:50

then when it's above

28:53

average carry, then you'd go long. When it's

28:55

below average carry, you'd go short.

28:58

and so it's just a slightly different cut

29:00

on this concept. Then

29:03

there's how you want to transform

29:05

that carry signal, right? So

29:08

a really simple way would say, well, I want

29:10

to be one unit long.

29:13

If the, or one volatility unit

29:15

long, as we'll talk about a little bit later,

29:18

if carry is positive, one volatility

29:20

unit short, if its carry is negative,

29:23

that's binary signals. Or

29:25

we could be long or short

29:28

in proportion to the, just

29:30

the strength of the raw

29:32

carry measure, or that

29:34

the

29:37

degree to which carry is above

29:40

or below its long term average. And

29:43

then also we can rank the

29:45

markets in, in the portfolio

29:48

by their carry as well and use the rank score,

29:51

positive or negative rank score as their,

29:53

carry measure. So there's all

29:55

these different sort of ways to skin the cat and

29:58

they all deliver, they all sort of

30:00

capture the same underlying

30:02

phenomenon, but they do things

30:04

from slightly different angles. And therefore they provide

30:06

some diversification benefits when you combine

30:08

them all together in a portfolio, as we'll discuss.

30:12

So just to reinforce this concept of

30:14

absolute carry. This is, again,

30:16

just kind of what we've already been talking about most

30:18

of the time so far. These two

30:21

futures markets, these term structures,

30:25

imply negative

30:27

carry in both cases, but

30:30

the blue line has a

30:32

higher negative carry or expected

30:34

negative carry than the black

30:36

line in this case on an absolute basis.

30:39

So this is like raw or absolute carry.

30:42

In contrast,

30:43

Can you go back for a second, Adam?

30:45

Sure.

30:46

So what you're saying here is there's two contracts.

30:48

So in a carry portfolio,

30:50

that's trying to decide whether

30:52

to invest based on carry.

30:55

All things equal. If they have the same volatility, same

30:57

type of correlation, the

30:59

blue line at the top would receive a higher

31:01

weighting than the black line, but they both

31:03

receive a negative would be shorting both

31:05

of them because they, they are, they,

31:08

you know, they're going to go from the

31:10

high price in the future

31:13

and roll down to spot. Is that

31:15

fair?

31:15

Yeah, that's a good point. And

31:18

so if it were a binary, transform,

31:22

then actually they would both be short

31:25

and have the same volatility adjusted

31:27

weight in the portfolio, right? if

31:29

they were ranked or if we were using the raw

31:31

score, then the market

31:33

represented by the blue line would be expected

31:35

to have a higher, negative weight in the portfolio

31:39

than the black line, right? For the reasons

31:41

it's discussed. So that's good. Now

31:43

this is relative value carry.

31:46

We call it carry Z because we're

31:48

actually doing a Z score. And

31:51

a Z score is just what is the

31:53

current value relative

31:55

to the mean value. So

31:58

that difference divided by

32:01

the amount that value varies over time

32:03

or the volatility of that value over

32:05

time. Okay. So that's why we sort

32:07

of use carry Z score and relative value carry

32:09

kind of interchangeably.

32:11

Let me clear that up a little bit

32:13

more. What do you mean by mean value? So basically the,

32:16

where is the carry today relative

32:19

to a historical average?

32:21

Exactly. Yeah. So we just take the historical

32:24

average of the entire term structure, and

32:27

then we look at, well, is it, is

32:29

the carry higher or lower

32:32

than what it, what the carry typically

32:34

is over time, right? So in this case, the

32:37

blue line represents, higher

32:40

than average negative carry. The

32:43

green line, while it also indicates

32:46

negative carry, it's lower

32:48

than negative, average negative carry.

32:51

And therefore we would be short

32:53

the blue line and long

32:55

the green line in this case, because

32:58

we're now measuring carry relative to

33:00

its long term average black line, not

33:03

on an absolute basis. Okay.

33:07

Okay. So now the fun part,

33:09

we're going to start with an examination

33:12

of whether, carry

33:16

has existed in each

33:18

individual market sector.

33:20

So equities, bonds, and

33:23

different commodity sectors and currencies

33:25

on their own. And then we're going to

33:27

kind of begin to combine things

33:30

together and see how that everything kind of works

33:32

together in a stepwise fashion.

33:35

So this will reinforce, because we're

33:37

dealing with markets in

33:40

sectors that have very different

33:42

ambient risk, like obviously

33:45

bonds are going to have very different long term average

33:48

volatility than say natural gas

33:50

or crude oil or copper.

33:53

we're going to, we're going to scale all of

33:55

the markets in each portfolio to

33:57

have the same volatility.

34:00

And then we're also going to scale

34:03

the volatility of each portfolio to

34:05

have the same target of 10 percent

34:08

annualized volatility. And when we

34:10

scale them every

34:12

single day, we're evaluating

34:15

the volatility of the underlying markets. They're

34:18

correlation to one another. And

34:20

then we're using those estimates to scale

34:22

that portfolio to,

34:25

the target that best approximates

34:28

what we've estimated from the

34:30

portfolio in the very recent past.

34:32

Right. So in that sense, you're not letting the maniacs

34:35

take over the asylum, right? You're making

34:37

sure that your bets are

34:40

equalized across the sector. And

34:43

then you also don't want to have any asylum

34:45

be too big over another asylum. And

34:47

you're getting that kind of like you're equalizing

34:50

the risk for the assignments.

34:51

So that all the sectors have the same volatility.

34:53

Exactly. But this is why, even though we're scaling

34:55

to target 10 percent volatility in these

34:57

portfolios, it never

34:59

actually gets to exactly 10%. Because there's

35:02

some error in our estimation of

35:04

what the portfolio volatility is going to

35:06

be in the next period, every

35:08

time we estimate it. Right. So we get close. But

35:11

we rarely get dead on,

35:13

right? And then we're just scaling, use a

35:16

fairly near term estimate

35:18

of volatility based on what

35:20

happened over the last 40 days. And

35:23

we're using, because it's an exponentially weighted moving

35:25

average, the nearer term

35:28

returns matter a bit more

35:30

in our estimate than the returns that

35:33

are more distantly in the future, in the past.

35:36

And you talk about the floor.

35:38

Oh, yeah. The floor just means that there

35:40

are some times when, you know, a

35:43

market seems to have just

35:45

extraordinarily low volatility

35:47

over a short period of time. And

35:50

when you're deep in that low volatility

35:52

tail, that's often a sign

35:54

that you're misjudging

35:57

the true volatility of the market. So we never

35:59

let our estimate of volatility

36:01

go below the fifth percentile

36:03

of our measured volatility over the full

36:08

period of history prior to

36:10

that date.

36:10

So that you're not levering up a

36:13

single security that has never been,

36:16

never shown or exhibited that low

36:18

level correlation, or not never, but very

36:20

rarely.

36:21

Okay. So again, these are just

36:23

sort of starting on, sector neutral

36:26

or cross sectional start strategies

36:29

and strategies that are using absolute

36:32

or raw carry to measure carry,

36:34

right? So raw carrying currencies

36:37

and all the way up through raw carrying equities. Now

36:39

you can see that enforcing sector

36:42

neutrality has a penalty.

36:45

We do not want to take on any

36:47

sector risk. And,

36:49

you know, that's okay across most of the sectors

36:52

historically, but a few of the sectors

36:54

have either very low or slightly

36:57

negative historical returns if

36:59

you're not willing to take on some sector

37:01

risk when the, the

37:04

carry skews towards

37:06

long or skews towards short in

37:09

any given sector over time, right? they

37:11

still all do relatively well with the exception of

37:13

equities. but not quite as well

37:16

as we'll see when

37:18

we adjust the

37:20

measure of carry for, you know, against the long

37:22

term average carry for each market in the

37:25

sector. We're still holding

37:28

the sectors market

37:30

neutral in this case, but we're

37:33

sorting them based on the

37:35

degree to which they're, the carry is above

37:38

or below the long term average within

37:40

each sector. And this

37:43

seems to have, deliver better performance over

37:45

the very long term. Now

37:47

on a time series sense. Now we're

37:49

allowing the sector

37:51

exposure to drift positive

37:53

or drift negative. If there's a

37:56

preponderance of markets that have positive carry

37:58

or negative carry at any given moment, right?

38:01

We do that using raw carry

38:03

as our signal, then the performance

38:05

generally improves across the board

38:08

and we see the same phenomenon

38:11

with relative value or carry

38:13

Z, signals where

38:15

allowing that sector

38:17

exposure to drift higher or lower

38:19

is long-term accretive. As

38:22

we're now accepting more sector

38:24

risk as well, when we

38:26

aggregate up all of the sectors

38:28

together and

38:30

we just look at the cross-sectional

38:33

carry for equal risk

38:35

weighting all of the different sectors and

38:37

are using a cross-sectional, portfolio

38:40

approach, then you

38:42

can see that the carry Z outperforms

38:45

the, the regular

38:47

carry, which is consistent with what we kind

38:49

of saw at the individual sector level. And

38:51

when you combine the regular

38:53

carry and the relative value carry Z

38:55

signals into an ensemble

38:58

for sector neutral strategies, it

39:01

actually rolls up pretty well with a long term Sharpe

39:03

ratio of around 0.55, right?

39:06

But still it doesn't compare

39:08

to the performance of time

39:10

series strategies where we're now allowing sector

39:13

exposure to drift over time, both

39:16

raw carry and carry Z strategies.

39:19

Both perform very well on a time

39:21

series basis. When you roll them up

39:23

together, they do even better with

39:25

a sharp ratio in the range of 0.

39:28

9. And even when you combine

39:30

the sector neutral strategies with

39:32

the time series strategies, because

39:34

of the low correlation, you still

39:37

preserve the majority of the

39:39

performance you get from the pure time series

39:41

strategies. when you combine everything

39:43

together, just the power of diversity

39:47

and ensembling, which we're going to discuss

39:49

as we go forward, we pretty well

39:52

use ensembles in all of our

39:54

strategies everywhere for this reason. It

39:58

should be noted by the way, because we brought up

40:00

the idea of you know,

40:02

raw signals, binary signals,

40:05

and rank signals, that in each of these

40:07

cases, we're just combining strategies

40:09

based on raw, strategies

40:12

based on binary, and strategies based

40:14

on rank all together

40:18

within individual sectors, within

40:20

individual cross sectional

40:22

versus time series ensembles. And

40:24

within the total ensemble portfolio,

40:27

right? So this is gives you a general

40:29

idea of the performance

40:31

of all of these time series

40:33

and cross sectional portfolios.

40:36

over time you see the,

40:38

you know, the cross, one of these cross sectional portfolios,

40:42

just didn't perform very well.

40:44

Again, if you're not willing to accept

40:46

sector risk, then you're

40:49

removing what turns out to

40:51

be a meaningful component of the carry

40:54

signal over time. So

40:56

that was individual sectors. Most

40:59

managers also

41:02

sort of contemplate or actually focus

41:04

their portfolio management on the

41:06

total portfolio and not just on individual

41:09

sectors. So in this case, we're allowing,

41:11

let's say, you know, all of the.

41:15

energy markets had negative carry,

41:18

but all of the bond markets had positive carry

41:20

or some mix, whatever we're allowing

41:22

those markets that have positive carry,

41:25

to be held in positive weight in

41:27

those markets with negative carry to be

41:29

held in negative weight in the portfolio

41:31

without sector constraints,

41:34

right? It's just, you're going to be held

41:36

in the direction of your raw

41:38

carry or your relative value

41:40

carry in the portfolio. We're

41:43

going to show an inverse volatility

41:46

weighted version of this, which is

41:48

just using, instead of just applying

41:50

inverse volatility weighting at the sector level.

41:53

Now we're going to apply this at the total

41:55

portfolio level. So we're measuring the volatility

41:57

of every market in the portfolio. The

42:01

exposure is going to be the

42:04

carry score, or the carry Z score,

42:07

divided by that market's volatility estimate

42:09

at that moment. And then we're going to

42:11

scale the, Exposure of all

42:13

markets in the portfolio to

42:15

hit a target of 10 percent

42:17

annualized volatility based

42:19

on our best estimates at any given moment. Okay.

42:22

So that's naive or inverse volatility weighting.

42:26

We're also going to explore a mean

42:28

variance optimization of the same concept.

42:30

In that case, we're trying to

42:32

maximize the total amount of

42:35

both long carry exposure and

42:37

negative carry exposure. So short

42:40

exposures while simultaneously

42:43

trying to minimize the total

42:45

portfolio volatility, right? The

42:47

way that you would in a typical mean variance optimization.

42:50

So it's just a carry score

42:53

is our return estimate. And

42:55

we're just trying to minimize the total portfolio

42:58

volatility while maximizing our exposure

43:00

to carry. So

43:02

the naive weight inverse volatility, and

43:05

then an optimization we're going to

43:07

get to after that. So starting with

43:09

the inverse volatility waiting. So

43:12

now we're only exploring

43:15

time series versions of this. We're going to leave

43:17

the sectoral sector neutral

43:19

versions of these behind. We're going

43:21

to accept the fact that

43:23

we're going to have some Sector exposure

43:26

over time. If all of the markets

43:28

in a given or most of the markets in a given sector

43:31

have positive carry, we're going to have

43:33

positive exposure to that

43:35

sector. And we're just going to live with

43:37

that. And we're going to also harvest

43:40

the excess premium we've got from

43:42

accepting that risk. So

43:45

looking at. normal carry,

43:48

but now examining just

43:50

using the carry score as a continuous

43:52

signal, or binary carry,

43:55

is it positive or is it negative, and

43:57

then rank carry, so the score

43:59

becomes the rank, then you can see all

44:02

of these do quite well, you

44:04

know, in this case, the raw carry

44:06

doesn't do as well as the

44:08

rank carry, the binary's

44:11

somewhere in the middle, we're going to see

44:13

that this is random noise, and that

44:15

all of these Carry signals

44:17

and transforms are

44:19

basically, they all give you approximately the

44:22

same strength of signal. It's

44:24

just, some of them have done worse

44:26

or better recently or what

44:28

have you, but it's just noise. So

44:31

exploring the same thing for

44:34

relative value carry, we see, you

44:36

know, a different, well, we're

44:38

preserving the order here in terms of sharp ratio,

44:41

but the relative value carry

44:43

in this case tends to do a little better

44:46

than the, raw carry in some

44:48

cases, a little worse in other cases, again, emphasizing

44:50

that we are using

44:53

all of these different cuts at the same

44:55

phenomenon, not because any

44:57

of them have better or worse expectancy

44:59

over time, but because they all do slightly different

45:01

things at different times and diversify one

45:04

another, but they all do well as

45:07

you can sort of see from their long term

45:10

profile. Now transitioning

45:13

to mean variance optimized portfolios.

45:16

We're again considering both volatility and

45:19

correlation to minimize portfolio

45:21

risk while maximizing total portfolio

45:24

carry. Just using

45:26

raw carry with various transforms.

45:30

All do very well, and they tend to do a

45:32

little bit better than the naive

45:34

portfolios that are not accounting for

45:36

correlation differences over time.

45:39

Carry Z showing equally

45:42

strong performance with mean variance

45:44

optimization. And

45:47

you can see these are a little bit more tightly

45:50

grouped over time, then

45:52

the inverse volatility or naive weighting,

45:55

again, they do slightly better than

45:57

naive portfolio weightings, but

45:59

for the most part, it's just noise and

46:02

what is better is

46:04

to combine a naive weighting, which makes

46:07

fewer assumptions about

46:09

the portfolio with the

46:12

mean variance optimization, which

46:14

makes a couple of more assumptions

46:16

about the portfolio. Mainly that we, that

46:20

correlation estimates are a little bit persistent.

46:22

So when we're measuring the current correlations,

46:25

those correlations are likely to be approximately

46:28

the same in the next period. Combining

46:32

those different approaches is

46:34

highly creative, right? For

46:37

diversification reasons. And

46:39

when we do combine them, we

46:41

observe smoother performance,

46:44

higher sharp ratios over time,

46:47

right? So we're just, this is

46:49

just building up again to

46:51

our final meta, meta ensemble,

46:54

but just focusing on all of the different

46:57

inverse volatility portfolios,

46:59

right? using regular carry,

47:01

all of them using relative value carry, Z

47:03

carry. And when you combine them both,

47:06

you go from about a 0. 85 sharp to

47:08

a one sharp. This is just with the inverse volatility,

47:11

but ensembling. Right.

47:13

And there's the green line above both of the,

47:16

constituent ensembles. Rodrigo,

47:19

I don't know if that's confusing. Yeah.

47:21

No. So I just want to, I

47:24

don't think it's confusing, but I think for

47:26

those who haven't read our research in the past. I

47:29

think it's important to put

47:31

a stake on the ground right here and make sure that

47:33

everybody understands the difference between

47:36

over optimizing and

47:39

narrowly data mining. And

47:41

creating a robust portfolio

47:43

that is likely to work out of

47:45

sample, meaning in real life. And

47:48

it may seem like all these layers that Adam's

47:50

been talking about is about, you know, getting

47:53

more narrow and more optimized, but in fact,

47:55

it's the complete opposite. It is

47:57

the idea of trying to be broadly

47:59

correct about capturing

48:02

the carry signal rather than being

48:04

specifically wrong and

48:06

assessing all of these individual

48:09

parameters. Finding the one that had

48:11

the best back test and choosing that one,

48:13

right? So an example that I often

48:15

use with regard to the value

48:17

of being humble about your

48:20

ability to capture any signal, the

48:22

way we're doing here through ensembles is, I'm

48:24

sure everybody a couple of years ago heard that the,

48:27

that we captured as humanity, the first image

48:29

of a black hole and

48:31

the headline said the event horizon

48:33

telescope captures the first image of a

48:35

black hole. And it was a very well defined black hole. It's exactly

48:37

what we imagined high def. It was a beautiful

48:39

image. What few people know is that

48:42

wasn't one telescope. The event horizon

48:44

telescopes is actually hundreds of telescopes

48:47

across the world in many

48:49

different sites that are capturing

48:51

different types of signals, radio

48:53

waves, infrared, you know,

48:55

they're all measuring the black

48:57

hole in their own way. And what

48:59

the team had to do is over a couple of months in the U.S.

49:02

is grab all of that data. Put

49:04

it together, eliminate the

49:07

error terms. And what they got was

49:09

that beautiful image that we received in

49:11

the news. Any single telescopes

49:13

image, if you watch the, there's a documentary

49:15

on Netflix is garbage. It's doesn't make

49:17

a lot of sense. It's kind of the outline, right?

49:20

So it really is ensembles is the

49:22

most robust way that we

49:25

have found as humanity in terms

49:27

of noise to ratio, noise to signal, to

49:30

make sure that we are broadly correct

49:32

about what we're trying to do.

49:34

Yeah, no, that's a really great metaphor.

49:37

and I love it that you saw the opportunity

49:39

to use that. And this is just the

49:42

most widely document phenomenon in,

49:45

data science and machine learning. So the winners

49:47

of Kaggle competitions invariably

49:50

every time are using ensemble type

49:52

techniques. All right. It's just vastly

49:55

superior to view a problem from a wide variety

49:57

of different angles and aggregate the signals

50:00

up when you aggregate the signals, you

50:02

reduce the noise and emphasize

50:06

the signal. And so that's all we're doing here

50:08

with at the portfolio level. Great

50:10

point. So now it's just the

50:13

optimization. It's

50:15

also, I think, worth saying that when we're portfolio

50:17

optimizing, we do this every

50:20

single day, right? So we're, you

50:22

know, it's today. We look back

50:24

over the last Several days,

50:27

several months, we're estimating

50:29

the, variances and correlations

50:33

of between all the different markets at

50:35

that time. We're estimating the carry and

50:37

we're forming a new portfolio. Then we

50:39

move forward one step, we look back

50:41

again, and we, you know, we're, we

50:43

use that information to form a brand new portfolio. So

50:46

we're constantly rebalancing into

50:49

a portfolio seeking to emphasize

50:51

or maximize carry while minimizing portfolio

50:53

volatility, right? So when

50:55

we're doing that, you know, when we ensemble

50:58

all the different transforms

51:00

of raw carry, We do very

51:03

well, carries the relative

51:05

value that does well when you put them together,

51:07

you go from kind of a 0. 9 sharp ratio

51:10

to almost a 1. 1 sharp ratio.

51:12

Historically gain just the power

51:14

of approaching it with ensembles

51:18

and the ensemble line above

51:20

either of the constituents line scaled to the same

51:22

volatility. Now,

51:26

you know, an actual question to ask

51:28

at this point is, well, you don't

51:30

know if you've been reading the disclaimers, but they're all showing gross,

51:32

returns gross of estimated

51:35

trading costs and commissions.

51:38

So, you know, it's a good question. Well,

51:40

do these returns survive?

51:43

Estimated real trade frictions,

51:46

real trade slippage, commissions

51:48

paid, et cetera. And,

51:50

so, you know, we've been running future strategies

51:52

since 2016.

51:55

So we've got seven or eight years

51:57

of live data from

51:59

our own trading that we are able to use to

52:02

get really good estimates on

52:05

the cost of trading these. And then there are papers

52:08

that we lean on for the

52:10

cost of. You know, trade frictions

52:12

on different markets going

52:14

back to earlier points in history. And

52:16

we're able to net these out and get estimated

52:19

net results. It's also important

52:21

to know that we are,

52:24

we do with some smoothing, right? So when

52:26

we ensemble all of these different,

52:28

approaches together, they all

52:31

recommend slightly different Portfolios

52:34

at any given time. And what that means

52:36

is it averages out the amount of

52:38

trading that you need to do from day to day.

52:41

So that alone sort of reduces

52:44

the amount of trading and therefore the amount of trade friction

52:46

that you experience, the amount of commission

52:48

you pay, and that demands

52:51

you place on the market to absorb

52:53

the liquidity that you're sourcing.

52:57

We also smooth the waste through time

52:59

using a 5 day exponentially

53:01

weighted moving average. We find that

53:04

smoothing like this has no effect on

53:06

performance, but does have a nice effect

53:08

on reducing trading frictions. so

53:11

when we apply these smoothing and ensembling

53:13

techniques and we also embed our,

53:17

trade slippage estimates, then

53:20

we see that we lose about

53:23

1 percent a year in terms

53:25

of returns, which works out to about 0.

53:27

1 sharp ratio. and

53:29

everything kind of just drops by 1%, you know,

53:31

slightly larger drawdown, slightly

53:34

lower sharp ratio, et cetera.

53:37

But I mean, this is just a tremendously

53:40

resilient strategy once you back

53:42

out estimating trading costs. Right.

53:46

so a common question is

53:49

great. But if carry

53:51

is very highly correlated to the other assets

53:53

I hold in my portfolio, it

53:56

may not be very useful still. Right. So

53:59

it's important to wonder how

54:01

the correlation experience

54:03

for carry evolves over time. Here we plot

54:05

the, rolling one

54:08

year or late. Oh, sorry. Rolling

54:10

three year correlation between

54:13

the carry strategy and the

54:15

S& P 500. And

54:17

the U S 10 year treasury future.

54:21

And you can see that, you know, it does

54:23

go through multi year periods

54:25

where bonds have

54:27

a positively sloping yield curve

54:30

and our carry bond exposure

54:32

is predominantly positive.

54:34

equities, the dividend

54:37

yield on most

54:39

global equity markets is

54:41

higher than their local short term rates.

54:44

And so they have positive carry. And so

54:46

it, you know, we have a proper ponderance

54:48

of long equity exposure or

54:51

vice versa. Right. So it does fluctuate

54:53

over time. That said

54:56

the long term average correlation

54:59

between stocks and bonds

55:01

and carry is about zero.

55:04

Yeah, sorry, the correlation between

55:07

carry and trend is in the neighborhood

55:09

of 0. 3 to 0. 4, depending

55:11

on the, the frequency that you're

55:13

measuring at. Right? So, whether

55:15

you measure daily returns or monthly returns,

55:17

et cetera, it's in the neighborhood of 0.

55:19

3 to 0. 4, which is still very much

55:22

in the range of a strategy that is.

55:24

Where two strategies can be combined and be nicely

55:27

complementary to one another, which

55:29

we'll see a little bit later on.

55:33

Now, it's important to examine how

55:36

Carry performs in different

55:38

market regimes. And

55:40

we define regimes in a few different ways,

55:43

but a common way Is inflation

55:46

currently trending higher than expected

55:48

or lower than expected? And

55:50

is growth currently

55:52

coming in a little higher than expected

55:55

or a little lower than expected? And

55:57

therefore we can divide things broadly in this kind of four

55:59

different regimes. And

56:01

I think, Rodrigo, you had a poll question

56:03

that you wanted to ask.

56:04

Yeah, Ani, if you don't mind pushing the

56:06

next poll question, why don't you go to the next slide? the

56:08

question is, Given everything that we've reviewed,

56:11

where do you think, carry loses

56:13

money, right? Cause everything, everything that this chart

56:15

shows is really, you

56:18

know, we can expect gold and

56:20

commodities to do well in rising inflation environments,

56:22

but we can likely expect them to lose

56:24

money generally in lower inflation

56:26

environments and low growth environments. And so there's

56:29

winners and losers. Even when you think about trend

56:31

following, you know, we can be very clear

56:33

about. When there are very

56:35

persistent trends, almost

56:37

all the time when there's a bear market, you

56:39

can count on trend to, to likely

56:42

be there and provide really strong offset.

56:44

So there's an intuition there. I'm just curious to know

56:46

from the crowd, what the intuition is for carry

56:48

here. What is the, what

56:50

regime does it lose money in? All

56:53

right, given that we have Not a lot of time,

56:55

Ani. Why don't you push that through? Okay.

56:59

All right. So that's interesting. Okay. So we got pretty

57:02

evenly distributed. Why don't we show, what

57:04

we actually found there, Adam?

57:05

Yeah. So in

57:07

fact, we generated

57:10

the equity line for

57:12

the carry strategy, conditioning

57:15

on each of these different regimes.

57:18

And so each of these lines represents

57:21

the cumulative growth of

57:23

the carry strategy only

57:26

during periods that are

57:28

aligned with, you know, inflationary

57:31

growth, deflationary growth,

57:33

inflationary stagnation, or Deflationary

57:36

stagnation. So

57:38

the flat periods here are when the

57:41

strategy is not in that regime,

57:44

right? And then it moves up

57:46

or down based on how

57:48

it performs conditioned on being

57:50

in that regime. And what we see is

57:52

that in broad strokes, carry

57:55

is not really very sensitive

57:57

to any of these, these broad regimes.

58:01

and that's partly due to the fact that

58:05

Because of the diversity of

58:09

markets, That are held in the portfolio

58:13

and the propensity for the portfolio

58:15

to be relatively diversified

58:17

most of the time, it ends

58:20

up having a very stable

58:22

return stream. We don't have a lot of really

58:24

big, monthly or quarterly

58:27

losses or gains. And

58:29

so that the historical

58:32

frequency distribution, if you kind of compare

58:34

it to trend or to stocks, is

58:37

a little bit more normal

58:39

or Gaussian in shape, which

58:42

is kind of the holy grail of what you,

58:44

I mean, people would maybe

58:46

prefer positive skew

58:49

and I can get behind that, but you

58:51

know, just avoiding negative skew, we

58:53

think is a big win. And when you, again,

58:55

when you combine carry with

58:57

trend, with equities, with bonds,

59:00

Then that distribution becomes

59:03

even more normal. it's

59:05

also, you know, curious,

59:08

how does carry perform during the best

59:10

and worst period for stocks? So

59:12

what we've done here is sorted the,

59:16

returns on the S& P 500 into their

59:18

worst quintiles on their left,

59:21

going all the way up to their best 20

59:23

percent of, orders on their,

59:25

on the right. Okay. And you can see, obviously

59:28

the light blue line is the S& P 500

59:31

in its worst quarters, it does the worst, right?

59:34

But turns out carry and trend do

59:37

just fine during the worst quarters

59:39

for, or have historically

59:41

done just fine. Interestingly,

59:43

in the second worst quarter, neither

59:46

carry nor trend really does much, right?

59:49

In, then in the sort of, top

59:52

three quintiles, not

59:54

quarter, quintiles, The

59:56

carry and trend both have a tendency to

59:59

do reasonably well and obviously these

1:00:01

are very good quarters

1:00:04

for stocks. For bonds

1:00:06

we see a similar profile

1:00:09

generally sort of agnostic to

1:00:13

the how bonds are doing

1:00:15

in any given quarter. Both trend

1:00:17

and carry tend to do

1:00:19

relatively well even in the worst bond

1:00:22

quarters and then they

1:00:24

go on to do actually quite well in the

1:00:26

best bond quarters. And

1:00:29

then you wanted to add to those, Rodrigo, or

1:00:32

no, just kind of broad, broadly speaking,

1:00:34

we're kind of on time here. So,

1:00:36

yeah, let me sort of zip through. We just wanted to go through

1:00:39

the profile of these strategies during

1:00:41

the worst drawdown periods for

1:00:44

both equities and bonds. So this is the, October,

1:00:47

2007 to August 16th,

1:00:49

2012. So the S and P 500 global

1:00:52

financial crisis drawdown, and you

1:00:54

can see carry It was kind of like

1:00:56

going sideways for the early part and

1:00:59

then went on to deliver nice returns. during

1:01:01

the tech rec, Carry did very

1:01:03

well. Very nice offset. COVID

1:01:06

crash was particularly challenging

1:01:09

for Carry. It was probably the most challenging

1:01:11

period for Carry, as the,

1:01:15

authorities both on the fiscal side

1:01:18

and on the central bank side

1:01:21

were way behind in terms of implementing

1:01:24

policy to keep up with the news flow.

1:01:27

And, what we find is that carry strategies

1:01:30

are a little bit more susceptible to

1:01:32

miscommunication or blunders.

1:01:36

by central banks, right?

1:01:38

So, WEN has carried on particularly

1:01:40

badly when central banks have

1:01:42

either been behind the curve or

1:01:44

they've been misreading the messaging from the markets.

1:01:47

And in this case, obviously, the authorities

1:01:49

were very behind the curve during the COVID

1:01:52

crash, and then they moved

1:01:55

extremely aggressively, probably more

1:01:57

than the market expected, immediately

1:01:59

after the crash and and things went

1:02:02

in a different direction than the market expected

1:02:04

and that wasn't very good for carry. Eventually

1:02:07

evened out and went on to deliver

1:02:10

very solid gains. For

1:02:13

treasuries, This is just the post

1:02:15

COVID bond bear market. so

1:02:17

actually that wasn't too bad for Carry. Recovering

1:02:21

from the global financial crisis. So Carry did

1:02:24

fairly well during the global financial

1:02:26

crisis, but once the global

1:02:28

authorities stepped in, they implemented

1:02:30

quantitative easing, markets

1:02:32

began to settle, then Carry

1:02:34

kind of went sideways, struggled for a little bit before

1:02:37

recovering. This is the one

1:02:39

to focus on, the bond massacre of 94.

1:02:41

Because this is a, an example

1:02:43

of where the Fed was

1:02:46

miscommunicating or not communicating

1:02:49

with the market about their intentions

1:02:51

and about their expectations. And

1:02:54

they came in with a ver with a surprise

1:02:57

rate rise at aggressive surprise

1:02:59

rate rise, caught the bond market

1:03:02

off guard, and. You

1:03:04

know, bonds basically crashed overnight

1:03:07

and everyone was sort of offside. and

1:03:09

that was an example of where carry kind of struggled in the

1:03:11

short term before again, going on, and doing very well.

1:03:14

we'd be, I think, leaving

1:03:16

people, wondering

1:03:18

if we also didn't examine how carry

1:03:21

worked alongside trend and alongside

1:03:24

equities of bonds in a stacking framework.

1:03:27

So, you know,

1:03:29

because carry and trend both

1:03:32

have low correlation to both stocks and

1:03:34

bonds, They're just both

1:03:36

really accretive when

1:03:39

stacked on stocks.

1:03:41

And here you see both carry

1:03:43

stacked on stocks, trend stacked

1:03:46

on stocks, and a 50 50 combination

1:03:49

of carry and trend stacked on

1:03:51

stocks. Obviously, all three

1:03:53

looking very attractive historically. Same

1:03:56

story stacking on top of bonds, just very

1:03:58

attractive. And, turning

1:04:01

up a negative return over the recent

1:04:03

bond bear market period into

1:04:06

reasonable positive returns. Just

1:04:09

isolating the performance of

1:04:11

carry and trend stacks on equities,

1:04:14

obviously boosting equity returns,

1:04:17

lowering equity

1:04:19

risk, or not boosting

1:04:22

equity risk by a meaningful amount, despite

1:04:26

the higher returns. And

1:04:29

trend and carry combining to be a

1:04:31

little better than either on its own. Similar

1:04:34

with, with bonds

1:04:37

and just combining everything

1:04:39

together. 50 50 stocks,

1:04:42

bonds, 50 50

1:04:44

carry trend, just has

1:04:46

a, an astonishingly attractive historical

1:04:49

profile.

1:04:50

Yeah. And what's important here when we think about stacking

1:04:53

is a lot of people think, okay, I'm stacking returns.

1:04:55

I'm also stacking a lot of risk. But I think what

1:04:57

we need to point out here is how little,

1:05:00

extra risk is taken to

1:05:03

stack a hundred percent of these

1:05:05

factors, right? So in

1:05:07

the first column there 18.09,

1:05:11

S& P 500 plus, you know,

1:05:13

10 percent volatility targeted carry

1:05:15

is at 20. 66. So not

1:05:17

a lot more risk is taken to

1:05:20

double the returns, from 1991

1:05:22

to now, right? With all the caveats that, you

1:05:25

know, it can at any given time

1:05:27

correlate and so on. But it is the

1:05:30

benefits of diversification, the zigging

1:05:32

and the zagging. Right? Two asset

1:05:34

classes and or strategies that

1:05:36

make positive, have positive outcomes,

1:05:38

have an expected positive return, but

1:05:41

move differently from each other to create

1:05:43

lower, low volatility,

1:05:45

high return, strategies. So examine

1:05:48

that table, examine it in the white paper and,

1:05:51

and then reach out for questions if you want

1:05:53

to get more granular than that..

1:05:54

Yeah, I think it's worth adding quickly that,

1:05:56

just think of a carry on equities, right?

1:05:59

So carry versus trend on equities. So

1:06:02

you think about an equity bull market, equities are rising.

1:06:04

As they rise over time, they're

1:06:07

getting further away from what,

1:06:09

you know, levels where trend

1:06:11

would flip from being long

1:06:13

to short, right? Carry

1:06:16

is a little different as equities rise

1:06:18

and rise toward a peak in

1:06:20

a bull market. The

1:06:22

equity dividend yield is

1:06:25

getting lower and lower.

1:06:28

And oftentimes as we're coming

1:06:30

into, a peak in equities,

1:06:33

it's corresponding with the Fed

1:06:35

raising rates. The economy is getting too hot.

1:06:37

The Fed is raising rates so that

1:06:40

the yield on cash is rising. Well, then

1:06:42

the yield on stocks is declining at some

1:06:44

point, the yield on cash

1:06:46

is exceeds the

1:06:48

dividend yield on equities. So

1:06:50

while trend continues to buy

1:06:52

into the equity rally,

1:06:55

there's a point at which Carry comes in and

1:06:58

starts getting short equity

1:07:00

markets as the cash

1:07:03

return exceeds the dividend yield. So

1:07:05

it ends up being at least mechanically

1:07:08

having the potential to be a nice offset

1:07:10

for what's going on the trend side.

1:07:12

That's just one example how, you

1:07:14

know, carry can mechanically diversify

1:07:17

trend in equity markets. And

1:07:19

there are different types of examples in different sectors.

1:07:22

And look, another question that just kind of falls

1:07:25

across the same vein here, which is roughly

1:07:27

speaking. The question is about carry, trend,

1:07:30

gold, you know, should gold

1:07:32

be avoidable if you can allocate to carry.

1:07:34

and, you know, our view has always been, these

1:07:37

are all idiosyncratic risks

1:07:40

that you should probably add to your portfolio

1:07:42

because we don't know the answer to that. And in fact,

1:07:44

I remember vividly a

1:07:46

recent gold and was it two or three years ago

1:07:48

when gold was rallying and,

1:07:51

you know, trend strategies were long

1:07:53

gold. And carry was shorted.

1:07:56

Carry was wrong because the carry

1:07:58

was negative for gold. So no,

1:08:00

I don't think necessarily if gold is going,

1:08:03

is doing its thing, there will be times when

1:08:05

carry is dead wrong on that strategy,

1:08:07

I promise you. And so the idea

1:08:09

of just eliminating, if you believe that

1:08:11

these, that assets that you can stack on

1:08:13

top are one of two things, are have a positive

1:08:16

risk premia and are lowly correlated to everything else.

1:08:19

Any one of those two will, will be a benefit.

1:08:21

If you expect gold to be,

1:08:24

you know, zero returning real returns,

1:08:27

but you can stack it on top and it happens to be

1:08:29

non correlated to everything else. It is accretive to

1:08:31

the portfolio. So I think the answer

1:08:33

is always yes and.

1:08:35

And the average long term correlation

1:08:37

of gold to stocks and bonds

1:08:39

is zero. The average correlation

1:08:42

of gold to carry and trend

1:08:44

is zero. It just

1:08:46

protects against 'em, it's the

1:08:48

only thing that can protect against a certain

1:08:50

kind of risk. I think

1:08:52

gold belongs in every portfolio. And,

1:08:55

you know, obviously talk to your

1:08:57

advisor, but to

1:08:59

me, yeah, it's highly complimentary

1:09:02

along with all of these other diversification

1:09:05

opportunities.

1:09:07

Why don't we wrap it up, Adam, and then we'll

1:09:09

see if we have, you know, you go ahead with the

1:09:11

benefit of carry strategies in the portfolio.

1:09:13

and yeah, we can take questions.

1:09:15

Yeah, obviously. Look, we're just to reiterate,

1:09:17

right. In conclusion, I think carry has a unique

1:09:19

place in the portfolio. It is an under loved

1:09:22

strategy that, I

1:09:24

think many people have tried to

1:09:26

bring to market and failed, and we're trying to like,

1:09:28

do our best to really present a

1:09:31

thoughtful case for why it is so

1:09:33

unique. Why it's so useful. If you're

1:09:35

at, people ask me all the time, you have your bonds, you

1:09:37

have your equities, you have your trend, what's

1:09:39

the next thing you would do? it's always

1:09:41

been carry. And there's enough, video

1:09:43

footage of Adam just pounding the table on

1:09:45

this over the years that, you know, it's true. And we've

1:09:47

been at, we have used it for, for

1:09:50

a long time right now. So now the

1:09:52

question is, you know, is this webinar

1:09:54

is one of the things that, will give us a good reading

1:09:56

as to whether there is an appetite for it. I

1:09:58

hope there is. and if you design

1:10:01

it properly, you design it thoughtfully,

1:10:03

you do ensembles, then it can

1:10:05

be just as a creative, not caveat emptor.

1:10:08

It is normally distributed roughly. It

1:10:10

has volatility. It will have drawdowns,

1:10:12

right? So if you do a 10 vol,

1:10:15

Let's say it's a sharp of one. A good heuristic

1:10:17

here is to say, okay, so the return

1:10:19

is 10%. What is a three standard deviation

1:10:22

event for strategy at 10 vol? It could be like

1:10:24

10 minus 10 is zero minus 10. Two

1:10:26

standard deviation is negative 10 minus 10 is

1:10:28

negative 20. You know, something a bit higher

1:10:30

than that is probably a good heuristic as to what to expect

1:10:32

in terms of drawdown. One hopes it doesn't

1:10:35

happen at the same time as what

1:10:37

you're matching it up with. Okay. So there is

1:10:39

risk involved. This is not a, Panacea,

1:10:42

it is a unique diversifier

1:10:45

to add to other many things. So,

1:10:49

just recap. Look, we do a lot of this stuff.

1:10:51

we tend to go long form. We're 15 minutes

1:10:53

over the webinar. That's not surprising to me at all.

1:10:56

It's a miracle we got here in such a short amount of

1:10:58

time. I thought it

1:10:58

was a 90 minute webinar. I thought we were doing

1:11:00

so well.

1:11:01

No, sadly that's, that's incorrect, but people

1:11:03

are here. So that's good. If you want to learn more. Go

1:11:05

to our website, investresolve. com. we

1:11:08

have just revamped it. So there's a lot of research

1:11:10

for you guys to dig into. The white paper is available

1:11:12

there. The executive summary is available there.

1:11:15

There's a couple of videos of Adam kind of like

1:11:17

in two minutes describing the benefits

1:11:19

of risk parity versus carry,

1:11:22

you know, there's some interesting dynamics there. I've answered

1:11:24

a few questions. I typed out as

1:11:26

Adam was talking a few questions about risk

1:11:28

parity and carry. If anybody wants to take a look at those,

1:11:31

we have our book available on Amazon, and

1:11:33

then you can explore our strategies that, that

1:11:35

span far and wide across,

1:11:38

you know, evolution strategies, which is, All

1:11:40

encompassing, long, short market, neutral

1:11:43

managed future strategy. You have a carry program.

1:11:45

You have more kind of all terrain strategies,

1:11:47

all types of stacking stuff. So take a look at our

1:11:50

strategies page, explore that. if

1:11:52

you have any questions, you can reach out to the team. and,

1:11:55

yeah, I'll see if I can, the last couple

1:11:57

of seconds here. There's a few more questions, Adam.

1:12:00

is there. Is there any

1:12:02

risk that keeps carry? I'm

1:12:04

going to combine two. Okay. Earlier on in the presentation,

1:12:06

there's a question about, and I think this

1:12:08

is super important to address. are you worried about

1:12:11

the, short volatility character

1:12:13

of carry with that's he assumed, I think

1:12:15

this was early on before you went through everything. and

1:12:18

then this other question is what is the risk that keeps

1:12:20

you up at night? Right. So let's address

1:12:22

both of those.

1:12:24

Yeah. I mean,

1:12:25

does carry have a short volatility tilt?

1:12:28

Carry, yeah.

1:12:30

Shortfall carry.

1:12:33

If you run carry on individual

1:12:36

sectors like currencies or equities

1:12:38

or bonds, then you do

1:12:40

see some left tail

1:12:42

events for sure. And there's

1:12:45

good reasons why they occur. because

1:12:47

there's a flight to quality during financial

1:12:49

crises. the magic.

1:12:51

in these carry strategies

1:12:53

is the diversity of the holdings. It's

1:12:57

that when there's a crisis in equities,

1:12:59

often there's an offsetting

1:13:02

move in bonds, or

1:13:05

there's an offsetting move in gold

1:13:07

or energies. Or

1:13:10

metals or what have you. And,

1:13:13

as a result of that, you actually do observe

1:13:15

a, quite a normal, return

1:13:17

distribution on carry in

1:13:20

stark contrast to the,

1:13:23

I think, boogeyman

1:13:25

version that those who

1:13:27

had heard of carry a few

1:13:29

decades ago had in their mind.

1:13:32

and. What

1:13:34

keeps me up at night? I

1:13:36

think what keeps me up at night is that people

1:13:38

would lean too heavily into

1:13:40

any one strategy.

1:13:43

You know what the miracle

1:13:46

here is the ability

1:13:48

to combine stocks

1:13:50

and bonds and, you know, maybe gold

1:13:53

with carry and trend and.

1:13:57

You know, hopefully other diversifying

1:14:00

strategies that, continue

1:14:02

to become available and that maybe some of them

1:14:04

we will bring to market over time. But,

1:14:08

you know, none of these strategies, including

1:14:10

equities, in my opinion,

1:14:13

should be held in isolation. the

1:14:15

real magic here is combining

1:14:17

all of them together and

1:14:19

relying on the fact that

1:14:22

they all deliver their

1:14:24

returns for different reasons at different times

1:14:26

based on different types of risk and

1:14:28

will therefore manifest their

1:14:30

risks at different times and

1:14:33

average out to deliver a much more

1:14:36

reliable and smooth return

1:14:38

stream to get you

1:14:41

more reliably to your financial objectives.

1:14:44

Yeah, that's a great answer, Adam. I wouldn't add anything

1:14:47

more to that. The,

1:14:51

there was one question about risk parity and

1:14:53

carry, and we talked a lot about that. there's, if

1:14:55

you look up, you know, risk parity, carry

1:14:58

in YouTube, you'll see us talk a lot

1:15:00

about this and the

1:15:02

complementarity of it. And so

1:15:04

the question is risk parity to be replaced

1:15:06

by carry or not? I just kind of see this

1:15:08

as two separate things. When you think about

1:15:11

risk parity, you make certain

1:15:13

assumptions. You make an assumption that

1:15:15

the, that returns are commensurate

1:15:18

to risk, that all assets

1:15:20

that you're going to be investing in have the same sharp

1:15:22

ratio. So you're not making a return assumption whatsoever.

1:15:25

You're assuming that all the assets you're

1:15:27

investing in have a positive

1:15:29

expect a positive risk premium. always

1:15:32

right. And the reason that risk parity

1:15:34

has become popular, because it was the thing

1:15:36

that I think at least when

1:15:38

Dalio talked about it, it was what he was

1:15:40

going to do when he died, that he didn't want anybody to screw

1:15:43

up. It's a low

1:15:45

maintenance, high conviction strategy

1:15:47

over a long period of time, right?

1:15:50

It's probably, from a theoretical perspective,

1:15:52

the best thing you could do if you can't touch

1:15:54

your portfolio, except for a rebalance here and there,

1:15:57

over 10 years, 20 years, and 30 years, right? And

1:16:00

I think where people get confused is, Well,

1:16:02

you could do this to improve it and that to improve it. Yes. But now

1:16:04

it's an active strategy that you have to maintain. And if you

1:16:07

die, you have to trust the person to maintain that thing.

1:16:09

All right. So there's a place for risk parity. Also,

1:16:11

there's a place to, to assume that those assumptions are correct.

1:16:14

Carry is actually an actively

1:16:16

managed approach that

1:16:18

doesn't assume that there's positive

1:16:21

risk premium all the time. It can

1:16:23

short bonds when it's

1:16:25

appropriate to short bonds that risk

1:16:28

parity will not do based on

1:16:30

the carry reading. And so there's two

1:16:32

things. And number one, it removes the shorting constraint.

1:16:34

And number two, it doesn't make an assumption

1:16:37

that anything is stable. And

1:16:39

ultimately though, you need an

1:16:42

active manager that knows how to do this day in and

1:16:44

day out. Right? So do they

1:16:46

compliment each other? Absolutely. The correlation between

1:16:48

risk parity and carry is also quite

1:16:50

low. It uses the same universe, but it just.

1:16:53

And that's, that's,

1:16:56

let's see if there's any other questions and then we can close it up.

1:16:59

lots of questions about product, which we cannot

1:17:01

answer sadly in this venue. If

1:17:04

you have any questions, please reach out independently

1:17:06

over, Twitter. So if you guys have

1:17:08

any questions, reach out at info at investresolve.

1:17:10

com or reach out to Adam for any, carry

1:17:13

white paper, specific questions at GestaltU,

1:17:16

my Twitter handle is at RodGordilloP.

1:17:20

And, And, you know, we write,

1:17:22

talk about this all the time. If you have not been

1:17:25

a listener of a Resolve Riffs podcast, you should.

1:17:27

We have a Resolve Masterclass series that talks

1:17:29

about this 12 episodes that walks through all the

1:17:31

elements of this that you guys can listen to. It's

1:17:33

a separate channel. We've recently launched

1:17:35

a new channel that if you haven't signed up for,

1:17:37

I would sign up for now, the first episode was killer, second

1:17:40

episode's coming up soon, the Get Stacked

1:17:42

Investment Podcast with Corey Hofstein,

1:17:45

our partner in crime and a lot of this, this

1:17:47

stuff. So, yeah, we will, we have talked

1:17:50

about this. We'll take any other questions that we didn't get

1:17:52

to today and see if we

1:17:54

can address those in one of those two podcast

1:17:56

series. So with that, I think

1:17:59

Adam, thank you so much for your time. I think

1:18:01

you worked your butt off for this one,

1:18:03

you and Andrew. Mr. Andrew Butler,

1:18:05

I know you're out there, buddy. well done. great paper,

1:18:08

great effort. I know that you've lost, you almost got

1:18:10

divorced a couple of times to get this out the door. So

1:18:13

kudos to you. Well done. And I

1:18:15

think the investment world is all

1:18:17

the better for it.

1:18:19

Thanks for coming.

1:18:21

Thanks everyone.