0:02

This is mesters in Business

0:04

with Very Results on Bloomberg

0:06

Radio. This

0:09

week on the podcast, I have an extra

0:11

special guest. Gerard O'Riley is

0:14

a double threat. He is the chief

0:16

investment officer as well as

0:19

the co CEO of

0:21

Dimensional Funds UH. They

0:23

are a factor giant, managing

0:26

about six and fifty billion

0:28

dollars and total assets. This

0:31

is really a master class in how

0:33

to think about investing,

0:36

how to be systematic, how to

0:38

approach it from a evidence

0:41

based scientific basis,

0:44

how to incorporate the

0:47

best of academic research into your

0:49

process. One of the things

0:51

that I found really interesting was the

0:54

d F A focus on costs,

0:57

convenience and customization. Not

1:00

every giant investment firm UH

1:02

takes that approach. Really,

1:05

I've interviewed a number of folks from d

1:07

F a UM, from

1:10

David Booth to Gene Parma

1:12

and throughout the rest of the

1:14

organization. I think you

1:16

will find this to be absolutely

1:19

fascinating and and really informative.

1:21

So, with no further ado, my

1:24

conversation with d F as Gerard

1:26

O'Reilly, this

1:29

is mesters in Business with Very

1:32

Redults on Bloomberg Radio.

1:36

My extra special guest this week

1:38

is Gerard O'Reilly. He is

1:41

the Chief Investment Officer and co

1:43

CEO of Dimensional Fund Advisors,

1:46

a leader and factor based investing

1:48

for the past forty years. D

1:50

f A has about employees

1:53

across thirteen offices globally and

1:55

full disclosure my firm Results.

1:57

Wealth Management is a client of

2:00

Dimensional Funds and we manage a substantial

2:02

chunk of our assets with

2:04

their products. They manage six

2:06

hundred and fifty billion dollars in assets,

2:09

about eight percent of that is equity.

2:12

Girard O'Reilly, Welcome to

2:14

Bloomberg. Thank you, Barry, and thanks

2:16

for the invitation. I've been looking forward to speaking

2:18

with you for some time, so so have I. You

2:20

have such an interesting background. I

2:22

was really excited to talk to you, especially

2:25

given you have a PhD in

2:27

aeronautics from the California

2:29

Institute of Technology. What what were

2:31

your original career plans. Well,

2:35

I've always liked mathematics, and

2:38

as an undergrad in Ireland studied

2:41

mathematics and physics and so on extensively.

2:44

I was thinking about what to do next and said,

2:46

well, cal Tech does a lot of great

2:48

stuff in fluid mechanics and

2:51

particular in aeronautics and So I didn't have a

2:53

specific set of career plans. I

2:55

just know that's the subject that I wanted to study and that

2:57

I enjoyed. So I set off

3:00

or cal Tech and

3:02

and I really enjoyed my time. They're working

3:05

on various different projects, many theoretical

3:07

in nature, but very mathematically oriented.

3:10

So when you're looking at aeronautics

3:13

in the United States, there aren't a whole lot

3:15

of career paths out of that other than academia

3:18

or going to Nassau or one of the defense

3:21

um companies. What led

3:24

the shift from aeronautics to finance

3:26

as a career, Well, I

3:28

wanted to learn some more about finance. Number one.

3:31

I hadn't taken a finance course ever

3:33

in my life before joining Dimensional,

3:35

and Dimensional was the firm that I joined

3:37

straight out of college. Also, you

3:40

know, the academic path wasn't one that

3:42

appealed to me. I really enjoyed grad school,

3:45

but I preferred to tackle something

3:48

that was I would say more than here and now,

3:50

where your projects that you're

3:52

working on have impact very

3:54

quickly on you know, the end customer,

3:57

the end consumer, and

3:59

then also where on the engineering

4:01

side. You know, you mentioned in the U S well,

4:03

I'm not a US citizens, so it's hard for non US

4:06

citizen to work in the aeronautics field here in

4:08

the US because largely required security

4:10

clearance. So it's looking around. A

4:13

friend of mine was working for Dimensional,

4:15

knew that person at Caltech, and

4:18

that person was talking about you

4:20

know, Dimensional has all these great academic connections.

4:24

They really take finance from a scientific

4:26

perspective. Went down, checked

4:28

it out and said, this sounds interesting.

4:30

I really want to give this a shot for a period of time. So

4:33

so let's talk a little bit about those academic

4:36

um connections. Ken

4:38

French has been at Dartmouth for a long time.

4:41

His colleague Gene Farmer, Nobel

4:43

Prize winner at University of Chicago. Another

4:46

Nobel Prize winner, Robert Merton, also

4:49

at Dimensional funds. What's it like working

4:52

with all these Nobel Prize winning

4:54

economists could be a little intimidating

4:56

to some folks. It's always intimidating

4:59

when you start off working with somebody who's

5:01

very, very talented and you're getting to know them

5:03

for the first time. But it's a privilege

5:06

and it's great fun because those

5:09

folks, you know, have have worked

5:11

incredibly hard to hone their craft,

5:14

hone their skills, and when you think

5:16

about Ken or Gene or Bob or Meer

5:18

and any of those folks, they're very, very

5:20

generous with their time and so they're willing

5:23

to teach because they're in academia,

5:25

and if you're willing to work hard, they're willing

5:27

to put the time and effort into you. So I started

5:30

off with no background in finance

5:32

and got to learn finance

5:34

from some of the most amazing minds

5:37

in the field. So it was just it was

5:39

great Ken, Jean and

5:41

Bob. I've never heard of those three

5:43

gentlemen referred to quite in that way,

5:45

but I guess when you work with them as

5:47

frequently as you do UM, it

5:49

becomes Ken, Jean and Bob. So, so what

5:52

are the parallels between academia

5:55

and UM

5:58

working in finance professionally? And

6:00

then I have to ask what are the parallels

6:03

between aeronautics and fluid dynamics

6:05

and finance and investment. Well,

6:08

working in academia, you know,

6:10

you're always trying to solve a problem. You're

6:12

looking for interesting problems to solve

6:15

that haven't really been tackled before,

6:17

or an aspect that you're working on hasn't been tackled

6:20

before, and you're saying, well, can

6:22

I bring something new to the table, something innovative,

6:25

and that's incredibly rewarding and incredibly

6:28

interesting. Working in finance is

6:30

no different. You're looking for

6:33

new problems to solve. Those

6:35

problems are largely driven by what

6:37

it is your clients are looking for, what types

6:39

of investment solutions do they

6:42

require to solve

6:44

the investment problems that they have, and

6:46

then you're coming up with innovative ways to solve

6:48

those problems. So in that respect, there's a

6:50

lot of similarities. The time scale

6:53

and the time frames are a little bit tighter

6:55

and faster when it comes to finance

6:57

than in academia. In academia

6:59

it may be multi years, and there's multi

7:02

year projects that happen in finance, but

7:04

you want to be able to live or something for your

7:06

clients in shorter time frames than that.

7:09

When you think about engineering or mathematics

7:12

or physics, and then how

7:14

does how do those skill sets translate over to

7:16

finance. Well, again, it's all about problem

7:18

solving and what you're looking

7:20

for is how do I number

7:23

one pose the question

7:26

correctly? How do I ask the right question?

7:29

Because that's as important as trying to solve

7:31

the problem, you have to set it up in the right way. And

7:34

that's true whether it's in mathematics or physics,

7:36

or engineering, or it's in finance. Then

7:38

how do I gather data to help

7:41

me address and find the answer to

7:43

this question? And that's true of

7:45

both fields. And then how do I interpret

7:47

the data? What are the tools and the models

7:49

that I can use such that I'm going to be able

7:52

to organize these data in such a way to draw inferences

7:54

about how I want to act going

7:57

forward. And that's true of both,

7:59

Madam, physics, engineering, and finance.

8:02

I think the big differences are the

8:04

laws of physics tend not to change

8:07

over time, but the

8:09

laws of that government finance

8:11

can change through time. There

8:14

are many repeatable experiments in physics,

8:17

there are no repeatable experiments in finance.

8:20

But there is a kind of a common truth in both,

8:22

which is that in finance and investing, people

8:25

demand return for bearing

8:27

uncertainty. That doesn't change through time, but

8:29

how you go about implementing that can

8:32

change through time because the laws are changing. On

8:35

behalf of Isaac Newton, I'm

8:37

going to raise an objection that at least

8:39

our understanding of the laws of physics

8:41

have changed over time. So

8:45

so maybe the underlying laws themselves

8:47

are the same, but our perception seems

8:49

to have evolved. I think that's that's

8:52

a good way to put it, in a nice precise way to put

8:54

it, is that the underlying drivers

8:57

don't change, but our perception changes.

8:59

And that's it's an interesting observation because their

9:01

perceptions change because the models

9:04

that we use to explain and

9:06

understand those underlying drivers

9:08

evolve over time. All models are incomplete,

9:11

none of them are true, none of them are perfect descriptions

9:13

of reality. And that's true of physics and

9:15

it's true of finance. But

9:17

you can improve those models over time. You

9:20

can improve the data that you can collect over time,

9:22

and that enhances your understanding over time. I'm

9:24

a big fan of George Box. I love

9:27

the quote all models are wrong, but some are

9:29

useful, and it sounds like you very

9:31

much embrace that philosophy as well. And

9:35

it's an important philosophy to embrace when

9:37

you're, you know, working in the field

9:39

of finance, because ultimately,

9:41

what you're doing is you're investing money

9:43

on behalf of others. It's their life

9:45

savings. Often so it's what they've made sacrifice

9:49

to put together so they can

9:51

afford a better retirement or something that's

9:54

important to them in their future. And

9:56

if you ever believe that the model is reality,

9:58

you're probably going to build non robust solutions

10:00

and do them a disservice. So having a

10:02

healthy skepticism around

10:05

all models and basically

10:07

all data sources that you see is

10:10

important because it leads you to, well,

10:12

what if I'm wrong, do I still

10:15

have a good in solution even if this model

10:17

it turns out to be incorrect? And

10:20

I think that's that's a good way of looking at the world.

10:22

So let's talk a little bit about your career.

10:25

You began at d f A in two thousand

10:27

four in the research department. A

10:29

little more than a decade later your

10:32

chief investment officer, and

10:34

not that many years after that you become

10:37

co chief executive officer.

10:40

That's a pretty rapid career path. Explain

10:42

to us, if you would, the concept of

10:44

co CEO S or co c i O S

10:47

and how you managed to um

10:50

advance so rapidly in

10:52

a firm that was led

10:54

by David Booth for so many decades.

10:57

Yeah, So let me let me start with the ladder. How

11:00

how how do you advance? And my

11:02

viewpoint on success is there's a

11:04

combination of three things, and I'm

11:06

not sure which one is most important, but they probably

11:09

all are equally important at different stages.

11:11

One is a little bit of luck, a

11:14

little bit of luck in the

11:16

things that you've learned up to

11:18

that point in time when the opportunity comes, a

11:21

little bit of luck. For example, finding Dimensional

11:24

was well suited to the way that I thought

11:27

about the world. And then there's

11:29

some talent. Do you have

11:31

the right skill set that will

11:33

be helpful in that particular organization?

11:36

And it turns out that a quantitative and

11:38

analytical type skill set was very

11:41

helpful for an organization like Dimensional

11:43

in our clients. And then hard work.

11:46

Are you willing to do whatever it takes to

11:49

complete projects to move

11:52

the ball forward to help your

11:54

clients succeed. And

11:56

when you have all three of those, I think good

11:58

things can happen. And was fortunate that

12:01

had a little bit of each one of those when I came to

12:03

Dimensional, and Dimensional

12:05

has been a growing firm for money

12:08

money decades and when I came in two

12:10

thousand or four, we had about fifty billion

12:12

under management and you

12:14

know, that grew rapidly, so there was a lot of opportunities

12:17

for those folks that were willing willing to step

12:19

up. And so I consider myself

12:21

fortunate and very happy by how

12:23

that's turned out, because I've had a blast doing

12:25

it and it's been it's been rewarding. And

12:28

then in terms of the co c i O s and co CEOs,

12:31

we do a lot of CODs. We have cos of different

12:33

department heads. From my particular

12:35

case, Dave Butler

12:37

is the other co CEO. And

12:41

it tends to work well when you have

12:43

people who number one get along well

12:45

with each other, they respect each

12:47

other and each other's ideas, and

12:50

then they have maybe complementary skill sets. And

12:52

so the way that Dave and I have worked

12:55

in that job together I

12:57

think has been much more all

13:00

my preference I would have. I'm much preferred to have done

13:02

it with him then without them, because

13:05

you can do some dividing and conquering. But

13:07

also what I find is that as you get promotions,

13:10

and this is a little bit facetious, but you

13:13

tend to become, at least if you judge

13:15

it by the input that you get from

13:18

your peers, smarter and

13:20

funnier in that the input that you

13:22

get from your peers becomes

13:25

less informationally rich. But

13:27

when you have a troop heer like Dave and

13:29

I are are troop peers, anything

13:31

goes. We can have robust, open, honest

13:33

conversations and with David as well,

13:36

which read us a lot of pressure test things

13:38

before we have to go and talk about

13:40

them with the rest of the firm. And

13:42

that really, you know, I always think iron sharpens

13:45

iron, that you have to have people

13:47

who you can, you know, spar with on a

13:49

daily basis test your ideas.

13:51

They'll push you, you will push them so

13:53

that you can improve every

13:55

day. So it's it's worked very very

13:57

well. We do a divide and conquered. We've

14:00

thirteen global departments at dimensional. Four

14:02

comes straight to me, four go straight

14:04

to him, and then the

14:07

five in the middle kind of go to both of

14:09

us either through the CEO we have a CEO Lisa

14:11

Dalmer, are directly like legal and

14:13

compliance come to both of us directly, and

14:16

that way that it's it's just worked

14:19

well. We've been very pleased with

14:21

what we've been able to accomplish over the past five years

14:23

working together. It's I guess you each

14:25

keep each other sharp and keep each other

14:27

honest. That's right, really interesting.

14:30

So so let's talk about factors

14:32

a little bit um. How did the academic

14:36

research that that Rex

14:38

and David, the two co founders

14:40

of d f A, How

14:43

did that become part of the investment process.

14:45

So I guess there's a couple of salient

14:48

points there. One is factor research

14:50

in itself, and we talked a little bit earlier

14:52

on about models and what they're useful

14:55

for and how you draw inferences from

14:57

them. I really look on factor models as way

14:59

to organize historical data so

15:01

you can try to understand better what

15:04

really drove differences and returns

15:06

across different groups of securities, different

15:08

groups of stocks, different group of bonds,

15:11

and from those you can glean very

15:13

important insights about the

15:16

drivers of expected returns,

15:18

the drivers of differences and risk

15:21

across different asset categories.

15:24

And so I think that's the important aspect

15:26

of factor models.

15:28

So when you put them dimensional and its founding

15:31

in context of kind

15:33

of a burgeoning field in the eighties and in the nineties,

15:36

when more and more factor models were being

15:39

developed and tested and so on. The

15:42

founding was two

15:45

I would say, address an institutional

15:47

need that David had identified,

15:49

which was there weren't

15:51

many systematic strategies that targeted

15:53

the returns of small cap stocks, and

15:57

he found that that that was a hole in many

16:00

institutional investor portfolios. And

16:02

along the around the same time, because

16:04

David had done his MBA at the University Chicago

16:07

now Both School of Business, around

16:09

that same time, there was evidence

16:11

coming out that smaller cap stocks also had

16:14

higher average returns historically and reasons

16:17

you know, promoted about why that would be higher expected return

16:19

is going forward, and so around

16:22

that time was kind of when those factor models were

16:24

developing. So I started with the client need, and

16:26

then it was well, let me go to

16:28

the academics and understand, what

16:31

are the research around this client

16:34

need. Am I going to do something

16:36

here that makes sense or not makes sense from an academic

16:38

perspective? And then how do I

16:40

build a good robust solution

16:43

to address that client need. And then,

16:45

of course, in the nineties you had the three factor model

16:48

come along, and then in the mid nineties you had momentum

16:50

come along, and in the two thousands you had things

16:52

like profitability and investment come along.

16:55

So we had lots of different factors uncovered

16:57

over time. But the way that we look on each one

16:59

of those is their models. They

17:01

give us insights from the data. How do you use

17:03

that to build robust portfolios?

17:05

And I would say that's been kind of part of

17:07

our heritage for forty years. How do

17:09

we build portfolios that can target

17:12

these premiums but be

17:14

robust regardless of the market environment.

17:17

And we've been through many different market crises

17:19

with a broad range of investment strategies that

17:21

have come out quite well the other side.

17:24

So we're we're pretty familiar in

17:27

modern times with small cap

17:29

indices like the Russell two thousand,

17:31

or the S and P six hundred

17:34

or whatever it happens to be. But when

17:36

Sinkfeld and Booth were forming

17:39

um D f A in the early eighties,

17:42

these weren't really household names,

17:45

if they even existed at all. It's

17:48

amazing to think that there was a period

17:51

where small caps weren't

17:53

their own category. Tell us a little bit

17:55

about how that evolved. Yeah,

17:57

if you go back even further, so dimension was found

18:00

in than eighty one, But if you go back a decade

18:02

earlier, and I'll

18:04

focus on David a little bit and his work

18:06

with mc McCown, who

18:08

was at Wells Fargo at the time, and he's

18:10

a director of the firm,

18:13

and so David and Mac were

18:15

working on indexes. So

18:17

in the very early seventies, the

18:21

Max team with David created the first index

18:24

fund. It wasn't for retail, it was for

18:26

an institutional client, and it was based on US

18:28

large cap stocks, So he's very familiar

18:30

with index based approaches. Then

18:32

David subsequently left and worked

18:35

a gibecker for a while, understood more about what

18:37

clients were interested in looking

18:39

for required and so there

18:41

wasn't a Russell two thousand available

18:44

when he was building the firm, so there

18:46

wasn't an index to attach the strategy

18:48

to. The Other thing that was kind

18:51

of feedback from academia is yes,

18:53

small cap investing makes sense, but you're going to get

18:55

killed on trading costs. And so

18:57

then you have this kind of environment where

19:00

there wasn't an index, it wasn't a household name.

19:03

To your point, you know small cap stocks

19:05

as an asset category, so you kind of have

19:07

a blank canvas. If I

19:09

know, knowing everything that I know, what's the right

19:11

way to build a small cap strategy

19:14

that hopefully then will be efficient

19:17

and won't suffer too greatly from

19:19

trading costs and implementing and investing

19:22

client flows. So I think that

19:24

it was in some respects a

19:26

very big advantage starting

19:29

with that blank canvas of how do you design the

19:31

best portfolio, you know how, with

19:33

as few constraints as possible, because

19:36

you weren't worried about an index. And then subsequently

19:38

Russell had the Russell two

19:41

thousand, and then of course in the nineties, value

19:43

versus growth became, you know, well established

19:46

asset categories, and so asset categories

19:48

have been added over time. So

19:50

so let's talk a little bit about Gene

19:52

Parma and Ken French is what

19:55

started out as a three factor model,

19:57

it eventually became five and seven. Now they're

19:59

a hundred of factors, many

20:02

of which um

20:04

don't really add a whole lot of alpha or not

20:07

consistent enough alpha to justify

20:09

their complications and costs. Tell

20:12

us a little bit about the Farmer French factor

20:14

model. Yeah, so you know,

20:16

when you when you go to the eighties, there was a lot of

20:18

empirical evidence being uncovered that

20:21

the prevailing model from the sixties and the

20:23

seventies, the capital asset pricing model,

20:26

didn't explain the data very well,

20:29

so when you look at it, it was it was a beautiful model.

20:31

It was very you know, intuitive,

20:34

but it didn't explain the data all that well. And

20:37

so Ken and Gene in the early

20:39

nineties started to organize all the data

20:41

to say, can we put some of these observations

20:43

in one kind of unified viewpoint

20:47

of the historical data. And from

20:49

that, you know, exercise came

20:51

a better model

20:54

in the sense that it could explain

20:56

the returns that you saw among

20:59

stocks are better than the capital as sur pricing

21:01

models, so explain more of the returns, more of

21:03

the variation that you saw on the returns

21:05

across stocks, and so that

21:07

so subsequently came the three factor model.

21:10

Then to your point, lots of factors have been added. If

21:13

you look at family frenches are even Ken's

21:15

website, now you'll see a profitability

21:17

factor, you'll see an investment factor, you'll

21:19

see momentum factors.

21:22

You'll see all different types of factors. And as I mentioned

21:24

earlier, factors are really great to help

21:26

you organize the historical data. But

21:29

you don't want to get kind of two stereoid

21:31

about the latest factor model. I

21:34

kind of view a lot of the academic research

21:36

over the past thirty years as doing

21:38

variance on a theme, and

21:40

so it's not that kind of a have

21:43

brand new discovery, but it refines

21:45

your understanding of existing factors.

21:47

So there's probably twenty or thirty or forty different value

21:50

factors out there, but you don't need all twenty

21:52

or thirty or forty when you're managing a strategy.

21:54

But you can get insights from the different

21:57

factors on how to manage a strategy effectively.

22:00

And so what I mean by that is if

22:02

you if if you think about what datas are are

22:05

available. You have security prices,

22:08

you have data from income statements, so things like

22:10

income or profits or revenues or

22:12

expenses, and you have data from balance

22:14

sheets, assets and liabilities. They're the broadly

22:17

the data that are available to go test.

22:20

And when you look at all of those factor

22:22

models their variants on the theme, the right are

22:24

looking at current values of those

22:26

variables, whether it's current income or

22:28

current price to book ratios or price earnings

22:31

ratios, are they're looking at how they've changed, How

22:33

to have prices changed over the past number

22:35

of months, How have assets grown

22:37

over the past number of months, How is profitability

22:40

changed over the past number of months. So there's three data

22:42

sources and people do two things with them,

22:45

so there's actually really kind of six that

22:47

you can think about that kind of encompass

22:50

most of the hundreds of factors that you

22:52

see out there. And I think that if you have coverage

22:54

of those six current prices, current

22:57

balance sheet items, current income statement items,

23:00

and then how each one of those have changed in

23:02

recent past, you have pretty broad

23:04

coverage of all the various

23:06

different factor literature that's available.

23:08

And that's what we do at Dimensional.

23:11

So so let's for the lay person

23:13

get a little more granular with

23:16

some of the more popular and

23:18

effective factors um.

23:22

The four biggest ones I think are

23:24

size, value, quality,

23:26

and momentum. Is there anything you would add

23:28

to that beyond beta which is just a

23:30

given? So there's five? What

23:33

else would you add to that list? I

23:35

would add probably investment

23:37

and proxy for investment is how a firm is growing

23:40

their assets over time. And

23:42

when you think about all of the ones that you just listed,

23:45

Barry, all of them are momentum,

23:47

have something in common, and what's

23:49

that that they have in common? They're basically picking

23:52

up differences and discount rates

23:54

that the market has applied to different

23:57

investment opportunities. So when you think about

23:59

something like value, you you're taking price

24:01

and you're dividing it by some company fundamental

24:03

so some fundamental measure of firm size,

24:06

and you're saying, why do you want to do that? Because

24:09

you want to see who has low price today relative

24:11

to who has high price today. So there's

24:14

firms in the marketplace, some of them will trade at low

24:16

prices, some of them will trade at high prices. You need

24:18

to scale price, normalize price to

24:20

be able to make that determination. When

24:22

you say quality, quality often comes

24:24

down to profitability. And

24:27

what we know from the historical data

24:29

is the firms that have the highest profits

24:31

or the highest profitability, so profits divided

24:33

by assets or profits divided by book value

24:37

in the marketplace tend to

24:39

continue to have that high

24:41

profitability over the next year, two, three,

24:43

four, or five years. But what do those

24:45

profits lead to? Those profits lead

24:47

to client cash flows or investor

24:50

clash flows. I should say the higher the profits,

24:52

the more cash flows investors can expect to get

24:54

from their investments. So it's telling

24:56

you something about expected cash flows from that

24:59

investment in the future. Here I say

25:01

investment because asset growth. Let's

25:04

imagine a company has to retain

25:07

a lot of earnings, or has to issue a

25:09

lot of debt, or has to issue a lot of stock

25:11

in order to drive those profits going forward.

25:13

That leads fewer cash flows for investors.

25:16

So that also tells you something about

25:18

expected cash flows. So when you

25:21

talk size, value, profitability,

25:23

or quality and investment,

25:26

they're all telling you something about expecting

25:28

cash flows. Are the prices people are willing to pay. It's

25:30

a discount rate effect. Momentum

25:33

is the outlier. There's no equally

25:36

simple, compelling story

25:39

that lets you know why should you expect

25:41

that firms that have outperformed the market in the past

25:43

three the twelve months to continue to outperform

25:45

the market in the next three the twelve months, and vice versa.

25:49

But it's there, loud and clear in the historical

25:51

data, and so the question we ask ourselves

25:53

is how do we use that information

25:56

with as low opportunity costs as possible because

25:58

we don't know why it's there, so we don't know if it will be

26:00

there in the future. But if it's not there in the

26:02

future, we don't want to

26:04

have incurred unnecessary costs

26:07

on behalf of investors pursuing

26:09

something that we don't know why it exists

26:11

in the data to begin with. Really

26:14

really interesting, when when I think of momentum,

26:17

I have I tend to think of a

26:19

persistency because

26:22

either fund managers or investors

26:25

have gone through the whole process of

26:27

selecting that stock, and as

26:30

long as it's working out, trending

26:32

in the right direction at market um

26:34

returns or better, there's no reason

26:37

to remove it. So it becomes a little

26:39

bit of a self fulfilling prophecy

26:41

until there's a substantial

26:44

enough misstep and then throw in

26:46

all of the four oh one k regular

26:49

contributions. If that if you're in fund

26:52

X and it owns company

26:54

A, B, S and C, and all

26:56

three of those are doing well, money continues

26:58

to flow to those funds automatically,

27:01

and those funds tend to buy their top

27:03

performers. It's almost like

27:05

a virtuous cycle. You know, that's a

27:08

possible explanation, and that it's certainly it's certainly

27:11

a little bit of narrative fallacy

27:13

and hindsight bias. To say the least,

27:15

it's been tested. I mean, academics have looked

27:18

at you know, overreaction, under

27:20

reaction, and why is there continuation

27:22

in returns. There's an interesting area

27:24

of research going on right now, and

27:27

Professor Novi Marx had one of the

27:29

kind of first, well not one of the first,

27:31

but I kind of I would say, an instrumental paper

27:34

on on this recently that looks

27:36

at profitability growth. So how

27:38

have affirms profits grown

27:40

are declined over

27:43

the past three months to a

27:45

year and does that explain the

27:47

returns pattern that you see related to momentum?

27:50

And that seems like a promising area of research

27:53

if there is a lot of explanatory power in

27:56

how affirms profits have changed or how

27:58

their profitability has changed, and

28:00

that has the power to predict future profitability

28:03

i e. Firms that have grown their profits more quickly

28:05

than other firms may continue to grow

28:07

their profits more quickly than other firms. Then

28:10

if that explains momentum, then

28:12

you start to get momentum back into that field

28:15

of differences in discount

28:17

rates, and then that becomes a

28:20

much more easy story

28:22

to understand in the sense that firm

28:25

characteristics are much more straightforward

28:28

to predict than future private prices.

28:30

Well run firms tend to remain well run firms

28:33

for some period of time. But given that their

28:35

well run firms when you think about the price,

28:37

that's said in the stock market, that's

28:39

the aggregive view of

28:42

what expected return people require to

28:44

hold that investment. So they already understand it's a

28:46

well run firm, and so we think that

28:48

it's priced fairly given

28:50

all that information. So it may have information

28:52

about how well run that firm has

28:55

been over the past number of quarters, and

28:57

that has predictive power on how well run

28:59

that firm is expected to be over

29:01

the next few quarters. So so let's get into

29:03

the weeds a little bit. How can

29:05

you distinguish between factor

29:07

research that's significant and

29:10

factor work that's either statistical

29:12

noise or backwards looking

29:14

form fitting, Because it seems like everybody

29:18

has developed a new model of

29:20

their own which looks great on paper.

29:23

Um, the back tests are always wonderful,

29:25

but then in reality it doesn't seem to

29:27

work. So so how do you draw the line

29:29

between hey, this really

29:32

is substantial versus

29:34

just a just a good backdest. Yeah, you

29:36

hit on it perfectly. Very You're never going to

29:38

see a bad back test, in particular from an

29:40

asset manager. Well, because

29:42

that's where they all go to diet. It's all survivorship

29:45

by it's all survivorship by it. So it is

29:47

a real challenge, and that's true even of the academic

29:49

work, because in academia, how

29:52

do you get tenure? You published

29:54

papers. The types of papers

29:56

that get published are those with startling empirical

29:59

observation, and so the

30:01

hundred experiments that were run that didn't

30:04

lead to a startling empirical observation are never

30:06

published and the one that did is published.

30:08

So you have that bias when

30:11

it comes to academic

30:14

and practitioner work. The way that

30:16

we think about it is kind

30:18

of nuanced. First off, we start

30:20

with the broader view of the academic literature,

30:22

what's the latest and greatest out there in academia.

30:25

Then at Dimensional, we've developed

30:28

a lot of in house proprietary data sets

30:31

that go back many many decades

30:34

that include data with a

30:36

level of tendiness, I would say, and

30:38

precision that's probably kind of second

30:40

to none and with respect

30:42

to all the data sets available out there.

30:45

And of course you know we're

30:47

here at Bloomberg Studios who love data

30:49

and we love data too. You guys

30:52

um were involved in the early

30:54

days of the CRISP data set.

30:56

Let's talk a little bit about what an advantage

30:59

it was having not only

31:01

access to that, but the ability to really

31:05

do a deep dive and manipulate that data.

31:07

Tell us a little bit about Chris. Yeah,

31:09

CRISP was started back in

31:12

the sixties and it was basically

31:15

an effort by University Chicago

31:18

and folks there to

31:20

gather all the stock price data

31:22

and dividend data and corporate action

31:24

data to say, can we were computer return

31:26

on the U S stock market? Because

31:29

pre nineteen sixties you couldn't get that with

31:31

a great deal of precision. It's amazing,

31:34

it really is amazing. And so

31:36

so then over time you

31:38

know you had CRISP, and then you had

31:40

other supplements were company

31:42

financials were added to the data set and

31:44

all joined and linked up together so

31:47

effectively you could test things well. And

31:49

the way that we think about testing things well

31:52

is number one, do you expect to see this in

31:54

the data before you look? Why are you looking

31:56

for this for this thing? And

31:58

so that kind of juices some of

32:00

them, the issues with

32:02

biases and back tests. You expect

32:05

it before you go see, and then you see

32:07

the data tells you how strong it has been or hasn't

32:09

been. Then you want to do a lot of robustness

32:12

checks because robustness is the name of the game. So

32:15

you've tested it in one data sample, can

32:17

you test it in multiple data samples?

32:19

Can you test it out of sample? So I'll give

32:22

you I'll give you an example, and

32:24

I think this experiment is kind of unique

32:26

when it comes to academia. When you

32:28

look at Famine French in their ninety two

32:30

paper, they used US stock data

32:33

from the sixties to the nineties and

32:35

they tested value, premiums and leverage

32:37

and all sorts of things in that paper over

32:39

that data sample and produced the three factor

32:42

model. Then they came up

32:44

with a prescription or a kind of like almost

32:46

a list

32:48

of ingredients. Here's how you create a factor model. And

32:50

that's been used by most

32:52

academic since so the formula that

32:54

they used has been used by most academics. Sins.

32:57

So, then later on in the nineties, with

33:00

Jim Davis who used to work at

33:03

Dimensional, he gathered a whole bunch of

33:05

pre nineteen sixties data, so he was able

33:07

to extend the original family

33:09

French analysis to completely out of sample

33:12

test and that went from the twenties

33:14

to the sixties. Then non

33:16

US developed market data were collected

33:19

and the same tests that Feminine

33:21

French had round on. Their original sample was

33:23

run on non US developed markets, and

33:26

then it was run on emerging market data

33:28

because that was collected. And now we're

33:30

thirty years past the family French original

33:32

experiment. So now we have another out of sample

33:34

test. And so you have five out

33:37

of sample tests, and in four

33:39

of those five you see very

33:41

very strong and reliable value premiums,

33:44

and you can't actually tell the difference between any

33:46

of those five about the magnitude statistically

33:48

speaking, between the realization of those

33:50

premiums. That's robustness. You've

33:53

seen it in sample and you've seen

33:55

it in many out of sample tests.

33:58

That gives you high confidence that what

34:00

you're observing in the data happened by more than just

34:02

chance. It's something real

34:05

and you should expect to see it going

34:07

forward. But that's the type of rigorous

34:10

analysis that we're able to apply

34:12

to new observations because now we have

34:14

so many different data sets that we can test the

34:16

observation on, we can shape up the

34:18

experiment, we can find out where the bodies are buried,

34:21

how robust it is, and that gives

34:23

us confidence in the in

34:26

the patterns that were observing in the data, whether

34:28

they're real or it's just noise, really

34:31

really interesting stuff. So

34:33

so let's talk a little bit about the growth

34:35

of d f A and and your role

34:37

there. Um, you're a bit younger

34:40

than the typical member of your management

34:42

team. How does that

34:44

affect how you do your job? What

34:46

do you bring to the table that some of the more

34:49

senior managers might be missing.

34:52

So I've never really thought about it, to be

34:54

perfectly honest, And maybe that's in part because

34:57

I've always been on the younger side, whether it was

34:59

in high school relative to the rest of the folks in my

35:01

class. I went to college when I was sixteen, and

35:04

so it was a little younger than the other folks

35:06

in my class. And then when

35:09

I started working at Dimensional after doing a PhD,

35:11

was younger than some of the other folks in the research

35:13

team. So it's always been kind of the state of play.

35:15

So I don't think about it too

35:18

much. I would say, a Dimensional we

35:20

have a very academic view of

35:22

how to interact with each other. So interact with each

35:24

other with respect, but

35:27

challenge and argue the facts

35:29

and the issues, and the

35:32

best ideas win. And so I

35:34

think that when it comes to how to

35:37

interact with colleagues, whether they're younger

35:39

or they're older. It's exactly

35:41

under that formula. You have to operate

35:44

with respect, listen to the ideas,

35:46

and then the best idea wins. Our view

35:49

is, don't defend the idea just because

35:51

it's your idea. Embrace the best

35:53

idea and the right idea because ultimately,

35:55

long term, that's going to be better for the clients.

35:58

And if you make it better for the clients, you're going

36:00

to have a better business. So you

36:02

know, when it comes to business, clients

36:05

first, makes business very straightforward

36:07

on how to make decisions and what decisions to make.

36:10

And I think that at that atmosphere, I've

36:12

always enjoyed a dimensional and so therefore

36:14

age has never been, never been an important

36:17

ingredient. So let me flip that question

36:19

around and ask what advantages

36:22

do you find when you're working with some

36:24

older, more experienced folks. What if

36:27

they bring to the table for you. Some

36:29

of the things that come, in my view,

36:32

with wisdom and wisdom

36:34

comes with experience, I believe, is

36:37

how to communicate, how to message,

36:40

and how to help people understand

36:42

your point of view without alienating those

36:44

folks. And I think that's something that

36:47

has been very helpful for me in

36:49

working with my colleagues at Dimensional

36:52

Butler. Dave Butler is a master of

36:54

that, of course, and so, Okay,

36:56

you have a great idea, but if you can

36:59

communicate that great idea and

37:01

you can't help people understand

37:03

why it's a great idea, it's going to die

37:06

on the vine. You really need to have

37:08

the great idea and also have

37:10

an understanding of how people

37:12

receive the information. And I

37:14

think that's something that I've always tried

37:17

to pay close attention to how

37:19

my colleagues do that, In the colleagues that do it effectively,

37:22

how do they do it effectively? Because

37:24

ultimately, the best ideas

37:26

win, but only those ideas that can be communicated

37:29

can be considered the best ideas. So

37:31

I mentioned earlier the trillion dollar

37:33

club. You mentioned uh

37:36

in an interview. I think it was the Financial Times

37:39

that you think Dimensional

37:41

Funds should be a member of that rarefied

37:44

club that is managing a trillion dollars

37:46

in client assets. Tell us a little

37:49

bit about how you're going

37:51

to achieve that fairly lofty

37:53

goal. Yeah, we we definitely

37:55

feel that Dimensional has a lot of run

37:57

way for growth and there's a few different

38:00

reasons behind that. One. We view that

38:02

many different investors and managers

38:04

have come around to our point of view that

38:07

systematic strategies are very, very

38:09

beneficial for the end investor. And

38:12

by systematic I mean more

38:14

rules based approaches, approaches

38:16

where you can communicate up front,

38:19

here's what you can expect from this strategy,

38:21

and then validate after the fact that you

38:23

got and delivered what you said you were

38:25

going to deliver. And I think that's incredibly

38:27

important for investors

38:30

to build trust and confidence in the strategies over

38:32

time, and Eventual has been doing that for forty years.

38:35

So I think that's one reason that

38:38

best ideas win, and we have

38:40

some of the best ideas in my view, and

38:42

therefore that will serve

38:45

clients well. And if you're serving your clients while you'll

38:47

grow. Second kind of component

38:49

there is exactly what I said, serving clients well. It's

38:52

clients first. We think that if we deliver

38:54

a great client experience, the great support

38:56

for that systematic approach so clients can understand

38:59

know what to expect to be able to have conversations.

39:02

We work with financial professionals, so they have to have conversations

39:05

with their constituencies and

39:07

who they're accountable to. We

39:09

think that that will also help

39:11

us grow. And then in terms of the tactics to

39:13

get there, Dave and I have really

39:16

discussed this over the past number of years and

39:18

we think that our investment philosophy is very, very

39:20

powerful and I can get into that in a

39:22

moment. However, the means

39:25

for delivering that investment philosophy have evolved

39:27

over time, and our view

39:30

is you get to learn our investment philosophy one time,

39:32

but then choose your own adventure on what vehicle you

39:34

like to consume that under. So you

39:36

know that we've launched e t F s recently

39:39

and we've had what I would view as a lot of success

39:42

on the e t F space. Our first e t F went

39:44

live in November of and

39:48

we're around forty eight billion in

39:50

e t F assets over the

39:53

course of that time period,

39:55

and so I think that's been a good outcome. So

39:57

same investment philosophy is what we've had in commingled

40:00

mutual funds, but now an ETS separately

40:02

managed accounts. How do we use new technology

40:04

to take that minimum down to a half million

40:06

dollars from where we used to be twenty million dollar minimum

40:08

for our separately managed accounts, and

40:11

we've built that technology, a true fintech solution

40:13

to that problem, so that we can serve

40:16

those types of clients as well. So

40:18

how we'll get there is by identifying

40:20

the needs that our clients have and

40:23

keeping in mind the three c's, which

40:25

is, there's a lot of complexity in the world that

40:28

requires customization to come

40:30

with good solutions, but people want it conveniently.

40:33

So can we identify the complexity,

40:35

can we provide the tools so that people

40:37

can customize the right solution, and can we do all

40:40

that very conveniently for our customers? And

40:42

if we do that, I think we'll be successful. So

40:44

full disclosure, My firm is a client

40:46

of Dimensional Funds. Redults Wealth Management

40:49

uses Dimensional Funds as

40:51

one of our primary asset managers

40:53

along with Vanguard, Black Rock, et

40:56

cetera. But Dimensional is definitely one of

40:58

our UM large JR. Fun

41:01

providers, and I'm very

41:03

aware of the process that Dimensional

41:06

goes through in order

41:08

to make sure that their clients

41:11

understand the philosophy. You understand the model

41:14

with an eye towards avoiding

41:17

the sort of flavor of the month. Hey,

41:19

I'm chasing this hot

41:22

manager. No, now I'm chasing that hot

41:25

fun family. E t f

41:27

s are very much a break from that prior

41:31

um embrace of of

41:33

working very closely with clients. Tell

41:35

us a little bit about the internal discussions

41:38

that must have taken place before

41:41

you switch to e t f s, which, hey,

41:43

anybody could go to their online training

41:45

account or robin Hood or whatever it

41:47

is and and by the e t F how

41:49

have you managed around that? So

41:52

there was two big drivers of that decision.

41:54

The first was input from

41:56

clients. And as I mentioned around, we work with financial professionals,

41:59

so we don't work the end

42:01

retail consumer. We work with

42:03

financial advisors like firms like yourself, who

42:06

can get that level of understanding

42:08

and knowledge and experience so they understand what

42:10

we're what we're trying to accomplish.

42:14

A lot of those firms were saying, we're using e

42:16

t f s more and more frequently on behalf

42:18

of our clients, and we'd like to be able

42:20

to use dimensional ETFs. Could

42:23

you launch ETFs please? And so we

42:25

took that away. We thought a lot about it, and

42:28

that was kind of twenty eighteen time frame,

42:30

and on the books with the SEC

42:33

back then was a new proposed

42:36

e t F rule, and what that rule

42:38

effectively did was it made

42:40

e t f s much more straightforward to bring to the market,

42:43

much more straightforward for the end investor

42:45

to evaluate, but then also clarified

42:48

some things around the inner workings of e t f

42:50

s that were important to us because

42:52

we're not an index manager. We

42:55

have a lot of the benefits of an index based

42:57

approach that include broad diversification, load

43:00

and over low costs, but

43:02

we have an active implementation and so

43:04

those rules got passed in the

43:06

fourth quarter of is when the SEC

43:08

adopted those rules Rule six C eleven

43:10

for anybody who's nerdy enough to want to look

43:12

into them, and that

43:14

was a bit of a game changer for us. We could do now

43:17

what we had done in our mutual funds for decades

43:20

in an e t F rapper, so there was no

43:22

give up on the investment proposition. As

43:25

soon as that rule was passed, we went

43:27

into full launch mode.

43:29

By June, we had announced that we were

43:31

going to launch. By November of twenty

43:34

so almost a year after the rule came out, we

43:37

had launched. Those were the two big

43:39

drivers on the tax efficiency side

43:42

that wasn't as big a driver for us largely

43:45

because, and you're familiar with this, our mutual funds

43:47

tend to be highly tax efficient, and we

43:49

had tax managed mutual funds that

43:52

had similar tax efficiency ratios

43:54

to e t f s, So we had

43:56

very very tax efficient approach. E

43:58

t f s taken up a bit our e T S

44:00

two, but it was more what

44:03

our clients were asking for, and the

44:05

rules changed such that we could

44:07

deliver a investment proposition

44:10

that was on par with our mutual fund investment

44:12

proposition. And your turnover in

44:14

your various funds is relatively

44:17

low compared to the average mutual

44:19

funds at a fair statement. That's a fair statement

44:21

on the equity side, for sure, on the fixed income side,

44:24

where we do things that lead to slightly

44:26

higher turnover because of the information

44:28

that you can take out of yield curves at any point in time.

44:31

But on the equity side, you know, a core strategy has

44:34

ten percent turnover, value strategy

44:36

twenty turnover in a given year. And

44:38

how to think about that is like in the value strategy,

44:40

when you buy a stock, you expect to hold it for about

44:43

five years at turnover. That's

44:45

how how you can kind of translate that into

44:47

holding period on fixed income? Is

44:49

it primarily duration versus

44:51

credit risk that that the activity comes

44:53

from. It's a combination of duration,

44:56

it's a combination of credit, and then it's

44:58

also a combination of currency of issuance. When

45:01

you think about fixed income, a lot of people

45:03

focus on the FED and what the FED is going to do. That's

45:05

one rate among hundreds of rates

45:07

out there, because there's different currency of issuance,

45:09

different durations, different credit qualities. And

45:12

what we do is we take in five six hundred

45:15

different interest rates from around the world

45:17

and we use that information every day to say,

45:20

how do we increase expected returns the return

45:22

of this portfolio, but manage risk very

45:25

very robustly. So again it's has

45:27

an index feel, but it goes beyond

45:29

indexing with an active implementation to add

45:32

value and manage risk. Really interesting.

45:35

So let's talk a little bit about what's going

45:37

on in the market this year. Pretty

45:39

tough start. First quarter

45:42

was a bit shaky. It was a little carry

45:44

over from the end of uh

45:47

SO growth investors have been doing so well

45:50

for so long, hasn't

45:52

hasn't been a great couple of quarters for them. How

45:55

is the f A navigating this volatility?

45:58

Yeah, you're right. It has been a rocky started the year

46:00

in absolute terms, and when you look at the first quarter

46:02

of two a lot of the major indicries,

46:05

whether that's US or non US, developed

46:07

or emerging or in the negative territory, You're

46:10

right, Value has continued on it's good

46:12

run, and value has been having almost like a

46:14

two year a good relative

46:17

performance, which is more what we expect from the world,

46:20

and that continued on in the in the in the first

46:22

quarter for sure, where value

46:25

stocks help performed growth stocks by as much

46:27

as ten percentage points and lots of different

46:29

regions around the world. So that's

46:32

been good for the investors

46:34

in dimensional strategies because a lot of our strategies

46:36

on the equity side overweight value stocks and

46:38

stocks with high profitability and so on. In

46:41

terms of navigating the volatility. You

46:43

know, when you go back to our investment principles, there's

46:46

probably three that I would highlight. One

46:48

systematic approach is a good

46:50

approach for investors with the right support,

46:53

the right continued information, innovation, and

46:55

the right price point. So that's one

46:57

one basic principle. The other

46:59

two are that prices are predictions

47:02

of the future. Market prices are forward

47:04

looking, how do you use those prices

47:06

to manage risk and increase expected

47:09

returns? And the third is that optionality

47:11

has value. We should capture on behalf of

47:13

our clients. So when you go through a time

47:15

period like what we've just been through, where

47:17

you have Russia invading

47:20

Ukraine, all the sanctions that then

47:22

subsequently came on Russian

47:24

companies, Russian stocks, Russian individuals,

47:27

that flexibility or optionality is critical.

47:29

Because what we were able to do was

47:32

in January, when you know, there

47:34

was a lot of talk of sanctions versus

47:37

various different companies and individuals,

47:41

that we were able to freeze purchases

47:43

on all Russian securities, which was an important

47:46

part of our process. We said, okay, let's take a weight

47:48

and see approach. And that was in part

47:50

because if you go back to and

47:53

when the annexation of the Crimea by

47:55

Russia, at that point, we have a set of criteria

47:57

that we go through rule of law, you

47:59

know, how our foreigners treated versus locals,

48:02

the local infrastructure, and we said, you know what,

48:05

that criteria for that country right

48:07

now is not quite being perfectly

48:09

well met. So we reduced Russia to a half weight in

48:12

ten. So we already had that flexibility

48:14

built in, but that's very

48:16

helpful when you go through time periods like this

48:19

because you have a systematic approach that's largely

48:21

rules based. But you can't come

48:24

with a set of rules that will contemplate every

48:26

state of the world. So you need to have people

48:29

who have pragmatic and practical experience

48:32

to say, well, what can we actually implement

48:35

in the real world, and then how does that citizen

48:37

overlay on top of what

48:40

we do. So I think that this year that

48:42

has been helpful in our strategies

48:44

and how do we stay flexible to adapt

48:46

to what's going on in the world and

48:48

in markets around the world. So so

48:51

let's talk a little bit more about the value

48:53

versus growth um relative

48:56

performance. Growth has

48:59

really had a great decade. The growth

49:01

was beating value. That started to change

49:04

last year. What do you attribute that too?

49:07

Is an inflation the end of quantitative

49:09

easing and zero interest rate policy,

49:12

or or something else. And I'm

49:14

sure the investors who are listening are going to want

49:17

to know and how long can this last?

49:19

Yeah, it's a it's a very interesting question. I'm gonna

49:21

flip it around on your barry, which is

49:24

why did we have such a long run of

49:27

growth out performing value over the because

49:30

that's the unexpected outcome. Value

49:32

out performing growth is not the unexpected outcome

49:35

because when you think about value stocks, there

49:37

are stocks that have lower prices and

49:39

higher expected cash flows. So by definition,

49:42

investors have applied a higher discount rate to them,

49:45

and that's every day, and so you expect them

49:47

to outperform growth stocks. When growth outperforms,

49:49

that's the unexpected outcome, and that happens

49:51

plenty, because returns over

49:54

the short pull are driven by the unexpected

49:57

things that happened, not they expected. When

49:59

you look over the past decade, there was probably

50:01

unexpectedly good outcomes for

50:04

the facebooks and the Amazons and the Netflix.

50:06

If you go back, you know, fifteen years

50:09

and say do you expect this group of fang

50:11

stocks or whoever to have an annulyzed

50:13

compound rate of return of thirty percent a year

50:15

for the next decade. Not many people would have said

50:17

yes. But they did very very

50:20

well. They improved their earnings profile quite

50:22

dramatically over that period and were rewarded

50:25

when you go then into the later

50:27

time period. Um, you know, those

50:30

value stocks in particular

50:32

in the US, when you look at the price to earnings

50:35

or price to book ratios

50:37

of value stocks for versus growth those

50:40

ratios and those differences had grown

50:42

dramatically large. So growth had become higher,

50:45

higher, higher, higher in terms of their valuations,

50:48

whereas value had stayed kind of right around where

50:50

it was because value had come in kind of

50:52

like it's long term average, but growth

50:54

had come in well ahead of its long term average in

50:57

terms of returns, and so value

50:59

was still in the same position to deliver those

51:01

good returns going forward, whereas the expected

51:03

returns on growth stocks had probably dropped given

51:06

those higher valuations. So so

51:09

let me phrase my hindsight bias

51:11

and in the form of a question, which

51:13

is, isn't it obvious

51:15

today that post financial

51:17

crisis the financials

51:20

would lag for quite a while, and there they

51:22

tend to be big value stocks.

51:24

And then when we look at the growth side, Hey,

51:27

this was a societal transformation,

51:29

a generational shift, uh,

51:32

towards mobile, towards internet,

51:35

towards technology. Again,

51:37

with the benefit of hindsight, how

51:39

did we not see? Why was this a surprise?

51:41

It's perfectly obvious after the fact that

51:44

this massive change was taking place. It's

51:47

obvious after the fact that in the middle

51:49

of it. You never know exactly what's going to

51:51

happen because there's always new technologies. People

51:54

often talk about the new normal, and there is no new

51:56

normal because technologies have been

51:58

developed persistently a

52:01

decade by decade for the past hundred

52:03

years, and those technologies give rise

52:05

to uncertainty about who will adapt

52:07

and use them in the best manner, and who

52:10

will be the winners and who will be the users once

52:12

that new technology comes into place. So there's

52:14

always a massive amount of uncertain It existed a

52:16

decade ago and exists today. And

52:19

what we look to markets to do is process that information

52:22

to say, given that uncertainty, who am

52:24

I going to demand the higher return to hold or a lower

52:26

returned to hold? So I think that's the state

52:29

of the world. And but even

52:31

things by you know, like who's going to predict

52:33

that COVID would come along and be such

52:36

a boon to the Amazons and the netflix

52:38

of the world because everybody who was locked

52:40

in their house for some period of time

52:42

that is unexpected. That's an unexpectedly

52:45

good outcome, not for society but

52:48

for the firms that

52:50

were well positioned to meet the needs

52:52

of society. When that unexpected

52:54

event began to unfold. So

52:57

so let's talk about another surprise

53:00

in return, which has been since

53:02

the financial crisis. The US

53:05

has just trounced international

53:07

returns for far longer

53:09

than I think even the most ardent

53:12

US investor expected. How

53:15

do we explain the dominance of US

53:17

equities versus either developed

53:19

x US or emerging markets.

53:22

And there, I'd point to you to the last

53:24

decade, which was the previous

53:27

decade, where you know, small

53:29

cap stocks, non U S stocks,

53:31

emerging market stocks greatly

53:33

outward outpaced US large cap stocks.

53:36

And then in the decade that you're referring to, it flipped

53:39

completely and US large

53:41

cap stocks outpaced everybody else, in particular

53:43

US large cap growth stocks. Again,

53:46

i'd put that down there's an unexpected component

53:48

to that, and I'd put it down to the success

53:50

of some of those U S firms that are now the largest

53:53

firms in the US marketplace.

53:56

That doesn't mean they will continue to be the largest firms in the

53:58

US marketplace, because what we've seen over

54:00

time, the largest firms tend

54:02

to get there by outperforming everybody else.

54:05

And in the global marketplace. Now

54:07

the US has many of those largest firms

54:10

and then in the you know, one to five years

54:12

after they become the largest firms in the world, they tend to

54:14

underperform everybody else's

54:16

other firms innovate and try

54:19

to take that top spot. So

54:21

there it's just you know, success of

54:24

those companies, and that's

54:27

driven the investor

54:29

demand for those companies because they've been able to satisfy

54:32

so much client demand. Those are well run

54:34

companies, and investors

54:37

see high cash flows from those companies

54:39

and they're willing to build up the prices. So

54:41

so let's talk about a couple of things that are

54:44

in the midst of changing

54:46

and what you guys are doing about

54:49

it. And I guess I have to start

54:51

with volatility. We saw a giant spike

54:54

in O eight or nine during the financial crisis,

54:57

another big spike in during

54:59

the pain endemic, and the

55:02

VIX the measure of volatility

55:05

was high thirties, and just just

55:07

a month or so ago that seems

55:09

to be rolling over and coming back down. First,

55:13

what have we learned about volatility and how

55:15

can investors use it to their

55:17

advantage? And second, what do you think

55:20

this softening of volatility today

55:23

might imply for the rest at least of this

55:26

calendar year. So what we've learned

55:28

over time about volatility is

55:30

that when there's

55:32

a market crisis, and this goes without saying

55:34

volatility increases. Why because uncertainty

55:37

increases. There's a lot more uncertainty

55:39

about what the range of outcomes maybe, and

55:41

that uncertainty leads to a few different things. Increases

55:45

in the volume of stocks and bonds that

55:47

are traded, increases in

55:49

bid off or spread, so the cost to trade

55:52

those stocks and bonds, increases

55:55

in volatility. All of those things

55:57

come in a crisis. We

55:59

had a crisis in March of when

56:02

Russia invaded Ukraine. We had another

56:04

crisis, how would that translate into

56:07

global markets? And volatility tends to spike.

56:09

But we've also learned over time is

56:12

that spikes and volatility are unpredictable.

56:15

So it's a shock, it's unexpected for a

56:17

reason because it's unpredictable.

56:20

And then once it's spikes, it tends to decay

56:22

slowly unless there's another big shock

56:25

that comes along to spike it back up. So

56:27

it tends to decay over the over course

56:29

of three to six months, goes back down to

56:31

normal levels, and you can actually see that from market

56:34

prices there's different

56:36

market prices that tell you about the implied volatility

56:38

of markets over the next thirty days, over

56:41

the next thirty days following that, the thirty days

56:43

following that, and so on the forth. And what you

56:45

see from market prices is that when you get a big spike,

56:48

it from market prices is

56:50

expected to decline over you

56:53

know, the next subsequent months. And we saw that clearly

56:55

in March. Volatility spiked,

56:58

but the markets told you that it expects

57:00

the decline over the next a

57:02

few months. It's the same with inflation. Right now,

57:05

you can look at break even inflation and

57:07

it's expected to be about six percent as

57:09

of the end of Q two,

57:13

But if you look at over five years, it's

57:15

expected to be six percent over the next twelve months

57:17

and then declined to something sub three in

57:19

the subsequent four years. Right, So markets

57:22

always tell you something about what's expected right

57:24

now and what's expected in the future.

57:26

So since you brought up inflation, let's talk a

57:28

little bit about that. Um, what

57:31

is the f A doing in preparation

57:33

for higher interest rates? If

57:35

the Fed keeps raising rates

57:38

and if bond investors keep selling short

57:40

duration holdings, how

57:42

are you going to adjust to that, what do you think about

57:44

things like high grade corporates and

57:46

tips versus high yield

57:49

and and risk of your bonds, your

57:51

inflation and interest rates. Inflation has

57:53

been high. Everybody knows that over the past while.

57:56

And the way that we view inflation is there's

57:59

two things that you can do the

58:01

markets. You can look at get understanding of what the

58:03

market expects. But the unexpected

58:05

often happens. Nobody can predict the unexpected,

58:08

so therefore you can but you can plan for the unexpected,

58:11

and you can plan to outpace it or

58:13

to hedge it. And so if you want to outpace

58:16

things like what you mentioned, corporate bonds,

58:19

globally diversified bond strategies,

58:21

equities and so on over time

58:23

have had positive real returns, so returns

58:26

in excessive inflation, in

58:28

high inflationary environments and low inflationary environments.

58:30

And if you look back the thirty past thirty four years, you

58:32

see that if you want to hedge

58:34

it, you can use treasury inflation

58:36

protective bonds, and we think that they're a good solution. You

58:39

can also then if you don't want to give up so much expected

58:41

return by corporates, are bonds

58:44

like that and then hedge it with different

58:46

types of instruments like inflation swaps and so on

58:48

that can hedge out your inflation exposure. They're

58:50

the two ways to deal with inflation

58:53

in our view. You can plan for it. You can't predict

58:55

when you're getting the spike, but you can plan for it. When

58:58

it comes to interest rates and increasing interest rates,

59:00

again, you can't predict when

59:02

they're going to shoot up. That's not a

59:05

something that you can predict, but you can plan for it. How

59:07

do you plan for it? Well, we mentioned

59:09

earlier on that there's an obsession over the Fed

59:11

Funds rate. But if you look over the past

59:13

thirty years, thirty to forty years, the

59:16

Fed has increased the Fed Funds

59:18

rate one month out of six, has

59:20

decreased the Fed Funds rate one month out

59:22

of six, and has left it flat in

59:25

the other four months out of six. That's been about the pattern

59:27

over the past forty years. And when

59:29

you look at the months in which has increased

59:32

the Fed funds rate, about half the time

59:34

the third year rate has gone up and about half the time

59:37

the third year rate has gone down. So what does that tell

59:39

you? It tells you that other

59:41

rates out there other interest rates don't move

59:43

in lockstep with what the FED is doing.

59:46

So if you think about that and you extrapolate,

59:49

you have interest rates on the short end, the intermediate

59:52

end, the long end. You have interest rates

59:54

as they applied to corporate bonds from

59:56

triple A s down the double B s. You have interest

59:58

rates from a current from bonds issued

1:00:00

in euros and British pounds in

1:00:03

Assie dollars and so and so forth, and

1:00:05

none of them move in lockstep with this FED. So

1:00:07

you can diversify. That's how you plan.

1:00:10

The FED may do what it's going to do, but it's one

1:00:12

interest rate among money, and that's

1:00:15

going all of those other interest rates. You wanted to drive

1:00:17

the returns of your probably diversity by portfolio because

1:00:19

if you look from oh eight on the subsequent ten

1:00:21

years, the FED funds rate was basically at zero for a

1:00:23

decade, but it globally diversified

1:00:26

portfolio stocks and bonds returned about four. So

1:00:29

in a zero FED funds rate, you've

1:00:32

got about a four return. So

1:00:34

again it goes back to you don't

1:00:36

have to be able to predict the unexpected. You

1:00:39

just have to be able to plan for it and then

1:00:41

stick with that plan, regardless of

1:00:43

what the unexpected brings brings

1:00:46

the past. So let's talk a little

1:00:48

bit about your career. Uh,

1:00:51

pretty much, since you've been in the world of finance,

1:00:54

we've only seen low rates

1:00:56

and we've only seen mostly low

1:00:59

inflation. Does that impact

1:01:01

you're thinking, there's a color your perspective

1:01:04

having lived um as

1:01:06

of financial professional in

1:01:08

this somewhat aberrational environment,

1:01:11

or are you looking at the academic research

1:01:13

and able to pull yourself out of it.

1:01:16

So I would say it's a little

1:01:18

bit of yes, a little bit of no. Um in

1:01:21

the yes category is that certainly,

1:01:24

after the financial crisis, the global financial crisis,

1:01:27

there were a lot of client questions about

1:01:29

the role of fixed income in a portfolio

1:01:33

because if you're used to headier times

1:01:35

when interest rates were higher, you

1:01:38

might have a different perspective on how to use that

1:01:40

strategy than when you know interest rates

1:01:42

are low. And so that has informed

1:01:45

Okay, what are the things that our clients are caring

1:01:47

about and what is

1:01:49

it that we need to deliver to clients given

1:01:52

that those are the concerns and

1:01:54

these are the problems that they're trying to solve in a low

1:01:57

interest rate environment. So that's a little bit of yes

1:01:59

because it's been on client's minds. The

1:02:01

little bit of no is that we've

1:02:03

had We have decades upon

1:02:05

decades, fifty sixty years and

1:02:08

longer of data on

1:02:10

the returns of bonds, both

1:02:12

here in the US of corporates and of

1:02:15

other bonds around the world issued

1:02:17

in different currencies, and so we can

1:02:19

look at lots of different high interest

1:02:21

rate low interest rate environments, transitions

1:02:24

between those when the interest rates

1:02:27

were had gone up or gone down, and

1:02:29

so we can understand are there certain

1:02:31

strategies that work better or worse than each of those environments,

1:02:34

and we can then we can design strategies that work well

1:02:36

for both environments. So that long term view

1:02:39

is something that we always keep in mind, which means

1:02:41

that you know something that happens over a decade or fifteen

1:02:44

years. It does give us new information,

1:02:46

but doesn't necessarily change dramatically

1:02:48

our investment priors. Really

1:02:51

really interesting. Before I get

1:02:53

to my favorite questions, I just have to throw

1:02:55

a curveball at you. So, in

1:02:59

your bachelor's in

1:03:02

theoretical physics from Trinity College,

1:03:05

what were you studying in theoretical physics?

1:03:07

What areas did you concentrate in

1:03:10

because I'm familiar with that space

1:03:12

and find it absolutely fascinating.

1:03:15

Yeah, it is really a very very interesting

1:03:17

space. And you know, when I

1:03:19

have was a it was a kid, I

1:03:21

like to read Stephen Hawkings and those

1:03:24

types of books. So I

1:03:26

was very interested in relativity

1:03:29

and so kind of that that side

1:03:31

of what Einstein worked on, and

1:03:34

I found that very interesting. We had a lot of we

1:03:36

have courses on relativity when

1:03:38

we were in in university

1:03:41

in theoretical physics. The other side is quantum

1:03:44

mechanics. And quantum mechanics is very

1:03:46

very interesting because you never

1:03:48

know anything with certainty, so it kind of has

1:03:50

parallels to to the real

1:03:53

world. You can't know something's position

1:03:55

and its speed at the same time,

1:03:58

you can only know one perfectly,

1:04:01

or you can know both in a with

1:04:03

a lot of uncertainty. But quantum

1:04:06

mechanics is also incredibly interesting

1:04:08

because everything has multiple

1:04:10

states of the world is and as in those multiple

1:04:12

states all the time with some set

1:04:14

of probability. So that's also a very fascinating

1:04:17

field of study.

1:04:19

And I enjoyed those quite a lot

1:04:21

when when I was working on them back

1:04:24

in Trinity College in Dublin. So so

1:04:26

if you're a fan of um Hawk

1:04:28

Professor Hawkings and some of his

1:04:31

work. Can we all admit that

1:04:33

dark matter and dark energy is a cheat

1:04:35

and we really have no idea what's going on with

1:04:39

the expansion in the universe, because every

1:04:41

explanation I've heard from various theoretical

1:04:44

physicists have been, well, we're not sure,

1:04:46

but we've made up this thing that we

1:04:48

hope to figure out one day. It

1:04:51

seems like it seems like it's um

1:04:54

you know, a short cut.

1:04:57

You know it may be a short cut. But I'd go back

1:04:59

to your earlier statement

1:05:01

was, which is around how

1:05:04

our models evolve over time, our

1:05:06

data evolves over time. Like you saw from

1:05:09

a couple of weeks ago there was a new discovery

1:05:11

from the Hubble Telescope

1:05:13

of the oldest star yet which

1:05:16

is older than the universe, which is which

1:05:19

seems to be a little confused, a little confusing,

1:05:22

and so new data emergence

1:05:24

all the time, and then you create models to try and

1:05:27

understand those data, but you

1:05:29

know it's not what understood yet are

1:05:31

not I would say it's what understood not completely

1:05:33

understood, and there's a lot left that's

1:05:37

not known yet for people to Discover fair

1:05:40

enough. So so let's jump to a little

1:05:42

less heavy material and talk

1:05:44

about our favorite questions, starting

1:05:47

with tell us, what you've been

1:05:49

streaming during the past couple of years

1:05:51

of Lockdown and Pandemic, either

1:05:54

podcast or Amazon and Netflix.

1:05:57

What's been keeping you entertained? Yeah,

1:06:00

couple of different shows have been keeping me entertained.

1:06:02

So it was in a board meeting, one of the

1:06:04

Advisor board meetings, and one of the board members

1:06:06

mc McCown said that he had been watching

1:06:09

a documentary series called The Prize, and The

1:06:11

Prize is from a while ago. It's it's about

1:06:14

the kind of the history of oil and

1:06:16

you know how it started and where it evolved

1:06:19

two and all the various different issues

1:06:22

that have arisen as a result. So that was super interesting

1:06:25

and I'd recommend that to anybody who's

1:06:27

kind of interested in those types

1:06:30

of historical shows. Other

1:06:33

things that I find interesting

1:06:35

over the past few years, I've watched a lot of documentaries

1:06:38

about you know, World War two, World

1:06:40

War One, Vietnam War. Ken

1:06:43

Burns has some great stuff even on the US

1:06:45

Civil War that have been very

1:06:48

interesting. The Fog of War

1:06:50

that that was another interesting show.

1:06:53

I find those particularly interesting,

1:06:55

just how do you ever get there? Because

1:06:57

war is in a rational act, so what what

1:07:00

are the things that have to happen

1:07:02

in order to get there? Because it's much more rational

1:07:04

to cooperate and to trade than it

1:07:07

is to go to Everybody will be better off in the former

1:07:09

and worse off in the latter, So how do you actually

1:07:11

get to that state of the world? Is interesting.

1:07:14

I have a six year old daughter and so

1:07:16

we watch shows together and

1:07:19

that also keeps me entertained. She loves If

1:07:22

I were an Animal. I don't know if you've seen that show

1:07:24

on Netflix, but that's a goody.

1:07:27

And then another one that came out recently a Netflix

1:07:29

is Old Enough. I don't know if you've seen this as a Japanese

1:07:32

show, and they have like

1:07:34

little three year olds, four year olds, five year olds,

1:07:37

and their parents give them a task to do and

1:07:39

then they have to go off into the round town into

1:07:41

the shop and they're followed by a camera coup by themselves,

1:07:44

and they accomplished this task. It's hilarious.

1:07:46

It's it's really really fun to old

1:07:49

Enough to check. That's a fun one.

1:07:51

Let's talk about some of your mentors,

1:07:53

who were some of the folks who helped shape

1:07:56

your career. Yeah, I would say that in

1:07:58

terms of folks that have my career. Some

1:08:01

of the names that you mentioned, whether it's a

1:08:03

Fama, French, Martin, have all been

1:08:06

very helpful to me over time.

1:08:09

David of course, has been very

1:08:11

very helpful to me over time. EDWARDO

1:08:14

used to work at Dimensional, has been very helpful

1:08:16

to me over time. And then

1:08:18

I'd be remiss if I didn't say my parents, because

1:08:21

there you know, up until the time that

1:08:23

you leave the home, and they're

1:08:26

your ultimate mentors in terms of shaping

1:08:29

how you approach problems, how you view

1:08:31

the world, what you prioritize.

1:08:34

My parents have always emphasized education

1:08:38

and the importance of keeping

1:08:40

your mind active and trying to better yourself. How

1:08:43

do you become better than your were the day before?

1:08:46

And that's a spirit that I think it's

1:08:48

important for anybody to keep kind

1:08:50

of pulling towards for as

1:08:53

long as they're on this planet, because what else

1:08:55

is there to do but try to improve your

1:08:57

skills and and how you interact

1:09:00

with the world. So let's talk about books.

1:09:02

This is everybody's favorite questions. What are

1:09:04

you reading right now and what are some of

1:09:07

your favorites. You know, I

1:09:09

am not reading any book right

1:09:11

now. I've been consumed

1:09:14

with work over the past few years

1:09:16

and by reading for pleasure

1:09:19

has taken a back seat, unfortunately.

1:09:21

But some of my favorite books over time, I

1:09:24

would say one Freedom

1:09:26

to Choose. I don't know if you've read that book by

1:09:29

Milton Freeman. I think is a great book

1:09:32

and timeless, I mean written many

1:09:34

decades ago, but but very

1:09:37

very timeless. They wro't deserved

1:09:39

them. I think is one of the all time classics

1:09:41

as well by you know That's

1:09:44

uh is an all time

1:09:46

classic. So you're going to get my idea from

1:09:49

I like books about markets, about

1:09:52

how to organize people and

1:09:55

how do you get to a state

1:09:57

of affairs where you're making the most efficient

1:09:59

use of the resources, where

1:10:02

people have freedom to pursue

1:10:04

what interests them. I find

1:10:06

that an interesting area of reading. What

1:10:08

sort of advice would you give to a recent

1:10:11

college grad or someone who was interested

1:10:14

in a career in investing

1:10:16

and finance? So two

1:10:18

big areas one and this is

1:10:21

something that is kind of I call it a dimensional

1:10:23

motto, and it's do

1:10:25

the right thing, do it the right way, and do it right

1:10:27

now. And so when

1:10:31

you're pursuing a career in any field, you

1:10:33

want to feel good about what you're doing. You want to feel

1:10:36

that you're helping people. Do

1:10:38

you want to do well while you're helping people.

1:10:41

But that's the right thing, And

1:10:43

then do it the right way is how do you

1:10:46

come with a path

1:10:49

to make a decision that uses

1:10:51

as much of the information that's available to you.

1:10:54

There's gonna be a lot of noise in the outcome, but you want

1:10:56

to be proud of the decision that you made given the information

1:10:58

that you had at the time. I think that's doing

1:11:00

things in the right way. And then do it right now.

1:11:03

Never sit on your hands, be

1:11:05

proactive, get after it, close

1:11:07

projects. If you can't close it, move on,

1:11:10

ask for help, and don't sit

1:11:12

on your hands, go out and get it done.

1:11:15

Then, when it comes to finance in particular, remember

1:11:18

what you're doing. You're taking people's life

1:11:20

savings and you're trying

1:11:23

to help them achieve objectives

1:11:25

and goals, and they're taking risk to achieve these oblection

1:11:27

and goals that they couldn't achieve without taking those

1:11:29

risks. And that's a very,

1:11:32

very meaningful responsibility. So

1:11:34

don't take it lightly. And you're

1:11:37

moving into a field that you can really

1:11:39

help people have a better life, but

1:11:42

you can also harm people

1:11:44

if you do things in the wrong way. So

1:11:47

I think that that's a something that you've

1:11:49

got to keep in mind when

1:11:51

it comes to finance. It's not your money,

1:11:53

it's somebody else's money. Be fiduciary,

1:11:56

be prudent, and then you can

1:11:58

really help people. Be it off

1:12:00

really interesting answer and our final

1:12:03

question, what do you know about the world

1:12:05

of investing today? You wish

1:12:07

you knew about twenty years ago or

1:12:10

so when you were first getting started. When

1:12:12

I was first getting started, I had this view

1:12:15

of the world because I had never taken a course in finance

1:12:17

before a dimensional which

1:12:19

and I didn't understand markets that well. I

1:12:22

had the view of the world that all

1:12:24

you had to come was with was a better

1:12:26

mathematical model than anybody

1:12:28

else out there, and then that would be able to

1:12:31

predict where prices were going to go. And

1:12:33

of course I was quickly disabused of that notion after

1:12:37

having conversations with Ken and Geene

1:12:39

and Bob, and you

1:12:41

just need a better model, And so

1:12:44

I wish I had known that then, but

1:12:46

now I certainly know it, and it's

1:12:48

really helped shape how I view

1:12:51

what good investment solutions are for

1:12:53

clients and what really the power

1:12:55

of markets are and can be really

1:12:58

really interesting uh stuff.

1:13:01

We have been speaking with Gerardo Riley.

1:13:04

He is the c i O and

1:13:06

co CEO of Dimensional Funds.

1:13:09

If you enjoy this conversation, well,

1:13:11

be sure and check out any of our four

1:13:13

hundred or so previous interviews. You

1:13:16

can find those at iTunes or Spotify

1:13:19

or wherever you get your podcasts.

1:13:21

We love your comments, feedback and suggestions

1:13:24

right to us at m IB podcast

1:13:26

at Bloomberg dot net. You can

1:13:28

sign up from my Daily reads at

1:13:31

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1:13:33

ritlts. I would be remiss

1:13:35

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1:13:37

put these conversations together each

1:13:40

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1:13:50

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1:13:52

Ritalts. You've been listening to Masters

1:13:55

in Business on Bloomberg Radio.