Speaker 1: This is mesters in Business with Very Results on Bloomberg Radio.
Speaker 1: This week on the podcast, I have an extra special guest.
Speaker 1: Gerard O'Riley is a double threat. He is the chief
Speaker 1: investment officer as well as the co CEO of Dimensional
Speaker 1: Funds UH. They are a factor giant, managing about six
Speaker 1: and fifty billion dollars and total assets. This is really
Speaker 1: a master class in how to think about investing, how
Speaker 1: to be systematic, how to approach it from a evidence
Speaker 1: based scientific basis, how to incorporate the best of academic
Speaker 1: research into your process. One of the things that I
Speaker 1: found really interesting was the d F A focus on costs,
Speaker 1: convenience and customization. Not every giant investment firm UH takes
Speaker 1: that approach. Really, I've interviewed a number of folks from
Speaker 1: d F a UM, from David Booth to Gene Parma
Speaker 1: and throughout the rest of the organization. I think you
Speaker 1: will find this to be absolutely fascinating and and really informative. So,
Speaker 1: with no further ado, my conversation with d F as
Speaker 1: Gerard O'Reilly, this is mesters in Business with Very Redults
Speaker 1: on Bloomberg Radio. My extra special guest this week is
Speaker 1: Gerard O'Reilly. He is the Chief Investment Officer and co
Speaker 1: CEO of Dimensional Fund Advisors, a leader and factor based
Speaker 1: investing for the past forty years. D f A has
Speaker 1: about employees across thirteen offices globally and full disclosure my
Speaker 1: firm Results. Wealth Management is a client of Dimensional Funds
Speaker 1: and we manage a substantial chunk of our assets with
Speaker 1: their products. They manage six hundred and fifty billion dollars
Speaker 1: in assets, about eight percent of that is equity. Girard O'Reilly,
Speaker 1: Welcome to Bloomberg. Thank you, Barry, and thanks for the invitation.
Speaker 1: I've been looking forward to speaking with you for some time,
Speaker 1: so so have I. You have such an interesting background.
Speaker 1: I was really excited to talk to you, especially given
Speaker 1: you have a PhD in aeronautics from the California Institute
Speaker 1: of Technology. What what were your original career plans. Well,
Speaker 1: I've always liked mathematics, and as an undergrad in Ireland
Speaker 1: studied mathematics and physics and so on extensively. I was
Speaker 1: thinking about what to do next and said, well, cal
Speaker 1: Tech does a lot of great stuff in fluid mechanics
Speaker 1: and particular in aeronautics and So I didn't have a
Speaker 1: specific set of career plans. I just know that's the
Speaker 1: subject that I wanted to study and that I enjoyed.
Speaker 1: So I set off or cal Tech and and I
Speaker 1: really enjoyed my time. They're working on various different projects,
Speaker 1: many theoretical in nature, but very mathematically oriented. So when
Speaker 1: you're looking at aeronautics in the United States, there aren't
Speaker 1: a whole lot of career paths out of that other
Speaker 1: than academia or going to Nassau or one of the
Speaker 1: defense um companies. What led the shift from aeronautics to
Speaker 1: finance as a career, Well, I wanted to learn some
Speaker 1: more about finance. Number one. I hadn't taken a finance
Speaker 1: course ever in my life before joining Dimensional, and Dimensional
Speaker 1: was the firm that I joined straight out of college. Also,
Speaker 1: you know, the academic path wasn't one that appealed to me.
Speaker 1: I really enjoyed grad school, but I preferred to tackle
Speaker 1: something that was I would say more than here and now,
Speaker 1: where your projects that you're working on have impact very
Speaker 1: quickly on you know, the end customer, the end consumer,
Speaker 1: and then also where on the engineering side. You know,
Speaker 1: you mentioned in the U S well, I'm not a
Speaker 1: US citizens, so it's hard for non US citizen to
Speaker 1: work in the aeronautics field here in the US because
Speaker 1: largely required security clearance. So it's looking around. A friend
Speaker 1: of mine was working for Dimensional, knew that person at Caltech,
Speaker 1: and that person was talking about you know, Dimensional has
Speaker 1: all these great academic connections. They really take finance from
Speaker 1: a scientific perspective. Went down, checked it out and said,
Speaker 1: this sounds interesting. I really want to give this a
Speaker 1: shot for a period of time. So so let's talk
Speaker 1: a little bit about those academic um connections. Ken French
Speaker 1: has been at Dartmouth for a long time. His colleague
Speaker 1: Gene Farmer, Nobel Prize winner at University of Chicago. Another
Speaker 1: Nobel Prize winner, Robert Merton, also at Dimensional funds. What's
Speaker 1: it like working with all these Nobel Prize winning economists
Speaker 1: could be a little intimidating to some folks. It's always
Speaker 1: intimidating when you start off working with somebody who's very,
Speaker 1: very talented and you're getting to know them for the
Speaker 1: first time. But it's a privilege and it's great fun
Speaker 1: because those folks, you know, have have worked incredibly hard
Speaker 1: to hone their craft, hone their skills, and when you
Speaker 1: think about Ken or Gene or Bob or Meer and
Speaker 1: any of those folks, they're very, very generous with their
Speaker 1: time and so they're willing to teach because they're in academia,
Speaker 1: and if you're willing to work hard, they're willing to
Speaker 1: put the time and effort into you. So I started
Speaker 1: off with no background in finance and got to learn
Speaker 1: finance from some of the most amazing minds in the field.
Speaker 1: So it was just it was great Ken, Jean and Bob.
Speaker 1: I've never heard of those three gentlemen referred to quite
Speaker 1: in that way, but I guess when you work with
Speaker 1: them as frequently as you do UM, it becomes Ken,
Speaker 1: Jean and Bob. So, so what are the parallels between
Speaker 1: academia and UM working in finance professionally? And then I
Speaker 1: have to ask what are the parallels between aeronautics and
Speaker 1: fluid dynamics and finance and investment. Well, working in academia,
Speaker 1: you know, you're always trying to solve a problem. You're
Speaker 1: looking for interesting problems to solve that haven't really been
Speaker 1: tackled before, or an aspect that you're working on hasn't
Speaker 1: been tackled before, and you're saying, well, can I bring
Speaker 1: something new to the table, something innovative, and that's incredibly
Speaker 1: rewarding and incredibly interesting. Working in finance is no different.
Speaker 1: You're looking for new problems to solve. Those problems are
Speaker 1: largely driven by what it is your clients are looking for,
Speaker 1: what types of investment solutions do they require to solve
Speaker 1: the investment problems that they have, and then you're coming
Speaker 1: up with innovative ways to solve those problems. So in
Speaker 1: that respect, there's a lot of similarities. The time scale
Speaker 1: and the time frames are a little bit tighter and
Speaker 1: faster when it comes to finance than in academia. In
Speaker 1: academia it may be multi years, and there's multi year
Speaker 1: projects that happen in finance, but you want to be
Speaker 1: able to live or something for your clients in shorter
Speaker 1: time frames than that. When you think about engineering or
Speaker 1: mathematics or physics, and then how does how do those
Speaker 1: skill sets translate over to finance. Well, again, it's all
Speaker 1: about problem solving and what you're looking for is how
Speaker 1: do I number one pose the question correctly? How do
Speaker 1: I ask the right question? Because that's as important as
Speaker 1: trying to solve the problem, you have to set it
Speaker 1: up in the right way. And that's true whether it's
Speaker 1: in mathematics or physics, or engineering, or it's in finance.
Speaker 1: Then how do I gather data to help me address
Speaker 1: and find the answer to this question? And that's true
Speaker 1: of both fields. And then how do I interpret the data?
Speaker 1: What are the tools and the models that I can
Speaker 1: use such that I'm going to be able to organize
Speaker 1: these data in such a way to draw inferences about
Speaker 1: how I want to act going forward. And that's true
Speaker 1: of both, Madam, physics, engineering, and finance. I think the
Speaker 1: big differences are the laws of physics tend not to
Speaker 1: change over time, but the laws of that government finance
Speaker 1: can change through time. There are many repeatable experiments in physics,
Speaker 1: there are no repeatable experiments in finance. But there is
Speaker 1: a kind of a common truth in both, which is
Speaker 1: that in finance and investing, people demand return for bearing uncertainty.
Speaker 1: That doesn't change through time, but how you go about
Speaker 1: implementing that can change through time because the laws are changing.
Speaker 1: On behalf of Isaac Newton, I'm going to raise an
Speaker 1: objection that at least our understanding of the laws of
Speaker 1: physics have changed over time. So so maybe the underlying
Speaker 1: laws themselves are the same, but our perception seems to
Speaker 1: have evolved. I think that's that's a good way to
Speaker 1: put it, in a nice precise way to put it,
Speaker 1: is that the underlying drivers don't change, but our perception changes.
Speaker 1: And that's it's an interesting observation because their perceptions change
Speaker 1: because the models that we use to explain and understand
Speaker 1: those underlying drivers evolve over time. All models are incomplete,
Speaker 1: none of them are true, none of them are perfect
Speaker 1: descriptions of reality. And that's true of physics and it's
Speaker 1: true of finance. But you can improve those models over time.
Speaker 1: You can improve the data that you can collect over time,
Speaker 1: and that enhances your understanding over time. I'm a big
Speaker 1: fan of George Box. I love the quote all models
Speaker 1: are wrong, but some are useful, and it sounds like
Speaker 1: you very much embrace that philosophy as well. And it's
Speaker 1: an important philosophy to embrace when you're, you know, working
Speaker 1: in the field of finance, because ultimately, what you're doing
Speaker 1: is you're investing money on behalf of others. It's their
Speaker 1: life savings. Often so it's what they've made sacrifice to
Speaker 1: put together so they can afford a better retirement or
Speaker 1: something that's important to them in their future. And if
Speaker 1: you ever believe that the model is reality, you're probably
Speaker 1: going to build non robust solutions and do them a disservice.
Speaker 1: So having a healthy skepticism around all models and basically
Speaker 1: all data sources that you see is important because it
Speaker 1: leads you to, well, what if I'm wrong, do I
Speaker 1: still have a good in solution even if this model
Speaker 1: it turns out to be incorrect? And I think that's
Speaker 1: that's a good way of looking at the world. So
Speaker 1: let's talk a little bit about your career. You began
Speaker 1: at d f A in two thousand four in the
Speaker 1: research department. A little more than a decade later your
Speaker 1: chief investment officer, and not that many years after that
Speaker 1: you become co chief executive officer. That's a pretty rapid
Speaker 1: career path. Explain to us, if you would, the concept
Speaker 1: of co CEO S or co c i O S
Speaker 1: and how you managed to um advance so rapidly in
Speaker 1: a firm that was led by David Booth for so
Speaker 1: many decades. Yeah, So let me let me start with
Speaker 1: the ladder. How how how do you advance? And my
Speaker 1: viewpoint on success is there's a combination of three things,
Speaker 1: and I'm not sure which one is most important, but
Speaker 1: they probably all are equally important at different stages. One
Speaker 1: is a little bit of luck, a little bit of
Speaker 1: luck in the things that you've learned up to that
Speaker 1: point in time when the opportunity comes, a little bit
Speaker 1: of luck. For example, finding Dimensional was well suited to
Speaker 1: the way that I thought about the world. And then
Speaker 1: there's some talent. Do you have the right skill set
Speaker 1: that will be helpful in that particular organization? And it
Speaker 1: turns out that a quantitative and analytical type skill set
Speaker 1: was very helpful for an organization like Dimensional in our clients.
Speaker 1: And then hard work. Are you willing to do whatever
Speaker 1: it takes to complete projects to move the ball forward
Speaker 1: to help your clients succeed. And when you have all
Speaker 1: three of those, I think good things can happen. And
Speaker 1: was fortunate that had a little bit of each one
Speaker 1: of those when I came to Dimensional, and Dimensional has
Speaker 1: been a growing firm for money money decades and when
Speaker 1: I came in two thousand or four, we had about
Speaker 1: fifty billion under management and you know, that grew rapidly,
Speaker 1: so there was a lot of opportunities for those folks
Speaker 1: that were willing willing to step up. And so I
Speaker 1: consider myself fortunate and very happy by how that's turned out,
Speaker 1: because I've had a blast doing it and it's been
Speaker 1: it's been rewarding. And then in terms of the co
Speaker 1: c i O s and co CEOs, we do a
Speaker 1: lot of CODs. We have cos of different department heads.
Speaker 1: From my particular case, Dave Butler is the other co CEO.
Speaker 1: And it tends to work well when you have people
Speaker 1: who number one get along well with each other, they
Speaker 1: respect each other and each other's ideas, and then they
Speaker 1: have maybe complementary skill sets. And so the way that
Speaker 1: Dave and I have worked in that job together I
Speaker 1: think has been much more all my preference I would have.
Speaker 1: I'm much preferred to have done it with him then
Speaker 1: without them, because you can do some dividing and conquering.
Speaker 1: But also what I find is that as you get promotions,
Speaker 1: and this is a little bit facetious, but you tend
Speaker 1: to become, at least if you judge it by the
Speaker 1: input that you get from your peers, smarter and funnier
Speaker 1: in that the input that you get from your peers
Speaker 1: becomes less informationally rich. But when you have a troop
Speaker 1: heer like Dave and I are are troop peers, anything goes.
Speaker 1: We can have robust, open, honest conversations and with David
Speaker 1: as well, which read us a lot of pressure test
Speaker 1: things before we have to go and talk about them
Speaker 1: with the rest of the firm. And that really, you know,
Speaker 1: I always think iron sharpens iron, that you have to
Speaker 1: have people who you can, you know, spar with on
Speaker 1: a daily basis test your ideas. They'll push you, you
Speaker 1: will push them so that you can improve every day.
Speaker 1: So it's it's worked very very well. We do a
Speaker 1: divide and conquered. We've thirteen global departments at dimensional. Four
Speaker 1: comes straight to me, four go straight to him, and
Speaker 1: then the five in the middle kind of go to
Speaker 1: both of us either through the CEO we have a
Speaker 1: CEO Lisa Dalmer, are directly like legal and compliance come
Speaker 1: to both of us directly, and that way that it's
Speaker 1: it's just worked well. We've been very pleased with what
Speaker 1: we've been able to accomplish over the past five years
Speaker 1: working together. It's I guess you each keep each other
Speaker 1: sharp and keep each other honest. That's right, really interesting.
Speaker 1: So so let's talk about factors a little bit um.
Speaker 1: How did the academic research that that Rex and David,
Speaker 1: the two co founders of d f A, How did
Speaker 1: that become part of the investment process. So I guess
Speaker 1: there's a couple of salient points there. One is factor
Speaker 1: research in itself, and we talked a little bit earlier
Speaker 1: on about models and what they're useful for and how
Speaker 1: you draw inferences from them. I really look on factor
Speaker 1: models as way to organize historical data so you can
Speaker 1: try to understand better what really drove differences and returns
Speaker 1: across different groups of securities, different groups of stocks, different
Speaker 1: group of bonds, and from those you can glean very
Speaker 1: important insights about the drivers of expected returns, the drivers
Speaker 1: of differences and risk across different asset categories. And so
Speaker 1: I think that's the important aspect of factor models. So
Speaker 1: when you put them dimensional and its founding in context
Speaker 1: of kind of a burgeoning field in the eighties and
Speaker 1: in the nineties, when more and more factor models were
Speaker 1: being developed and tested and so on. The founding was
Speaker 1: two I would say, address an institutional need that David
Speaker 1: had identified, which was there weren't many systematic strategies that
Speaker 1: targeted the returns of small cap stocks, and he found
Speaker 1: that that that was a hole in many institutional investor portfolios.
Speaker 1: And along the around the same time, because David had
Speaker 1: done his MBA at the University Chicago now Both School
Speaker 1: of Business, around that same time, there was evidence coming
Speaker 1: out that smaller cap stocks also had higher average returns
Speaker 1: historically and reasons you know, promoted about why that would
Speaker 1: be higher expected return is going forward, and so around
Speaker 1: that time was kind of when those factor models were developing.
Speaker 1: So I started with the client need, and then it
Speaker 1: was well, let me go to the academics and understand,
Speaker 1: what are the research around this client need. Am I
Speaker 1: going to do something here that makes sense or not
Speaker 1: makes sense from an academic perspective? And then how do
Speaker 1: I build a good robust solution to address that client need.
Speaker 1: And then, of course, in the nineties you had the
Speaker 1: three factor model come along, and then in the mid
Speaker 1: nineties you had momentum come along, and in the two
Speaker 1: thousands you had things like profitability and investment come along.
Speaker 1: So we had lots of different factors uncovered over time.
Speaker 1: But the way that we look on each one of
Speaker 1: those is their models. They give us insights from the data.
Speaker 1: How do you use that to build robust portfolios? And
Speaker 1: I would say that's been kind of part of our
Speaker 1: heritage for forty years. How do we build portfolios that
Speaker 1: can target these premiums but be robust regardless of the
Speaker 1: market environment. And we've been through many different market crises
Speaker 1: with a broad range of investment strategies that have come
Speaker 1: out quite well the other side. So we're we're pretty
Speaker 1: familiar in modern times with small cap indices like the
Speaker 1: Russell two thousand, or the S and P six hundred
Speaker 1: or whatever it happens to be. But when Sinkfeld and
Speaker 1: Booth were forming um D f A in the early eighties,
Speaker 1: these weren't really household names, if they even existed at all.
Speaker 1: It's amazing to think that there was a period where
Speaker 1: small caps weren't their own category. Tell us a little
Speaker 1: bit about how that evolved. Yeah, if you go back
Speaker 1: even further, so dimension was found in than eighty one,
Speaker 1: But if you go back a decade earlier, and I'll
Speaker 1: focus on David a little bit and his work with
Speaker 1: mc McCown, who was at Wells Fargo at the time,
Speaker 1: and he's a director of the firm, and so David
Speaker 1: and Mac were working on indexes. So in the very
Speaker 1: early seventies, the Max team with David created the first
Speaker 1: index fund. It wasn't for retail, it was for an
Speaker 1: institutional client, and it was based on US large cap stocks,
Speaker 1: So he's very familiar with index based approaches. Then David
Speaker 1: subsequently left and worked a gibecker for a while, understood
Speaker 1: more about what clients were interested in looking for required
Speaker 1: and so there wasn't a Russell two thousand available when
Speaker 1: he was building the firm, so there wasn't an index
Speaker 1: to attach the strategy to. The Other thing that was
Speaker 1: kind of feedback from academia is yes, small cap investing
Speaker 1: makes sense, but you're going to get killed on trading costs.
Speaker 1: And so then you have this kind of environment where
Speaker 1: there wasn't an index, it wasn't a household name. To
Speaker 1: your point, you know small cap stocks as an asset category,
Speaker 1: so you kind of have a blank canvas. If I know,
Speaker 1: knowing everything that I know, what's the right way to
Speaker 1: build a small cap strategy that hopefully then will be
Speaker 1: efficient and won't suffer too greatly from trading costs and
Speaker 1: implementing and investing client flows. So I think that it
Speaker 1: was in some respects a very big advantage starting with
Speaker 1: that blank canvas of how do you design the best portfolio,
Speaker 1: you know how, with as few constraints as possible, because
Speaker 1: you weren't worried about an index. And then subsequently Russell
Speaker 1: had the Russell two thousand, and then of course in
Speaker 1: the nineties, value versus growth became, you know, well established
Speaker 1: asset categories, and so asset categories have been added over time.
Speaker 1: So so let's talk a little bit about Gene Parma
Speaker 1: and Ken French is what started out as a three
Speaker 1: factor model, it eventually became five and seven. Now they're
Speaker 1: a hundred of factors, many of which um don't really
Speaker 1: add a whole lot of alpha or not consistent enough
Speaker 1: alpha to justify their complications and costs. Tell us a
Speaker 1: little bit about the Farmer French factor model. Yeah, so
Speaker 1: you know, when you when you go to the eighties,
Speaker 1: there was a lot of empirical evidence being uncovered that
Speaker 1: the prevailing model from the sixties and the seventies, the
Speaker 1: capital asset pricing model, didn't explain the data very well,
Speaker 1: so when you look at it, it was it was
Speaker 1: a beautiful model. It was very you know, intuitive, but
Speaker 1: it didn't explain the data all that well. And so
Speaker 1: Ken and Gene in the early nineties started to organize
Speaker 1: all the data to say, can we put some of
Speaker 1: these observations in one kind of unified viewpoint of the
Speaker 1: historical data. And from that, you know, exercise came a
Speaker 1: better model in the sense that it could explain the
Speaker 1: returns that you saw among stocks are better than the
Speaker 1: capital as sur pricing models, so explain more of the returns,
Speaker 1: more of the variation that you saw on the returns
Speaker 1: across stocks, and so that so subsequently came the three
Speaker 1: factor model. Then to your point, lots of factors have
Speaker 1: been added. If you look at family frenches are even
Speaker 1: Ken's website, now you'll see a profitability factor, you'll see
Speaker 1: an investment factor, you'll see momentum factors. You'll see all
Speaker 1: different types of factors. And as I mentioned earlier, factors
Speaker 1: are really great to help you organize the historical data.
Speaker 1: But you don't want to get kind of two stereoid
Speaker 1: about the latest factor model. I kind of view a
Speaker 1: lot of the academic research over the past thirty years
Speaker 1: as doing variance on a theme, and so it's not
Speaker 1: that kind of a have brand new discovery, but it
Speaker 1: refines your understanding of existing factors. So there's probably twenty
Speaker 1: or thirty or forty different value factors out there, but
Speaker 1: you don't need all twenty or thirty or forty when
Speaker 1: you're managing a strategy. But you can get insights from
Speaker 1: the different factors on how to manage a strategy effectively.
Speaker 1: And so what I mean by that is if you
Speaker 1: if if you think about what datas are are available.
Speaker 1: You have security prices, you have data from income statements,
Speaker 1: so things like income or profits or revenues or expenses,
Speaker 1: and you have data from balance sheets, assets and liabilities.
Speaker 1: They're the broadly the data that are available to go test.
Speaker 1: And when you look at all of those factor models
Speaker 1: their variants on the theme, the right are looking at
Speaker 1: current values of those variables, whether it's current income or
Speaker 1: current price to book ratios or price earnings ratios, are
Speaker 1: they're looking at how they've changed, How to have prices
Speaker 1: changed over the past number of months, How have assets
Speaker 1: grown over the past number of months, How is profitability
Speaker 1: changed over the past number of months. So there's three
Speaker 1: data sources and people do two things with them, so
Speaker 1: there's actually really kind of six that you can think
Speaker 1: about that kind of encompass most of the hundreds of
Speaker 1: factors that you see out there. And I think that
Speaker 1: if you have coverage of those six current prices, current
Speaker 1: balance sheet items, current income statement items, and then how
Speaker 1: each one of those have changed in recent past, you
Speaker 1: have pretty broad coverage of all the various different factor
Speaker 1: literature that's available. And that's what we do at Dimensional.
Speaker 1: So so let's for the lay person get a little
Speaker 1: more granular with some of the more popular and effective
Speaker 1: factors um. The four biggest ones I think are size, value, quality,
Speaker 1: and momentum. Is there anything you would add to that
Speaker 1: beyond beta which is just a given? So there's five?
Speaker 1: What else would you add to that list? I would
Speaker 1: add probably investment and proxy for investment is how a
Speaker 1: firm is growing their assets over time. And when you
Speaker 1: think about all of the ones that you just listed, Barry,
Speaker 1: all of them are momentum, have something in common, and
Speaker 1: what's that that they have in common? They're basically picking
Speaker 1: up differences and discount rates that the market has applied
Speaker 1: to different investment opportunities. So when you think about something
Speaker 1: like value, you you're taking price and you're dividing it
Speaker 1: by some company fundamental so some fundamental measure of firm size,
Speaker 1: and you're saying, why do you want to do that?
Speaker 1: Because you want to see who has low price today
Speaker 1: relative to who has high price today. So there's firms
Speaker 1: in the marketplace, some of them will trade at low prices,
Speaker 1: some of them will trade at high prices. You need
Speaker 1: to scale price, normalize price to be able to make
Speaker 1: that determination. When you say quality, quality often comes down
Speaker 1: to profitability. And what we know from the historical data
Speaker 1: is the firms that have the highest profits or the
Speaker 1: highest profitability, so profits divided by assets or profits divided
Speaker 1: by book value in the marketplace tend to continue to
Speaker 1: have that high profitability over the next year, two, three, four,
Speaker 1: or five years. But what do those profits lead to?
Speaker 1: Those profits lead to client cash flows or investor clash flows.
Speaker 1: I should say the higher the profits, the more cash
Speaker 1: flows investors can expect to get from their investments. So
Speaker 1: it's telling you something about expected cash flows from that
Speaker 1: investment in the future. Here I say investment because asset growth.
Speaker 1: Let's imagine a company has to retain a lot of earnings,
Speaker 1: or has to issue a lot of debt, or has
Speaker 1: to issue a lot of stock in order to drive
Speaker 1: those profits going forward. That leads fewer cash flows for investors.
Speaker 1: So that also tells you something about expected cash flows.
Speaker 1: So when you talk size, value, profitability, or quality and investment,
Speaker 1: they're all telling you something about expecting cash flows. Are
Speaker 1: the prices people are willing to pay. It's a discount
Speaker 1: rate effect. Momentum is the outlier. There's no equally simple,
Speaker 1: compelling story that lets you know why should you expect
Speaker 1: that firms that have outperformed the market in the past
Speaker 1: three the twelve months to continue to outperform the market
Speaker 1: in the next three the twelve months, and vice versa.
Speaker 1: But it's there, loud and clear in the historical data,
Speaker 1: and so the question we ask ourselves is how do
Speaker 1: we use that information with as low opportunity costs as
Speaker 1: possible because we don't know why it's there, so we
Speaker 1: don't know if it will be there in the future.
Speaker 1: But if it's not there in the future, we don't
Speaker 1: want to have incurred unnecessary costs on behalf of investors
Speaker 1: pursuing something that we don't know why it exists in
Speaker 1: the data to begin with. Really really interesting, when when
Speaker 1: I think of momentum, I have I tend to think
Speaker 1: of a persistency because either fund managers or investors have
Speaker 1: gone through the whole process of selecting that stock, and
Speaker 1: as long as it's working out, trending in the right
Speaker 1: direction at market um returns or better, there's no reason
Speaker 1: to remove it. So it becomes a little bit of
Speaker 1: a self fulfilling prophecy until there's a substantial enough misstep
Speaker 1: and then throw in all of the four oh one
Speaker 1: k regular contributions. If that if you're in fund X
Speaker 1: and it owns company A, B, S and C, and
Speaker 1: all three of those are doing well, money continues to
Speaker 1: flow to those funds automatically, and those funds tend to
Speaker 1: buy their top performers. It's almost like a virtuous cycle.
Speaker 1: You know, that's a possible explanation, and that it's certainly
Speaker 1: it's certainly a little bit of narrative fallacy and hindsight bias.
Speaker 1: To say the least, it's been tested. I mean, academics
Speaker 1: have looked at you know, overreaction, under reaction, and why
Speaker 1: is there continuation in returns. There's an interesting area of
Speaker 1: research going on right now, and Professor Novi Marx had
Speaker 1: one of the kind of first, well not one of
Speaker 1: the first, but I kind of I would say, an
Speaker 1: instrumental paper on on this recently that looks at profitability growth.
Speaker 1: So how have affirms profits grown are declined over the
Speaker 1: past three months to a year and does that explain
Speaker 1: the returns pattern that you see related to momentum? And
Speaker 1: that seems like a promising area of research if there
Speaker 1: is a lot of explanatory power in how affirms profits
Speaker 1: have changed or how their profitability has changed, and that
Speaker 1: has the power to predict future profitability i e. Firms
Speaker 1: that have grown their profits more quickly than other firms
Speaker 1: may continue to grow their profits more quickly than other firms.
Speaker 1: Then if that explains momentum, then you start to get
Speaker 1: momentum back into that field of differences in discount rates,
Speaker 1: and then that becomes a much more easy story to
Speaker 1: understand in the sense that firm characteristics are much more
Speaker 1: straightforward to predict than future private prices. Well run firms
Speaker 1: tend to remain well run firms for some period of time.
Speaker 1: But given that their well run firms when you think
Speaker 1: about the price, that's said in the stock market, that's
Speaker 1: the aggregive view of what expected return people require to
Speaker 1: hold that investment. So they already understand it's a well
Speaker 1: run firm, and so we think that it's priced fairly
Speaker 1: given all that information. So it may have information about
Speaker 1: how well run that firm has been over the past
Speaker 1: number of quarters, and that has predictive power on how
Speaker 1: well run that firm is expected to be over the
Speaker 1: next few quarters. So so let's get into the weeds
Speaker 1: a little bit. How can you distinguish between factor research
Speaker 1: that's significant and factor work that's either statistical noise or
Speaker 1: backwards looking form fitting, Because it seems like everybody has
Speaker 1: developed a new model of their own which looks great
Speaker 1: on paper. Um, the back tests are always wonderful, but
Speaker 1: then in reality it doesn't seem to work. So so
Speaker 1: how do you draw the line between hey, this really
Speaker 1: is substantial versus just a just a good backdest. Yeah,
Speaker 1: you hit on it perfectly. Very You're never going to
Speaker 1: see a bad back test, in particular from an asset manager. Well,
Speaker 1: because that's where they all go to diet. It's all
Speaker 1: survivorship by it's all survivorship by it. So it is
Speaker 1: a real challenge, and that's true even of the academic work,
Speaker 1: because in academia, how do you get tenure? You published papers.
Speaker 1: The types of papers that get published are those with
Speaker 1: startling empirical observation, and so the hundred experiments that were
Speaker 1: run that didn't lead to a startling empirical observation are
Speaker 1: never published and the one that did is published. So
Speaker 1: you have that bias when it comes to academic and
Speaker 1: practitioner work. The way that we think about it is
Speaker 1: kind of nuanced. First off, we start with the broader
Speaker 1: view of the academic literature, what's the latest and greatest
Speaker 1: out there in academia. Then at Dimensional, we've developed a
Speaker 1: lot of in house proprietary data sets that go back
Speaker 1: many many decades that include data with a level of tendiness,
Speaker 1: I would say, and precision that's probably kind of second
Speaker 1: to none and with respect to all the data sets
Speaker 1: available out there. And of course you know we're here
Speaker 1: at Bloomberg Studios who love data and we love data too.
Speaker 1: You guys um were involved in the early days of
Speaker 1: the CRISP data set. Let's talk a little bit about
Speaker 1: what an advantage it was having not only access to that,
Speaker 1: but the ability to really do a deep dive and
Speaker 1: manipulate that data. Tell us a little bit about Chris. Yeah,
Speaker 1: CRISP was started back in the sixties and it was
Speaker 1: basically an effort by University Chicago and folks there to
Speaker 1: gather all the stock price data and dividend data and
Speaker 1: corporate action data to say, can we were computer return
Speaker 1: on the U S stock market? Because pre nineteen sixties
Speaker 1: you couldn't get that with a great deal of precision.
Speaker 1: It's amazing, it really is amazing. And so so then
Speaker 1: over time you know you had CRISP, and then you
Speaker 1: had other supplements were company financials were added to the
Speaker 1: data set and all joined and linked up together so
Speaker 1: effectively you could test things well. And the way that
Speaker 1: we think about testing things well is number one, do
Speaker 1: you expect to see this in the data before you look?
Speaker 1: Why are you looking for this for this thing? And
Speaker 1: so that kind of juices some of them, the issues
Speaker 1: with biases and back tests. You expect it before you
Speaker 1: go see, and then you see the data tells you
Speaker 1: how strong it has been or hasn't been. Then you
Speaker 1: want to do a lot of robustness checks because robustness
Speaker 1: is the name of the game. So you've tested it
Speaker 1: in one data sample, can you test it in multiple
Speaker 1: data samples? Can you test it out of sample? So
Speaker 1: I'll give you I'll give you an example, and I
Speaker 1: think this experiment is kind of unique when it comes
Speaker 1: to academia. When you look at Famine French in their
Speaker 1: ninety two paper, they used US stock data from the
Speaker 1: sixties to the nineties and they tested value, premiums and
Speaker 1: leverage and all sorts of things in that paper over
Speaker 1: that data sample and produced the three factor model. Then
Speaker 1: they came up with a prescription or a kind of
Speaker 1: like almost a list of ingredients. Here's how you create
Speaker 1: a factor model. And that's been used by most academic
Speaker 1: since so the formula that they used has been used
Speaker 1: by most academics. Sins. So, then later on in the nineties,
Speaker 1: with Jim Davis who used to work at Dimensional, he
Speaker 1: gathered a whole bunch of pre nineteen sixties data, so
Speaker 1: he was able to extend the original family French analysis
Speaker 1: to completely out of sample test and that went from
Speaker 1: the twenties to the sixties. Then non US developed market
Speaker 1: data were collected and the same tests that Feminine French
Speaker 1: had round on. Their original sample was run on non
Speaker 1: US developed markets, and then it was run on emerging
Speaker 1: market data because that was collected. And now we're thirty
Speaker 1: years past the family French original experiment. So now we
Speaker 1: have another out of sample test. And so you have
Speaker 1: five out of sample tests, and in four of those
Speaker 1: five you see very very strong and reliable value premiums,
Speaker 1: and you can't actually tell the difference between any of
Speaker 1: those five about the magnitude statistically speaking, between the realization
Speaker 1: of those premiums. That's robustness. You've seen it in sample
Speaker 1: and you've seen it in many out of sample tests.
Speaker 1: That gives you high confidence that what you're observing in
Speaker 1: the data happened by more than just chance. It's something
Speaker 1: real and you should expect to see it going forward.
Speaker 1: But that's the type of rigorous analysis that we're able
Speaker 1: to apply to new observations because now we have so
Speaker 1: many different data sets that we can test the observation on,
Speaker 1: we can shape up the experiment, we can find out
Speaker 1: where the bodies are buried, how robust it is, and
Speaker 1: that gives us confidence in the in the patterns that
Speaker 1: were observing in the data, whether they're real or it's
Speaker 1: just noise, really really interesting stuff. So so let's talk
Speaker 1: a little bit about the growth of d f A
Speaker 1: and and your role there. Um, you're a bit younger
Speaker 1: than the typical member of your management team. How does
Speaker 1: that affect how you do your job? What do you
Speaker 1: bring to the table that some of the more senior
Speaker 1: managers might be missing. So I've never really thought about it,
Speaker 1: to be perfectly honest, And maybe that's in part because
Speaker 1: I've always been on the younger side, whether it was
Speaker 1: in high school relative to the rest of the folks
Speaker 1: in my class. I went to college when I was sixteen,
Speaker 1: and so it was a little younger than the other
Speaker 1: folks in my class. And then when I started working
Speaker 1: at Dimensional after doing a PhD, was younger than some
Speaker 1: of the other folks in the research team. So it's
Speaker 1: always been kind of the state of play. So I
Speaker 1: don't think about it too much. I would say, a
Speaker 1: Dimensional we have a very academic view of how to
Speaker 1: interact with each other. So interact with each other with respect,
Speaker 1: but challenge and argue the facts and the issues, and
Speaker 1: the best ideas win. And so I think that when
Speaker 1: it comes to how to interact with colleagues, whether they're
Speaker 1: younger or they're older. It's exactly under that formula. You
Speaker 1: have to operate with respect, listen to the ideas, and
Speaker 1: then the best idea wins. Our view is, don't defend
Speaker 1: the idea just because it's your idea. Embrace the best
Speaker 1: idea and the right idea because ultimately, long term, that's
Speaker 1: going to be better for the clients. And if you
Speaker 1: make it better for the clients, you're going to have
Speaker 1: a better business. So you know, when it comes to business,
Speaker 1: clients first, makes business very straightforward on how to make
Speaker 1: decisions and what decisions to make. And I think that
Speaker 1: at that atmosphere, I've always enjoyed a dimensional and so
Speaker 1: therefore age has never been, never been an important ingredient.
Speaker 1: So let me flip that question around and ask what
Speaker 1: advantages do you find when you're working with some older,
Speaker 1: more experienced folks. What if they bring to the table
Speaker 1: for you. Some of the things that come, in my view,
Speaker 1: with wisdom and wisdom comes with experience, I believe, is
Speaker 1: how to communicate, how to message, and how to help
Speaker 1: people understand your point of view without alienating those folks.
Speaker 1: And I think that's something that has been very helpful
Speaker 1: for me in working with my colleagues at Dimensional Butler.
Speaker 1: Dave Butler is a master of that, of course, and so, Okay,
Speaker 1: you have a great idea, but if you can communicate
Speaker 1: that great idea and you can't help people understand why
Speaker 1: it's a great idea, it's going to die on the vine.
Speaker 1: You really need to have the great idea and also
Speaker 1: have an understanding of how people receive the information. And
Speaker 1: I think that's something that I've always tried to pay
Speaker 1: close attention to how my colleagues do that, In the
Speaker 1: colleagues that do it effectively, how do they do it effectively?
Speaker 1: Because ultimately, the best ideas win, but only those ideas
Speaker 1: that can be communicated can be considered the best ideas.
Speaker 1: So I mentioned earlier the trillion dollar club. You mentioned
Speaker 1: uh in an interview. I think it was the Financial
Speaker 1: Times that you think Dimensional Funds should be a member
Speaker 1: of that rarefied club that is managing a trillion dollars
Speaker 1: in client assets. Tell us a little bit about how
Speaker 1: you're going to achieve that fairly lofty goal. Yeah, we
Speaker 1: we definitely feel that Dimensional has a lot of run
Speaker 1: way for growth and there's a few different reasons behind that. One.
Speaker 1: We view that many different investors and managers have come
Speaker 1: around to our point of view that systematic strategies are very,
Speaker 1: very beneficial for the end investor. And by systematic I
Speaker 1: mean more rules based approaches, approaches where you can communicate
Speaker 1: up front, here's what you can expect from this strategy,
Speaker 1: and then validate after the fact that you got and
Speaker 1: delivered what you said you were going to deliver. And
Speaker 1: I think that's incredibly important for investors to build trust
Speaker 1: and confidence in the strategies over time, and Eventual has
Speaker 1: been doing that for forty years. So I think that's
Speaker 1: one reason that best ideas win, and we have some
Speaker 1: of the best ideas in my view, and therefore that
Speaker 1: will serve clients well. And if you're serving your clients
Speaker 1: while you'll grow. Second kind of component there is exactly
Speaker 1: what I said, serving clients well. It's clients first. We
Speaker 1: think that if we deliver a great client experience, the
Speaker 1: great support for that systematic approach so clients can understand
Speaker 1: know what to expect to be able to have conversations.
Speaker 1: We work with financial professionals, so they have to have
Speaker 1: conversations with their constituencies and who they're accountable to. We
Speaker 1: think that that will also help us grow. And then
Speaker 1: in terms of the tactics to get there, Dave and
Speaker 1: I have really discussed this over the past number of
Speaker 1: years and we think that our investment philosophy is very,
Speaker 1: very powerful and I can get into that in a moment. However,
Speaker 1: the means for delivering that investment philosophy have evolved over time,
Speaker 1: and our view is you get to learn our investment
Speaker 1: philosophy one time, but then choose your own adventure on
Speaker 1: what vehicle you like to consume that under. So you
Speaker 1: know that we've launched e t F s recently and
Speaker 1: we've had what I would view as a lot of
Speaker 1: success on the e t F space. Our first e
Speaker 1: t F went live in November of and we're around
Speaker 1: forty eight billion in e t F assets over the
Speaker 1: course of that time period, and so I think that's
Speaker 1: been a good outcome. So same investment philosophy is what
Speaker 1: we've had in commingled mutual funds, but now an ETS
Speaker 1: separately managed accounts. How do we use new technology to
Speaker 1: take that minimum down to a half million dollars from
Speaker 1: where we used to be twenty million dollar minimum for
Speaker 1: our separately managed accounts, and we've built that technology, a
Speaker 1: true fintech solution to that problem, so that we can
Speaker 1: serve those types of clients as well. So how we'll
Speaker 1: get there is by identifying the needs that our clients
Speaker 1: have and keeping in mind the three c's, which is,
Speaker 1: there's a lot of complexity in the world that requires
Speaker 1: customization to come with good solutions, but people want it conveniently.
Speaker 1: So can we identify the complexity, can we provide the
Speaker 1: tools so that people can customize the right solution, and
Speaker 1: can we do all that very conveniently for our customers?
Speaker 1: And if we do that, I think we'll be successful.
Speaker 1: So full disclosure, My firm is a client of Dimensional Funds.
Speaker 1: Redults Wealth Management uses Dimensional Funds as one of our
Speaker 1: primary asset managers along with Vanguard, Black Rock, et cetera.
Speaker 1: But Dimensional is definitely one of our UM large JR.
Speaker 1: Fun providers, and I'm very aware of the process that
Speaker 1: Dimensional goes through in order to make sure that their
Speaker 1: clients understand the philosophy. You understand the model with an
Speaker 1: eye towards avoiding the sort of flavor of the month. Hey,
Speaker 1: I'm chasing this hot manager. No, now I'm chasing that
Speaker 1: hot fun family. E t f s are very much
Speaker 1: a break from that prior um embrace of of working
Speaker 1: very closely with clients. Tell us a little bit about
Speaker 1: the internal discussions that must have taken place before you
Speaker 1: switch to e t f s, which, hey, anybody could
Speaker 1: go to their online training account or robin Hood or
Speaker 1: whatever it is and and by the e t F
Speaker 1: how have you managed around that? So there was two
Speaker 1: big drivers of that decision. The first was input from clients.
Speaker 1: And as I mentioned around, we work with financial professionals,
Speaker 1: so we don't work the end retail consumer. We work
Speaker 1: with financial advisors like firms like yourself, who can get
Speaker 1: that level of understanding and knowledge and experience so they
Speaker 1: understand what we're what we're trying to accomplish. A lot
Speaker 1: of those firms were saying, we're using e t f
Speaker 1: s more and more frequently on behalf of our clients,
Speaker 1: and we'd like to be able to use dimensional ETFs.
Speaker 1: Could you launch ETFs please? And so we took that away.
Speaker 1: We thought a lot about it, and that was kind
Speaker 1: of twenty eighteen time frame, and on the books with
Speaker 1: the SEC back then was a new proposed e t
Speaker 1: F rule, and what that rule effectively did was it
Speaker 1: made e t f s much more straightforward to bring
Speaker 1: to the market, much more straightforward for the end investor
Speaker 1: to evaluate, but then also clarified some things around the
Speaker 1: inner workings of e t f s that were important
Speaker 1: to us because we're not an index manager. We have
Speaker 1: a lot of the benefits of an index based approach
Speaker 1: that include broad diversification, load and over low costs, but
Speaker 1: we have an active implementation and so those rules got
Speaker 1: passed in the fourth quarter of is when the SEC
Speaker 1: adopted those rules Rule six C eleven for anybody who's
Speaker 1: nerdy enough to want to look into them, and that
Speaker 1: was a bit of a game changer for us. We
Speaker 1: could do now what we had done in our mutual
Speaker 1: funds for decades in an e t F rapper, so
Speaker 1: there was no give up on the investment proposition. As
Speaker 1: soon as that rule was passed, we went into full
Speaker 1: launch mode. By June, we had announced that we were
Speaker 1: going to launch. By November of twenty so almost a
Speaker 1: year after the rule came out, we had launched. Those
Speaker 1: were the two big drivers on the tax efficiency side
Speaker 1: that wasn't as big a driver for us largely because,
Speaker 1: and you're familiar with this, our mutual funds tend to
Speaker 1: be highly tax efficient, and we had tax managed mutual
Speaker 1: funds that had similar tax efficiency ratios to e t
Speaker 1: f s, So we had very very tax efficient approach.
Speaker 1: E t f s taken up a bit our e
Speaker 1: T S two, but it was more what our clients
Speaker 1: were asking for, and the rules changed such that we
Speaker 1: could deliver a investment proposition that was on par with
Speaker 1: our mutual fund investment proposition. And your turnover in your
Speaker 1: various funds is relatively low compared to the average mutual
Speaker 1: funds at a fair statement. That's a fair statement on
Speaker 1: the equity side, for sure, on the fixed income side,
Speaker 1: where we do things that lead to slightly higher turnover
Speaker 1: because of the information that you can take out of
Speaker 1: yield curves at any point in time. But on the
Speaker 1: equity side, you know, a core strategy has ten percent turnover,
Speaker 1: value strategy twenty turnover in a given year. And how
Speaker 1: to think about that is like in the value strategy,
Speaker 1: when you buy a stock, you expect to hold it
Speaker 1: for about five years at turnover. That's how how you
Speaker 1: can kind of translate that into holding period on fixed income?
Speaker 1: Is it primarily duration versus credit risk that that the
Speaker 1: activity comes from. It's a combination of duration, it's a
Speaker 1: combination of credit, and then it's also a combination of
Speaker 1: currency of issuance. When you think about fixed income, a
Speaker 1: lot of people focus on the FED and what the
Speaker 1: FED is going to do. That's one rate among hundreds
Speaker 1: of rates out there, because there's different currency of issuance,
Speaker 1: different durations, different credit qualities. And what we do is
Speaker 1: we take in five six hundred different interest rates from
Speaker 1: around the world and we use that information every day
Speaker 1: to say, how do we increase expected returns the return
Speaker 1: of this portfolio, but manage risk very very robustly. So
Speaker 1: again it's has an index feel, but it goes beyond
Speaker 1: indexing with an active implementation to add value and manage risk.
Speaker 1: Really interesting. So let's talk a little bit about what's
Speaker 1: going on in the market this year. Pretty tough start.
Speaker 1: First quarter was a bit shaky. It was a little
Speaker 1: carry over from the end of uh SO growth investors
Speaker 1: have been doing so well for so long, hasn't hasn't
Speaker 1: been a great couple of quarters for them. How is
Speaker 1: the f A navigating this volatility? Yeah, you're right. It
Speaker 1: has been a rocky started the year in absolute terms,
Speaker 1: and when you look at the first quarter of two
Speaker 1: a lot of the major indicries, whether that's US or
Speaker 1: non US, developed or emerging or in the negative territory,
Speaker 1: You're right, Value has continued on it's good run, and
Speaker 1: value has been having almost like a two year a
Speaker 1: good relative performance, which is more what we expect from
Speaker 1: the world, and that continued on in the in the
Speaker 1: in the first quarter for sure, where value stocks help
Speaker 1: performed growth stocks by as much as ten percentage points
Speaker 1: and lots of different regions around the world. So that's
Speaker 1: been good for the investors in dimensional strategies because a
Speaker 1: lot of our strategies on the equity side overweight value
Speaker 1: stocks and stocks with high profitability and so on. In
Speaker 1: terms of navigating the volatility. You know, when you go
Speaker 1: back to our investment principles, there's probably three that I
Speaker 1: would highlight. One systematic approach is a good approach for
Speaker 1: investors with the right support, the right continued information, innovation,
Speaker 1: and the right price point. So that's one one basic principle.
Speaker 1: The other two are that prices are predictions of the future.
Speaker 1: Market prices are forward looking, how do you use those
Speaker 1: prices to manage risk and increase expected returns? And the
Speaker 1: third is that optionality has value. We should capture on
Speaker 1: behalf of our clients. So when you go through a
Speaker 1: time period like what we've just been through, where you
Speaker 1: have Russia invading Ukraine, all the sanctions that then subsequently
Speaker 1: came on Russian companies, Russian stocks, Russian individuals, that flexibility
Speaker 1: or optionality is critical. Because what we were able to
Speaker 1: do was in January, when you know, there was a
Speaker 1: lot of talk of sanctions versus various different companies and individuals,
Speaker 1: that we were able to freeze purchases on all Russian securities,
Speaker 1: which was an important part of our process. We said, okay,
Speaker 1: let's take a weight and see approach. And that was
Speaker 1: in part because if you go back to and when
Speaker 1: the annexation of the Crimea by Russia, at that point,
Speaker 1: we have a set of criteria that we go through
Speaker 1: rule of law, you know, how our foreigners treated versus locals,
Speaker 1: the local infrastructure, and we said, you know what, that
Speaker 1: criteria for that country right now is not quite being
Speaker 1: perfectly well met. So we reduced Russia to a half
Speaker 1: weight in ten. So we already had that flexibility built in,
Speaker 1: but that's very helpful when you go through time periods
Speaker 1: like this because you have a systematic approach that's largely
Speaker 1: rules based. But you can't come with a set of
Speaker 1: rules that will contemplate every state of the world. So
Speaker 1: you need to have people who have pragmatic and practical
Speaker 1: experience to say, well, what can we actually implement in
Speaker 1: the real world, and then how does that citizen overlay
Speaker 1: on top of what we do. So I think that
Speaker 1: this year that has been helpful in our strategies and
Speaker 1: how do we stay flexible to adapt to what's going
Speaker 1: on in the world and in markets around the world.
Speaker 1: So so let's talk a little bit more about the
Speaker 1: value versus growth um relative performance. Growth has really had
Speaker 1: a great decade. The growth was beating value. That started
Speaker 1: to change last year. What do you attribute that too?
Speaker 1: Is an inflation the end of quantitative easing and zero
Speaker 1: interest rate policy, or or something else. And I'm sure
Speaker 1: the investors who are listening are going to want to
Speaker 1: know and how long can this last? Yeah, it's a
Speaker 1: it's a very interesting question. I'm gonna flip it around
Speaker 1: on your barry, which is why did we have such
Speaker 1: a long run of growth out performing value over the
Speaker 1: because that's the unexpected outcome. Value out performing growth is
Speaker 1: not the unexpected outcome because when you think about value stocks,
Speaker 1: there are stocks that have lower prices and higher expected
Speaker 1: cash flows. So by definition, investors have applied a higher
Speaker 1: discount rate to them, and that's every day, and so
Speaker 1: you expect them to outperform growth stocks. When growth outperforms,
Speaker 1: that's the unexpected outcome, and that happens plenty, because returns
Speaker 1: over the short pull are driven by the unexpected things
Speaker 1: that happened, not they expected. When you look over the
Speaker 1: past decade, there was probably unexpectedly good outcomes for the
Speaker 1: facebooks and the Amazons and the Netflix. If you go back,
Speaker 1: you know, fifteen years and say do you expect this
Speaker 1: group of fang stocks or whoever to have an annulyzed
Speaker 1: compound rate of return of thirty percent a year for
Speaker 1: the next decade. Not many people would have said yes.
Speaker 1: But they did very very well. They improved their earnings
Speaker 1: profile quite dramatically over that period and were rewarded when
Speaker 1: you go then into the later time period. Um, you know,
Speaker 1: those value stocks in particular in the US, when you
Speaker 1: look at the price to earnings or price to book
Speaker 1: ratios of value stocks for versus growth those ratios and
Speaker 1: those differences had grown dramatically large. So growth had become higher, higher, higher,
Speaker 1: higher in terms of their valuations, whereas value had stayed
Speaker 1: kind of right around where it was because value had
Speaker 1: come in kind of like it's long term average, but
Speaker 1: growth had come in well ahead of its long term
Speaker 1: average in terms of returns, and so value was still
Speaker 1: in the same position to deliver those good returns going forward,
Speaker 1: whereas the expected returns on growth stocks had probably dropped
Speaker 1: given those higher valuations. So so let me phrase my
Speaker 1: hindsight bias and in the form of a question, which is,
Speaker 1: isn't it obvious today that post financial crisis the financials
Speaker 1: would lag for quite a while, and there they tend
Speaker 1: to be big value stocks. And then when we look
Speaker 1: at the growth side, Hey, this was a societal transformation,
Speaker 1: a generational shift, uh, towards mobile, towards internet, towards technology. Again,
Speaker 1: with the benefit of hindsight, how did we not see?
Speaker 1: Why was this a surprise? It's perfectly obvious after the
Speaker 1: fact that this massive change was taking place. It's obvious
Speaker 1: after the fact that in the middle of it. You
Speaker 1: never know exactly what's going to happen because there's always
Speaker 1: new technologies. People often talk about the new normal, and
Speaker 1: there is no new normal because technologies have been developed
Speaker 1: persistently a decade by decade for the past hundred years,
Speaker 1: and those technologies give rise to uncertainty about who will
Speaker 1: adapt and use them in the best manner, and who
Speaker 1: will be the winners and who will be the users
Speaker 1: once that new technology comes into place. So there's always
Speaker 1: a massive amount of uncertain It existed a decade ago
Speaker 1: and exists today. And what we look to markets to
Speaker 1: do is process that information to say, given that uncertainty,
Speaker 1: who am I going to demand the higher return to
Speaker 1: hold or a lower returned to hold? So I think
Speaker 1: that's the state of the world. And but even things
Speaker 1: by you know, like who's going to predict that COVID
Speaker 1: would come along and be such a boon to the
Speaker 1: Amazons and the netflix of the world because everybody who
Speaker 1: was locked in their house for some period of time
Speaker 1: that is unexpected. That's an unexpectedly good outcome, not for
Speaker 1: society but for the firms that were well positioned to
Speaker 1: meet the needs of society. When that unexpected event began
Speaker 1: to unfold. So so let's talk about another surprise in return,
Speaker 1: which has been since the financial crisis. The US has
Speaker 1: just trounced international returns for far longer than I think
Speaker 1: even the most ardent US investor expected. How do we
Speaker 1: explain the dominance of US equities versus either developed x
Speaker 1: US or emerging markets. And there, I'd point to you
Speaker 1: to the last decade, which was the previous decade, where
Speaker 1: you know, small cap stocks, non U S stocks, emerging
Speaker 1: market stocks greatly outward outpaced US large cap stocks. And
Speaker 1: then in the decade that you're referring to, it flipped
Speaker 1: completely and US large cap stocks outpaced everybody else, in
Speaker 1: particular US large cap growth stocks. Again, i'd put that
Speaker 1: down there's an unexpected component to that, and I'd put
Speaker 1: it down to the success of some of those U
Speaker 1: S firms that are now the largest firms in the
Speaker 1: US marketplace. That doesn't mean they will continue to be
Speaker 1: the largest firms in the US marketplace, because what we've
Speaker 1: seen over time, the largest firms tend to get there
Speaker 1: by outperforming everybody else. And in the global marketplace. Now
Speaker 1: the US has many of those largest firms and then
Speaker 1: in the you know, one to five years after they
Speaker 1: become the largest firms in the world, they tend to
Speaker 1: underperform everybody else's other firms innovate and try to take
Speaker 1: that top spot. So there it's just you know, success
Speaker 1: of those companies, and that's driven the investor demand for
Speaker 1: those companies because they've been able to satisfy so much
Speaker 1: client demand. Those are well run companies, and investors see
Speaker 1: high cash flows from those companies and they're willing to
Speaker 1: build up the prices. So so let's talk about a
Speaker 1: couple of things that are in the midst of changing
Speaker 1: and what you guys are doing about it. And I
Speaker 1: guess I have to start with volatility. We saw a
Speaker 1: giant spike in O eight or nine during the financial crisis,
Speaker 1: another big spike in during the pain endemic, and the
Speaker 1: VIX the measure of volatility was high thirties, and just
Speaker 1: just a month or so ago that seems to be
Speaker 1: rolling over and coming back down. First, what have we
Speaker 1: learned about volatility and how can investors use it to
Speaker 1: their advantage? And second, what do you think this softening
Speaker 1: of volatility today might imply for the rest at least
Speaker 1: of this calendar year. So what we've learned over time
Speaker 1: about volatility is that when there's a market crisis, and
Speaker 1: this goes without saying volatility increases. Why because uncertainty increases.
Speaker 1: There's a lot more uncertainty about what the range of
Speaker 1: outcomes maybe, and that uncertainty leads to a few different things.
Speaker 1: Increases in the volume of stocks and bonds that are traded,
Speaker 1: increases in bid off or spread, so the cost to
Speaker 1: trade those stocks and bonds, increases in volatility. All of
Speaker 1: those things come in a crisis. We had a crisis
Speaker 1: in March of when Russia invaded Ukraine. We had another crisis,
Speaker 1: how would that translate into global markets? And volatility tends
Speaker 1: to spike. But we've also learned over time is that
Speaker 1: spikes and volatility are unpredictable. So it's a shock, it's
Speaker 1: unexpected for a reason because it's unpredictable. And then once
Speaker 1: it's spikes, it tends to decay slowly unless there's another
Speaker 1: big shock that comes along to spike it back up.
Speaker 1: So it tends to decay over the over course of
Speaker 1: three to six months, goes back down to normal levels,
Speaker 1: and you can actually see that from market prices there's
Speaker 1: different market prices that tell you about the implied volatility
Speaker 1: of markets over the next thirty days, over the next
Speaker 1: thirty days following that, the thirty days following that, and
Speaker 1: so on the forth. And what you see from market
Speaker 1: prices is that when you get a big spike, it
Speaker 1: from market prices is expected to decline over you know,
Speaker 1: the next subsequent months. And we saw that clearly in March.
Speaker 1: Volatility spiked, but the markets told you that it expects
Speaker 1: the decline over the next a few months. It's the
Speaker 1: same with inflation. Right now, you can look at break
Speaker 1: even inflation and it's expected to be about six percent
Speaker 1: as of the end of Q two, But if you
Speaker 1: look at over five years, it's expected to be six
Speaker 1: percent over the next twelve months and then declined to
Speaker 1: something sub three in the subsequent four years. Right, So
Speaker 1: markets always tell you something about what's expected right now
Speaker 1: and what's expected in the future. So since you brought
Speaker 1: up inflation, let's talk a little bit about that. Um,
Speaker 1: what is the f A doing in preparation for higher
Speaker 1: interest rates? If the Fed keeps raising rates and if
Speaker 1: bond investors keep selling short duration holdings, how are you
Speaker 1: going to adjust to that, what do you think about
Speaker 1: things like high grade corporates and tips versus high yield
Speaker 1: and and risk of your bonds, your inflation and interest rates.
Speaker 1: Inflation has been high. Everybody knows that over the past while.
Speaker 1: And the way that we view inflation is there's two
Speaker 1: things that you can do the markets. You can look
Speaker 1: at get understanding of what the market expects. But the
Speaker 1: unexpected often happens. Nobody can predict the unexpected, so therefore
Speaker 1: you can but you can plan for the unexpected, and
Speaker 1: you can plan to outpace it or to hedge it.
Speaker 1: And so if you want to outpace things like what
Speaker 1: you mentioned, corporate bonds, globally diversified bond strategies, equities and
Speaker 1: so on over time have had positive real returns, so
Speaker 1: returns in excessive inflation, in high inflationary environments and low
Speaker 1: inflationary environments. And if you look back the thirty past
Speaker 1: thirty four years, you see that if you want to
Speaker 1: hedge it, you can use treasury inflation protective bonds, and
Speaker 1: we think that they're a good solution. You can also
Speaker 1: then if you don't want to give up so much
Speaker 1: expected return by corporates, are bonds like that and then
Speaker 1: hedge it with different types of instruments like inflation swaps
Speaker 1: and so on that can hedge out your inflation exposure.
Speaker 1: They're the two ways to deal with inflation in our view.
Speaker 1: You can plan for it. You can't predict when you're
Speaker 1: getting the spike, but you can plan for it. When
Speaker 1: it comes to interest rates and increasing interest rates, again,
Speaker 1: you can't predict when they're going to shoot up. That's
Speaker 1: not a something that you can predict, but you can
Speaker 1: plan for it. How do you plan for it? Well,
Speaker 1: we mentioned earlier on that there's an obsession over the
Speaker 1: Fed Funds rate. But if you look over the past
Speaker 1: thirty years, thirty to forty years, the Fed has increased
Speaker 1: the Fed Funds rate one month out of six, has
Speaker 1: decreased the Fed Funds rate one month out of six,
Speaker 1: and has left it flat in the other four months
Speaker 1: out of six. That's been about the pattern over the
Speaker 1: past forty years. And when you look at the months
Speaker 1: in which has increased the Fed funds rate, about half
Speaker 1: the time the third year rate has gone up and
Speaker 1: about half the time the third year rate has gone down.
Speaker 1: So what does that tell you? It tells you that
Speaker 1: other rates out there other interest rates don't move in
Speaker 1: lockstep with what the FED is doing. So if you
Speaker 1: think about that and you extrapolate, you have interest rates
Speaker 1: on the short end, the intermediate end, the long end.
Speaker 1: You have interest rates as they applied to corporate bonds
Speaker 1: from triple A s down the double B s. You
Speaker 1: have interest rates from a current from bonds issued in
Speaker 1: euros and British pounds in Assie dollars and so and
Speaker 1: so forth, and none of them move in lockstep with
Speaker 1: this FED. So you can diversify. That's how you plan.
Speaker 1: The FED may do what it's going to do, but
Speaker 1: it's one interest rate among money, and that's going all
Speaker 1: of those other interest rates. You wanted to drive the
Speaker 1: returns of your probably diversity by portfolio because if you
Speaker 1: look from oh eight on the subsequent ten years, the
Speaker 1: FED funds rate was basically at zero for a decade,
Speaker 1: but it globally diversified portfolio stocks and bonds returned about four.
Speaker 1: So in a zero FED funds rate, you've got about
Speaker 1: a four return. So again it goes back to you
Speaker 1: don't have to be able to predict the unexpected. You
Speaker 1: just have to be able to plan for it and
Speaker 1: then stick with that plan, regardless of what the unexpected
Speaker 1: brings brings the past. So let's talk a little bit
Speaker 1: about your career. Uh, pretty much, since you've been in
Speaker 1: the world of finance, we've only seen low rates and
Speaker 1: we've only seen mostly low inflation. Does that impact you're thinking,
Speaker 1: there's a color your perspective having lived um as of
Speaker 1: financial professional in this somewhat aberrational environment, or are you
Speaker 1: looking at the academic research and able to pull yourself
Speaker 1: out of it. So I would say it's a little
Speaker 1: bit of yes, a little bit of no. Um in
Speaker 1: the yes category is that certainly, after the financial crisis,
Speaker 1: the global financial crisis, there were a lot of client
Speaker 1: questions about the role of fixed income in a portfolio
Speaker 1: because if you're used to headier times when interest rates
Speaker 1: were higher, you might have a different perspective on how
Speaker 1: to use that strategy than when you know interest rates
Speaker 1: are low. And so that has informed Okay, what are
Speaker 1: the things that our clients are caring about and what
Speaker 1: is it that we need to deliver to clients given
Speaker 1: that those are the concerns and these are the problems
Speaker 1: that they're trying to solve in a low interest rate environment.
Speaker 1: So that's a little bit of yes because it's been
Speaker 1: on client's minds. The little bit of no is that
Speaker 1: we've had We have decades upon decades, fifty sixty years
Speaker 1: and longer of data on the returns of bonds, both
Speaker 1: here in the US of corporates and of other bonds
Speaker 1: around the world issued in different currencies, and so we
Speaker 1: can look at lots of different high interest rate low
Speaker 1: interest rate environments, transitions between those when the interest rates
Speaker 1: were had gone up or gone down, and so we
Speaker 1: can understand are there certain strategies that work better or
Speaker 1: worse than each of those environments, and we can then
Speaker 1: we can design strategies that work well for both environments.
Speaker 1: So that long term view is something that we always
Speaker 1: keep in mind, which means that you know something that
Speaker 1: happens over a decade or fifteen years. It does give
Speaker 1: us new information, but doesn't necessarily change dramatically our investment priors.
Speaker 1: Really really interesting. Before I get to my favorite questions,
Speaker 1: I just have to throw a curveball at you. So,
Speaker 1: in your bachelor's in theoretical physics from Trinity College, what
Speaker 1: were you studying in theoretical physics? What areas did you
Speaker 1: concentrate in because I'm familiar with that space and find
Speaker 1: it absolutely fascinating. Yeah, it is really a very very
Speaker 1: interesting space. And you know, when I have was a
Speaker 1: it was a kid, I like to read Stephen Hawkings
Speaker 1: and those types of books. So I was very interested
Speaker 1: in relativity and so kind of that that side of
Speaker 1: what Einstein worked on, and I found that very interesting.
Speaker 1: We had a lot of we have courses on relativity
Speaker 1: when we were in in university in theoretical physics. The
Speaker 1: other side is quantum mechanics. And quantum mechanics is very
Speaker 1: very interesting because you never know anything with certainty, so
Speaker 1: it kind of has parallels to to the real world.
Speaker 1: You can't know something's position and its speed at the
Speaker 1: same time, you can only know one perfectly, or you
Speaker 1: can know both in a with a lot of uncertainty.
Speaker 1: But quantum mechanics is also incredibly interesting because everything has
Speaker 1: multiple states of the world is and as in those
Speaker 1: multiple states all the time with some set of probability.
Speaker 1: So that's also a very fascinating field of study. And
Speaker 1: I enjoyed those quite a lot when when I was
Speaker 1: working on them back in Trinity College in Dublin. So
Speaker 1: so if you're a fan of um Hawk Professor Hawkings
Speaker 1: and some of his work. Can we all admit that
Speaker 1: dark matter and dark energy is a cheat and we
Speaker 1: really have no idea what's going on with the expansion
Speaker 1: in the universe, because every explanation I've heard from various
Speaker 1: theoretical physicists have been, well, we're not sure, but we've
Speaker 1: made up this thing that we hope to figure out
Speaker 1: one day. It seems like it seems like it's um
Speaker 1: you know, a short cut. You know it may be
Speaker 1: a short cut. But I'd go back to your earlier
Speaker 1: statement was, which is around how our models evolve over time,
Speaker 1: our data evolves over time. Like you saw from a
Speaker 1: couple of weeks ago there was a new discovery from
Speaker 1: the Hubble Telescope of the oldest star yet which is
Speaker 1: older than the universe, which is which seems to be
Speaker 1: a little confused, a little confusing, and so new data
Speaker 1: emergence all the time, and then you create models to
Speaker 1: try and understand those data, but you know it's not
Speaker 1: what understood yet are not I would say it's what
Speaker 1: understood not completely understood, and there's a lot left that's
Speaker 1: not known yet for people to Discover fair enough. So
Speaker 1: so let's jump to a little less heavy material and
Speaker 1: talk about our favorite questions, starting with tell us, what
Speaker 1: you've been streaming during the past couple of years of
Speaker 1: Lockdown and Pandemic, either podcast or Amazon and Netflix. What's
Speaker 1: been keeping you entertained? Yeah, couple of different shows have
Speaker 1: been keeping me entertained. So it was in a board meeting,
Speaker 1: one of the Advisor board meetings, and one of the
Speaker 1: board members mc McCown said that he had been watching
Speaker 1: a documentary series called The Prize, and The Prize is
Speaker 1: from a while ago. It's it's about the kind of
Speaker 1: the history of oil and you know how it started
Speaker 1: and where it evolved two and all the various different
Speaker 1: issues that have arisen as a result. So that was
Speaker 1: super interesting and I'd recommend that to anybody who's kind
Speaker 1: of interested in those types of historical shows. Other things
Speaker 1: that I find interesting over the past few years, I've
Speaker 1: watched a lot of documentaries about you know, World War two,
Speaker 1: World War One, Vietnam War. Ken Burns has some great
Speaker 1: stuff even on the US Civil War that have been
Speaker 1: very interesting. The Fog of War that that was another
Speaker 1: interesting show. I find those particularly interesting, just how do
Speaker 1: you ever get there? Because war is in a rational act,
Speaker 1: so what what are the things that have to happen
Speaker 1: in order to get there? Because it's much more rational
Speaker 1: to cooperate and to trade than it is to go
Speaker 1: to Everybody will be better off in the former and
Speaker 1: worse off in the latter, So how do you actually
Speaker 1: get to that state of the world? Is interesting. I
Speaker 1: have a six year old daughter and so we watch
Speaker 1: shows together and that also keeps me entertained. She loves
Speaker 1: If I were an Animal. I don't know if you've
Speaker 1: seen that show on Netflix, but that's a goody. And
Speaker 1: then another one that came out recently a Netflix is
Speaker 1: Old Enough. I don't know if you've seen this as
Speaker 1: a Japanese show, and they have like little three year olds,
Speaker 1: four year olds, five year olds, and their parents give
Speaker 1: them a task to do and then they have to
Speaker 1: go off into the round town into the shop and
Speaker 1: they're followed by a camera coup by themselves, and they
Speaker 1: accomplished this task. It's hilarious. It's it's really really fun
Speaker 1: to old Enough to check. That's a fun one. Let's
Speaker 1: talk about some of your mentors, who were some of
Speaker 1: the folks who helped shape your career. Yeah, I would
Speaker 1: say that in terms of folks that have my career.
Speaker 1: Some of the names that you mentioned, whether it's a Fama, French, Martin,
Speaker 1: have all been very helpful to me over time. David
Speaker 1: of course, has been very very helpful to me over time.
Speaker 1: EDWARDO used to work at Dimensional, has been very helpful
Speaker 1: to me over time. And then I'd be remiss if
Speaker 1: I didn't say my parents, because there you know, up
Speaker 1: until the time that you leave the home, and they're
Speaker 1: your ultimate mentors in terms of shaping how you approach problems,
Speaker 1: how you view the world, what you prioritize. My parents
Speaker 1: have always emphasized education and the importance of keeping your
Speaker 1: mind active and trying to better yourself. How do you
Speaker 1: become better than your were the day before? And that's
Speaker 1: a spirit that I think it's important for anybody to
Speaker 1: keep kind of pulling towards for as long as they're
Speaker 1: on this planet, because what else is there to do
Speaker 1: but try to improve your skills and and how you
Speaker 1: interact with the world. So let's talk about books. This
Speaker 1: is everybody's favorite questions. What are you reading right now
Speaker 1: and what are some of your favorites. You know, I
Speaker 1: am not reading any book right now. I've been consumed
Speaker 1: with work over the past few years and by reading
Speaker 1: for pleasure has taken a back seat, unfortunately. But some
Speaker 1: of my favorite books over time, I would say one
Speaker 1: Freedom to Choose. I don't know if you've read that
Speaker 1: book by Milton Freeman. I think is a great book
Speaker 1: and timeless, I mean written many decades ago, but but
Speaker 1: very very timeless. They wro't deserved them. I think is
Speaker 1: one of the all time classics as well by you
Speaker 1: know That's uh is an all time classic. So you're
Speaker 1: going to get my idea from I like books about markets,
Speaker 1: about how to organize people and how do you get
Speaker 1: to a state of affairs where you're making the most
Speaker 1: efficient use of the resources, where people have freedom to
Speaker 1: pursue what interests them. I find that an interesting area
Speaker 1: of reading. What sort of advice would you give to
Speaker 1: a recent college grad or someone who was interested in
Speaker 1: a career in investing and finance? So two big areas
Speaker 1: one and this is something that is kind of I
Speaker 1: call it a dimensional motto, and it's do the right thing,
Speaker 1: do it the right way, and do it right now.
Speaker 1: And so when you're pursuing a career in any field,
Speaker 1: you want to feel good about what you're doing. You
Speaker 1: want to feel that you're helping people. Do you want
Speaker 1: to do well while you're helping people. But that's the
Speaker 1: right thing, And then do it the right way is
Speaker 1: how do you come with a path to make a
Speaker 1: decision that uses as much of the information that's available
Speaker 1: to you. There's gonna be a lot of noise in
Speaker 1: the outcome, but you want to be proud of the
Speaker 1: decision that you made given the information that you had
Speaker 1: at the time. I think that's doing things in the
Speaker 1: right way. And then do it right now. Never sit
Speaker 1: on your hands, be proactive, get after it, close projects.
Speaker 1: If you can't close it, move on, ask for help,
Speaker 1: and don't sit on your hands, go out and get
Speaker 1: it done. Then, when it comes to finance in particular,
Speaker 1: remember what you're doing. You're taking people's life savings and
Speaker 1: you're trying to help them achieve objectives and goals, and
Speaker 1: they're taking risk to achieve these oblection and goals that
Speaker 1: they couldn't achieve without taking those risks. And that's a very,
Speaker 1: very meaningful responsibility. So don't take it lightly. And you're
Speaker 1: moving into a field that you can really help people
Speaker 1: have a better life, but you can also harm people
Speaker 1: if you do things in the wrong way. So I
Speaker 1: think that that's a something that you've got to keep
Speaker 1: in mind when it comes to finance. It's not your money,
Speaker 1: it's somebody else's money. Be fiduciary, be prudent, and then
Speaker 1: you can really help people. Be it off really interesting
Speaker 1: answer and our final question, what do you know about
Speaker 1: the world of investing today? You wish you knew about
Speaker 1: twenty years ago or so when you were first getting started.
Speaker 1: When I was first getting started, I had this view
Speaker 1: of the world because I had never taken a course
Speaker 1: in finance before a dimensional which and I didn't understand
Speaker 1: markets that well. I had the view of the world
Speaker 1: that all you had to come was with was a
Speaker 1: better mathematical model than anybody else out there, and then
Speaker 1: that would be able to predict where prices were going
Speaker 1: to go. And of course I was quickly disabused of
Speaker 1: that notion after having conversations with Ken and Geene and Bob,
Speaker 1: and you just need a better model, And so I
Speaker 1: wish I had known that then, but now I certainly
Speaker 1: know it, and it's really helped shape how I view
Speaker 1: what good investment solutions are for clients and what really
Speaker 1: the power of markets are and can be really really
Speaker 1: interesting uh stuff. We have been speaking with Gerardo Riley.
Speaker 1: He is the c i O and co CEO of
Speaker 1: Dimensional Funds. If you enjoy this conversation, well, be sure
Speaker 1: and check out any of our four hundred or so
Speaker 1: previous interviews. You can find those at iTunes or Spotify
Speaker 1: or wherever you get your podcasts. We love your comments,
Speaker 1: feedback and suggestions right to us at m IB podcast
Speaker 1: at Bloomberg dot net. You can sign up from my
Speaker 1: Daily reads at Ritalts dot com. Follow me on Twitter
Speaker 1: at ritlts. I would be remiss if I did not
Speaker 1: thank our crack staff that helps put these conversations together
Speaker 1: each week. Mohammed Remaui is my audio engineer. Attica val
Speaker 1: Bron is my product manager, Paris Wald is my producer.
Speaker 1: Sean Russo is my head of research. I'm Barry Ritalts.
Speaker 1: You've been listening to Masters in Business on Bloomberg Radio.