Gerard O’Reilly on Academic Research and Stocks

and fifty billion dollars and total assets. This is really a master class in how to think about investing, how to be systematic, how to approach it from a evidence based scientific basis, how to incorporate the best of academic research into your process. One of the things that I found really interesting was the d F A focus on costs, convenience and customization. Not every giant investment firm UH takes

that approach. Really, I've interviewed a number of folks from d F a UM, from David Booth to Gene Parma and throughout the rest of the organization. I think you will find this to be absolutely fascinating and and really informative. So, with no further ado, my conversation with d F as Gerard O'Reilly, this is mesters in Business with Very Redults on Bloomberg Radio. My extra special guest this week is

Gerard O'Reilly. He is the Chief Investment Officer and co CEO of Dimensional Fund Advisors, a leader and factor based investing for the past forty years. D f A has about employees across thirteen offices globally and full disclosure my firm Results. Wealth Management is a client of Dimensional Funds and we manage a substantial chunk of our assets with their products. They manage six hundred and fifty billion dollars in assets, about eight percent of that is equity. Girard O'Reilly,

Welcome to Bloomberg. Thank you, Barry, and thanks for the invitation. I've been looking forward to speaking with you for some time, so so have I. You have such an interesting background. I was really excited to talk to you, especially given you have a PhD in aeronautics from the California Institute of Technology. What what were your original career plans. Well, I've always liked mathematics, and as an undergrad in Ireland

studied mathematics and physics and so on extensively. I was thinking about what to do next and said, well, cal Tech does a lot of great stuff in fluid mechanics and particular in aeronautics and So I didn't have a specific set of career plans. I just know that's the subject that I wanted to study and that I enjoyed. So I set off or cal Tech and and I really enjoyed my time. They're working on various different projects,

many theoretical in nature, but very mathematically oriented. So when you're looking at aeronautics in the United States, there aren't a whole lot of career paths out of that other than academia or going to Nassau or one of the defense um companies. What led the shift from aeronautics to finance as a career, Well, I wanted to learn some more about finance. Number one. I hadn't taken a finance course ever in my life before joining Dimensional, and Dimensional

was the firm that I joined straight out of college. Also, you know, the academic path wasn't one that appealed to me. I really enjoyed grad school, but I preferred to tackle something that was I would say more than here and now, where your projects that you're working on have impact very quickly on you know, the end customer, the end consumer,

and then also where on the engineering side. You know, you mentioned in the U S well, I'm not a US citizens, so it's hard for non US citizen to work in the aeronautics field here in the US because largely required security clearance. So it's looking around. A friend of mine was working for Dimensional, knew that person at Caltech, and that person was talking about you know, Dimensional has all these great academic connections. They really take finance from

a scientific perspective. Went down, checked it out and said, this sounds interesting. I really want to give this a shot for a period of time. So so let's talk a little bit about those academic um connections. Ken French has been at Dartmouth for a long time. His colleague Gene Farmer, Nobel Prize winner at University of Chicago. Another Nobel Prize winner, Robert Merton, also at Dimensional funds. What's it like working with all these Nobel Prize winning economists

could be a little intimidating to some folks. It's always intimidating when you start off working with somebody who's very, very talented and you're getting to know them for the

first time. But it's a privilege and it's great fun because those folks, you know, have have worked incredibly hard to hone their craft, hone their skills, and when you think about Ken or Gene or Bob or Meer and any of those folks, they're very, very generous with their time and so they're willing to teach because they're in academia, and if you're willing to work hard, they're willing to

put the time and effort into you. So I started off with no background in finance and got to learn finance from some of the most amazing minds in the field. So it was just it was great Ken, Jean and Bob. I've never heard of those three gentlemen referred to quite in that way, but I guess when you work with them as frequently as you do UM, it becomes Ken, Jean and Bob. So, so what are the parallels between

academia and UM working in finance professionally? And then I have to ask what are the parallels between aeronautics and fluid dynamics and finance and investment. Well, working in academia,

you know, you're always trying to solve a problem. You're looking for interesting problems to solve that haven't really been tackled before, or an aspect that you're working on hasn't been tackled before, and you're saying, well, can I bring something new to the table, something innovative, and that's incredibly rewarding and incredibly interesting. Working in finance is no different.

You're looking for new problems to solve. Those problems are largely driven by what it is your clients are looking for, what types of investment solutions do they require to solve the investment problems that they have, and then you're coming up with innovative ways to solve those problems. So in that respect, there's a lot of similarities. The time scale and the time frames are a little bit tighter and

faster when it comes to finance than in academia. In academia it may be multi years, and there's multi year projects that happen in finance, but you want to be able to live or something for your clients in shorter time frames than that. When you think about engineering or mathematics or physics, and then how does how do those skill sets translate over to finance. Well, again, it's all about problem solving and what you're looking for is how do I number one pose the question correctly? How do

I ask the right question? Because that's as important as trying to solve the problem, you have to set it up in the right way. And that's true whether it's in mathematics or physics, or engineering, or it's in finance. Then how do I gather data to help me address and find the answer to this question? And that's true of both fields. And then how do I interpret the data?

What are the tools and the models that I can use such that I'm going to be able to organize these data in such a way to draw inferences about how I want to act going forward. And that's true of both, Madam, physics, engineering, and finance. I think the big differences are the laws of physics tend not to change over time, but the laws of that government finance can change through time. There are many repeatable experiments in physics,

there are no repeatable experiments in finance. But there is a kind of a common truth in both, which is that in finance and investing, people demand return for bearing uncertainty. That doesn't change through time, but how you go about implementing that can change through time because the laws are changing. On behalf of Isaac Newton, I'm going to raise an objection that at least our understanding of the laws of

physics have changed over time. So so maybe the underlying laws themselves are the same, but our perception seems to have evolved. I think that's that's a good way to put it, in a nice precise way to put it, is that the underlying drivers don't change, but our perception changes. And that's it's an interesting observation because their perceptions change because the models that we use to explain and understand

those underlying drivers evolve over time. All models are incomplete, none of them are true, none of them are perfect descriptions of reality. And that's true of physics and it's true of finance. But you can improve those models over time. You can improve the data that you can collect over time, and that enhances your understanding over time. I'm a big fan of George Box. I love the quote all models are wrong, but some are useful, and it sounds like

you very much embrace that philosophy as well. And it's an important philosophy to embrace when you're, you know, working in the field of finance, because ultimately, what you're doing is you're investing money on behalf of others. It's their life savings. Often so it's what they've made sacrifice to put together so they can afford a better retirement or

something that's important to them in their future. And if you ever believe that the model is reality, you're probably going to build non robust solutions and do them a disservice. So having a healthy skepticism around all models and basically all data sources that you see is important because it leads you to, well, what if I'm wrong, do I still have a good in solution even if this model it turns out to be incorrect? And I think that's that's a good way of looking at the world. So

let's talk a little bit about your career. You began at d f A in two thousand four in the research department. A little more than a decade later your chief investment officer, and not that many years after that you become co chief executive officer. That's a pretty rapid career path. Explain to us, if you would, the concept of co CEO S or co c i O S and how you managed to um advance so rapidly in a firm that was led by David Booth for so many decades. Yeah, So let me let me start with

the ladder. How how how do you advance? And my viewpoint on success is there's a combination of three things, and I'm not sure which one is most important, but they probably all are equally important at different stages. One is a little bit of luck, a little bit of luck in the things that you've learned up to that point in time when the opportunity comes, a little bit of luck. For example, finding Dimensional was well suited to the way that I thought about the world. And then

there's some talent. Do you have the right skill set that will be helpful in that particular organization? And it turns out that a quantitative and analytical type skill set was very helpful for an organization like Dimensional in our clients. And then hard work. Are you willing to do whatever it takes to complete projects to move the ball forward to help your clients succeed. And when you have all

three of those, I think good things can happen. And was fortunate that had a little bit of each one of those when I came to Dimensional, and Dimensional has been a growing firm for money money decades and when I came in two thousand or four, we had about fifty billion under management and you know, that grew rapidly, so there was a lot of opportunities for those folks

that were willing willing to step up. And so I consider myself fortunate and very happy by how that's turned out, because I've had a blast doing it and it's been it's been rewarding. And then in terms of the co c i O s and co CEOs, we do a lot of CODs. We have cos of different department heads. From my particular case, Dave Butler is the other co CEO.

And it tends to work well when you have people who number one get along well with each other, they respect each other and each other's ideas, and then they have maybe complementary skill sets. And so the way that Dave and I have worked in that job together I think has been much more all my preference I would have. I'm much preferred to have done it with him then without them, because you can do some dividing and conquering.

But also what I find is that as you get promotions, and this is a little bit facetious, but you tend to become, at least if you judge it by the input that you get from your peers, smarter and funnier in that the input that you get from your peers becomes less informationally rich. But when you have a troop heer like Dave and I are are troop peers, anything goes.

We can have robust, open, honest conversations and with David as well, which read us a lot of pressure test things before we have to go and talk about them with the rest of the firm. And that really, you know, I always think iron sharpens iron, that you have to have people who you can, you know, spar with on a daily basis test your ideas. They'll push you, you will push them so that you can improve every day. So it's it's worked very very well. We do a

divide and conquered. We've thirteen global departments at dimensional. Four comes straight to me, four go straight to him, and then the five in the middle kind of go to both of us either through the CEO we have a CEO Lisa Dalmer, are directly like legal and compliance come to both of us directly, and that way that it's it's just worked well. We've been very pleased with what we've been able to accomplish over the past five years working together. It's I guess you each keep each other

sharp and keep each other honest. That's right, really interesting. So so let's talk about factors a little bit um. How did the academic research that that Rex and David, the two co founders of d f A, How did that become part of the investment process. So I guess there's a couple of salient points there. One is factor research in itself, and we talked a little bit earlier on about models and what they're useful for and how

you draw inferences from them. I really look on factor models as way to organize historical data so you can try to understand better what really drove differences and returns across different groups of securities, different groups of stocks, different group of bonds, and from those you can glean very important insights about the drivers of expected returns, the drivers of differences and risk across different asset categories. And so

I think that's the important aspect of factor models. So when you put them dimensional and its founding in context of kind of a burgeoning field in the eighties and in the nineties, when more and more factor models were

being developed and tested and so on. The founding was two I would say, address an institutional need that David had identified, which was there weren't many systematic strategies that targeted the returns of small cap stocks, and he found that that that was a hole in many institutional investor portfolios.

And along the around the same time, because David had done his MBA at the University Chicago now Both School of Business, around that same time, there was evidence coming out that smaller cap stocks also had higher average returns historically and reasons you know, promoted about why that would be higher expected return is going forward, and so around that time was kind of when those factor models were developing.

So I started with the client need, and then it was well, let me go to the academics and understand, what are the research around this client need. Am I going to do something here that makes sense or not makes sense from an academic perspective? And then how do I build a good robust solution to address that client need.

And then, of course, in the nineties you had the three factor model come along, and then in the mid nineties you had momentum come along, and in the two thousands you had things like profitability and investment come along. So we had lots of different factors uncovered over time. But the way that we look on each one of those is their models. They give us insights from the data. How do you use that to build robust portfolios? And I would say that's been kind of part of our

heritage for forty years. How do we build portfolios that can target these premiums but be robust regardless of the market environment. And we've been through many different market crises with a broad range of investment strategies that have come out quite well the other side. So we're we're pretty familiar in modern times with small cap indices like the Russell two thousand, or the S and P six hundred

or whatever it happens to be. But when Sinkfeld and Booth were forming um D f A in the early eighties, these weren't really household names, if they even existed at all. It's amazing to think that there was a period where small caps weren't their own category. Tell us a little

bit about how that evolved. Yeah, if you go back even further, so dimension was found in than eighty one, But if you go back a decade earlier, and I'll focus on David a little bit and his work with mc McCown, who was at Wells Fargo at the time, and he's a director of the firm, and so David and Mac were working on indexes. So in the very early seventies, the Max team with David created the first

index fund. It wasn't for retail, it was for an institutional client, and it was based on US large cap stocks, So he's very familiar with index based approaches. Then David subsequently left and worked a gibecker for a while, understood more about what clients were interested in looking for required and so there wasn't a Russell two thousand available when he was building the firm, so there wasn't an index

to attach the strategy to. The Other thing that was kind of feedback from academia is yes, small cap investing makes sense, but you're going to get killed on trading costs. And so then you have this kind of environment where there wasn't an index, it wasn't a household name. To your point, you know small cap stocks as an asset category,

so you kind of have a blank canvas. If I know, knowing everything that I know, what's the right way to build a small cap strategy that hopefully then will be efficient and won't suffer too greatly from trading costs and implementing and investing client flows. So I think that it was in some respects a very big advantage starting with that blank canvas of how do you design the best portfolio, you know how, with as few constraints as possible, because

you weren't worried about an index. And then subsequently Russell had the Russell two thousand, and then of course in the nineties, value versus growth became, you know, well established asset categories, and so asset categories have been added over time. So so let's talk a little bit about Gene Parma and Ken French is what started out as a three

factor model, it eventually became five and seven. Now they're a hundred of factors, many of which um don't really add a whole lot of alpha or not consistent enough alpha to justify their complications and costs. Tell us a

little bit about the Farmer French factor model. Yeah, so you know, when you when you go to the eighties, there was a lot of empirical evidence being uncovered that the prevailing model from the sixties and the seventies, the capital asset pricing model, didn't explain the data very well, so when you look at it, it was it was a beautiful model. It was very you know, intuitive, but

it didn't explain the data all that well. And so Ken and Gene in the early nineties started to organize all the data to say, can we put some of these observations in one kind of unified viewpoint of the

historical data. And from that, you know, exercise came a better model in the sense that it could explain the returns that you saw among stocks are better than the capital as sur pricing models, so explain more of the returns, more of the variation that you saw on the returns across stocks, and so that so subsequently came the three factor model. Then to your point, lots of factors have

been added. If you look at family frenches are even Ken's website, now you'll see a profitability factor, you'll see an investment factor, you'll see momentum factors. You'll see all different types of factors. And as I mentioned earlier, factors are really great to help you organize the historical data. But you don't want to get kind of two stereoid

about the latest factor model. I kind of view a lot of the academic research over the past thirty years as doing variance on a theme, and so it's not that kind of a have brand new discovery, but it refines your understanding of existing factors. So there's probably twenty or thirty or forty different value factors out there, but you don't need all twenty or thirty or forty when

you're managing a strategy. But you can get insights from the different factors on how to manage a strategy effectively. And so what I mean by that is if you if if you think about what datas are are available. You have security prices, you have data from income statements, so things like income or profits or revenues or expenses, and you have data from balance sheets, assets and liabilities. They're the broadly the data that are available to go test.

And when you look at all of those factor models their variants on the theme, the right are looking at current values of those variables, whether it's current income or current price to book ratios or price earnings ratios, are they're looking at how they've changed, How to have prices changed over the past number of months, How have assets grown over the past number of months, How is profitability

changed over the past number of months. So there's three data sources and people do two things with them, so there's actually really kind of six that you can think about that kind of encompass most of the hundreds of

factors that you see out there. And I think that if you have coverage of those six current prices, current balance sheet items, current income statement items, and then how each one of those have changed in recent past, you have pretty broad coverage of all the various different factor literature that's available. And that's what we do at Dimensional. So so let's for the lay person get a little more granular with some of the more popular and effective

factors um. The four biggest ones I think are size, value, quality, and momentum. Is there anything you would add to that beyond beta which is just a given? So there's five? What else would you add to that list? I would add probably investment and proxy for investment is how a firm is growing their assets over time. And when you think about all of the ones that you just listed, Barry, all of them are momentum, have something in common, and

what's that that they have in common? They're basically picking up differences and discount rates that the market has applied to different investment opportunities. So when you think about something like value, you you're taking price and you're dividing it by some company fundamental so some fundamental measure of firm size, and you're saying, why do you want to do that? Because you want to see who has low price today

relative to who has high price today. So there's firms in the marketplace, some of them will trade at low prices, some of them will trade at high prices. You need to scale price, normalize price to be able to make that determination. When you say quality, quality often comes down

to profitability. And what we know from the historical data is the firms that have the highest profits or the highest profitability, so profits divided by assets or profits divided by book value in the marketplace tend to continue to have that high profitability over the next year, two, three, four, or five years. But what do those profits lead to? Those profits lead to client cash flows or investor clash flows. I should say the higher the profits, the more cash

flows investors can expect to get from their investments. So it's telling you something about expected cash flows from that investment in the future. Here I say investment because asset growth. Let's imagine a company has to retain a lot of earnings, or has to issue a lot of debt, or has to issue a lot of stock in order to drive those profits going forward. That leads fewer cash flows for investors. So that also tells you something about expected cash flows.

So when you talk size, value, profitability, or quality and investment, they're all telling you something about expecting cash flows. Are the prices people are willing to pay. It's a discount rate effect. Momentum is the outlier. There's no equally simple, compelling story that lets you know why should you expect that firms that have outperformed the market in the past three the twelve months to continue to outperform the market in the next three the twelve months, and vice versa.

But it's there, loud and clear in the historical data, and so the question we ask ourselves is how do we use that information with as low opportunity costs as possible because we don't know why it's there, so we don't know if it will be there in the future. But if it's not there in the future, we don't want to have incurred unnecessary costs on behalf of investors pursuing something that we don't know why it exists in

the data to begin with. Really really interesting, when when I think of momentum, I have I tend to think of a persistency because either fund managers or investors have gone through the whole process of selecting that stock, and as long as it's working out, trending in the right direction at market um returns or better, there's no reason to remove it. So it becomes a little bit of a self fulfilling prophecy until there's a substantial enough misstep and then throw in all of the four oh one

k regular contributions. If that if you're in fund X and it owns company A, B, S and C, and all three of those are doing well, money continues to flow to those funds automatically, and those funds tend to buy their top performers. It's almost like a virtuous cycle. You know, that's a possible explanation, and that it's certainly it's certainly a little bit of narrative fallacy and hindsight bias.

To say the least, it's been tested. I mean, academics have looked at you know, overreaction, under reaction, and why is there continuation in returns. There's an interesting area of research going on right now, and Professor Novi Marx had one of the kind of first, well not one of the first, but I kind of I would say, an instrumental paper on on this recently that looks at profitability growth.

So how have affirms profits grown are declined over the past three months to a year and does that explain

the returns pattern that you see related to momentum? And that seems like a promising area of research if there is a lot of explanatory power in how affirms profits have changed or how their profitability has changed, and that has the power to predict future profitability i e. Firms that have grown their profits more quickly than other firms may continue to grow their profits more quickly than other firms.

Then if that explains momentum, then you start to get momentum back into that field of differences in discount rates, and then that becomes a much more easy story to understand in the sense that firm characteristics are much more straightforward to predict than future private prices. Well run firms tend to remain well run firms for some period of time.

But given that their well run firms when you think about the price, that's said in the stock market, that's the aggregive view of what expected return people require to hold that investment. So they already understand it's a well run firm, and so we think that it's priced fairly given all that information. So it may have information about how well run that firm has been over the past number of quarters, and that has predictive power on how well run that firm is expected to be over the

next few quarters. So so let's get into the weeds a little bit. How can you distinguish between factor research that's significant and factor work that's either statistical noise or backwards looking form fitting, Because it seems like everybody has developed a new model of their own which looks great on paper. Um, the back tests are always wonderful, but then in reality it doesn't seem to work. So so how do you draw the line between hey, this really

is substantial versus just a just a good backdest. Yeah, you hit on it perfectly. Very You're never going to see a bad back test, in particular from an asset manager. Well, because that's where they all go to diet. It's all survivorship by it's all survivorship by it. So it is a real challenge, and that's true even of the academic work, because in academia, how do you get tenure? You published papers.

The types of papers that get published are those with startling empirical observation, and so the hundred experiments that were run that didn't lead to a startling empirical observation are never published and the one that did is published. So you have that bias when it comes to academic and practitioner work. The way that we think about it is kind of nuanced. First off, we start with the broader view of the academic literature, what's the latest and greatest

out there in academia. Then at Dimensional, we've developed a lot of in house proprietary data sets that go back many many decades that include data with a level of tendiness, I would say, and precision that's probably kind of second to none and with respect to all the data sets available out there. And of course you know we're here at Bloomberg Studios who love data and we love data too. You guys um were involved in the early days of

the CRISP data set. Let's talk a little bit about what an advantage it was having not only access to that, but the ability to really do a deep dive and manipulate that data. Tell us a little bit about Chris. Yeah, CRISP was started back in the sixties and it was basically an effort by University Chicago and folks there to gather all the stock price data and dividend data and corporate action data to say, can we were computer return

on the U S stock market? Because pre nineteen sixties you couldn't get that with a great deal of precision. It's amazing, it really is amazing. And so so then over time you know you had CRISP, and then you had other supplements were company financials were added to the data set and all joined and linked up together so effectively you could test things well. And the way that we think about testing things well is number one, do you expect to see this in the data before you look?

Why are you looking for this for this thing? And so that kind of juices some of them, the issues with biases and back tests. You expect it before you go see, and then you see the data tells you how strong it has been or hasn't been. Then you want to do a lot of robustness checks because robustness is the name of the game. So you've tested it in one data sample, can you test it in multiple

data samples? Can you test it out of sample? So I'll give you I'll give you an example, and I think this experiment is kind of unique when it comes to academia. When you look at Famine French in their ninety two paper, they used US stock data from the sixties to the nineties and they tested value, premiums and leverage and all sorts of things in that paper over that data sample and produced the three factor model. Then they came up with a prescription or a kind of

like almost a list of ingredients. Here's how you create a factor model. And that's been used by most academic since so the formula that they used has been used by most academics. Sins. So, then later on in the nineties, with Jim Davis who used to work at Dimensional, he gathered a whole bunch of pre nineteen sixties data, so he was able to extend the original family French analysis to completely out of sample test and that went from

the twenties to the sixties. Then non US developed market data were collected and the same tests that Feminine French had round on. Their original sample was run on non US developed markets, and then it was run on emerging market data because that was collected. And now we're thirty years past the family French original experiment. So now we

have another out of sample test. And so you have five out of sample tests, and in four of those five you see very very strong and reliable value premiums, and you can't actually tell the difference between any of those five about the magnitude statistically speaking, between the realization of those premiums. That's robustness. You've seen it in sample and you've seen it in many out of sample tests. That gives you high confidence that what you're observing in

the data happened by more than just chance. It's something real and you should expect to see it going forward. But that's the type of rigorous analysis that we're able to apply to new observations because now we have so many different data sets that we can test the observation on, we can shape up the experiment, we can find out where the bodies are buried, how robust it is, and that gives us confidence in the in the patterns that were observing in the data, whether they're real or it's

just noise, really really interesting stuff. So so let's talk a little bit about the growth of d f A and and your role there. Um, you're a bit younger than the typical member of your management team. How does that affect how you do your job? What do you bring to the table that some of the more senior

managers might be missing. So I've never really thought about it, to be perfectly honest, And maybe that's in part because I've always been on the younger side, whether it was in high school relative to the rest of the folks in my class. I went to college when I was sixteen, and so it was a little younger than the other folks in my class. And then when I started working at Dimensional after doing a PhD, was younger than some of the other folks in the research team. So it's

always been kind of the state of play. So I don't think about it too much. I would say, a Dimensional we have a very academic view of how to interact with each other. So interact with each other with respect, but challenge and argue the facts and the issues, and the best ideas win. And so I think that when it comes to how to interact with colleagues, whether they're younger or they're older. It's exactly under that formula. You have to operate with respect, listen to the ideas, and

then the best idea wins. Our view is, don't defend the idea just because it's your idea. Embrace the best idea and the right idea because ultimately, long term, that's going to be better for the clients. And if you make it better for the clients, you're going to have a better business. So you know, when it comes to business, clients first, makes business very straightforward on how to make

decisions and what decisions to make. And I think that at that atmosphere, I've always enjoyed a dimensional and so therefore age has never been, never been an important ingredient. So let me flip that question around and ask what advantages do you find when you're working with some older, more experienced folks. What if they bring to the table

for you. Some of the things that come, in my view, with wisdom and wisdom comes with experience, I believe, is how to communicate, how to message, and how to help people understand your point of view without alienating those folks. And I think that's something that has been very helpful for me in working with my colleagues at Dimensional Butler.

Dave Butler is a master of that, of course, and so, Okay, you have a great idea, but if you can communicate that great idea and you can't help people understand why it's a great idea, it's going to die on the vine. You really need to have the great idea and also

have an understanding of how people receive the information. And I think that's something that I've always tried to pay close attention to how my colleagues do that, In the colleagues that do it effectively, how do they do it effectively? Because ultimately, the best ideas win, but only those ideas that can be communicated can be considered the best ideas. So I mentioned earlier the trillion dollar club. You mentioned

uh in an interview. I think it was the Financial Times that you think Dimensional Funds should be a member of that rarefied club that is managing a trillion dollars in client assets. Tell us a little bit about how you're going to achieve that fairly lofty goal. Yeah, we we definitely feel that Dimensional has a lot of run way for growth and there's a few different reasons behind that. One.

We view that many different investors and managers have come around to our point of view that systematic strategies are very, very beneficial for the end investor. And by systematic I mean more rules based approaches, approaches where you can communicate up front, here's what you can expect from this strategy, and then validate after the fact that you got and

delivered what you said you were going to deliver. And I think that's incredibly important for investors to build trust and confidence in the strategies over time, and Eventual has been doing that for forty years. So I think that's one reason that best ideas win, and we have some of the best ideas in my view, and therefore that will serve clients well. And if you're serving your clients while you'll grow. Second kind of component there is exactly

what I said, serving clients well. It's clients first. We think that if we deliver a great client experience, the great support for that systematic approach so clients can understand know what to expect to be able to have conversations. We work with financial professionals, so they have to have conversations with their constituencies and who they're accountable to. We

think that that will also help us grow. And then in terms of the tactics to get there, Dave and I have really discussed this over the past number of years and we think that our investment philosophy is very, very powerful and I can get into that in a moment. However, the means for delivering that investment philosophy have evolved over time, and our view is you get to learn our investment philosophy one time, but then choose your own adventure on

what vehicle you like to consume that under. So you know that we've launched e t F s recently and we've had what I would view as a lot of success on the e t F space. Our first e t F went live in November of and we're around forty eight billion in e t F assets over the course of that time period, and so I think that's been a good outcome. So same investment philosophy is what we've had in commingled mutual funds, but now an ETS

separately managed accounts. How do we use new technology to take that minimum down to a half million dollars from where we used to be twenty million dollar minimum for our separately managed accounts, and we've built that technology, a true fintech solution to that problem, so that we can

serve those types of clients as well. So how we'll get there is by identifying the needs that our clients have and keeping in mind the three c's, which is, there's a lot of complexity in the world that requires customization to come with good solutions, but people want it conveniently. So can we identify the complexity, can we provide the tools so that people can customize the right solution, and can we do all that very conveniently for our customers?

And if we do that, I think we'll be successful. So full disclosure, My firm is a client of Dimensional Funds. Redults Wealth Management uses Dimensional Funds as one of our primary asset managers along with Vanguard, Black Rock, et cetera. But Dimensional is definitely one of our UM large JR. Fun providers, and I'm very aware of the process that Dimensional goes through in order to make sure that their clients understand the philosophy. You understand the model with an

eye towards avoiding the sort of flavor of the month. Hey, I'm chasing this hot manager. No, now I'm chasing that hot fun family. E t f s are very much a break from that prior um embrace of of working very closely with clients. Tell us a little bit about the internal discussions that must have taken place before you switch to e t f s, which, hey, anybody could go to their online training account or robin Hood or whatever it is and and by the e t F

how have you managed around that? So there was two big drivers of that decision. The first was input from clients. And as I mentioned around, we work with financial professionals, so we don't work the end retail consumer. We work with financial advisors like firms like yourself, who can get that level of understanding and knowledge and experience so they

understand what we're what we're trying to accomplish. A lot of those firms were saying, we're using e t f s more and more frequently on behalf of our clients, and we'd like to be able to use dimensional ETFs. Could you launch ETFs please? And so we took that away.

We thought a lot about it, and that was kind of twenty eighteen time frame, and on the books with the SEC back then was a new proposed e t F rule, and what that rule effectively did was it made e t f s much more straightforward to bring to the market, much more straightforward for the end investor to evaluate, but then also clarified some things around the inner workings of e t f s that were important

to us because we're not an index manager. We have a lot of the benefits of an index based approach that include broad diversification, load and over low costs, but we have an active implementation and so those rules got passed in the fourth quarter of is when the SEC adopted those rules Rule six C eleven for anybody who's nerdy enough to want to look into them, and that was a bit of a game changer for us. We could do now what we had done in our mutual funds for decades in an e t F rapper, so

there was no give up on the investment proposition. As soon as that rule was passed, we went into full launch mode. By June, we had announced that we were going to launch. By November of twenty so almost a

year after the rule came out, we had launched. Those were the two big drivers on the tax efficiency side that wasn't as big a driver for us largely because, and you're familiar with this, our mutual funds tend to be highly tax efficient, and we had tax managed mutual funds that had similar tax efficiency ratios to e t f s, So we had very very tax efficient approach.

E t f s taken up a bit our e T S two, but it was more what our clients were asking for, and the rules changed such that we could deliver a investment proposition that was on par with our mutual fund investment proposition. And your turnover in your various funds is relatively low compared to the average mutual

funds at a fair statement. That's a fair statement on the equity side, for sure, on the fixed income side, where we do things that lead to slightly higher turnover because of the information that you can take out of yield curves at any point in time. But on the equity side, you know, a core strategy has ten percent turnover, value strategy twenty turnover in a given year. And how to think about that is like in the value strategy, when you buy a stock, you expect to hold it

for about five years at turnover. That's how how you can kind of translate that into holding period on fixed income? Is it primarily duration versus credit risk that that the activity comes from. It's a combination of duration, it's a combination of credit, and then it's also a combination of currency of issuance. When you think about fixed income, a lot of people focus on the FED and what the

FED is going to do. That's one rate among hundreds of rates out there, because there's different currency of issuance, different durations, different credit qualities. And what we do is we take in five six hundred different interest rates from around the world and we use that information every day to say, how do we increase expected returns the return

of this portfolio, but manage risk very very robustly. So again it's has an index feel, but it goes beyond indexing with an active implementation to add value and manage risk. Really interesting. So let's talk a little bit about what's going on in the market this year. Pretty tough start. First quarter was a bit shaky. It was a little carry over from the end of uh SO growth investors have been doing so well for so long, hasn't hasn't been a great couple of quarters for them. How is

the f A navigating this volatility? Yeah, you're right. It has been a rocky started the year in absolute terms, and when you look at the first quarter of two a lot of the major indicries, whether that's US or non US, developed or emerging or in the negative territory, You're right, Value has continued on it's good run, and value has been having almost like a two year a good relative performance, which is more what we expect from the world, and that continued on in the in the

in the first quarter for sure, where value stocks help performed growth stocks by as much as ten percentage points and lots of different regions around the world. So that's been good for the investors in dimensional strategies because a lot of our strategies on the equity side overweight value stocks and stocks with high profitability and so on. In terms of navigating the volatility. You know, when you go back to our investment principles, there's probably three that I

would highlight. One systematic approach is a good approach for investors with the right support, the right continued information, innovation, and the right price point. So that's one one basic principle. The other two are that prices are predictions of the future. Market prices are forward looking, how do you use those prices to manage risk and increase expected returns? And the third is that optionality has value. We should capture on

behalf of our clients. So when you go through a time period like what we've just been through, where you have Russia invading Ukraine, all the sanctions that then subsequently came on Russian companies, Russian stocks, Russian individuals, that flexibility

or optionality is critical. Because what we were able to do was in January, when you know, there was a lot of talk of sanctions versus various different companies and individuals, that we were able to freeze purchases on all Russian securities, which was an important part of our process. We said, okay,

let's take a weight and see approach. And that was in part because if you go back to and when the annexation of the Crimea by Russia, at that point, we have a set of criteria that we go through rule of law, you know, how our foreigners treated versus locals, the local infrastructure, and we said, you know what, that criteria for that country right now is not quite being perfectly well met. So we reduced Russia to a half

weight in ten. So we already had that flexibility built in, but that's very helpful when you go through time periods like this because you have a systematic approach that's largely rules based. But you can't come with a set of rules that will contemplate every state of the world. So you need to have people who have pragmatic and practical experience to say, well, what can we actually implement in the real world, and then how does that citizen overlay

on top of what we do. So I think that this year that has been helpful in our strategies and how do we stay flexible to adapt to what's going on in the world and in markets around the world. So so let's talk a little bit more about the value versus growth um relative performance. Growth has really had a great decade. The growth was beating value. That started to change last year. What do you attribute that too? Is an inflation the end of quantitative easing and zero

interest rate policy, or or something else. And I'm sure the investors who are listening are going to want to know and how long can this last? Yeah, it's a it's a very interesting question. I'm gonna flip it around on your barry, which is why did we have such a long run of growth out performing value over the

because that's the unexpected outcome. Value out performing growth is not the unexpected outcome because when you think about value stocks, there are stocks that have lower prices and higher expected cash flows. So by definition, investors have applied a higher discount rate to them, and that's every day, and so

you expect them to outperform growth stocks. When growth outperforms, that's the unexpected outcome, and that happens plenty, because returns over the short pull are driven by the unexpected things that happened, not they expected. When you look over the past decade, there was probably unexpectedly good outcomes for the

facebooks and the Amazons and the Netflix. If you go back, you know, fifteen years and say do you expect this group of fang stocks or whoever to have an annulyzed compound rate of return of thirty percent a year for the next decade. Not many people would have said yes. But they did very very well. They improved their earnings profile quite dramatically over that period and were rewarded when

you go then into the later time period. Um, you know, those value stocks in particular in the US, when you look at the price to earnings or price to book ratios of value stocks for versus growth those ratios and

those differences had grown dramatically large. So growth had become higher, higher, higher, higher in terms of their valuations, whereas value had stayed kind of right around where it was because value had come in kind of like it's long term average, but growth had come in well ahead of its long term average in terms of returns, and so value was still in the same position to deliver those good returns going forward, whereas the expected returns on growth stocks had probably dropped

given those higher valuations. So so let me phrase my hindsight bias and in the form of a question, which is, isn't it obvious today that post financial crisis the financials would lag for quite a while, and there they tend to be big value stocks. And then when we look at the growth side, Hey, this was a societal transformation, a generational shift, uh, towards mobile, towards internet, towards technology. Again, with the benefit of hindsight, how did we not see?

Why was this a surprise? It's perfectly obvious after the fact that this massive change was taking place. It's obvious after the fact that in the middle of it. You never know exactly what's going to happen because there's always

new technologies. People often talk about the new normal, and there is no new normal because technologies have been developed persistently a decade by decade for the past hundred years, and those technologies give rise to uncertainty about who will adapt and use them in the best manner, and who will be the winners and who will be the users once that new technology comes into place. So there's always a massive amount of uncertain It existed a decade ago

and exists today. And what we look to markets to do is process that information to say, given that uncertainty, who am I going to demand the higher return to hold or a lower returned to hold? So I think that's the state of the world. And but even things by you know, like who's going to predict that COVID would come along and be such a boon to the Amazons and the netflix of the world because everybody who was locked in their house for some period of time

that is unexpected. That's an unexpectedly good outcome, not for society but for the firms that were well positioned to meet the needs of society. When that unexpected event began to unfold. So so let's talk about another surprise in return, which has been since the financial crisis. The US has just trounced international returns for far longer than I think even the most ardent US investor expected. How do we explain the dominance of US equities versus either developed x

US or emerging markets. And there, I'd point to you to the last decade, which was the previous decade, where you know, small cap stocks, non U S stocks, emerging market stocks greatly outward outpaced US large cap stocks. And then in the decade that you're referring to, it flipped completely and US large cap stocks outpaced everybody else, in

particular US large cap growth stocks. Again, i'd put that down there's an unexpected component to that, and I'd put it down to the success of some of those U S firms that are now the largest firms in the US marketplace. That doesn't mean they will continue to be the largest firms in the US marketplace, because what we've seen over time, the largest firms tend to get there

by outperforming everybody else. And in the global marketplace. Now the US has many of those largest firms and then in the you know, one to five years after they become the largest firms in the world, they tend to underperform everybody else's other firms innovate and try to take that top spot. So there it's just you know, success of those companies, and that's driven the investor demand for those companies because they've been able to satisfy so much

client demand. Those are well run companies, and investors see high cash flows from those companies and they're willing to build up the prices. So so let's talk about a couple of things that are in the midst of changing and what you guys are doing about it. And I

guess I have to start with volatility. We saw a giant spike in O eight or nine during the financial crisis, another big spike in during the pain endemic, and the VIX the measure of volatility was high thirties, and just just a month or so ago that seems to be rolling over and coming back down. First, what have we learned about volatility and how can investors use it to their advantage? And second, what do you think this softening of volatility today might imply for the rest at least

of this calendar year. So what we've learned over time about volatility is that when there's a market crisis, and this goes without saying volatility increases. Why because uncertainty increases. There's a lot more uncertainty about what the range of outcomes maybe, and that uncertainty leads to a few different things. Increases in the volume of stocks and bonds that are traded, increases in bid off or spread, so the cost to trade those stocks and bonds, increases in volatility. All of

those things come in a crisis. We had a crisis in March of when Russia invaded Ukraine. We had another crisis, how would that translate into global markets? And volatility tends to spike. But we've also learned over time is that spikes and volatility are unpredictable. So it's a shock, it's unexpected for a reason because it's unpredictable. And then once it's spikes, it tends to decay slowly unless there's another big shock that comes along to spike it back up.

So it tends to decay over the over course of three to six months, goes back down to normal levels, and you can actually see that from market prices there's different market prices that tell you about the implied volatility of markets over the next thirty days, over the next thirty days following that, the thirty days following that, and so on the forth. And what you see from market prices is that when you get a big spike, it from market prices is expected to decline over you know,

the next subsequent months. And we saw that clearly in March. Volatility spiked, but the markets told you that it expects the decline over the next a few months. It's the same with inflation. Right now, you can look at break even inflation and it's expected to be about six percent as of the end of Q two, But if you look at over five years, it's expected to be six percent over the next twelve months and then declined to

something sub three in the subsequent four years. Right, So markets always tell you something about what's expected right now and what's expected in the future. So since you brought up inflation, let's talk a little bit about that. Um, what is the f A doing in preparation for higher

interest rates? If the Fed keeps raising rates and if bond investors keep selling short duration holdings, how are you going to adjust to that, what do you think about things like high grade corporates and tips versus high yield and and risk of your bonds, your inflation and interest rates. Inflation has been high. Everybody knows that over the past while. And the way that we view inflation is there's two things that you can do the markets. You can look

at get understanding of what the market expects. But the unexpected often happens. Nobody can predict the unexpected, so therefore you can but you can plan for the unexpected, and you can plan to outpace it or to hedge it. And so if you want to outpace things like what you mentioned, corporate bonds, globally diversified bond strategies, equities and so on over time have had positive real returns, so returns in excessive inflation, in high inflationary environments and low

inflationary environments. And if you look back the thirty past thirty four years, you see that if you want to hedge it, you can use treasury inflation protective bonds, and we think that they're a good solution. You can also then if you don't want to give up so much expected return by corporates, are bonds like that and then hedge it with different types of instruments like inflation swaps and so on that can hedge out your inflation exposure.

They're the two ways to deal with inflation in our view. You can plan for it. You can't predict when you're getting the spike, but you can plan for it. When it comes to interest rates and increasing interest rates, again, you can't predict when they're going to shoot up. That's not a something that you can predict, but you can plan for it. How do you plan for it? Well, we mentioned earlier on that there's an obsession over the

Fed Funds rate. But if you look over the past thirty years, thirty to forty years, the Fed has increased the Fed Funds rate one month out of six, has decreased the Fed Funds rate one month out of six, and has left it flat in the other four months out of six. That's been about the pattern over the

past forty years. And when you look at the months in which has increased the Fed funds rate, about half the time the third year rate has gone up and about half the time the third year rate has gone down. So what does that tell you? It tells you that other rates out there other interest rates don't move in lockstep with what the FED is doing. So if you think about that and you extrapolate, you have interest rates on the short end, the intermediate end, the long end.

You have interest rates as they applied to corporate bonds from triple A s down the double B s. You have interest rates from a current from bonds issued in euros and British pounds in Assie dollars and so and so forth, and none of them move in lockstep with this FED. So you can diversify. That's how you plan. The FED may do what it's going to do, but it's one interest rate among money, and that's going all

of those other interest rates. You wanted to drive the returns of your probably diversity by portfolio because if you look from oh eight on the subsequent ten years, the FED funds rate was basically at zero for a decade, but it globally diversified portfolio stocks and bonds returned about four. So in a zero FED funds rate, you've got about a four return. So again it goes back to you

don't have to be able to predict the unexpected. You just have to be able to plan for it and then stick with that plan, regardless of what the unexpected brings brings the past. So let's talk a little bit about your career. Uh, pretty much, since you've been in the world of finance, we've only seen low rates and

we've only seen mostly low inflation. Does that impact you're thinking, there's a color your perspective having lived um as of financial professional in this somewhat aberrational environment, or are you looking at the academic research and able to pull yourself

out of it. So I would say it's a little bit of yes, a little bit of no. Um in the yes category is that certainly, after the financial crisis, the global financial crisis, there were a lot of client questions about the role of fixed income in a portfolio because if you're used to headier times when interest rates were higher, you might have a different perspective on how to use that strategy than when you know interest rates

are low. And so that has informed Okay, what are the things that our clients are caring about and what is it that we need to deliver to clients given that those are the concerns and these are the problems that they're trying to solve in a low interest rate environment. So that's a little bit of yes because it's been

on client's minds. The little bit of no is that we've had We have decades upon decades, fifty sixty years and longer of data on the returns of bonds, both here in the US of corporates and of other bonds around the world issued in different currencies, and so we can look at lots of different high interest rate low interest rate environments, transitions between those when the interest rates were had gone up or gone down, and so we can understand are there certain strategies that work better or

worse than each of those environments, and we can then we can design strategies that work well for both environments. So that long term view is something that we always keep in mind, which means that you know something that happens over a decade or fifteen years. It does give us new information, but doesn't necessarily change dramatically our investment priors. Really really interesting. Before I get to my favorite questions,

I just have to throw a curveball at you. So, in your bachelor's in theoretical physics from Trinity College, what were you studying in theoretical physics? What areas did you concentrate in because I'm familiar with that space and find it absolutely fascinating. Yeah, it is really a very very interesting space. And you know, when I have was a it was a kid, I like to read Stephen Hawkings

and those types of books. So I was very interested in relativity and so kind of that that side of what Einstein worked on, and I found that very interesting. We had a lot of we have courses on relativity when we were in in university in theoretical physics. The other side is quantum mechanics. And quantum mechanics is very very interesting because you never know anything with certainty, so it kind of has parallels to to the real world.

You can't know something's position and its speed at the same time, you can only know one perfectly, or you can know both in a with a lot of uncertainty. But quantum mechanics is also incredibly interesting because everything has multiple states of the world is and as in those multiple states all the time with some set of probability. So that's also a very fascinating field of study. And I enjoyed those quite a lot when when I was

working on them back in Trinity College in Dublin. So so if you're a fan of um Hawk Professor Hawkings and some of his work. Can we all admit that dark matter and dark energy is a cheat and we really have no idea what's going on with the expansion in the universe, because every explanation I've heard from various theoretical physicists have been, well, we're not sure, but we've made up this thing that we hope to figure out one day. It seems like it seems like it's um

you know, a short cut. You know it may be a short cut. But I'd go back to your earlier statement was, which is around how our models evolve over time,

our data evolves over time. Like you saw from a couple of weeks ago there was a new discovery from the Hubble Telescope of the oldest star yet which is older than the universe, which is which seems to be a little confused, a little confusing, and so new data emergence all the time, and then you create models to try and understand those data, but you know it's not what understood yet are not I would say it's what understood not completely understood, and there's a lot left that's

not known yet for people to Discover fair enough. So so let's jump to a little less heavy material and talk about our favorite questions, starting with tell us, what you've been streaming during the past couple of years of Lockdown and Pandemic, either podcast or Amazon and Netflix. What's been keeping you entertained? Yeah, couple of different shows have

been keeping me entertained. So it was in a board meeting, one of the Advisor board meetings, and one of the board members mc McCown said that he had been watching a documentary series called The Prize, and The Prize is from a while ago. It's it's about the kind of the history of oil and you know how it started and where it evolved two and all the various different issues that have arisen as a result. So that was super interesting and I'd recommend that to anybody who's kind

of interested in those types of historical shows. Other things that I find interesting over the past few years, I've watched a lot of documentaries about you know, World War two, World War One, Vietnam War. Ken Burns has some great stuff even on the US Civil War that have been very interesting. The Fog of War that that was another interesting show. I find those particularly interesting, just how do

you ever get there? Because war is in a rational act, so what what are the things that have to happen in order to get there? Because it's much more rational to cooperate and to trade than it is to go to Everybody will be better off in the former and worse off in the latter, So how do you actually get to that state of the world? Is interesting. I have a six year old daughter and so we watch shows together and that also keeps me entertained. She loves If I were an Animal. I don't know if you've

seen that show on Netflix, but that's a goody. And then another one that came out recently a Netflix is Old Enough. I don't know if you've seen this as a Japanese show, and they have like little three year olds, four year olds, five year olds, and their parents give them a task to do and then they have to go off into the round town into the shop and they're followed by a camera coup by themselves, and they accomplished this task. It's hilarious. It's it's really really fun

to old Enough to check. That's a fun one. Let's talk about some of your mentors, who were some of the folks who helped shape your career. Yeah, I would say that in terms of folks that have my career. Some of the names that you mentioned, whether it's a Fama, French, Martin, have all been very helpful to me over time. David of course, has been very very helpful to me over time. EDWARDO used to work at Dimensional, has been very helpful

to me over time. And then I'd be remiss if I didn't say my parents, because there you know, up until the time that you leave the home, and they're your ultimate mentors in terms of shaping how you approach problems, how you view the world, what you prioritize. My parents have always emphasized education and the importance of keeping your mind active and trying to better yourself. How do you

become better than your were the day before? And that's a spirit that I think it's important for anybody to keep kind of pulling towards for as long as they're on this planet, because what else is there to do but try to improve your skills and and how you interact with the world. So let's talk about books. This is everybody's favorite questions. What are you reading right now and what are some of your favorites. You know, I

am not reading any book right now. I've been consumed with work over the past few years and by reading for pleasure has taken a back seat, unfortunately. But some of my favorite books over time, I would say one Freedom to Choose. I don't know if you've read that book by Milton Freeman. I think is a great book and timeless, I mean written many decades ago, but but very very timeless. They wro't deserved them. I think is one of the all time classics as well by you

know That's uh is an all time classic. So you're going to get my idea from I like books about markets, about how to organize people and how do you get to a state of affairs where you're making the most efficient use of the resources, where people have freedom to pursue what interests them. I find that an interesting area of reading. What sort of advice would you give to a recent college grad or someone who was interested in

a career in investing and finance? So two big areas one and this is something that is kind of I call it a dimensional motto, and it's do the right thing, do it the right way, and do it right now. And so when you're pursuing a career in any field, you want to feel good about what you're doing. You want to feel that you're helping people. Do you want

to do well while you're helping people. But that's the right thing, And then do it the right way is how do you come with a path to make a decision that uses as much of the information that's available to you. There's gonna be a lot of noise in the outcome, but you want to be proud of the decision that you made given the information that you had at the time. I think that's doing things in the right way. And then do it right now. Never sit on your hands, be proactive, get after it, close projects.

If you can't close it, move on, ask for help, and don't sit on your hands, go out and get it done. Then, when it comes to finance in particular, remember what you're doing. You're taking people's life savings and you're trying to help them achieve objectives and goals, and they're taking risk to achieve these oblection and goals that they couldn't achieve without taking those risks. And that's a very,

very meaningful responsibility. So don't take it lightly. And you're moving into a field that you can really help people have a better life, but you can also harm people if you do things in the wrong way. So I think that that's a something that you've got to keep in mind when it comes to finance. It's not your money, it's somebody else's money. Be fiduciary, be prudent, and then you can really help people. Be it off really interesting answer and our final question, what do you know about

the world of investing today? You wish you knew about twenty years ago or so when you were first getting started. When I was first getting started, I had this view of the world because I had never taken a course in finance before a dimensional which and I didn't understand markets that well. I had the view of the world that all you had to come was with was a better mathematical model than anybody else out there, and then that would be able to predict where prices were going

to go. And of course I was quickly disabused of that notion after having conversations with Ken and Geene and Bob, and you just need a better model, And so I wish I had known that then, but now I certainly know it, and it's really helped shape how I view what good investment solutions are for clients and what really the power of markets are and can be really really interesting uh stuff. We have been speaking with Gerardo Riley. He is the c i O and co CEO of

Dimensional Funds. If you enjoy this conversation, well, be sure and check out any of our four hundred or so previous interviews. You can find those at iTunes or Spotify or wherever you get your podcasts. We love your comments, feedback and suggestions right to us at m IB podcast at Bloomberg dot net. You can sign up from my Daily reads at Ritalts dot com. Follow me on Twitter at ritlts. I would be remiss if I did not thank our crack staff that helps put these conversations together

each week. Mohammed Remaui is my audio engineer. Attica val Bron is my product manager, Paris Wald is my producer. Sean Russo is my head of research. I'm Barry Ritalts. You've been listening to Masters in Business on Bloomberg Radio.