Gerard O’Reilly: Control for the Unexpected, Focus on the Expected
DFA’s co-CEO and chief investment advisor shares the latest on the launch of its ETFs, the importance of client relationships, and the role of academic research in the firm.
Our guest this week is Gerard O'Reilly. Gerard is co-CEO and chief investment officer of Dimensional Fund Advisors, an asset manager headquartered in Austin, Texas, that manages more than $600 billion using a systematic investing approach. In his role, Gerard works with his co-CEO, Dave Butler, to set the firm's vision and strategy while also overseeing its investment processes. Prior to assuming his current post, Gerard was Dimensional's head of research. In addition to serving as a Dimensional director, Gerard co-chairs the firm's Investment Research Committee and is a member of its Investment Committee. Gerard obtained his doctorate in aeronautics from the California Institute of Technology and his master's degree in high-performance computing from Trinity College, Dublin.
“Dimensional Eyes the Fund Sector’s Trillion-Dollar Club,” by Owen Walker, thefinancialtimes, March 31, 2019.
“Dimensional Fund Advisors Significantly Expands ETF Offering,” dimensional.com, Nov. 17, 2020.
“Dimensional Investing in an Active ETF Structure,” dimensional.com, Nov. 17, 2020.
“Dimensional Funds ETFs Launch as Quant Plans to Convert Mutual Funds,” by Claire Ballentine, Bloombergquint, Nov. 19, 2020.
“Why Dimensional Fund Advisors Is Converting Six of its Mutual Funds to the ETF Format,” by Lizzy Gurdus, cnbc.com, Dec. 1, 2020.
Innovation, Value, and Return
“Market Beaters: A Different Dimension,” by Beverly Goodman, barrons.com, Jan. 6, 2014.
“3 Shades of Value,” by Daniel Sotiroff, Morningstar.com, Sept. 26, 2018.
“Robert Merton on Financial Innovation,” by Robert C. Merton, dimensional.com, June 24, 2019.
“The Real Reason Value Has Been Lagging Growth,” by Julie Segal, institutionalinvestor.com, Oct. 24, 2019.
“Tesla’s Charge Reveals Weak Points of Indexing,” dimensional.com, Jan. 15, 2020.
“Untangling Intangibles,” by Savina Rizova and Namiko Saito, dimensional.com, Sept. 28, 2020.
“An Exceptional Value Premium,” dimensional.com, Oct. 5, 2020.
“Securities Lending Fees as a Short-Term Driver of Stock Returns,” by Kaitlin Simpson Hendrix and Gavin Crabb, dimensional.com, Nov. 11, 2020.
“Sustainability Report,” dimensional.com, Dec. 31, 2020.
“Burton Malkiel: ‘I Am Not a Big Fan of ESG Investing,’ ” The Long View podcast with Christine Benz and Jeff Ptak, Morningstar.com, Aug. 5, 2020.
“Dimensional Finds ‘Little Evidence’ Emissions Are Linked to Expected Returns,” by Christine Idzelis, institutionalinvestor.com, Oct. 22, 2020.
Jeff Ptak: Hi, and welcome to The Long View. I'm Jeff Ptak, chief ratings officer at Morningstar Research Services.
Christine Benz: And I'm Christine Benz, director of personal finance for Morningstar.
Ptak: Our guest this week is Gerard O'Reilly. Gerard is co-CEO and chief investment officer of Dimensional Fund Advisors, an asset manager headquartered in Austin, Texas, that manages more than $600 billion using a systematic investing approach. In his role Gerard works with his co-CEO, Dave Butler, to set the firm's vision and strategy while also overseeing its investment processes. Prior to assuming his current post, Gerard was Dimensional's head of research. In addition to serving as a Dimensional director, Gerard co-chairs the firm's Investment Research Committee and is a member of its Investment Committee. Gerard obtained his doctorate in aeronautics from the California Institute of Technology and his master's degree in high-performance computing from Trinity College, Dublin.
Gerard, welcome to The Long View.
Gerard O'Reilly: Thanks for having me, Jeff, and thanks for having me, Christine. It's a pleasure to be here.
Ptak: To familiarize our listeners with your role at Dimensional, can you walk through what your specific responsibilities are as co-CEO, and how those might differ from Dave Butler's who is Dimensional's other co-CEO?
O'Reilly: Yeah, absolutely. And it might help to provide a bit of context. So, Dimensional is very much an investment and client-led firm and we strive for excellence in both of those areas. So, when you look at Dimensional as an organization, there's 13 global groups around the firm, four of them report directly to me, four to Dave, and five to both of us. So, the four that come to me are the investment-type functions: research, portfolio management, trading. Those that go to Dave--well, Dave has been with the firm for about 25 years, and he's grown up on the sales side of the firm--so it's the global client groups: marketing, HR, corporate services, and so on. Then those that come into us jointly are compliance, legal, and then we have a chief operating officer, Lisa Dallmer, and operations, finance, and those types of groups come up through her and she reports to us jointly. So, from day-to-day, he has a focus on sales and marketing, things of that nature. I have a focus on investments. And then, the overall operation of the firm, we jointly focus on. So, that's kind of how it splits up. And a lot of folks think that co is an unusual role, co-CEO. We use the co role quite frequently here at Dimensional, have done for many years. So, we had co-CIOs; we've had co-heads of different groups. So, it's something that's quite common, and we think that it's useful for redundancy, and also to help focus on different areas that are important to the firm.
Ptak: If we were to think of the investment function you oversee as capital, so to speak, and one of your responsibility is to allocate that capital like a capital allocator, how do the investments you're making in the firm today compared with, say, 10 years ago, what are the most striking differences we'd observe?
O'Reilly: It's a good question. And there have been differences and changes over time in how we allocate capital on behalf of our clients. When you think about Dimensional, I always think about innovation and transformation, where we innovate on how we invest and the types of products that we offer our clients, and transformation in that we've changed how our clients do business and they've changed how we do business.
So, when you look back 10 years--I'll give examples. When I joined the firm in 2004, so more than 10 years ago, we were preparing to launch a set of core strategies, which were – people call them different things, but multi-factor strategies is a common name for them. And if you look back 10 years ago, we had about $160 billion, give or take, in regional equity funds, so that's U.S. equity, non-U.S., developed, emerging. And that was about 40% these core: all-cap, large-cap, small-cap core 10 years ago and about 60% value. Fast-forward 10 years, and that's about 70-30. So, 70-30 large-cap, all-cap and small-cap core, 30% value. So, there's been a change in terms of how our clients like to consume our expertise and go for more all-market solutions and complement that with value portfolios.
I'd say another big change has been on fixed income. If you look at fixed income, fixed income has been kind of a steady-Eddie. It's been about 20% of our business for a long time. But over the past decade it's probably gotten more than its fair share of flows, because equity has grown quite quickly over the past decade, fixed income lower in terms of returns. But if you go back 10 years, we had about $40 billion in fixed income. And about 3% of that was in all of investment-grade strategies. Now, we've got $120 billion, and over 35% of it is in all of investment-grade strategies, so AAA down to BBB, and we do some BBs as well.
So, we had a whole book of business of $40 billion 10 years ago, and with very small amount of investment-grade, and now over $40 billion in all of investment-grade or core strategies. So, when you look at things like that, I think that you see this innovation over time and transformation. As markets evolve, as our clients require new solutions to solve the problems that they're working on, we develop strategies that manage toward those solutions, and they've been quite successful for our clients over time.
Benz: Dimensional recently entered the ETF market by launching three ETFs with more to come in 2021. You're kind of late to the party. Can you talk about some of the misgivings you previously had about launching ETFs and how you overcame those from an investing and a business standpoint?
O'Reilly: So, thanks for that question, Christine. I don't know if you guys saw there was an article yesterday on the Business Insider, and there was president of The ETF Store was quoted, Nate Geraci. And I'm going to quote this because I found it so funny. “DFA arriving late is like Brad Pitt walking through the door of an Oscars' afterparty at 1 a.m. Both are immediately the center of attention. It doesn't matter that the party is already in full swing.” I just found it so funny. We had a good laugh at it yesterday between Dave Butler, myself, and Catherine Newell, our general counsel. We were trying to decide who would be Brad Pitt, who would be George Clooney, and all that sort of fun stuff.
But kind of taking it a bit more seriously, when you look at the ETF evolution over time, if you look at equity ETFs, even today, it's about 99% index. And what does Dimensional bring to the table? Well, Dimensional is systematic fundamental. And what does that mean? That means that we bring many of the benefits of indexing--low fees, transparent, well-diversified, easy to monitor--but a lot of the benefits of an active implementation, whether that's the innovative research, daily rebalancing, flexible trading, all these types of things. And so, when we look back at the history of ETFs, because of the ways that ETFs were managed, there wasn't as much flexibility in managing investment strategy inside an ETF structure over the past 10 or 20 years.
But that changed. That changed at the end of 2019. At the end of 2019, there was a new ETF rule, and that new ETF rule paved the way for active transparent ETFs. I prefer to think of them as flexible, transparent ETFs. And that rule was an important impetus into us deciding to launch ETFs, because we could bring what we've done in separate accounts, what we've done in mutual funds, what we've done in trusts and so on, to the ETF landscape. We don't mind that it's transparent, because we're well-diversified. We minimize unnecessary turnover. So, having transparency of holdings is not really a big deal.
So, I would kind of turn it around a little bit and say that we're very early to the active transparent party, because that is somewhat in its nascency when you look at the overall ETF landscape. And it's been a good launch so far. We launched three ETFs, as you mentioned, in November/December--healthy volumes, healthy spreads. They're at about $500 million AUM over the first two months. So, they've done well. And then, we plan to convert six tax-managed funds over the course of 2021, and they're about $26 billion, give or take. So, I would say that it’s a new, innovative investment strategy coming to the ETF space, and hopefully will be well appreciated by investors.
Ptak: Your model has been to sell exclusively through financial advisors. Launching ETFs seems likely to change that. In fact, I know it changes that as anyone can access these ETFs you've referred to. Against that backdrop, what are some investment decisions you'd made previously that perhaps you wouldn't have made if you didn't have the advisor-driven model you had before? For instance, maybe it would be to incorporate a factor that you knew wouldn't confer immediate benefits, but that your advisor base would be OK with knowing that in the long term it would pay off?
O'Reilly: Thanks for that. We work with investment professionals. So, I'd probably change around a little bit. And what I mean by investment professionals, they span a wide range of clients. So, we work with financial advisors, as you mentioned, and they span $50 million to many billions in terms of the assets that they oversee or advise for mom and pops investors. But we also work with banks that provide advice to high-net-worth individuals; we work with corporations that provide investment options to their rank and file workers; we work with city and states who invest on behalf of their teachers, police force, firefighters; we work with sovereign wealth funds, central banks that invest on behalf of the citizens in their countries.
So, what's common between all those people that we work with directly are that they're investment professionals. And we think that's important, because they understand the goal of the assets. They understand what the assets are to be used for, either because of the mandate of the organization, or the relationship that that financial advisor may have with the mom or the pop that they're working with.
So, when you think about it in that way, we have quite a large and diverse set of clients. What's common between them all is that they understand investments, they understand the goal of the assets. And so, that means that our model or our distribution model then becomes more educational in nature. Where we spend a lot of time covering things like volatility, what to expect from our strategies, how to monitor us, how they can fit into various different asset allocations. And because we have a wide range of clients, that means, in part, that if we can do something better, we will, always. So, if we come up with some new piece of research, or if there's some new way to deliver an investment service to our clients, that's what we'll do. We keep that as one of our guiding lights is that if we can do something better, let's do it better. So, that model of education, I think, is an important model.
So, I would say that the fact that we work with financial advisors hasn't impeded us from bringing new solutions or new ideas to the table. In fact, the way that I view our relationship with clients is that we learn from them and they learn from us. It's a transformational relationship. We make them better, and they make us better, because they understand the goals of the assets. So, they tell you and they explain to you in great detail, here's what we're trying to accomplish so that we get a very good idea of, well, from a capital markets perspective, which we aren’t experts in, how do we accomplish those goals. So, I would say that we've plenty of examples of innovation over time, ETFs being one of the latest ones, but it's always about serving a broad range of clients, who then in turn, serve a big constituency of investors.
Benz: Do you think your proximity to advisors helped keep them in the fold? And if so, how do you mitigate the risk of seeing that bond weaken as you broaden the firm's focus to other types of investors going forward?
O'Reilly: I do think that our relationship overall with our clients has been very helpful. In particular times, like last year, when growth did exceptionally well, and value did relatively poorly by comparison. So, when you have time periods like that, that trust that you develop by having a close working relationship with your clients is important. And I go back to the educational model. We spend a lot of time before we work with any financial professional or an investment professional, on trying to explain: here's what volatility means, here's how good it can be, here's how disappointing it can be, here's what our strategies are designed to do. And so, that means that when you have a particular outcome, that outcome, the client very much understands what's driven that outcome. And you can demonstrate, here's all the ways that we added value, but that's in the context of very noisy market returns. So, we're very much committed to that and we think that that's an important aspect of our business going forward, Christine, and we think that that will be very important across different channels going forward.
But we're committed to education, whether that's through conferences, whether it's through communities, whether it's through practice management, and client communication. I'll give you some examples. So, last year, as we all know, that we went to a much more virtual environment. While Dimensional had been working on virtual interactions and the quality of those virtual interactions with their clients for many, many years and we have a set of studios here in our Austin building that we can do TV-quality broadcasts, we put on conferences and community events. A community event is where you either have a group of advisors or a group of CIOs from corporations, or a group of sovereign wealth funds, all get together so they can communicate with each other, but also hear from us. We had close to 100,000 attendees to those various conferences, communities, practice management events, and client communication events over the course of 2020. And we plan to do more again in 2021. So, I think that those types of investments that we're making in educational type of events for clients help deepen the relationship, engender that trust. And this ETF launch is really because our clients have been asking us for ETFs. They use those investment vehicles. They want our investment approach brought to an ETF investment vehicle. And so, they're designed with them in mind. And it's really for the financial professionals and investment professionals that we work with that we have launched these strategies. And that's evidenced by the, I would call it, relatively good success in the first two months--$0.5 billion in two months of three brand-new ETFs kind of tells you that, yeah, the advisors that we work with have been waiting for this for a period of time.
Ptak: I wanted to stay high level. We're going to talk about things like sources of returns, value, factors and the like. But to stay at a high level for a moment--the notion of innovation versus centeredness and keeping a firm focused on its core investing principles. As the firm's chief investment officer, how do you strike the balance between encouraging fresh thinking and innovation while at the same time safeguarding the firm's core investing principles? That has to be very challenging, and so, how do you strike that balance in practice?
O'Reilly: Yeah, it can be challenging, but there's probably a few different items that are important in striking that balance. And I would say one is the nature of our systematic approach. And I mentioned that a little bit earlier that it has the benefits of indexing but also active implementation. What I'd add to that is this view about market prices. And market prices are effectively forecasts of the future, and we believe that they're the best forecasts that we have available to us. And there's lots of data supporting that it's somewhat futile to try to outguess market prices. You're better off trying to understand what information market prices are presenting to you and how do you use that information.
Now, I highlight that because that gives you a framework for making improvements. You can get improvements through new markets opening up. Are new market prices being presented to you? So, an example would be when the investment-grade, corporate fixed-income market started to become more transparent in the mid-2000s was because market prices started to be disseminated more broadly and more quickly. So, that opens up a new market with which to do research in. It also gives you a framework to say, “How do you want to test the data, analyze the data?” And so, that's a very important framework for innovating. And then, I would say adding on to that, then some of our changes will happen slowly, some of our changes will happen fast. And that's exactly because of what you mentioned--this we're stewards of a large asset base.
And the example that I sometimes use, and I get this from Bob Merton, who works closely with the firm, is the example of pi. So, pi, not the pie that you eat, but pi, the number, 3.141, and so on. The way that I often think about it is that some changes are like enhancing pi by another digit. So, it was 3.1. Now, it's 3.14. Now, it's 3.141. And so, some recent research that we did on asset growth or investments was like taking pi from 3.14 to 3.141. We took a couple of years to do that research. We made sure that we dotted every i, crossed every t before we introduced it into our investment strategies.
I would say the ETF rule that came out in late 2019 was like saying: you used to think pi was 4, but now it's 3. “Oh, I have a new investment vehicle that I could do things today and that I couldn't do before. OK, that's important. Let's get on top of that pretty quickly.” From the time that rule came out to the time that we filed for ETFs was about six to eight months to the time that we launched was about a year. So, that's a brand-new registrant launched purely under rule 6c-11, brought to market in the course of a year. And again, because it's the right answer. I always believe the right answer wins in the end, so embrace it rather than defend the wrong answer, even if that wrong answer is your own. Just check your ego at the door and search for the right answer. And that's the important part of driving innovation.
Benz: Can you give an example of a concept your team did a lot of work on with your encouragement that you ultimately decided not to pursue? And how do you set expectations with your researchers about the kind of batting average they're likely to hit for in seeing their ideas come to fruition?
O'Reilly: Absolutely. And to set that in context, let's just talk a little bit about, I would call it the academic pedigree of Dimensional. So, it's probably common knowledge that Dimensional has very close ties to the University of Chicago Booth School of Business. When you think about that university, that university has something like 31 Nobel Prize in Economic Sciences. So, it's, I think, the most decorated university when it comes to that particular prize in the world. But we also have relationships with Dartmouth, Stanford, Yale, Ohio, University of Rochester, Harvard. There's a lot of academics like Eugene Fama, Ken French, Bob Merton, Myron Scholes that we work with. So, we have a very academic view on how to do research, on how to engage with each other, on how to question research, push research ideas, and basically be introspective and try to improve every day.
So, when it comes to the types of research that we do then, it starts off with looking at what's out there in the academic world. We take that, and we say, “Is it sensible? Does it survive a battery of robustness checks, which might include extending it to new data sets, modifying the empirical test parameters, controlling the things for we already know?” And then, if it survives those, we say, “Can it be useful in an investment strategy, given all what we already do?” So, I'll give you an example of some research that we did recently. And it's on corporate bond data.
So, there were a number of academic papers that were published, talking about differences in expected returns across bonds. And should you look at, if a company has stocks and bonds, its book value, or the bonds' momentum or things of that nature? Now, what a lot of that research missed in my view, was it didn't control for the most obvious thing that you should control for when you're looking at a bond's expected return, which is basically the forward rate, its yield plus the shape of the curve--it gives you a prediction of the expected return of that bond. An example of that is, imagine you're doing your daily commute. And you say, on average, traffic is heavier on a Wednesday. So, that's your best prediction of how traffic will be on your daily commute. But a different approach is to use Waze, and Waze gives you real-time live data all the time. So, rather than using your average, you use Waze. That's the same here. Forward rates are market prices updated real-time all the time that give you predictions of expected bond returns.
So, we took all that research. And then, we did it again internally, but controlled for forward rates. That's an example of where we took a lot of papers, but the results in the papers didn't hold up to our own internal analysis. But that doesn't count as something that is not a good thing, because it improves our knowledge. We wrote a number of papers about it. We have another one coming out this year. And it also helped highlight something that we could make more systematic that we've been doing for quite some time. The one thing out of all those variables that have been tested was the really short-term equity return. Let's imagine the stock--an issuer has a stock and a bond. And the stock has just gone down by 20%. Does that have information about the bond's return over the next week or month? And the answer is yes. And so, that's something that we use to enhance the process.
So, in short, the batting average tends to be reasonably high. If you consider improving your knowledge set, writing a paper that can be useful for clients, or implementing something in a strategy, it tends to be high, if that's the measure of success. And we're kind of open-minded about which of those outcomes will be the outcome. We don't go in with a preconceived notion that it has to be one of those three outcomes. It can be something that we use to enhance strategy, or it can be something that we find doesn't hold water, but let's help our clients understand these papers better. And so, that's the view that we generally take on those types of topics.
Ptak: Wanted to shift gears and talk about sources of returns. Markets, as we know, are competitive. Before we talk about how maybe that's impacted the sources of security returns, let's talk about some of the things you might have previously done more from a trading and operational standpoint to add value at the margins. For instance, how has your budgeted value-add from securities-lending changed compared with 10 years ago? And on the other hand, have you found potentially new and fruitful sources of return to add at the margins?
O'Reilly: The sources of value tend to change over time, because markets change over time. With respect to your specific question on sec lending, or securities-lending revenue, that has varied over time. Sometimes it's higher, sometimes it's lower, but it still is a very strong source of revenue for the funds. So, if you take the U.S. mutual fund complex as an example, the average AUM last year was around, let's call it, $450 billion. It was up higher and lower at the end of the year, and it ended at about $600 billion, give or take. But the lending revenue from that was about $220 million, give or take. So, it's about 10 basis points to the funds. So, Dimensional, the advisor, doesn't keep any of that lending revenue. That goes to the funds after the agents get paid. So, that's an example of the entire book of business. But that varies across strategy.
So, I'll give you examples like Dimensional Emerging Markets Small--50 to 60 basis points of revenue from securities lending. And that's a sizable number. That's a big number for that strategy. So, what do you have to do to try to maintain that over time? Well, one part of it is flexibility. And we've worked with many Nobel Prize winners, Myron Scholes, Bob Merton being two of them. And of course, they did a lot of work on trying to evaluate what the value of flexibility or optionality is. And flexibility in your investment process is important. So, examples from emerging, or any markets last year, when there are certain bands, short-selling bands, that go on into a market, or when there's markets that are harder to loan in, flexibility of how you integrate the portfolio-management process with how you do your sec lending is important for maximizing that revenue in a risk-managed way. So, that still is a good source of revenue for the portfolios.
Now, you talk about innovation, well, we use that information differently today than we did 10 years ago. Because what we found through our research is that when you look at the fees that people are willing to pay to borrow our security, it gives you pretty good real-time information about what stocks may be getting sold short, or where there may be information coming that might be negative information about a particular stock. Now, you might say, “Well, why do you lend those stocks at all if somebody is borrowing to short term?” Well, if they have information it's going to go into the price one way or another. So why not get paid for our investors when it's working its way into the price? But what we do with that, if the stock goes on loan at a very high fee, we don't purchase it over the next couple of days, because that tells us something about the expected return over the very short term. So, that's examples of that innovation.
One last one I'd point out, though, Jeff, is even on things like trading, I think that trading has become more efficient over time. I also think that we've gotten better at trading over time. And there's a great example from December, where Tesla was added to the S&P 500. A super liquid company, super liquid stocks. We have a large company portfolio that tries to approximate the returns of the S&P 500. And in December, it returned 3.92, 3.92%, S&P 500 3.84%. Where does those 8 basis points come from? Flexibility. We didn't need to trade Tesla on the day that it was added to the S&P 500 and all that price pressure came on it. We could work our way in slowly. That's 8 basis points return difference in a super liquid stock in one month. So, it kind of gives you a sense that optionality and flexibility has value; how you apply it needs to change over time. But the value doesn't necessarily degrade over time; that that value can hold through over many, many years. And the securities lending and the trading examples, I think, are two good examples to illustrate those points.
Benz: Dimensional has long preached the philosophy of courting risks for which you'll get paid as investors. On the equity side that's driven tilts toward small cap, value, emerging-markets stocks. On the flip side, it's argued against taking credit risk on the bond side where you've been wary of default risk. How has the firm's understanding of which risks pay, and the magnitudes of those payoffs, changed over the past decade?
O'Reilly: It's a very interesting question, actually. And I'll go back to some of the earlier examples that I gave. So, it was about in the mid-2000s, when this database came out called TRACE, Trade Reporting and Compliance Engine. And it was the first of many. Now you have these data over many different bond markets. And effectively what it did was when you had a corporate-bond trade that was a U.S.-dollar-denominated corporate bond, the price of that trade had to be disseminated within 15 minutes. And over the next four or five years, the number of bonds that were covered increased and improved, and the data set itself increased and improved. And we did a lot of work in, I would call it the, 2007-08 time period that preceded the launch of our first all of investment-grade strategy in 2009. And it was very important those data for us, because when you have a systematic approach, being well-diversified is important. But having good risk controls is also important. So, how do you manage default risk? How do you measure default risk? How do you manage it real-time?
So, to your question on default risk--what we use those data for is real-time models of what's the credit quality of different bonds so we can supplement what we get from the rating agencies with different types of market prices to help manage those risks in the portfolios. But we also get information on where those bonds are trading and that helps our trading process across a very broadly diversified portfolio. This is the fantastic thing about markets.
When you have public markets, the price of entry is to make a certain amount of information available. And you can get that information, you can gather it. And when somebody acts upon the information, because they form some type of an opinion, when they trade, that's also information. And what we know about aggregate demand is that it changes prices. So, basically, what you get is the wisdom of the crowds and these market prices that help you do things in a systematic fashion that you may not have been able to do without those data. And so, over time, I would say when it comes to corporate bonds, our view is that you have to manage credit risk real-time and correctly, you have to be flexible in your implementation, and then you have to design portfolios in such a way that clients understand what they're getting, how much can that portfolio allocate to different credit qualities, how much can it allocate to different currencies of issuance. And as I mentioned at the start of the podcast, we have about $40 billion-plus now in all of investment-grade strategies that have held up very well in different market environments that I think it's been impressive. Even the start of this year was quite impressive in terms of the market volatility in March, and so on, and what those portfolios were able to accomplish. So, I think what's evolved is our thinking of how to implement, how to do it in a systematic way, how to do it in a diversified way, and yet add value for our clients. That's certainly evolved over the past, I'd call it, 15 years.
Ptak: Let's talk about value. As we know, over the past decade-plus U.S. large and growth have led the way and so investors tilting toward factors like size and value are likely to have lagged. Given that, what have you said to clients who have come to question whether these tilts are still durable sources of excess return?
O'Reilly: The question about value has been a common one. And we always take it back to first principles. And first principles basically say that stock prices represent the discounted cash flows to equityholders. That's first principles. And then, when the discount rate of a stock is high--so that's the investor's expected return--either its price should be low relative to company fundamentals or it should be low relative to some forecasts of future cash flows that you have. That's basically first principles.
When you look at value, or profitability, or company size or investment, they’re all basically different forms of that basic valuation framework of the world. Value is saying something about who has low price relative to some other measures of firm size, your normalizing market price. When you look at company size, it's saying, given the size of the company, who's bigger, who's smaller, i.e., low price. Profitability gives you a prediction of future cash flows. It's saying something about the cash flows side of the equation. Asset growth tells you something about the cash flow side of the equation. So, we always break it back down to first principles.
And why is that? Because the only way that value premiums disappear is if the expected returns of all stocks become the same. As soon as you have differences in expected returns, you have some stocks trading at low prices relative to fundamentals, some stocks trading at high prices, some stocks with low expected future cash flow, some stocks with high expected cash flows. So, that's the state of the world in which value premiums disappear. Now, some things change over time--how you treat accounting variables, how you treat portfolios, markets, infrastructure, microstructure changes over time. But that's kind of a principle that is true through time. So, then it comes to, well, let's understand the past year. Let's take the past year as an example, Jeff.
So, growth beat value over the past year. And you would say, was that expected or unexpected? It certainly wasn't unprecedented. It's happened before. But it was unexpected in the sense that there were stocks that were priced low relative to their fundamentals with high expected cash flows that underperformed those that were priced high relative to fundamentals with low expected cash flows. And you say, well, how did that happen? Why is it unexpected? Well, did anybody expect COVID at the beginning of last year? Not too many. Did they expect what impact that would have had on Amazon or Netflix, some prototypical growth stocks that kind of fuel that side of the market or energy companies that were on the value side of the market? Often, the conversation with clients is about decomposing what's expected to what's unexpected and making sure they focus on the expected outcomes, because the expected value of the unexpected part of expected returns--and that's quite a tongue twister--is zero. So, you have to control for the unexpected, but you have to focus on the expected. And that's how we have those conversations with clients that helps them keep their eye on the long pull.
Benz: In research pieces, you've found that it's not unusual for value to underperform growth for prolonged stretches. But the current slump has been unusual in both its length and its magnitude. Why? And what are the implications?
O'Reilly: The current slump, when you look at it--I often break the past 10 years into the first seven and the next three, is often how I view it. Because the first seven, you had quite a few years where value outperformed growth. But end to end, in the first seven, there was a modestly negative value premium. When you look at the returns of U.S. value stocks, they outpaced their long-run average. But growth stocks outpaced their long-run average by even more. So, growth stocks up until that point had an average return in the U.S. of about 8%, 9%. They did about 16% year over the first seven. Value had an average long-pull return of about 12%, and they did about 14% in the first seven, in the U.S., I'm talking about now. So, that's an outcome that's not expected but unprecedented. You have about a negative 2% value premium probably in about 10% of rolling seven-year periods. It's not so uncommon. But the last three years is probably, again, unexpected. But also, we said no large return differences in terms of value versus growth. And so, that helps folks understand that it is very much a short time-period phenomenon.
What we've been through over the past three years, in particular, this 2020, and we don't know what 2021 will bring, is a very short-run phenomenon. And that helps them put it in context about: is there some type of fundamental shift? Or is this some large unexpected series of events that have led to this outcome? And I think that is more of the latter. And so, that's certainly something that we focus on when explaining that time period to our clients.
On the bright side, when you look at the long-pull research, you're right, you can go through these time periods where value underperforms, but the averages are positive. And when you look at when value premiums are positive, you don't know when they will be, but when they are, they tend to be much bigger than the long pole average. So, in the years when value has been positive, it's been 2 or 3 times the average. So, value on average has outperformed growth by 3% to 4%. In years when it's positive, it outperforms by almost 14%. And we've seen that in the last quarter, and the first week of 2021, where we've had very, very strong value premiums all around the world. And I think that time periods like that are just a constant reminder that when you're investing, keep your eye on the long pull, know that there's going to be ups and downs, and keep yourself focused on the premiums that you're after, because you don't know when they're going to show up.
Ptak: I think one of the things that you and your researchers examine is whether the slump in value could be explained if you were to capitalize certain types of expenses as intangible assets. Can you talk about what your team found when they conducted that research?
O'Reilly: Yeah, absolutely. And this is kind of a hot area of research, I would say, right now going across the money-management industry. And it goes back to this whole notion of what's reflected in the firm's price. And certainly, physical assets, reputation, quality of employees, ability to innovate, all those types of things are reflected in current prices. And when you look at all those things, the term “intangible assets” has come to mean basically anything that's not a physical asset that the market believes can help that firm generate future cash flows or manage risk better. And so, if the market perceives there's value in these assets, it's reflected in the market price.
So, where the conversation comes in then is, well, what about the variables that you use to scale market prices? And what do they include? Do they include physical assets? Do they include intangible assets? Are you capturing different components of the firm? And when you look at those assets, they include physical and they include certain parts of intangible assets that are acquired through merger and acquisitions, so go through a competitive bidding process. But they may not include other types of intangible assets that are developed internally. So, what we looked at was if you include those types of assets on a firm's balance sheet, does it make a difference to value portfolios, and in particular, after you're doing things like controlling for sector exposure, after you're doing things like already incorporating profitability, which reflects some of the expenses that are incurred to produce intangible assets. And we've done that for U.S. data; we're doing that now currently for non-U.S. and emerging-markets data. So, we're dotting our i's and crossing our t's, going through all the data sets.
And in short, I would say that the data so far that we've seen is not terribly compelling, in that, you have to make some very large approximations, and that those approximations tend to be sector-specific. So, if one sector spends more in R&D than another, that kind of difference tends to be sector-specific. And you adjust for intangibles. Then you change the sector composition of the portfolio. As soon as you control for that, you don't see too much difference left over. So, it seems to be more of a sector-specific type of an adjustment. But we're doing the additional research in global markets right now. And we'll probably write another paper in the next six months or so on our findings in global markets. And once we have all that done, then we'll take a long hard look to see, can we improve how we manage our investment portfolios by taking some of these types of considerations into account?
But again, I stress that the market prices already reflect intangibles. We already take care of the expenses incurred to produce intangibles through profitability. We already manage sector weights in our portfolios. So, it's a pretty high hurdle to say that one more addition on top of that would be beneficial. This is one of these cases where we might go from pi being 3.141 to adding one little extra digit on the end, which we will do if we think that it's helpful. But it's on that order of magnitude in terms of the impact it may have on the portfolios.
Benz: Wanted to ask about factors broadly speaking. Suppose I have picked out my basket of factors that I'm going to incorporate, now I need to incorporate them into my portfolio and that means figuring out how to weight them, rebalancing. Drawing on the work that you've done at Dimensional and incorporating factors into the different strategies that you run, what should I be mindful of in structuring my portfolio?
O'Reilly: The framework that we generally use for that, Christine, is to understand what time frame we expect that factor to operate over. I'll give you some examples. If you have a value portfolio, a value portfolio turns over by maybe 20% a year. So, when a stock becomes a value stock, you expect it to stay a value stock for about four or five years. And it gives you that higher expected return over that time period in the portfolio.
Let's take another example of the sec-lending exclusion that I mentioned earlier on. When a stock goes on loan at a high fee, that's over a period of weeks to months. It's very, very short term. And so, it gives you information about differences in expected returns maybe over the next few weeks to next few months. So, here, on the one hand, I have a factor, a value factor, that tells me something about expected returns over a multi-year horizon. On the other hand, I have some type of a proxy for short interest in the market to tell me something about differences in returns over weeks to months.
So, I think the first step is to categorize factors by that kind of a time frequency. Because the way that we view it is, the market is a pretty good starting place. The market offers good rates of returns--have delivered good rates of returns for investors over many, many decades, hundreds of years that we have the data to observe. And so, you shouldn't deviate too much from the market in pursuit of higher returns with respect to the level of turnover, the diversification, all of those types of things. So, the way that we think about broad asset allocation is allocate based on those long-term factors, so size, value, profitability, asset growth, things of that nature. Then use the factors that are maybe more short term in nature--people often refer to momentum, or liquidity or sec lending to say how should you get to that long-term asset allocation?
If I have 10 value stocks to buy today, and five of them are in downward momentum, i.e., they've underperformed the market over the past year, and five of them have not, well, they all give me the value premium, but five give me a negative-momentum premium and five give me no-momentum premium. OK, I focus on the five that give me no-momentum premium, so I get the full-value premium. So, it's kind of a balance of time scales that help you balance these factors. So, generally, asset allocation, long term, size, value, profitability, and then how you get to that asset allocation through time, consider some of the shorter-term factors. And that's how we generally view that problem.
Ptak: I wanted to shift gears and ask you a question that we've asked a few other leaders of investment-management organizations and it tends to yield interesting insights. And that's, what's the most hotly debated topic or issue at Dimensional today? And what's a topic that maybe where you disagree with your team, or where you've invoked your leader's prerogative, so to speak? And conversely, what's an example of something where your team has maybe turned you around on an issue and changed your mind?
O'Reilly: Yeah, great question, Jeff. And thanks for that question. I would say, again, going back to the systematic process, that “debate” might be a strong term, we certainly have discussion and its lively academic discussion. But ultimately, the data says what it says. And I think that helps resolve any types of differences of opinion. Now, we test the data in lots and lots of different ways. Because ultimately, you can make the data sing whatever tune you want it to sing. So, we look at the data from lots of different angles. And I can't stress to you how many experiments we may run to get to understand the data better, and what level of detail we will go into looking at individual companies and financial statements, and so on to understand the data better.
But ultimately, since we're not discussing where's prices wrong or right. We're discussing what information can I get from prices and how do I get that information out? The data says what it says. And I think that's very, very helpful to have those robust discussions, because ultimately, after you run your tests, there's not too much disagreement that ends up once you've run the tests. Because here's what the data says. We've looked at the data in the U.S., in developed markets, in emerging markets. Here's what it says. And if it says something, and that is sensible, and it's robust and compelling in the data, then, OK, that's what the data says. How do we use that then to improve returns? So, I would view it as there may be debate and discussion going in until you've gotten all the data run. But ultimately, I think that we generally get on the same page by the end of doing those experiments.
An example of where we might have had differences of opinion going in, but the data resolved those differences of opinion, I'd go back to the investment premium that we implemented in our portfolio, so basically, asset growth. That one took us a couple of years, because we wanted to really run a lot of different tests, and make sure that it wasn't a proxy for something like merger or acquisition, or a proxy for how firms may issue new shares or buy back shares. So, there was a lot of discussion: “Is it a proxy for x, or is it something that stands by itself?” And so, that one was where the research team did, and Savina Rizova, in particular, she runs the research team, did a lot of really great work to say, “No, this stands by x, and in fact, these other things are probably a proxy for it, rather than it a proxy for them. So, that would be an example.
And effectively, to put a bit more context for that, asset growth is how firms increase their asset base over time, that has predictive power over how they will do that asset growth in the future, and that tends to have a negative relation to returns. The faster you grow your assets, the lower your returns tend to be. That's the observation in the data.
Benz: We've asked a lot of our guests about environmental, social, governance, ESG investing, and we've gotten a wide variety of opinions. Burton Malkiel, for instance, was quite skeptical. Where does Dimensional come down on this topic? And why?
O'Reilly: I think it depends on the viewpoint that you take there. So, when I think about information, I think that all information that can be reliably and systematically used to improve returns or manager risk should be considered. So, let's put it that way. And that includes ESG information. When I think about ESG, there's a lot of different definitions out there. But generally, when you look at ESG risks or opportunities, their expected impact on future cash flows, we believe, is reflected in market prices. So, we know of no compelling evidence that strong ESG firms offer higher or lower expected returns after you control for size, value, profitability, etc. So, if that's the point that Burton was making, I agree with him there, that by focusing on strong ESG firms, if you already control for other things, it doesn't really improve your expected return profile.
How we view it then is that we look on the boards of companies to have a fiduciary obligation to the shareholders of those companies to oversee management, and in particular, oversee material risks, and those can be ESG-related risks. So, across our investment strategies, what we focus on our stewardship activities that advocate for strong board oversight of all risks but including ESG-related risks. And we believe that if you can get the board to focus on these risks that that can either improve returns by leading to higher future cash flows or lowering the discount rate. So, for example, a company has a dam break in South America. OK, that dam caused a lot of damage. That's a letter to the board understanding who on the board oversees these types of risks, what are your policies, what are your procedures? Not asking for information about how the company is run--we believe that's best done by management--but making sure that the board has the right skill set to oversee those risks. So, that's one aspect of it.
The other aspect, though, is that we can have targeted ESG solutions that can achieve certain types of sustainability objectives. So, I'll give you an example. Back about 10 to 15 years ago, we launched sustainability strategies with a strong focus on climate change. And there's a few reasons behind that. Number one, we have a relationship with Scripps. And they're one of the world leaders on climate change, and they've helped us understand the topic over time. But there's a pretty good consensus that the average surface temperatures have increased, and anthropogenic, or man-made emissions, are the cause of that. And there's also a pretty good economic literature that suggests that that may cause costs on future generations. And so, we have a lot of clients who care about that topic.
So, what we can do is we can use the data, and we are experts on data at Dimensional, to build strategies that don't really give up an investment proposition, they give up some diversification, but pursue these sustainability objectives. So, it's not an investment proposition, it's more “do good while doing good,” that type of an approach, where you can have strategies that lower basically for every dollar invested the carbon footprint of that dollar, but yet have a good investment proposition. So, we launched those in 2008. And we have about $12 billion now in those and a good track record. And so, they serve a particular set of clients. So, in some respects, I think that it's important that clients feel that their money is working in the ways that they want it to work for them. And that's a good investment proposition, but for some clients, also a good sustainable proposition. We do social funds, too.
Ptak: For a last question, I wanted to shift our gaze forward. Dimensional has been remarkably successful in expanding its business for most of its existence. But in recent years, that hasn't been the case, as you've been dealing with some outflows. So, my question is, how do you ensure the pressures of stemming some of those redemptions and returning to growth, that that doesn't intrude upon your research process and investment decision-making?
O'Reilly: The way that we think about--I guess I'll call it growth, for want of a better term--is that we want to be ready for growth, but growth is not the objective. When I joined the firm back in 2004, I remember David Booth--and this was one of the very first townhall-type sessions that we had--made the statement, and at that point we were about a $40 billion, $50 billion firm, that we need to think like a $500 billion firm. What does that mean? That means that we need to have the right solutions, the right infrastructure, so that our excellence when it comes to investments is the same as it is at $50 billion, and our excellence when it comes to client service is the same as it is at $50 billion. So, let's start making those investments now.
And that's how we kind of view growth. Growth, sometimes you lose assets, sometimes you win assets. That's just the nature of the business. When you win, understand why you've won; when you've lost, understand why you've lost. Learn from that, but never veer from excellence in investments and excellence in client service. Because if you get those items right, growth will take care of itself. So, Dave and I now think about what happens if we're a $1 trillion dollar firm or a $2 trillion firm? What are the processes that we need to have in place, the functions we need to have in place such that if that happens--it's not the objective, but we want to be ready if it does--that, we are able to have excellence in investments and client service. So, the way that we view it means that whether we have inflows or outflows does not change your investment philosophy, doesn't change your approach to client service, that we strive to do everything that we can in both of those areas.
Over the past year, I'd say, when there's a lot of volatility in the markets, generally assets move around more than in other periods. And that's what we've seen in the last year, people do reallocations, people do rebalancing between equity and fixed income. I think that's what we've seen over the past year more than anything else, rather than some type of a long-term trend. So, we've continued to be focused on investments and client service. And some of the items that I mentioned earlier on really highlight that focus, like almost 100,000 conference attendees. That's a focus on client service. OK, we can’t go see you in your offices, but we are going to do everything we can to make sure that you know what we're working on and what we're doing on your behalf.
Ptak: Well, Gerard, this has been a really interesting conversation. We greatly appreciate your time and insight. Thanks for sharing it with our listeners.
O'Reilly: Thank you very much, Jeff and Christine, for having me on. Great questions. And hopefully, your listeners find some of those responses somewhat useful, at least.
Benz: Great. Thank you so much.
Ptak: Thanks again.
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