#16 - Gerard O'Reilly: DFA, Financial Science, Indexing

27:59So Tesla was added to the S &P 500 a couple of years ago. It was in December of that year, maybe 2022, in and around there. And if you look at Tesla in that month, and the price of Tesla stock on the day that it was added to the S &P 500, there was enormous buying demand for that stock. So it went up. And the day after it was added, that buying demand came down. So it came down. And so we have a strategy called a large company portfolio, which basically looks a lot like the S &P 500, but we don't do things at the same time as the S &P 500. And it outperformed by about eight basis points, net of fees and expenses, the index in that one month, which is a reasonable estimate of how much it actually cost index trackers to add that stock in that month.

28:52That's built into the return of the S &P 500 index, by the way. So you could have matched the return of the S &P 500 index, but still borne eight basis points in cost, in trading cost, to add the index in that one month. That's an estimate. It's not a bad estimate, but it's an estimate. It's not perfect, it's an estimate. When you look at the expense ratios for S &P 500 funds, there are a handful of basis points. So in one month, you could estimate that three or four times the expense ratio, the annual expense ratio was spent to add a stock to the index. Now, it costs that much because everybody wanted to do it at the same time.

29:30Because it was being added to the index at that time. And if zero tracking error is your objective, then you're going to have to do it at the same time as everybody else. That's why the cost was high. And the market will step up and provide all that abnormal liquidity, but it demands to be compensated for providing that service to folks who want to consume that service. So you could say, what's a structural alpha? But don't trade at the same time that everybody else is trying to acquire a stock. That's an example of using flexibility to then not pay the same costs as others. And that's one example in a trading perspective where it's kind of very obvious that you can see that structural example.

30:13But there's other structural examples I would contend, Alex, and this is where we think financial professionals like yourselves are important because what's risk to one person may not be risk to another person or as risky to another person. And that all depends on what that person's goals are. And so in working with financial professionals, we think that families, moms and pops can better identify what their goals are. And if you can identify what your goals are, you can then come up with what are the truly risk mitigating assets with respect to those goals. And those are the things that reduce my uncertainty with respect to your goals.

30:53and that allows me to more efficiently put capital to work for achieving your goals and the risky part of your portfolio. And so that's another type of what I would call structural alpha that we work a lot with financial professionals to understand what are the different types of goals that investors are saving for. And then can we build the tools? And we view that our tools have expected outperformance or alpha relative to index-based approaches. but then if used in the right way by a financial professional then they have expected outperformance versus some naive approach who's not the person who's not starting with their goals and building an allocation that's really designed around an investor's goal so there's lots of ways to look at investing where i think that if you don't have the expertise there's potential to leave money on the table whether it's through the individual funds that you're using to implement the asset allocation or the goals and how that informs the asset allocation itself.

31:51I think there's a lot to this kind of structural kind of notion on giving investors better outcomes, ultimately by listening, understanding what it is that investors are acquiring and then building the best tools and putting them in the right place to meet those outcomes for investors. The other way I know you try to add value in your portfolios is by understanding that securities have different expected returns, you know, both stocks and bonds. And you've talked about that a little bit. Would you describe that in more detail and how you construct portfolios with that notion? We think that, and I think most everybody I've ever asked agrees with this statement, that there are differences in expected returns across stocks and across bonds.

32:40Not all stocks have the same expected return. And expected return is different than realized return because we all know that there's differences in realized return. But investors demand higher returns to hold some stocks than others, right? And that can be seen right now. The MAG7 had a big run-up in price. And maybe that's due to the fact that their expected cash flows to shareholders increased over the past year. But it may also be because the investors demand a lower return right now to hold those stocks. So certain stocks, we think of higher expected returns, certain stocks lower. Same with bonds.

33:17Some bonds have higher expected returns, some bonds have lower expected returns. And the question then we ask ourselves is, can we use that information in a systematic way such that we can build portfolios that are like the market so that you get market rates of return? because we know the market is a good place to be, whether it's the broad stock market, the broad bond market, but then put you in a position to earn more than market rates of return. So diversification is important, low cost is important, low turnover is important. And the way that we do that is you can describe it in different ways, whether it's factor-based investing or there's systematic investing, but effectively how I think about it is, is combining stock and bond prices each day with other measures of firm size to say here today, who in the stock market has been assigned a low price and who a high price?

34:11Because the lower the price that you pay for something, all else equal, the higher the expected return. If you have one in two investments, each expected to pay you$100 a year from now, and one you can buy for$10, and one you can buy for$99, well, they're each expected to give you 100, but the one with$10 has a higher expected return, all else equal, right? And so the way that we do it is we scale price in different ways. So the size premium, the excess return of small caps over big cap stocks, basically you're normalizing price by multiplying it by shares outstanding. The value premium, low relative price versus high relative price or value versus growth, you're taking price and you're dividing it by some other fundamental measure of firm size to say, Here's low price relative to something.

35:00Profitability. Profitability predicts future cash flows to investors. So you're combining that with the prices that people are willing to pay for what they're expecting to receive. So all in all, what you're trying to do is find low discount rate stocks, whether that's size, value, profitability. And that's how we inform our asset allocation within the stocks that we select is we want to have a greater than market weight in stocks with smaller market capitalization that have lower relative prices and higher expected profitability because we think those have higher expected returns in the market.

35:36And then kind of in contrast to that lower than market weight in high price stocks that are very, very mega cap with low profitability, that type of asset allocation is then how you take the market and shift the market to something that we think puts you in the position of having higher than market rates of return. But even if those premiums turn out to be zero over the next 10 or 20 years, i.e. value stocks return the same as growth stocks over the next 20 years, you still end up with market rates of return, right? So put you in the position to have higher market rates in return, take that 10 and turn it into an 11 or 12.

36:11But if the 11 and 12 doesn't show up, make sure you get that 10. And I think that's the way that we've generally approached investing. So people call that factor investing, where it's size factor, value factor, profitability factor. Other people call it systematic, where you're systematically using information and prices and complementing it with other pieces of information that you have about the firm so that you can categorize stocks by their expected return, more or less, into the high and low and market-like, and using that information to systematically, in, as I said, diversified, low turnover fashion, shift weight away from the low expected return stocks in the market to the high expected return stocks.

36:50And that's what we do across the equity side. And we do very similar things on the fixed income. You give us an asset category, we're trying to manage risk better, but then we're trying to overweight those stocks, our bonds with higher expected returns. I have two questions on that point. The first is, how do you deal with market cyclicality and the risk that you can go through a long period of time where the market earns 10, but you might earn 7 because those factors just were unfavorable or just didn't experience the same tailwind as maybe the rest of the market and the risk that investors lose patience and faith that it works over time when that period lasts a long time?

37:32How do you deal with that? So I think that you have to identify that right up front for investors. Because if you take a strategy like what we typically call core equity market or a core market type strategy, so market type of a strategy, it's not impossible, but very unlikely that you get that type of an outcome over 10 or 20 years, in particular if you invest globally, right? And so for that type of a strategy is not deviating from the market by so much that that outcome is very likely. That type of outcome tends to be more likely when you deviate from the market by a lot. Now, why would you deviate from the market by a lot?

38:16Well, you might do that because you want even higher expected outperformance, but you have to know and recognize up front that there's the chance of greater underperformance if that does indeed occur. So I think having that conversation, understanding of the end client up front to understand what their tolerances are, that helps you arrive at the right asset allocation. Because even, you know, over the past 10 or 20 years, there's a lot of talk about value and value and small value underperforming. That's largely based by the U.S. observation. In the U.S., we've had on characteristically high average returns for large growth stocks.

38:56So not very low returns for large value or small value stocks. Their returns have been quite strong, but uncharacteristically high average returns for large growth. And that's a US phenomenon. So large value, small value has done what is expected, but large growth has done better than expected. You go outside the US, you don't see that. Over the past 10 or 20 years, you see strong outperformance for small value versus large growth. So even with that observation here in the US, if you had a globally balanced portfolio, you're not really in that situation. As if you had a US and you went 50 % small value, 50 % large caps, right?

39:33Then that type of a level of a deviation from the market would lead to that outcome. So I think that's important. It's understanding what the investor is sensitive to, being upfront about how bad it can be so that there's no surprises about how bad it can be. But then being clear on, we're taking that level of deviation because maybe your investment horizon is X long or you're not sensitive to tracking error versus the broad market or you want those higher expected returns because it doesn't come without risk, but that the payoff is there. The other piece though, Alex, and this is kind of a more fundamental piece, people talk about cycles all the time, but cycles don't actually exist.

40:15They're not supported by the data in any way, shape or form. And what do I mean by that? Cycles are something that we've constructed by the way that we plot data. So people will plot a rolling 20-year return and say, look at these cycles. Well, when you plot a rolling 20-year return, you've built in massive what they call autocorrelation into the data because the next month that you've plotted has 19 years and 11 months in common with the previous month that you just plotted the data point. So you're basically plotting the same data point over and over and over and over and over and over again.

40:52So market cycles don't actually exist. We plot them and pretend they exist, but they don't exist. When you look at the data itself, what you find is there's very little autocorrelation in the data. Last month's returns tell you almost nothing about what next month's returns will be. Last year's returns tell you almost nothing about what next year's returns will be. And then when you look at what people categorize as a cycle, it's usually where you've gone from, like at a calendar year, 52 % winning rate to a 48 % winning rate. for example value versus growth over the past couple of decades there's been tons of years when value outperformed growth and some years when growth had to perform value but growth had to perform value by more so i think that's the other piece to keep in mind a lot of folks focus on cycles and i understand why because you have an experience of this was my return over the past 20 years, but it's one point.

41:45And so the reason that I bring up that point is because people often focus on cycles as a way to predict what's going to happen next. And the point that I'm making is that it gives you no ability to predict what's going to happen next. And it gives you no, it shouldn't be your expectation that you should go to a value cycle or a growth cycle, because there's no autocorrelation in the premium data. The observation, this one tells you nothing about what's going to happen next month. The second question on that topic relates to this notion of market efficiency. And how do you think about the possibility or the risk that when you look backwards and you study the data, that the market adapts to that data?

42:31So if there's a premium for value versus growth or a premium for small versus large, is there risk that that premium evaporates in the future as market participants become knowledgeable of that and then price that in? How do you think about that? Yeah, it's a very important question and it goes into how do you do the research. And I think that a part of it is why do you, what's the phenomenon that you're identifying with your piece of research? Because the research shouldn't be done to explain historical returns. It should be done to predict future returns. So let's take the example of size, value, profitability.

43:07For me, they're all a discount rate effect. They're basically saying, where can I identify the stocks where the market has applied a higher discount rate to future cash flows versus stocks that the market has applied a lower discount rate to future cash flows? I won't ever know all the reasons why the market applied a higher discount rate to the future cash flows of stock A versus stock B. I'll never be able to understand that reason. I can speculate and I can come up with very plausible answers, but I'll never be able to understand that reason. But do I expect all stocks to have the same expected return going forward?

43:41No, I don't think that state of the world is very likely. And so therefore, when I look back at those types of premiums and you say, what has to change? Basically, the main thing that has to change is that investors demand the same expected return to hold every stock, regardless of what that stock is. And I think that's a highly unlikely state of the world. So you say, okay, I look back historically at the data and size, value, profitability. I can pursue those with very low turnover portfolios, like let's call it 10%, 15 % turnover in a given year. I can pursue those with very diversified portfolios.

44:19They can hold many thousands of securities. I can pursue those globally. And I can pursue those with a reasonably low cost portfolio with good solid implementation, whether it's how you keep yourself focused on the premiums or how you keep yourself or your trading costs things of that nature and then you say okay for those premiums i think it's it's worth pursuing because it goes back to the point that i made earlier on the opportunity cost is small because if those premiums let's pretend that they weren't there because of risk and let's pretend that going forward the market says every stock has the same expected return it doesn't matter what's going on with that security.

44:57Even if that security has some terrible news happening with it right now, and it looks like that stock may not exist, we're still going to apply the same expected return for holding that stock. Let's just pretend that unlikely state of the world happens. You end up with a good portfolio. You end up with a market portfolio. There's other observations, though, that you say that may not be the outcome. The example that we often use is momentum. There's no really good reason why you should expect to see momentum in the data. Why is a stock that has just gone up in price over the past few months now have a lower discount rate applied to it than one that has just gone down in price?

45:31I don't know. But momentum is the tendency of stocks that have just gone up in price over the past 3 to 12 months to continue to do so relative to the market over the next three months or so. So there what we say is how can we use that information in a way that does no harm? and we delay the purchase of stocks that are in downward momentum and delay the sales of stocks in upward momentum. So that means we get a part of the premium without incurring costs to pursue it. So I think that your question is a really important one because the historical data tells you that there have been premiums, that they happen by more than just chance, right?

46:08That's part of the testing. This happened by a lot more than just chance. So you might want to expect it going forward. But then you should take that information and say, how do I build a portfolio around this that is still a good strategy, even if those premiums are zero in the future? And that's the question that we ask ourselves all the time. And that's what I mentioned at the start. Models are very, very helpful to understand the data, but don't ever pretend that the models give you the perfect solution to anything. It's, yes, the model gives you an understanding, but then it's the art of the science.

46:45How do you build something that's robust? And I think that's a very important question for managers to ask themselves because of what you mentioned, because you don't know what these premiums will be. Even if the market, there are value premiums over the next, you know, on expectation, you can go through long time periods when they don't show up. And so I think that's an important question to ask for portfolio design purposes. And obviously you're constantly testing the data. So it's not like you tested it 40 years ago and you haven't looked at it since. You're looking at it every day, every second.

47:17And if things look like they're actually changing and there's data support for that change and you understand why that change is occurring, obviously that would get implemented as well. 100%. Like when profitability came along about 10 years ago, we realized that we could replace, we were using earnings price, cash flow price, and some of our processes. We realized that we could replace those variables with profitability and that we had a better description of differences in expected returns. You know, when you look on the bond side, there's been a lot of evolution in bond markets over time, in particular about their transparency and the pricing that you have.

47:57So we realized that we could do real-time credit monitoring using bond prices over time that could enhance the strategies that is taking prices from lots of different sources so that we can actually assign real-time credit ratings that complement the rating agency credit ratings to thousands of bonds throughout the day you know that type of scale or if you look at how we trade that has evolved over time as market microstructures have changed or accounting practices we've changed the way that we measure certain variables over time. So the rules-based approach, in my view, is a good approach to investing.

48:34But Alex, to your point, innovation has to happen. And part of innovation is introspection, is saying, this is the way that I have been doing it. Is this still the best way that I know how to do it, given that I know more today than I did five years ago? So introspection is a very important important part of innovation. And then talking to clients and understanding their needs is another important part of innovation because I've learned more in the past five years. So the needs that I hear from my clients, I can probably more address in a more systematic fashion than I could in the past. So both of those are real key inputs into innovation.

49:14And in my view, you know, if you're not asking your manager how they're innovating and how they're learning and how they're improving, you're kind of missing something there because it's an important way to learn more about your manager's commitment to that way of investing and their commitment to serving clients well. And that recognition, I believe, is crucial because the markets are constantly evolving. The investors are getting smarter. They're learning from historical experiences. There's new insight about the future. And that gets reflected in market pricing. And so if you are too dogmatic about your approach and you don't evolve, you can effectively be left behind.

49:55I agree. I agree. And that is, again, Alex, why I think indexing is also something that leaves money on the table because you basically delegate asset allocation as a money manager to a third party. And that third party may not have the real-time insights that you have and that you're gaining from working in markets each and every day. And they're also not talking to the end investors on an ongoing basis and the financial professionals on an ongoing basis to understand what problems it is they're experiencing. And they may not be doing the same level of rigorous research that an organization like Dimensional prides itself on doing or the same connections with academia.

50:36We think the combination of all of those things is an important component to getting the right solutions in the hands of financial professionals to serve their end investors. I'd like to ask you a moment about private markets relative to public markets. We talked about public markets being relatively efficient. Is your sense that there's more alpha opportunity in private markets as obviously a lot of money has flown in that direction? Whenever I'm asked a question like that, it's always I go back to what I can say from the data. And the challenge with answering that question is that the private market data is not as rich as public market data and makes that question very challenging to answer.

51:19So we've done a lot of experiments with private market data here at Dimensional. And so we've gathered data from organizations like Burgess and Prequin and so on. And the things that we can say are that if you look at the dispersion among private market managers, whether it's in private equity or venture capital or private credit or private real estate, that the dispersion of returns tends to be very high. And so depending on the fund and the vintage that you invest in, there can be a large dispersion in returns. And that's something that should be considered when investing and selecting a private market manager because you could end up with a great outcome or a very, very poor outcome.

51:59So that has to be acknowledged, that there's a wide range of outcomes that are possible. The second observation is that private markets probably add some diversification to your portfolio. And if you look, I'll just focus on private equity for a moment. If you look at private equity and investable private equity, and you look at reasonable estimates of investable private equity, and I'm not talking about all private companies, I'm talking about the ones that you can actually invest in as an investor, that was at about six or seven percent of the size of the public markets for a long period of time.

52:31and more recently has climbed up to as high as 10 in and around there. I'm using rough numbers. And so that tells you that there's a lot of companies out there that you don't hold in your public market solutions, in your public market portfolios. And those companies can provide nice diversification if you can get a diversified exposure at an appropriate cost. And you see that in the returns of private market managers when you look and you look at things like correlations, although they're not perfect, You see diversification benefits to broad exposure to private markets. So I think that's a potential benefit.

53:10On the alpha question, you don't see as much on the alpha question at all. And that's because the returns are more challenging to benchmark. So let's unpack that a little bit. The way the private market managers report returns is not the same as public market managers, where in the public markets, a mutual fund or an ETF, it has an NAV every single day. And it's based on largely market prices, mark to market every single day. And you get a daily return every single day in real time, all the time for most mutual funds and ETFs. That's reported to everyone. So you get to see it as it happens. There's no hiding.

53:56And that allows you to compute returns in a particular way. When you're looking at things like IRRs and other types of ways that private market investments, the returns are reported, it makes that comparison to public market returns much more challenging. But what we've typically found is that a lot of reported alpha disappears as soon as you benchmark to more appropriate public market benchmarks. benchmarks. And so I think that I'm using that as a kind of the broad distribution. I'm not saying that there's not private market managers that have done very well and some that have done very poorly.

54:32I'm just saying the broad distribution looks like the alpha is not robust when you benchmark it in the right way. So from a returns perspective, I typically don't weigh in on the returns too much because the data don't allow you to say too much. And what I will say is that I I think due diligence is incredibly important if you're going to work with a private market manager. Pricing is incredibly important. So what's the way that you're going to get charged? If you can get diversification, I think that's an important thing to emphasize. So all of those things could lead to some kind of rationale for why private market solutions in your portfolio.

55:12The alpha part, though, that is probably a little bit of the due diligence. a little bit of a leap of faith, I would say, because it's particularly hard to say in the broad distribution, where the broad distribution is actually, if I pick one at random, then the data suggests that you're probably not getting alpha to the appropriate benchmark in the public market. And then the question you have to ask yourself is, am I doing better than picking at random when I'm picking a private market manager? I think that's the way I generally think about private market investing. And it certainly has become more popular over time.

55:50There's been a lot of cash flows that way for lots of different reasons that we probably know time to get into on this webinar. But I would say that if you're going to invest that way, it's important to work with a financial professional. It's important to do your due diligence and it's important to know what you're getting into and what to expect. Those things are very important. I suppose if you have broader dispersion across managers in private markets than you do potentially in public markets. There is theoretically more alpha potential, but you obviously need some alpha in finding the managers who have that potential and doing the underwriting and due diligence.

56:31Does that make sense? Yeah, that makes sense. I mean, there's potential to be, I don't know if it's surprise on the upside as well price on the downside or skill at identifying the upside or identifying the downside. I really don't know if it's one or the other. But as I said, you know, it's kind of the question you really have to ask yourself is if that skill exists, why are they willing to share it with you after their fees and expenses? Because ultimately, you know, a lot of private markets, and it's not all, but a lot, you know, you take a company from the public market into the private market, you think that you can add some skill or you can add some value to that company after it's been private and then you exit by selling it to another private market manager or back into the public market.

57:19So you're coming out and going in a public market valuations and you're hopeful that along the way through your own Rolodex or your own skill set that you can make the, as a private market manager, make that company more valuable. And then the question is, why would that person who can do that share that skill with you as the end investor? Because your cash is probably not the scarce resource there. That private market manager's skill, if it is indeed skill, is probably the scarce resource. So why do they share it with you? And I think that's the important question as you're doing your due diligence that you have to ask yourself.

57:50Net and fees and expenses, do I think that this is the right solution? The diversification question, though, is not nearly as involved. That one for me is, can I get two, three, 400 different private market companies in my private market portfolio at a reasonable cost? If I can do that, that has a diversification benefit that I think is more obvious, a more obvious benefit for an end investor. Let's talk about building a diversified portfolio. Would you talk about your framework for building both a well-diversified portfolio across assets and then also perhaps talk about how to build diversification within a bond portfolio or a stock portfolio?

58:35Yeah, I think that, you know, there it starts with the goals and understanding what it is that person is saving for. Understanding their other assets. What do I mean by that? Let's say you have a person who's kind of early in their career. well they have a lot of income for from their human capital going forward and so you might say that their most of their wealth today is in their human capital and what their human capital is going to translate into actual you know physical tangible wealth in the future so understanding that about a person and then that kind of allows you to arrive at what's an appropriate asset allocation given their risk tolerance between either what assets you consider less risky and assets that you consider more risky.

59:22So that is often the stock bond mix. What are they saving for? Are they saving for retirement? Are they saving for their kids' college? Are they saving for something else? I think that also helps you identify what assets reduce uncertainty relative to what it is they're actually saving for. And then that helps you identify the risk reduction versus the risky assets. And you allocate the risky assets usually because you can't meet your goals with your own saving, right? So you're willing to take some risk to grow the value of your wealth to meet your investment goals. That's why you take risk. If you could do that without having to take any risk, you just construct whatever is the riskless asset for you and invest all your money in that because you can meet all your goals.

1:00:04Why take any risk if you don't have to? So that's an important part. So when you look at diversification, there's that split between, you know stocks bonds maybe something like REITs other types of asset categories like that then within bonds I think you start in my view you start with the broad market and you decide why you're different and the same within stocks you start with the broad market and decide why you're different the broad market and bonds you have many yield curves around the world you have bonds issued in US dollars in British pounds in euros in Aussie dollars in Swiss franc You have bonds issued in lots of different currencies.

1:00:39You can hedge out the currency exposure if you choose, but that gives you diversification across currency of issuance. It also allows you to say, OK, if I want a broadly diversified bond portfolio, I have treasuries, I have corporates, I have bonds that are related to mortgages. So again, looking for value add in each one of those asset categories, but building yourself a broadly diversified portfolio of bonds, I think is appropriate. it. Other reasons why you might be different is if you want something that has a lot of certainty with respect to the value of your wealth, then you have to go short duration, high quality.

1:01:16If you want expected returns only and you're willing to put up with some volatility, then it's a more of a core like broad market bond offering. If you have liabilities that are very long dated, then you maybe want to go long duration to match the duration of your liability. So those will inform why you might be different in the market. On the equity side, again, start with the whole stock market in my view, which is 45 countries around the world, 10 ,000 plus individual stocks around the world. And with that, I'd say, how different are you willing to be? And then build a portfolio that overweights smaller cap value profitability so that you're willing to take on some differences versus the broad market in the pursuit of higher expected returns.

1:02:02That doesn't mean that you don't hold all those stocks. You can hold a strategy with all of those stocks. And we have many that hold many thousands of securities. So they're broadly diversified, but deviate from the market in order to increase returns. So I think it starts off with investor goals and risk preferences and risk tolerances. That kind of gives you a broad view of what are the appropriate bonds for those goals? What's the split between stocks and bonds for those goals, and then build diversification into each of those strategies once you've decided on what's appropriate for the end investor.

1:02:38It makes a lot of sense. One observation that I've had the last several years is investors, particularly the clients of people's money that we're all investing, they tend to chase returns. And for the last almost 10 plus years, the S &P S &P 500 has done really well. Big cap tech has done really well. And portfolios, in my observation, are less diversified today than they were 10 years ago because investors are flocking towards the things that have done really well. How do you think about that? And are there any important lessons you'd like to share? Yeah, let's take the S &P 500 since you mentioned that one as the example.

1:03:19And if you look coming towards the end of last year, you had close on 30 % of the weight of that index in about 10 stocks, right? So that's higher than it has been in the past. Now, we've had situations 10, 20, 30, 40 years ago where the S &P 500 has looked like that. Now, if that was all you had to invest in, I say that's appropriate diversification. 30 % of your weight in 10 stocks, that's all you have to invest in? Okay, that's appropriate diversification. But it's not all you have to invest in. You have small cap stocks within the US. you have even some large cap stocks in the US. I think this is a funny fact.

1:03:56If you look at the top 500 stocks and which of those are in the S &P 500, there's about$2 trillion of market cap, bigger than the whole Australian market cap of stocks in the top 500 that are not in the S &P 500. Because the S &P 500 is, again, it's important to understand the rules. The rules are not just market cap. There's other selection criteria that qualifies a stock for it. So there's even other large cap stocks that are not in the S &P 500 that you might want to include, U.S. small cap stocks, then the stocks of non-U.S. developed countries, the stocks of emerging market countries. So if you go from that S &P 500 viewpoint of the world and say 30 % in 10 names, that's, you know, okay, that's what that is.

1:04:43And then you go to a globally diversified view with overweights to small and value and profitability. Now you're down to about 10 % of your stock portfolio in the top 10 names. Which one sounds better? Well, the 10 % in the top 10 names, I think, well, I'm less likely to be negatively impacted by the fortunes of 10 specific companies. And I think that's just a basic tenet of diversification. So, you know, markets will ebb and flow. The makeup across market cap of securities will ebb and flow across time. And it's always a game of what's the alternative? Because there's nothing in absolute. So it's always relative to something else.

1:05:27So what's the alternative to the S &P 500? A globally diversified portfolio of stocks. And the immediate benefit is you lower your concentration risk immediately by moving from that to the other alternative. Does the S &P 500 have a higher expected return going forward than everybody else in the world? That's unlikely. And all you have to look at is the last decade here in the US where people were basically saying, you know, where the US and large cap stocks in the US had the lowest returns and developed markets next and emerging markets next and small caps next. So it was the exact opposite of this decade that we've just been through.

1:06:03So it's kind of, it's one of those things where you can't be so myopic and try out common sense for 10 years worth of data. Unfortunately, even though 10 years seems like a long time, it's actually quite a small amount of data to draw any type of strong inference from. And that's something that a lot of people have a hard time kind of wrapping their minds around because 10 years seems like a long time to most people, but it's just a blink in the eye when it really comes to investing. Yeah. In the real world, 10 years is a long time, but in the investment world, it's very short. DFA has made a significant move into ETFs.

1:06:41Would you talk about that and what has driven that focus? Yeah. Again, it comes back to some of the things I mentioned earlier on about working with clients and understanding what are the needs of our clients and then also understanding how markets are changing. So in 2019, the SEC brought in a new rule called the ETF rule. And before that, the way that ETFs were managed is different than today in the sense of, and I don't want to get into so much of the specifics, but the rule basically allowed active ETFs, so non-index-based ETFs to be much better managed than prior to the rule. And that's to do with how ETFs interact with authorized participants.

1:07:25So what they call the create and redeem process, how new ETF shares are created and then how ETF shares are taken off the market. So that's basically, there was a change that enabled a much more efficient approach to managing a non-index-based strategy in an ETF wrapper. We had been hearing from the financial professionals that we worked with that they wanted more choice in vehicle to consume Dimensional's investment approach. They didn't want to sacrifice anything in the investment approach when it came to the choice of vehicle and the ETF rule allowed us to deliver that. Basically we could take an investment approach that we had been kind of honing and developing for very large separate accounts for mutual funds for four decades and apply that investment approach in an ETF wrapper in what the SEC deems active transparent.

1:08:21So I basically call it non-index transparent way of investing. We're able to bring that to the table. And it's just worked out tremendously well for, I think, for the financial professionals we work with, their clients, and dimensional. So it's kind of a win-win all around. Because we listen to the folks that we work with, understand what their needs were, and then we're able to launch strategies that met their needs. So within three years, just a little over three years, we're already at$130 billion assets under management in ETF, in the ETF wrapper. By AUM across the full suite, we have 38 ETFs.

1:09:03we're the largest active transparent ETF manager in the US in just three years. So we think that we understood the need well and met the need well of the financial professionals that we work with. And we're able to deliver an investment approach such that whether it's the expense ratios, because sister ETFs and mutual funds, we've worked to have very similar expense ratios. The investment approach, sister ETFs and mutual funds, a very similar investment approach and a tax profile, actually, that we've been able to accomplish with our mutual funds and our ETFs, very similar tax profile or tax efficiency ratios.

1:09:38So basically, we want to take all those off the table so financial professionals can choose the vehicle that's right for them, whether it's a mutual fund, an SMA or an ETF. You choose the vehicle, you can get a very similar investment approach and overall investment experience, regardless of the vehicle wrapper. And we've been able to accomplish that working well with the financial professionals and other institutions over that time period. So it's been a real, I would call it, success story. We're very pleased with how that's come out. The regulations changed. We understood a need in the marketplace.

1:10:11We said, here's how we can build a value-add solution better than anything else that we see out there. And then the outcome that you see three years later is kind of the proof of getting that right. That's great. We're running out of time. There's one last topic that I wanted to ask you about. But, you know, given that you're a data scientist and the hot topic these days is artificial intelligence, AI, and you've been working on that long before it became mainstream, how do you foresee the impact on your investment process and just the industry in general? Yeah, I think that there's a few different ways of describing it.

1:10:51One that we often like to say, and I got this one from Professor Bob Merton, is we shouldn't call it artificial intelligence. We should call it assisted implementation. Because I think that is the real power of what a lot of the tools that have been developed over the past many decades and will continue to be developed bring to the table. And so they allow you to be more efficient in how you implement a strategy, more efficient in how you understand the data. I don't think they're going to give you any different insights in mispricing. They're not going to allow you to outguess market prices. Because guess what?

1:11:25As soon as you have better information and you trade upon it, everybody has that information because you just traded upon it. And that goes to your point, Alex, where the tools that people use and the infrastructure changes over time. So markets get better over time. And I think that AI will probably lead to some innovations that will help markets become more efficient to run over time. And the more efficient to run, the lower the cost they are to run. And therefore, the better and more smoothly they would run on expectation. So the way I think about it is it's very important for organizations like Dimensional to understand what those tools can bring to the table.

1:12:04how we can gain efficiencies ourselves as an organization such that then as we become more efficient how do we pass on those cost savings to the end investors and so it's an aspect of whether we use it on the research side of things whether we use it on how we implement the strategies day in day out how we service our clients all of those things i think will lead to efficiencies and i think that's really where the where the big the big gains come from i think that some people focus too much on certain companies because if there's tools that enable productivity across companies in the marketplace, then all of those companies get lifted up.

1:12:47So for example, the internet many, many years ago, there was tools that came to the table and you didn't have to invest in just internet stocks to benefit from the productivity that things like a Google search enabled organizations to have. So that from an investment perspective, I think that invest in the markets. And if AI is really something very, very important and helps people become more efficient and helps people become more productive, then market prices will be lifted up across the board. And you don't have to identify just the companies associated with delivering AI tools to actually have a good experience from it.

1:13:27So as an individual, learn from the tools, develop your skill set, and then as an investor, invest in the broad markets. And I think that you'll be okay with respect to the innovations that AI will bring. That's great. Gerard, I appreciate your time and for sharing your insights with us today. Thank you for joining us. Thanks, Alex, for having me on the show and a good conversation and appreciate the questions and all you do for your clients and appreciate the relationship that we have with our two organizations. Thanks for listening. We hope you enjoyed this episode. Please visit our website at insightfulinvestor.org to access past shows and learn more about our podcast.

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