transcript

Speaker 1:
[00:00] Lots of firms throw a couple flashy funds your way and call it a day, but not Vanguard. Vanguard bonds are institutional quality. Institutional quality isn't a tagline, it's a commitment to your clients. It means top-grade products across the board. So if you're looking to give your clients consistent results year in and year out, go see the record for yourself at vanguard.com/audio. That's vanguard.com/audio. All investing is subject to risk. Vanguard Marketing Corporation Distributor.

Speaker 2:
[00:27] Please stay tuned for important disclosure information at the conclusion of this episode.

Speaker 3:
[00:33] Hi and welcome to Long View. I'm Ben Johnson, Head of Client Solutions with Morningstar. Today's guest on The Long View is Michael Gates. Michael is the Managing Director, Head of Model Portfolio Solutions for the Americas, and the Lead Portfolio Manager for Target Allocation Models at BlackRock. BlackRock's target allocation models have been adopted by a large and growing contingent of advisors in the US. Four of the model families within BlackRock's target allocation suite have been awarded Morningstar Medalist Ratings of Gold. In 2025, Morningstar's Manager Research Team nominated Michael for a Morningstar Award for Investing Excellence in the Outstanding Allocation Portfolio Manager category. Michael's time at BlackRock dates to 1999, including his years with Barclays Global Investors, which merged with BlackRock in 2009. Prior to founding the Model Portfolio Solutions Group, Michael conducted quantitative trading research for equities in BlackRock's global trading group. At Barclays Global Investors, Michael led in the development and implementation of the firm's quantitative approach in fixed income and alternative asset investing. Michael earned his MS in Economics from the University of California, Davis. He also earned a BS with honors from the University of California, Davis. Michael is a CFA Charter Holder. Michael, welcome to The Long View. So glad you could join me.

Speaker 1:
[02:04] Pleasure to be here. Thanks for having me.

Speaker 3:
[02:06] So Michael, while you and your team have been building model portfolios for quite some time now, I'd be remiss if I assumed familiarity with the space, the concept on behalf of our listeners. So I want to start with a very basic question. What are model portfolios?

Speaker 1:
[02:25] Great question. Models are actively managed multi-asset portfolios with strategic allocations and tactical tweaks, in this case by BlackRock's team. We typically use broad index ETFs and active funds for core exposures. At BlackRock, the model kind of flagship family that I manage is called target allocation. We manage these models across a range of risk profiles, so advisors can appropriately select a model for a given client. We've chosen to organize them in an intuitive way, which is we focus on the equity bond split. So you've probably heard of the 60-40, which would be 60% equity, 40% bond target. That would be kind of our plurality allocation. That's the one that gets chosen the most frequently. But we support a range of targets all the way from all fixed income to all equity. And this business as it's grown has seen a proliferation of the kinds of models that we support. So, of course, taxable and non-taxable, but also models with alternatives are very popular. There's a wide range of models at this point. And advisors who use models are experiencing a competitive advantage in the US. And I think that competitive dynamic underlies the growth in models that we've seen. Right now at BlackRock, since we launched the strategy over 10 years ago, we've grown to over $220 billion in assets that I'm responsible for here in the US at this point.

Speaker 3:
[04:04] I'd love to understand a bit more about the why behind that, Michael, from zero to now over $220 billion. What are some of the common themes that you're hearing among the advisors that you're working with as to why they've begun to get out of the business of picking individual securities or even picking funds and building portfolios themselves and gravitating towards these effectively ready-made portfolio solutions?

Speaker 1:
[04:34] Absolutely. Let's kind of size the opportunity for a moment. The amount in the US that is estimated to be tracking models at this point, 220 billion is an exciting number, but the total amount we believe right now is about $4.2 trillion in assets to track models in the US. But the total addressable market at this point is $11.5 trillion. So in advisory, the number of financial advisors who are using a models-based practice has a lot of room to run. And for those advisors who do adopt models into their practice, they get a lot of advantages. Some of them use models at a turnkey fashion, so adopt the allocations that are released from BlackRock directly. Some engage with BlackRock in custom model solutions, so creating predictable variability to a target model based on their preferences, say, for certain fund managers on different asset classes. And then others will choose to take model guidance and directly modify it themselves as we publish it, and it's made public at the same time for everyone. So advisors who are able to do this can increasingly integrate a wide range of investment options into their practice. So some of the most advanced advisors are using things like separately managed accounts, which we offer through our Perio offering. They're able to use option overlays. At BlackRock, that's through SpiderRock advisors. And they're pairing these with planning tools. So adopting a models-based practice allows the advisor to spend more time on activities that clients value, such as paying attention to client-specific constraints, paying attention to financial planning, and getting really strong performance from the allocation side. So one thing I've heard a lot in interacting with advisors is that the uplift in performance is notable. They feel it. And that's because the performance that we've been able to deliver has been very strong.

Speaker 3:
[06:46] You mentioned some of the interesting ways in which these models are ultimately being implemented by advisors. Everything from kind of a paint by numbers, I'm going to look at a paper model and make my own modifications that I see fit to effectively turnkey solutions, oftentimes offered by turnkey asset management platforms. Which of these areas, which of these delivery mechanisms do you see more advisors gravitating to hit the margin today versus maybe your experience in the past?

Speaker 1:
[07:21] We're definitely seeing the turnkey adoption sustaining itself and picking up in terms of where it's coming from. So we interact with a number of platforms that carry our models and platforms that have held the models for longer have a certain momentum to them. And then platforms that have added us in more recent years are really gaining steam right now. So we're seeing asset gathering across an array of these turnkey platforms. Meanwhile, the custom model solutions business is growing at a heady rate that typically is for registered investment advisors or independent advisors across the US. And so we have a lot of advisors on ramping on that right now. And BlackRock's support for advisors outside of those two groups, let's say, for advisors who are doing custom model portfolios, but on their own, you know, taking the guidance and making those adjustments. You know, BlackRock's client facing operation works with advisors to help them do that. So we're sort of agnostic, shall we say, to how that gets done. What we want is to help advisors manage their practice with models because we think it's good for them and what's good for them is good for us.

Speaker 3:
[08:45] You know, Michael, I want to circle back to one of the other interesting vectors that you touched on in passing earlier, which is, I think, emblematic of how this space is evolving by virtue of folding in other interesting capabilities. You mentioned tax loss harvesting, things like options, overlays. What's driving demand for and what's driving adoption of those particular offerings and how does that start to, you know, fold seamlessly into your core offering as opposed to being something that might be sort of cumbersome, incremental and almost begin to detract from the value proposition, a key pillar of which, as you alluded to, is really efficiency and time savings and scale on the part of the end advisor.

Speaker 1:
[09:34] Yeah, it's one of the advantages that we have is this technological integration. There are two ways that tax is coming to effect. One is simply through a model that is cognizant of taxes, both in terms of what kinds of assets it holds, as well as the kinds of trading that we introduce. So, typically, those models have lower turnover, obviously. But with the technological angle, we're partnering with firms for the different platforms that advisors are accessing models through. And the technology can do tax loss harvesting on an account-by-account basis. So, that's an efficient way to reduce the tax load. The other mechanism is through the use of separately managed accounts. So, I mentioned to Purio, a moment ago, they've got... We have outstanding technology in separately managed accounts. The model that is being used, we call it TASMA, Target Allocation with SMAs. And that's a whole kind of another level of taxless harvest opportunity because you're now using the underlying kind of stock portfolio and the underlying bond portfolios, which has more dispersion, which allows you to get tax gains harvested. The other thing I mentioned are option overlays. So, SpiderRock advisors at BlackRock have a number of programs for generating hedges on individual positions, working out positions over time. These kinds of programs are very attractive to larger accounts. And I think it's worth noting that models have been very effective for covering a large number of accounts, so smaller average account sizes. But what we've seen in the last couple years has been adoption larger and larger accounts. And as the accounts get bigger, it lends itself to use of SMAs and use of option overlay programs. And I think that really is the future.

Speaker 3:
[11:36] So I'm curious if you were to look back, Michael, and put yourself in the shoes that you were in in 2012 when you first worked with a group to stand up, BlackRock's Model Portfolio Solutions team, what's the thing, or maybe a handful of things, that have most surprised you about how this particular corner of the industry has evolved over the ensuing 14 years?

Speaker 1:
[12:01] I think it's this competitive advantage that's emerged, because that really creates quality business momentum. So that's been interesting. We hypothesized at the beginning that advisors taking on a models-based practice model would have competitive advantages. But to see it play out has been really exciting. And yeah, the breadth of adoption at this point is also interesting. We see it everywhere. We see it in kind of what we call core wire platforms, the largest platforms. We see it in independent broker dealers. We see it in independent advisors, RIA. So the breadth of adoption has been really important to the success we've gotten.

Speaker 3:
[12:47] So Michael, I'm wondering if you could share with us what some of the central tenets of your team's approach to building multi-asset portfolios are.

Speaker 1:
[12:57] We start with a whole portfolio mindset. So this means we're very focused on diversification and risk budgeting. The allocations are centered around a risk target benchmark. And the purpose of the portfolio construction exercise is to deliver a portfolio that's as good or better than what that benchmark provides. We're able to leverage the Aladdin risk management framework. Aladdin is a risk management tool that's widely used across the asset management industry. What it allows us to do in portfolio management is look through to the underlying holdings in the model portfolios down to every security and see the underlying risk factors that are shared across them. So, risk management is at the forefront of the process that we're using to build and manage models and to adjust them over time.

Speaker 3:
[13:59] I'm curious in that respect, considering this whole portfolio mindset, if you think of the Global Investment Opportunity Set, involves needing a lot of insights across a lot of assets across virtually every corner of the globe. So, how do you think about setting up your team and ultimately aligning that with this investment process and ultimately manifesting these principles in the portfolios you're building for advisors?

Speaker 1:
[14:32] Well, we've built a team that has specialization across it. So, researchers with institutional backgrounds that focus on different elements of portfolio construction, including equity selection and timing, fixed income selection and timing, cross-asset selection, currency and commodity markets. So, really building a team of specialists has been important to getting performance into the models. And then the other thing I'd point out is just on the assets we're using in terms of ETFs, funds, separate managed accounts. We have a manager selection process anytime that an active product is being considered. There's a specialist kind of group for manager selection that comes into play. And then kind of from my seat, what we're focused on is managing the exposures over time and trying to get the big topics right. So, in the last decade, it's been really important to have your emphasis within equities placed correctly at different times. So different times call for different kinds of exposures. Recently we had a call for non-US exposures that went up in late 2024 that was really important to delivering total performance in the portfolio. These things change all the time. And so having a systematic process and a specialist team allows us to get that right.

Speaker 3:
[16:17] So I'm curious, as we shift from process to beginning to land closer to where the portfolios are positioned today, I want to shift our focus away from your team, your structure, your guiding principles and towards what we're seeing in markets. And to start, just what are some of the key macroeconomic and market themes that you and your team are keeping close tabs on today?

Speaker 1:
[16:46] We're entering into a period of sustained productivity growth in the US, and especially and globally. A lot of that precedes AI's penetration throughout the economy, but that kind of ability for AI to have a meaningful impact on productivity is starting to appear. And we think it's going to become more important in coming years. So the economic backdrop has that positive aspect to it, which is a productivity led growth impulse. The interesting thing to me about productivity led growth is that when you get a productivity shock, it's positive to real GDP and it's negative to inflation. So it's a supply side shock and has that dual benefit. So if we extend that forward, that's a positive environment for stocks. And right now, it's a time when even with that, it's not easy to invest. So for instance, in fixed income at the moment, credit spreads are very tight. For fixed income investment, you're not going to get more than the yield to maturity in most instances. So the fact that the compensation over treasuries is very low for credit-sparing assets is something we're taking note of recently. Still have a pretty positive outlook for stocks. I think the other thing right now to pay attention to, and this will come as no surprise, is what's happening geopolitically. And that's changing week to week.

Speaker 3:
[18:24] It's interesting, Michael, in the most recent rebalance to the target allocation ETF model suite, this was something you implemented on March 9. You typified your response, your repositioning within the portfolios, as remaining risk on, but sort of refining and not retreating from risk. So curious, like as you alluded to, especially with respect to exposure to, say, credit in fixed income markets, what are some of these refinements to risk exposure? And how do they net out in a way that remains risk on, given your broader macro view and especially expectations for maybe a bit of a productivity boom?

Speaker 1:
[19:14] Yeah. So one thing we've seen is a shift in the last six months especially toward wider dispersion and returns in US equity markets. And this has been accompanied by a widening in earnings performance across equities, especially in the US. So in this most recent trade, some of the largest stocks got reduced out of our US equity position. We have an S&P 100 exposure that got cut out. And if you think about the S&P 500, you take the 400 smaller stocks in the S&P 500, their average capitalization is $200 billion. That's a pretty large company. In the smallest 80% of companies of the S&P. And so that's where we're seeing some changes. You know, we have a systematic process that's geared towards identifying what styles make sense to be overweight and underway at different times. And we've seen a reduction in the attractiveness of those largest stocks recently. That trade reflects that. The other adjustment we made that's probably worth noting, just from a risk perspective, as we slashed our precious metals positions in gold and silver and models containing alternatives, we had silver. Simply because the reason we added that position in late 2024 was because we identified central bank purchases of gold as being persistent and predictable. And indeed, they've proven to be that. The central bank bid globally for gold has been sustained over the period since we got in. But last summer, flows began to appear from a wider set of purchasers, both retail and institutional. And that's a set we have less insight into in terms of how persistent it is. And so, felt the time was right to cut that as a source of risk in the portfolios with this most recent trade. And I think I mentioned a moment ago on the fixed income side that credit spreads are very tight. You know, if you look at the credit indexes for US high yield, US investment grade, or dollar-based EM bonds, they're very correlated to each other once you take the effect of interest rate duration out. So, focusing strictly on credit returns, the three major kinds of credit risk-bearing instruments you can get exposed to are highly correlated. 80%, 90%, and then they are also very correlated to the stock market. So, they're economically sensitive, and yet they have a limited payout because they're fixed income. So we took the opportunity in this trade to go underweight to credit risk overall in the portfolios as a way to kind of reduce the overall risk in the portfolios while keeping an equity overweight, which is something we think will pay off better if things go the way we think they're going to go.

Speaker 3:
[22:20] It seems to be a common theme, at least based on my interpretation of how you've repositioned your portfolio, specifically the examples you mentioned, whereby you reduced your stake in gold, you reduced your exposure to credit that really your cell discipline, for lack of a better term, points back to a risk-based framework, specifically the risks that you might be looking to either mitigate or capitalize on with a gold position seem to have been priced in based on the run-up we saw in prices for the yellow medal. And similarly, maybe even more starkly in the case of credit, it just became clear that you weren't getting paid for the increment of risk you were taking in those markets. Is that a fair characterization of how you think about when it's time to either pair back or fully sort of remove a position from the portfolios?

Speaker 1:
[23:20] Yeah. And maybe one way to frame this is just in terms of scenarios. So our central scenario is for continued growth in the economy. But if we look at history, do a quantitative exercise of trying to predict the likelihood of a recession in the next 12 months. There are tools for doing that. There are models that do that that are available. And right now, those predictive models show a likelihood of recession in the next 12 months of around 20 percent. So one in five odds. It's pretty low, but it's not zero. And one in five is a risk. We had a similar situation back in 2019 where our kind of central outlook was for continued growth. But we were cognizant of the fact that there were risks. There always are. And credit spreads then were very tight. And so we did a similar thing at that time, which was go outright underweight to credit fairly aggressively. Lots of firms throw a couple flashy funds your way and call it a day, but not Vanguard. Vanguard bonds are institutional quality. Institutional quality isn't a tagline. It's a commitment to your clients. It means top grade products across the board. So if you're looking to give your clients consistent results year in and year out, go see the record for yourself at vanguard.com/audio. That's vanguard.com/audio. All investing is subject to risk. Vanguard Marketing Corporation Distributor. And at the beginning of 2020, we heard that you're also with a 3% equity over weight and something happened, which was the pandemic. And the markets went down very quickly. And that credit underweight put us in a great position to feel comfortable to make a decision in March of 2020 to add back to equity risk, following a very dramatic and quick sell off to add to credit risk at cheaper levels then and also in April. So, it doesn't mean that, you know, we're going to not participate in a downturn. But it does mean that, you know, if you put these kind of risk controls into the portfolio, you're positioned so that you can capitalize on any opportunities that might emerge in such a shock event.

Speaker 3:
[25:50] Yeah, I liked your earlier sort of summary. I think you described it as strategic allocations with tactical tweaks. So I think we're talking about some of these tactical tweaks here that you're regularly making at the margins. I want to go back to Michael, the topic of AI because it seems these days we can't possibly spend enough time or money on this topic in this particular vein of innovation. I'm curious just to get your overall AI thesis and how it might be either different from or more nuanced than just what seems to be a prevailing AI is going to eat everything and everyone and take all of our jobs thesis and how that's beginning to manifest in the portfolios.

Speaker 1:
[26:45] Why don't we start at the end? So let's just go right for that concern. The answer to that is we don't know. But let's build a mental construct here quickly. Imagine that you have an elasticity, a sensitivity of the labor market to GDP growth. So in the numerator, you have the monthly change in non-farm payrolls. How many jobs net are created in the economy this month or on any month? In the denominator, you have the GDP growth rate for that month or every quarter. So you have a kind of per unit of GDP growth, how many jobs are created? So that's the elasticity of the labor market to real growth. Imagine that that number, that elasticity is reducing. So for every unit of GDP growth, the job intensity of that, the number of jobs created is less than you would have expected if you were looking at the average over the last five years. If the productivity boom drives GDP up substantially though, you can make up for or more than make up for that drop in the elasticity. So you can see a labor intensity in the economy reduced, but an overall level of jobs creation sustained under such a scenario. You can imagine such a break even. I think that's going to be a part of this. If you listen to leaders like Jensen Wang, who's spoken about at his company, how profits are recycled into new and exciting projects. You can imagine that writ large across corporate America firms that are succeeding, adopting AI and infusing AI into their operations, have higher profitability, and continue to see a need for labor. But the labor intensity per unit of revenue goes down. So the amount of revenue that we see per employee in the S&P 500 is rising and I think that's going to continue. But the amount of growth in the economy is going to determine the net of this in terms of the job market.

Speaker 3:
[29:03] And I think maybe Michael, on behalf of the entire global labor force, I want to thank you for one of the rosiest pictures of the future for employment and productivity, certainly that I've heard in quite some time. And I think in many ways what you've described is just congruent with our long experience with all form of productivity growth more broadly. When you think about how specifically you make investments against this narrative in the AI sector, how that manifests specifically in the context of the model portfolios.

Speaker 1:
[29:40] A couple of ways, just taking a look at models, one of the decisions we've made is to carve allocations away from the benchmark asset into thematic assets. So what do I mean by that? So if I replicated my performance benchmark within US equities, my advisors would recover something like the Russell 1000 or the S&P 500. They'd get back a cap-weighted benchmark asset and nothing else. Instead, what they get is an equity portfolio that's very similar to that large cap benchmark, but differentiated. And one of the ways we've differentiated has been to have a carve-out position to technology. We had that up until, I think, 2022. Then we got out for a while and then came back in late 2023 or sometime in 2023. So that's one level of active risk-taking, is to carve away from the core asset and overweight a specific sector in the market. Something we did last year was then to move that allocation to a specialist team. So we're using an active exposure for the tech position, and that active exposure is focused strictly on the AI theme. And that's been a very accretive decision. The actively managed AI-specific exposure has substantially outperformed that capitalization-weighted tech index, and both have in turn outperformed the funding asset, which is that US equity market benchmark. In the most recent repositioning that we did, I mentioned that we cut off the top in terms of taking out the top 100 stock exposure and moving to a broader kind of exposure. We could have just done that with an index, and some of the money that came out of the largest stocks did go into index exposures in the US. But a portion of the proceeds went into an active US core manager, and that's got, in the Morningstar data, the top one percentile of performance, not top decile, but top percentile performance in the trailing 12 months. In his investment process, his and his team's investment process, it's very focused on earnings, and they also have a thematic overlay looking for firms within each sector. So we're talking in addition to the IT and communication services sector, within each sector looking to identify firms that are effectively embedding AI into their operations. They've had a lot of success doing that. There's a number of anecdotes in the portfolio, and it's a concentrated portfolio, less than 50 names, where the firms they're identifying competing in a given space are growing their earnings and having a very strong stock performance relative to firms that are not being very effective at adopting AI into their practices and their earnings are not growing as quickly and their stock prices are languishing. So this is a dynamic that's happening inside of the equity market, inside of all the sectors of the market, in addition to tech. And so that's part of the broadening out that we're seeking to capitalize upon.

Speaker 3:
[33:10] Michael, I'd like to get your take just more generally in scenarios like this, how you and your team approach thematic investments and specifically thematic fund selection. This is an area I've licked into in the past, is kind of the trifecta bet of sorts. You're betting you're getting the theme right, that it's substantive and durable. You're betting you're getting the portfolio right, that you're picking the right underlying securities, and fundamentally you're always betting that you're getting the timing, the valuations right. And what we've seen in the data, and you and I are looking at the same Morningstar data oftentimes, is that this is an area where investors have tended to show some of their worst instincts and behaviors. They've been bad market timers from the.com era to more recently trying to bet on the future of the cannabis industry. So how do you think about trying to thread that needle when you're going sort of outside of your core allocations in the portfolios to add value?

Speaker 1:
[34:14] A couple points. One is when we do add these thematic positions, they are scaled based on how much risk they add to the model. So the sizing is a function of the volatility of the new asset relative to the benchmark assets. That's the first point. And then in terms of investment process, and I mentioned some of the specialists we have on team, and then the wider kind of investment community at BlackRock, the ability to get subject matter expertise in a topic area is potentially unsurpassed for us as portfolio managers. So we stress test the heck out of thematic ideas. And at this point, over the last 10 years, we've made a number of decisions like the one I described with Teck, where we phase into and then out of thematic positions, and we've got a really high hit ratio or percentage of times it being right. It's not 100%, but it's far greater than the 50%. And some of those thematic traits have been very straightforward. So an example would be post the pandemic, we identified in late 2020 a hypothesis V for vaccine, that the inoculation that was being kind of spread across the population through vaccinations was going to lead to a reopening for the economy. And then interlacing that with what was happening on oil inventories and fuel inventories and production rates, we became convinced that there was going to be a shortfall in energy assets, both commodities and then that would be beneficial to energy stocks. And so that was something we introduced to models in 2021 as a theme. And just as important as getting into a theme is getting out. So those kind of more energy producer exposures, we held them for some time and then cut them over the subsequent couple of years. So, you know, you've got to identify when to get in, when to get out, and how to size.

Speaker 3:
[36:33] I want to touch quickly on, Michael, another more timely thematic repositioning of the portfolio you've made, specifically looking at the global defense industry. I'm wondering if you'd share the thesis there in where you've gone with respect to that positioning in the portfolio from, you know, predecessor position, kind of point A to point B, and, you know, how you arrived at the rationale behind that move.

Speaker 1:
[37:02] Yeah, similar things. So, you know, the aerospace and defense industry sits inside of the wider industrials sector of the market. And so we've had a desire to kind of get overweight to get industrials thematically for some time. And then that interlaced with this insight that's hidden in plain sight, which is increased fiscal allocations to defense globally, both for the United States as well as for our allies globally. The budgets to defense are increasing, and we have pretty good fidelity on that. So, the other feature of this is the technological innovation feature, and that comes into play especially with our choice to use an active specialist manager to implement the defense position. So you can think of aerospace and defense as being aerospace and defense and technology, especially once we're using an active team on this. We have an active ETF that's implementing our overweight to the defense theme. Since we introduced the defense theme in the second half of last year, it's been a strong performer relative to what we funded from the broad market, and we think that's set to continue. We don't expect it to be an outperformer consistently quarter after quarter after quarter, but as long as the kind of technological innovations, the budgeting, and the revenue and earnings continue to flow through the way we expect, I think it's something you can expect to see in our models.

Speaker 3:
[38:47] Michael, in this same vein of thematic investing in fund selection, your approach to the age-old question of active versus passive, so discretionary versus index, and if you've either got a framework or an express reference, when it comes to these more tactical thematic positions?

Speaker 1:
[39:12] Well, for active management, generally, it's not enough to have a high percentile raking in the Morningstar Peer Set. I think that in the last 10 years, being a 70th percentile active manager has meant that you've underperformed your benchmark net of fees. So, part of the issue with active management is that the fees are so high that the statement I just made is true. As an asset allocator, as a portfolio manager then, who's using active and index products, I am not asking the question of how is this manager doing relative to peers. I'm asking how much better are they than what I can get with an efficient low-cost index. And given that that's the question, getting an answer back of it's a good idea to go active is seldom the answer I get back. So, we use active sparingly and very selectively. I can get into some of the places where that's been effective, but I want you to stop right there.

Speaker 3:
[40:19] I am curious and I would love to pull on that a little bit further. Where are specifically some of those areas where it's been most effective and maybe more generally, how do you think about or do you explicitly manage an active risk budget?

Speaker 1:
[40:36] So, if the facts were more favorable in terms of the relative performance of active managers versus low cost indexes, then the risk story would benefit active management. So, the reason for that is most inequities, active managers tell a tale of security selection and kind of stock-specific risks they're adding to the portfolio. So, they describe themselves as stock pickers. And so, that kind of risk, being it is syncretic and not common, so not just a market risk or a style factor risk, are generally very complementary to the kinds of benchmarks that we're evaluating against. So, where we've been able to use active effectively in equities, especially has been with a strategy that is differentiated versus that stock selection narrative though, and that's dynamic factor timing ETF strategy. And so, we, in addition to using stock picker type of approaches, which we have in those thematic defense and the thematic tech and a small thematic broad market exposure, we have a substantial exposure to a style timing strategy ETF. And you add all these risks up, you can get back to a total amount of tracking error relative to benchmark that's not very big. So because the kinds of risks that get introduced to the model are complementary to each other and differentiated, the free lunch of finance is diversification. And that's especially true when you're building a multi-asset portfolio. The other place I'd identify is in fixed income. We've had some good success buying certain fixed income asset classes that are not found in the core benchmark, which is the Barclays US aggregate. So things like securitized assets that sit outside the benchmark have tended to have wider spreads, higher yields persistently. And that is something that's given that kind of actively managed exposure a tailwind.

Speaker 3:
[43:01] Michael, I want to finish by asking you the forward-looking version of the backward-looking question I asked you a bit ago. Specifically, when you think forward another 14 years, as you've been building models for investors for 14 now, what do you think are going to be some of the most meaningful changes in how you and your team are building and delivering portfolios to the end investor?

Speaker 1:
[43:34] I think the role of technology in this creates some uncertainty. But if I had to venture a guess, I would say that the use of separately managed or direct index strategies in models, it's got an uptrend now, it's going to continue as a trend. So you're going to see that there's just a lot of economic arguments for it, especially in taxable accounts. And use of options overlays, like we're seeing with Spider Rock Advisors, sensitivity to costs, that's been in place for quite a while now. I don't see that going away. So I think you're going to see 14 years forward, lower cost portfolios that embed options and separately managed single stock, single bond accounts being much more common than they are today.

Speaker 3:
[44:32] I think that's a solid bet. Certainly if past is any guide, Michael, that investors are going to be ever focused on value for money. And I don't think we're not going to be dealing with taxes 14 years from now, which is probably the safest of all bets.

Speaker 1:
[44:48] So maybe AAL solve that.

Speaker 3:
[44:50] Yeah, we'll see. I mean, let's hope so. No, I don't know. Certainly CPAs are not hoping so. But thank you, Michael, so much for spending your time sharing your insights with us today. Really appreciate it. Thank you. Thank you for joining us on The Long View. If you could, please take a moment to subscribe to and rate the podcast on Apple, Spotify, or wherever you get your podcasts. You can follow me on social media at mstarbenjohnson on X or at benjohnson, CFA, on LinkedIn. George Cassidy is our engineer for the podcast. Jessica Bevel produces the show notes each week. Jennifer Garrett copy edits our transcripts. Finally, we'd love to get your feedback. If you have a comment or a guest idea, please e-mail us at thelongviewatmorningstar.com. Until next time, thanks for joining us.

Speaker 2:
[45:57] This recording is for informational purposes only, and should not be considered investment advice. Opinions expressed are as of the date of recording, and are subject to change without notice. The views and opinions of guests on this program are not necessarily those of Morningstar Inc. and its affiliates, which together we refer to as Morningstar. Morningstar is not affiliated with guests or their business affiliates, unless otherwise stated. Morningstar does not guarantee the accuracy or the completeness of the data presented herein. This recording is for informational purposes only, and the information, data, analysis, or opinion it includes or their use should not be considered investment or tax advice, and therefore is not an offer to buy or sell security. Morningstar shall not be responsible for any trading decisions, damages, or other losses resulting from or related to the information, data, analysis, or opinions or their use. Past performance is not a guarantee of future results. All investments are subject to investment risk, including possible loss of principal. Individuals should seriously consider if an investment is suitable for them by referencing their own financial position, investment objectives, and risk profile before making any investment decision. Please consult a tax and or financial professional for advice specific to your individual circumstances.

Speaker 4:
[47:17] Morningstar Investment Management LLC is a registered investment advisor and subsidiary of Morningstar Inc. The Morningstar name and logo are registered marks of Morningstar Inc.