Vanguard's Return Forecasts Explained: What the Percentiles Really Mean with Kevin DiCiurcio

We all need to make smart decisions with our money. The Long Term Investor podcast shows you how by distilling complex financial matters into easily digestible lessons. And now, here's your host, Chief Investment Officer at PlanCorp and the author of Making Money Simple, Peter Lazaroff. Welcome back to the Long Term Investor. Last week, I shared with you how PlanCorp sets its capital market assumptions, which is the expected return, the volatility, and the correlations that drive our financial planning models, and I also explained how a number of other people approach that activity. Now, one of the ways that some people do it is adopting institutional assumptions. And today, I have Kevin DeCercio, head of Vanguard's Capital Market Model Development. He's going to talk about the process they use to set up those models and develop the outlook that a wide range of people use. Now, I personally like using these capital market assumptions for setting expectations. And if you go back and listen to the last episode, you'll see how we do it at PlanCorp. But Kevin was actually really, really important to evaluating that process a few years ago and thinking through the different solutions that were available to us, the way that we had been doing it, and we do touch on some of those things in this conversation. But this is a pretty wonky conversation. I must warn you. I do think it has a lot of value and one detail that kind of sticks out to me. And I'm recording this right after we got off of the conversation. Every time I thought about how much effort they are putting into their models, my goodness, think about all the people on Wall Street or in all financial markets across the world who are putting so much information into their buy/sell decisions. And I'm not saying markets are perfectly efficient, but the competition in markets does make it blatantly obvious to me, at least, that if you think you have some sort of gut feeling or intuition that makes you want to do something to your portfolio based on the information you have, just listen to a conversation like this and see how much thought Kevin and his team put into return assumptions because chances are there is already some sort of probabilistic consideration for what has you worried or what has you excited about a certain return or asset class. As always, you can find detailed show notes at the longterminvestor.com and at the top of this episode description, there is a link to sign up to my newsletter, which comes out every other Wednesday in links to resources that I am currently reading, as well as gives you access to exclusive content that you cannot find on my website or on this podcast. All right, let's dive in. Here is my conversation with Vanguard's Kevin DeCercio. Welcome back to the longterm investor. Today I'm thrilled to be joined by Vanguard's Kevin DeCercio who has a very long title, Kevin. We've been talking about this in advance, but when I asked you to come on the show, the real reason that I wanted to speak with you in front of our audience is that you're in charge of the capital markets research and helping develop those models that put out the return assumptions, many advisors like myself, and I suspect many individual investors see at the beginning and the middle of the year. So Kevin, welcome to the show and tell the audience maybe in a better job than I have exactly what it is that you are doing at Vanguard. Hi, Peter. And thank you so much for the opportunity to be on your show today. And as you mentioned, yeah, my name is Kevin DeCercio and I am a research team lead leading a global research effort to inform on our capital markets outlook in our investment strategy group. And our primary or bread and butter of our research is the research and development and operation that we've run, which is the Vanguard capital markets model, which is a financial simulation engine that is projecting global asset return distributions to help inform on economic and market outlook, as well as understand portfolio construction implications. So this is an important part of the research we do, but we also have a broader strategist function within our group, which is really about making sense of what's going on in markets. We may not be able to predict what's happening tomorrow, but I think a lot of our job is an attribution about understanding of what has happened to help our investors to maintain discipline with their long-term investment approaches. When I reached out to you, you and I spoke, we were trying to figure this out. I don't know, maybe four or five years ago. And PlaneCorp, we update our capital market assumptions every year, actually the episode just before this one goes exactly through that process. But while the process doesn't change that much, we do go a deep dive into the process itself and make sure that the process we're using is valid. But let me look at Vanguard. I reached out right when I saw the numbers come out. Maybe you could share a little bit with us. How do you develop these assumptions, these return assumptions? And how is it that you are hoping that people use them? Yeah, absolutely. I thought maybe I'd start by setting some context and definitions. I don't want to lose your audience at the start with they're using any kind of insider or vernacular. But you'll probably hear me refer to the model as BCMM throughout our conversation. Today, acronyms are very widespread at Vanguard, but it pretty much rolls off the tongue at this point. But VCMM stands for the Vanguard Capital Markets model. And as I previously mentioned, it's a global asset return forecasting simulation engine. It's really become an integral tool for outlook and portfolio construction considerations that are used across the Vanguard enterprise. Our Capital Markets research team sits in the investment strategy group and we own the research development and forecast operation of the model. We're also the gatekeeper of the model use. So we advocate and enable clients to use VCMM in primarily three ways. First is really encouraging clients and advisors to set reasonable asset and portfolio return expectations. Investors have myriad accumulation goals, most importantly, retirement for most, but also called savings, other general savings goals and being able to set reasonable return expectations is important information to really facilitate the success of saving and investing. So in the VCMM model, from a high level concept, we're leaning on statistical predictability to set these return expectations. This concept of predictability simply means that our belief about future returns varies through time. Doesn't mean that asset returns lack uncertainty, just that there's information that exists today that informs on a pattern of returns in the future. I think a useful analogy to describe this is seasonal air temperatures. We may not know exactly what the temperature will be each day throughout the winter, but we do know that winter days on average have a lower temperature than summer days. So similarly throughout history, intermediate to long-term equity and bond market returns have had "seasons" and these can partially be explained by starting market valuations. So therefore these time varying returns projected by VCMM give clients a good framework for understanding whether they should consider saving more, spending less, perhaps changing their asset allocation risk posture in order to give themselves a best chance to achieve those accumulation goals. A second important use case is really, VCMM has become an important portfolio analytical tool that helps our clients understand the risk and return trade-offs of their investment decisions. So first point here really relates to the previous one about setting reasonable return expectations, but because we all invest under uncertainty and ultimately are compensated for that uncertainty, understanding the likelihood of meeting your savings and investing goals really requires a full appreciation of the range of potential outcomes. So VCMM being a globally connected model captures statistical relationships among global asset return drivers, simulates them jointly, meaning we're really trying to capture correlations and higher moments that make this a really important portfolio construction tool. So this is used in advice settings at Vanguard to quantify probabilities for achieving success, given personal information about savings rates, asset allocation, retirement age, etc. We also have portfolio analytical users who will use VCMM simulations to get insight into how the range of their potential return outcomes or probabilities for downside risk events change based on changes in risk taking in the portfolios. So I would argue that this type of analysis is also useful in revealing preferences about one's own risk tolerance. We can show you a comparison of your projected risk and return profiles between an 80/20 stop bond portfolio in a 60/40 and you may just decide that you're a little bit more comfortable with that range of potential long-term outcomes or the downside risk expectations in the 60/40 to the 80/20. That reveals a risk tolerance. So analytically outlook reasons, those are the first two primary use cases and lastly what we're using more and more of is VCMM is being used in asset allocation frameworks that will now provide recommendations for the portfolio mix. And really briefly here, we offer valuation conscious portfolios whose allocations will change through time in order to anchor to either a long-term return target or to help you stay more consistent with that preferred risk and return profile. And then we also perform due diligence on static portfolios and glide pads based on what we call the equilibrium premium assumptions in the model. Well, I will add one more use case to that, Kevin. I feel like I use it to behaviorally coach my clients to stay the course on their diversifying asset classes, particularly in the last handful of years where US large cap stocks were just beating everything on the planet and a common question is, "Well, why do I even own international?" And I could often point to Vanguard's 10 and 30 year projections to say, "Well, look, they're forecasting and there is a range of outcomes, but they're forecasting to be higher. I am going to get into some of those numbers a little bit later, but I actually would first love to hear a little bit of an insider's look on the process of developing these numbers in the first place." I appreciate your use case. I think that's a really informed and educated use case of VCMM. I know we spoke a few years ago, so I appreciate the work that you're doing with your clients as we think about using the capital markets model to benefit a wide-do-it-yourself investment audience and an advised audience. In terms of the process, so we have a 10-person global research team responsible for the research development and operation. As I mentioned, we have governance from internal operational risk teams. Given the widespread nature of its use, we have a lot of governance oversight from operational risk teams, audit teams, as well as internal oversight from our regional and global chief economists and who are really weighing in with opinions on model methodologies and the forecasts that we're releasing to our clients. The forecast operation itself is really designed to be as systematic as possible. We re-estimate the model quarterly as new data is finalized and we will run forecast monthly based on those prevailing market conditions. We try not to touch it. There is no ad hoc overlays. A lot of that we try to build into the model methodology, but ultimately we generally release a new long-term outlook each quarter, but in the event of volatile market conditions that are creating big impacts to the forecast, we'll certainly share updates in true quarter through our corporate communications teams or strategic communication teams internally. But ultimately forecast will change only as market conditions change, forward-looking information changes, or model methodologies change. The research development effort really is separate from operating the model on a monthly basis. Any methodology changes or changes to forward-looking assumptions resulting from that research and development effort. That's then part of a broader new model implementation and a change management process. Any model development is only as good as its inputs, but in my understanding it's a very probabilistic approach. Is that a fair assessment? Yeah. The goal of the model is to capture the full range of potential outcomes, absolutely. There are so many capital market assumptions that are made by large financial institutions, again, by financial advisors who then need to populate financial planning software with capital market assumptions. Would you mind just quickly touching on maybe some of the different approaches that you did not take that others maybe take in the industry with capital market assumption development? Yeah. There's primarily, I think a couple of ways to do this and then maybe a lot of ways on the how, but a lot of the forecasting really goes back to some of the original Robert Schiller Cape work that identified this inverse relationship between starting market valuations and future returns. There is this predictive regression approach where you're really just mapping a starting condition to a long-term return and the visualizing that scatter plot. That's where the predictability came from, but from an explainability perspective, there's that asset pricing aspect to this as well, where any financial security is getting its returns from two sources. You have an income return and you have a price return, so you're starting with this accounting identity. And then from a asset pricing perspective, thinking in terms of discounted cash flows that income return on an equity side is coming from dividend yield and your price return can be decomposed into its valuation change and then the growth in the fundamental that's for using that valuation, so a lot of approaches you see the sum of parts where you're making an informed view, maybe independently on, okay, dividend yield is today 1.5%, so maybe I'm looking at a historical average there or maybe I'm just locking in the current condition. Valuation change, maybe my expectation is there's some reversion to a historical average there, so that you're just computing that impact to return based on some assumption of what valuations are going to do and then earnings growth, you see a lot in literature, historical averages, it's hard to find predictability there, we may dispute that a little bit, but I think you have some combination of these two approaches of predicting based on mapping current conditions to a future return or making some decisions on those sum of parts. So I think where we are is we're taking advantage of this academic literature of predictability, but we want to give that an explainability, but we want to model it holistically. And I think that's where maybe the approach is a little bit different as we're taking any ad hoc nature out of views on these sum of parts and you're limited to these ways of looking at it. And then otherwise you can just say look historical averages, I throw my hands up. I don't know what returns are going to be, historically they've been 10% in US markets, but I think what we're trying to do is again, statistically capture predictability, do it in a consistent way that we're not heavy handed, making a lot of assumptions in the model while we're running it, and that it is statistically capturing the full range of outcome. Again, it's not a different way of thinking it, but it's more of a holistic, consistent, systematic model is what we're trying to achieve. Let me zoom out to the end result. And if you're listening to us on the podcast, there's no way for me to really showcase this without saying it, although if you're watching us on YouTube or you're watching us on chatter, you go to the longterminvestor.com and you can get a better look at some of these returns. But what I'm doing right now is I'm looking at the 10 year forecast. I'm going to start there, maybe we'll go to the 30 year. And what you showcase are the 5th percentile, 25th percentile, 50th, 75th, 95th. So you have your normal distribution, and I'll try not to keep it too wonky for our listeners who don't want to talk stats, but if we're going to look at the 50th percentile outcome or base case, over the next 10 years, US equities earning an average annualized return of 3.8 percent, developed internationally, you have 6.3 percent. I think interestingly you have bonds at 4.3 percent. So outperforming US stocks, when you're looking at these different percentiles though, I got to the 95th percentile, well, yes, equities are in their 95th percentile, they end up beating bonds. But when you showcase those percentiles, Kevin, how is it that you are thinking that people are going to be using those percentiles and thinking about them? Yeah, that's a great question, Peter. And I'm happy to try to shed some light on this. The percentiles are used to describe that full probability distribution, which is generated via a deterministic regression approach, and then simulating all of the uncertainty in the model. We don't have to go deep in that. So what we have here is this full probability distribution function that we're summarizing in these percentiles. There really is this literal interpretation here is like, if you're looking at the range of outcomes between the 25th and 75th percentile for US equities, for instance, that has the interpretation of the, there's a 50 percent chance of returns at the end of the 10 year horizon falling between that return range. The nuance, I think in VCMM, is that there are time horizon implications though to the interpretation of model medians or the model averages as you've pointed out. Around the 10 year time horizon for equities, and maybe it's the five year for broad market or intermediate duration fixed income, we do have this increased confidence in the model's ability to capture realized outcomes somewhere in that middle 50 percentile range. That's why we really define the median returns at those time horizons as return expectations because of that increasing accuracy in the forecasts. If you were to look at shorter term distributions, which we don't really share publicly because again, we want our investors to use them properly. So we're thinking about the long term because I know investors are fixated on that 50th percentile. That's the average. That's the return expectation. But for the more shorter term time horizons, basically what we see in the model is a much more uniform distribution, equally likely to get any return between negative 20 percent and positive 20 percent for US equities over the next year. And therefore we have less confidence in saying, okay, well, the median is a return expectation. It's just not as accurate. So as a research effort, what we try to achieve is really the best forecast accuracy to set those return expectations over the median long term. And then over shorter term time horizons, we're really aiming to ensure that VCMM can capture realized outcomes somewhere in the distributions. Now, I think the 2022 experience was really instructive for us in this regard. So you probably remember 2022 finally the beginning of the year, January 2022 was the beginning of this steep decline in US equity markets over the next nine months or so that coincided with the rapid rise in long term bond yields as we experienced that inflation spike and Fed signaling and aggressive hiking campaign. This caused simultaneous capital losses across both markets and the notorious spike in the stock bond correlation. Equities were down near 25 percent. I think bonds were down 15 aggregate bonds in the US market. So we looked at this, you know, are we capturing this in the model? So a 40 60 US stock bond portfolio, and I'm doing a conservative one because, you know, how frequently have bonds had a 15 percent draw down in nine months. I mean, it's almost unprecedented. The aggressive hiking campaign that we saw, but the 40 60 US stock bond portfolio was down by 19 percent over those nine months. So we went back looking at the forecast produced as of December 2021 conditions, if VCMM projected a 19 percent draw down of the 40 60 portfolio somewhere around the 95th percentile of its max draw down projections. So in that case, we felt like we reasonably well captured that outcome. In other words, with these percentiles, VCMM's design to set return expectations with the median or the averages of the model from the long term, that's where you're fixating on that 50 percent. But we really want to fully describe the probabilistic nature of returns over all time horizons. I'll take a moment to explain to people why I think this conversation is interesting, particularly if you are a client of financial advisor running Monte Carlos or you are a do-it-yourself investor who's using modeling is that sometimes you might be worried about something bad happening in the market. And the modeling process takes into account, you know, you mentioned 2022 was in the range of expected outcomes, roughly. And I think the mistake people make when they're trying to set up return forecasts, particularly when they're for 30 plus years as they forget, well, if we're going to have these modeling, it doesn't mean that that return you're assuming, assuming you're going to vary it and put a standard deviation on it. It's going to capture downturns of a similar magnitude and frequency as we've experienced in the past. And I'm curious, Kevin, when I click over from the 10 year returns to the 30 year returns, 30 year returns hugged the average pretty closely over the long term. So I guess does the base rate, does the fact that the long term average does it get a bigger weighting in the model and thus drag returns closer to their long term average? Such an interesting question. The way the model, and I don't know if you have questions on this, but the model really has two important states, right? The first is the predictability that we described. We want to capture the impact of current market conditions on our expectation of the future. And then we let the statistics in the model tell us how long that takes and not as concerned with the path sometimes necessarily, but more it's the, okay, well, how long do these relationships with starting conditions tend to play out trend growth rates matter certainly as well in that time horizon? We think about the very long term. We do have this equilibrium aspect of the model and you can consider equilibrium as this is when market conditions would have no impact on our return expectations on the mediums. We're looking at sort of the steady state. So you can think of steady state as maybe years 21 through 30 in a 30 year forecast. Maybe you want to go a little bit further and say maybe it's 31 to 40 in that state in the model. Initial conditions have decayed off and you're left with these long term premium assumptions. So there's a big body of research where we're looking at that and saying, okay, well, it's our long term average has been for country equities. How should we position that in the model that going to get into that in this question here? But the relationship between the two matters on time horizon. So our 10 year forecast is almost entirely determined by current market conditions. And then you get to this 30 year horizon. In most normal times, your 30 year horizon really resembles that equilibrium set of assumptions. It's not going to vary that much on it, but depending on how extreme current market conditions are, they can begin to start drying that higher or lower. And I think in the case you have them in front of you, I presume that our 30 year forecast are drug down. I think our steady state assumptions around 8%, which is different than the 10 year 10% that we've seen historically. There's reasons for that. But we think where current market conditions are today have a big impact on even the 30 year time horizon. That's an interesting point we can pivot to over the next 5 to 10 years. What does Vanguard see as the strongest risk return profiles based on your modeling? So I would encourage, I don't know if you're a reader of the economic and market outlook work, Peter, but I would definitely encourage you and your listeners to read the report that we published in December. That will probably lay out all the numbers, the rationale, economic rationale, and maybe statistical rationale for those numbers as well. Think long term, so things tend to move fairly slowly. We're considering bigger picture things, mega trends, being one of them. But ultimately we continue to see strong value and fixed income, expected equity risk premium, which I often estimate if you're looking at those return tables by looking at the difference in the median 10 year forecast between broad equities and broad bonds. That is my estimate of expected equity risk premium, might be different than how some others look at it. That's based on our forecasts and based on the statistical probability that's very compressed by historical standards. So this suggests that over the long term, we do not believe that global stock bond portfolios are really expected to receive adequate compensation for taking on marginal equity risk. And this is particularly related to the US exposures in that VCMM equilibrium state that I described where you're not impacted by market conditions. We look at credit risk within the fixed income space, credit bonds in equilibrium provide really reliable risk premium over the long term. So we can actually make this argument from a strategic equilibrium perspective. You want additional credit bonds in your portfolio. But given the spread compression that we've seen in both IG and high yield spaces, we just don't think that risk return trade off is as attractive today. And would argue that to really capture the benefit from your fixed income exposures, you want to be up in quality, your treasuries, you know, really high grade investment grade bonds, if you're going to be taking that credit risk. From an equity perspective, we again continue to believe that US value provides more attractive prospects than US growth. And this is even in consideration of different AI scenarios playing out, you know, whether it's an AI led growth boom or if AI disappoints, we actually think value performs well relative to growth in both cases. And kind of let me lay that out in the boom case, we think value out performance is mostly driven by creative destruction processes in which competition for the AI profits begins to erode the excess profitability that we see today in the handful of firms. Other sectors in this AI boom will start to experience profit growth as well. And then some of these productivity gains really show up in consumer surplus as well. So they're not always materialized in profits in a handful of firms. The broadening out of profitability and the erosion of excess profitability, we think of the strong case continued for US value. If AI disappoints, what's a different reason? But that's when the profits don't materialize from the heavy cap ex expenditures and those handful of hyperscalers driving the tech performance today will likely underperform in that situation. And certainly given their large share in the market that had, you know, puts pressure on the broader market as well. From a global equity perspective, and you mentioned this before, we know the history here quite well. The US equity market is trounced its global peers over the last decade and even longer. When we look at the joint probabilities of the model and this is a use case that we haven't always talked about. I mean, if you're comparing the mediums of two asset categories, it doesn't give you all the information of the joint probabilities. So when we actually do this joint probability, the viso model shows about a 35% probability of continued US equity outperformance, which by just looking at the differences and mediums, you know, you really, you might get the false impression that, okay, we believe 100% that develop XUS, for instance, it is going to outperform, but 35% is a non-trivial probability. It's mostly related to the growth challenges that we've seen in the XUS markets and the economies, but really given the level of US valuations today and our view of over valuation being driven by this increasing expectations for expanding corporate profit margins, earnings growth rates. You see this in analyst consensus estimates. We just see that very low probability of being able to be sustained and increasing those growth rates over the long term. So from a risk return profile, we definitely favor developed XUS, but in the event of long-term US outperformance, we just don't believe it can possibly be at the magnitude, you know, as the last decade, right? So that 35% probability, if you look into that, you know, the very few of those simulations have a 10% outperformance of US there. So through your point earlier, we don't believe you're going to be penalized from holding a balanced global equity portfolio, as was the case maybe the last 10 years, you know. So when we look at that forward looking, we still wanted to stay balanced with our global equity portfolio. Well, I got to be honest, Kevin. I had my first client conversation since, I don't know, the early 2010s, where in the late knocks, so like 2007 through, I don't know, 2012 clients would keep saying, like, why do we have so much US reached out more international? US had started winning, but the sentiment was still there. And for a decade plus, it's why do we even have international? We should just be all US. In 2025, I had my first client conversation where they were ready to move from US to international. While I was thrilled that I maybe get to talk about something else, it was a little discouraged like, oh, no, we're going to just performance chase the other direction now, aren't we? Maybe different people, but that is human nature for you. You mentioned some of the AI stuff. I was going to ask you that anyways, I mean, a lot of the economic report incorporates ways to think about AI-driven returns and growth and how the winners of AI aren't just big technology. You've already touched on it a little bit, but before I move on, if there's anything else you want to mention related AI, I kind of want to give you that space to do so. A lot of this results from, I don't know if you've seen Joe Davis or Chief Strategist Global Economist. He started modeling and thinking about these mega trends, how these mega trends in his mind will play out in the future. And you know, it really emerged was this, again, this new modeling effort, we're calling the mega trends model, which has been a collaboration across a couple different research teams in the group, understanding the probabilistic future of, I think you've really started with, okay. The consensus believes we're going back to this mid-2000s moderate growth, low inflation environment. That's the consensus. So I think he was really setting out to understand, okay, well, we have this AI phenomenon that's emerging. We have demographic and physical challenges. Can I get a better understanding of these mega trends? And what he found was this likelihood of going back to this moderate growth and low inflation is really unlikely. And it's this tug of war between those two polar things, which is this AI-driven productivity boom, or will deficits dominate and demographics dominate leading to a quite different situation. So I think we spent a lot of time in the report really talking about the AI driven because we should be. That's what's really important right now. But I think we lay out really three scenarios in the outlook. That would say the extreme bull case, which is what we see as having a relatively low probability. This is where AI, it's economic transformation, even is stronger than expected. And this is probably that continuation. This is that 35% U.S. outperformance probability, maybe a little bit lower with the magnitudes. But our economics team is laying this out as maybe a 10, 15% probability I could come back to the report. I apologize for not having that number handy. But this really results in earnings growth, 8 plus percent, equity valuations, been this type of environment remain at present levels or even marginally expand under those conditions. So we think in this environment, U.S. equity returns really would continue to be very strong around 10%. But the baseline view, that baseline view is where we're assigning most of the probability. And that is really the belief that AI will emerge as this general purpose technology that is innovative across the economy. And this results in a productivity boom that it generates, I think, maybe 3% real economic trend growth rates. But it's really this case where growth were made in strong, but this is where the competition for profits and creative disruption, consumer surpluses process, as I described, normalize that excess profitability. We just think that's going to put pressure on valuations at some point in this kind of negative trends future. So that has equity returns in that five, seven percent range. But then we have the bear case, and this is this non-trivial probability of AI disappointing. And you know, this is a scary case, broad earnings growth at this point is looking like trend GDP growth. But with valuations price to perfection today, we just think it would fall markedly. And this is the scenario that gets you to that decade ended 2008, early 2009, which valuations have a long way to go, which creates this drawdown as well as a long term, maybe 2% annualized return. When you think of VCMM as we connect this to the mega trends thinking, VCM today is really this probability weighted average of those scenarios, which is why we're in this four to five percent range for 10 year US equity returns over the decade. The other topic that seems to come up a lot these days, although I'm pausing because it seems like it has been coming up throughout my whole career. But at the moment, I get a lot of questions about geopolitical considerations. How does that impact the way that you guys both model returns or think about the inputs? You follow markets probably as much as I do if not more clearly prices in the short term move on headlines and they move on geopolitical considerations, wars, political environment, etc. And when we think about it, we don't have a specific geopolitical factor that enters this core macro financial model that those are the drivers over asset returns. But there's nothing geopolitically that will aid in this deterministic 10 year forecast. However, when we think about the longer term, we think about that steady state that I described, we spend a lot of time looking at long term average returns, there's some great resources on this. But when you look at long term, like really long term, 80 years, decades, historical average country returns, what you're going to see is like Australia equities, for instance, will have a higher average return than most European countries, every 80 years annualized. So a time series, a naive time series model, we need to talk about this much today. But a time series model wants to converge to the sample average. What we do in VCMM, it's always been a mean adjusted model. We've done different things with that date today to make it more dynamic and systematic. But whenever we think the expectations for the future will not resemble historical averages, we can make these mean adjustments think of declining inflation rates since the 70s, think of risk free rates, which is a component of that. We're really not letting those experiences inform what the Fed is trying to do with inflation. That's kind of why we have a 2% inflation average in VCMM in steady state rather than something that looks more like history. When we do this for risk premia, comparing Australia to Europe, once the model converges to that point where initial conditions don't matter anymore, you're left with that return advantage. So if we just left it naive, we would have starting at about year 15 or 20 in the model, you would have this expectation for Australia outperformance over Europe. But then when you really consider the history, there was a world war fought on the European continent. There was two of you go back even further. So we just think that may well have explained why you get these average return differences. So for us, we're not trying to pick equity winners over the next 80 years, we're really just relying on that near term predictability. So even though there's not this deterministic forecast geopolitical consideration, I could suggest to you that these play into our thoughts on long term risk premium for sure. So a few questions I have to wrap us up, although neither of them may be all that short, but are there two or three or four sign posts that you would be watching in 2026 that would most change your conviction around the modeling or cause the greatest change to what you've projected at least as a right this moment? Thinking about those current market conditions that you said, play a heavier weight on the 10-year returns as opposed to the 30-year, and I'm putting you a little on the spot here. I think for us, Peter, at this point, really because we are continuing to take this long-term view, there's an element of our economics research team, which are peers of mine. We work in the same group. They do not have a view on recession or low probability view on recession in 2026. We don't want to talk out of both side of our math, but in the long term, we believe in this lower average return environment. In the short term, we believe there's a lot of momentum reasons why US equity and US stock market can continue its good performance. Probability distribution in the long-term may not resemble the one in the short term, but as we're thinking again of this longer term time horizon, I think for us, Peter, it's really views on how these mega-trend scenarios play out. Are we getting increasing or less conviction in AI as a transformational economic tool? Is it a boom? Is it electricity or is it something else? These kind of probability weights to those scenarios. I think that's what's most likely going to inform bigger changes to our long-term US equity outlook. And for 2026, as you think about what the research agenda at Vanguard will be, or maybe you already know, given that it's already January 2026, what is your team looking into right now? That's a good question. I think we have a focus on a couple things, enabling multi-asset portfolio decision-making. So there's always an element of the work that we do to ensure that we're meeting the demands from our clients on adding new asset coverage, adding countries to the model. And then, on the other hand, there is this view that the way we talk about markets and economy is tightly connected to it. What you can see from our research agenda is we want our forecast methodologies to be mega-trends aware. We've gotten a lot of insight into the probabilistic macro and AI future. So we want to make sure that we're connected to those views, as well as being very consistent with our economics research team as saying, I think, as their views on policy inflation change, we want to make sure that's well represented in VCMM, so that's really this tool that, yes, it helps strategic decision-making, but also cyclically consistent with the view points coming from our economics research team. And then, ultimately, it's an agenda to help our investors and clients stay disciplined with their investment approach. So I think Ambassadorship is another big thing that we want to do. We want to be talking to our clients. So in addition to the rigorous research and statistical modeling, we want to have these conversations with clients to ensure that we're helping with our mission of giving every investor the best chance of success in what they're trying to accomplish. Well, Kevin, it's always a pleasure speaking with you. We get to have a wonky or conversation than my typical everyday conversation. So this is really fun for me. I'll be sure to link to the outlook and the show notes at the longterminvestor.com, where you can also find some of those return assumptions that we were referencing throughout the process. And I would encourage listeners and viewers, if you think that you're doing research and modeling, predicting the market, just listen to what Kevin and his team are doing to put all of this information into a long looking forecast, and I use that word forecast lightly, because I know that you don't feel like you're predicting the future. You're very good about producing ranges of outcomes. You are arguably the first major financial institution to publish financial outcomes. And it's part of what I've been drawn to all long. And so whether you're an advisor or an individual investor and you can make your way to Vanguard, they really have a lot of great stuff for you to learn from and help set those expectations. So Kevin, again, thank you so much for joining me here on this show, and I cannot wait to talk to you again soon. Peter, it's my pleasure and, you know, grateful to be here and having an opportunity to adjust your audience. So I couldn't be more thankful of your time, so thanks again. My pleasure. Take care, everybody. Thanks for listening to the longterm investor podcast. To access free financial resources and submit questions to be answered on the show, visit the longterminvestor.com. Peter Lazerov is an employee of PlanCorp and BrightPlan. All opinions expressed by Peter and any podcast guests are solely their own opinions and do not reflect the opinions of PlanCorp or BrightPlan. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of PlanCorp and BrightPlan may maintain positions in the securities discussed in this podcast.