The financial advisor and adjunct professor discusses elevated equity valuations, the limitations of random-walk assumptions, and why bonds are riskier than you might think.
Our guest on the podcast today is Scott Bondurant. Scott is the founder and chief investment officer of Bondurant Investment Advisory, a registered investment advisor based in the Chicago suburbs. He is also an adjunct professor at Northwestern University, where he teaches an undergraduate course on the history of investing. He recently published a white paper about the importance of incorporating mean reversion in financial planning and portfolio construction, which we’ll be discussing in this podcast. Scott has a BA from Stanford University and an MBA from Duke University’s Fuqua School of Business. He started his career at Kidder Peabody and also worked for Paine Webber and Morgan Stanley before becoming a managing director for UBS.
“Hidden in Plain Sight: The Dramatic Impact on Financial Planning and Portfolio Construction When Mean Reversion Is Incorporated in Risk and Return Expectations,” by Scott Bondurant, papers.ssrn.com, Nov. 25, 2024.
“Understanding ‘Mean Reversion’ Can Make or Break Retirement,” by Scott Bondurant, rethinking65.com, June 13, 2024.
“Mean Reversion: Unlocking a Foundational Investing Principle,” sbondinvest.com, Feb. 8, 2024.
Fair Disclosure, Regulation FD
“Charley Ellis: Indexing Is a Marvelous Gift,” The Long View podcast, morningstar.com, Aug. 5, 2025.
Devil Take the Hindmost: A History of Financial Speculation, by Edward Chancellor
Stocks for the Long Run: The Definitive Guide to Financial Market Returns & Long-Term Investment Strategies, by Jeremy Siegel
“Anomalies: The Equity Premium Puzzle,” by Jeremy Siegel and Richard Thaler, The Journal of Economic Perspectives, Winter, 1997.
“Do Stock Prices Move Too Much to Be Justified by Subsequent Changes in Dividends?” by Robert Shiller, papers.ssrn.com, April 12, 2004.
(Please stay tuned for important disclosure information at the conclusion of this episode.)
Amy Arnott: Hi, and welcome to The Long View. I’m Amy Arnott, portfolio strategist for Morningstar.
Christine Benz: And I’m Christine Benz, director of personal finance and retirement planning for Morningstar.
Arnott: Our guest on the podcast today is Scott Bondurant. Scott is founder and chief investment officer of Bondurant Investment Advisory, a registered investment advisor based in the Chicago suburbs. He is also an adjunct professor at Northwestern University, where he teaches an undergraduate course on the history of investing. He recently published a white paper about the importance of incorporating mean reversion in financial planning and portfolio construction, which we’ll be discussing in this podcast. Scott has a BA from Stanford University and an MBA from Duke University’s Fuqua School of Business. He started his career at Kidder Peabody and also worked for Paine Webber and Morgan Stanley before becoming a managing director for UBS.
Scott, welcome to The Long View.
Scott Bondurant: Thank you.
Arnott: So, we wanted to talk about the concept of mean reversion and the paper that you’ve written about that topic and what it means for financial planning and portfolio construction. But before we get into this, we wanted to talk a little bit about your background. You were a top-ranked tennis player when you were in college at Stanford and you spent a few years playing professionally after college. How did you end up choosing a career and business after that?
Bondurant: Well, it wasn’t a straight path. So, when I played professional tennis, I had a fairly clear-eyed view that the odds of making it weren’t really very good. The good news was I had pretty good grades and so I was thinking about potentially going to business or law school. I sat in some classes at Stanford Law and liked them, and so I decided that’s a good route to go. And I ended up going to a year of law school and not liking it and then working at a real estate law firm, which was interesting in a lot of ways but in the end it was a real grind and I kind of sensed that that was going to be the nature of a lot of the legal work that I might end up doing.
So, I looked for greener pastures and talked to a few of my friends from college, one of which was an analyst at Wellington, and he invited me up to his offices up in Philly and showed me what he was doing and then took me to the trading desk where they were trading big blocks of stock, and I said that was pretty cool. And so, the guy there said, well, you need to go to some of these Wall Street firms and check in with them, and so I got very interested in the equity sales and trading thing. But the best way to get a job there was to get an MBA and so I went to Duke, got an MBA, spent a lot of time on the job hunt and got an internship at Bear Stearns and then ended up getting a full-time job in sales and trading for institutional equities for Kidder Peabody. So, always good to be with the firm that—we’ve been in the business long enough; these firms don’t exist anymore. So, that’s how I got going into the investment world. And so yeah, that’s kind of my story and then I migrated to a few different things throughout my career.
Benz: We wanted to follow up on that, Scott. You started out, as you just said, working on the sell side at firms like Morgan Stanley, Paine Webber, Kidder Peabody. So, how did you end up deciding to start your own investment advisory firm, which you run today?
Bondurant: Yes. So, when I was at Morgan Stanley in the ‘90s in institutional equities it was just a boom time and just a ton of fun. So, back in the day, before the internet companies had to go through investment banks to basically tell their story and give investors an opportunity to understand what they’re doing. And so, we had a monopoly kind of on that information flow and so we were able to obviously take advantage of that. And then, in the ‘90s when the internet happened and people didn’t really understand it, and [there was] lots of trading and activity, and we had basically groundbreaking research on it too because we had the connections to the company. So that was a lot of fun, and I think I would have kept doing it if it weren’t for what I say three different things.
So, the first off is the internet happened, and so everybody gets the information at the same time and by the way, Reg FD, fair disclosure, happened, and companies had to provide everybody at the same time. So, we no longer had that edge. Then there was electronic trading, so the spreads and the trading weren’t as good. And finally, Eliot Spitzer came around and made it so the very best analysts that were able to generate investment banking deals couldn’t get paid for that and so the very top talent I think left. So, in many ways, very good evolutionary reasons that became not as interesting a job.
And what had started to happen in the early aughts was the new boom in hedge funds, and we were always encouraging hedge funds and they were big clients and profitable business and prime brokerage, and so I encouraged my clients to do hedge funds and one of them the legacy Brinson Partners, which was bought by UBS Global Asset Management, had an investment process they identified under and overvalued securities that lent itself to equity long-short investing and they said, yes, we want to do this, you come over and run it. So, I did that for about 10 years, and we built up a really nice business all over the world and that was fun, although at some point I think—probably a lot of listeners can relate—but these big firms are very kind of challenging in a lot of different ways. And so, I kind of got tired of the big firm and working for somebody else. And so, I was in a position to take full career retirement. Having said that, one of the reasons I got into this business was trying to figure out if you have enough using an excel spreadsheet, and speculative forecasts was not an easy task.
So anyway, I did my best, said I think I got enough and took full career retirement, started teaching at Northwestern. I’ve taught the history of investing for over 11 years. And then, I love the investment business, and somebody said that they could bring me some clients, and so I got registered and started my own investment advisory firm. That pipeline for clients never happened, but I got going. And I love the business, and you can tell from what I think we’re going to talk about that I think there’s a real opportunity to kind of do it better, if you will. So anyway, I’ve been doing that for seven years and have really enjoyed it and I’ve continued to do teaching as well.
Benz: We want to follow up on some of the themes that you’ve just referenced, but I wanted to ask about Reg FD, which you referenced in your response. We had Charley Ellis on the podcast not too long ago and he felt that Reg FD was absolutely seismic in terms of its impact on the investment management space and the active investment management space. Do you share that view?
Bondurant: Yeah. I love Charley Ellis for starters. And yes, it was incredibly different. So, there was a huge volume of information increase, and so people were better able to value securities. And I would also make a note that more and more people kind of became CFAs and were very rigorous about their approach to valuing securities and so the whole thing got to be just much more efficient. So, yes, I think that was a huge moment for active management.
Arnott: You mentioned that you teach a course about the history of investing at Northwestern. What do you enjoy most about teaching?
Bondurant: So, a bunch of things. One, it’s fun to be with younger enthusiastic people and kind of get a sense for the way they’re looking at the world. And I also think that it’s satisfying to be able to help them and “give back” a little bit. I kind of have set up this class as in many ways the class that I wanted to have when I was back at Stanford in college and we had one class on the stock market with a guy, a broker who came in from E.F. Hutton, and it was fascinating to see how the real world worked. And so, I’m able to kind of bring that real world in, and by the way, I bring in guest lecturers that are leaders in their respective fields and that are relevant for the particular class I’m teaching and so they get, again, a broad view from a lot of different practitioners, and I think that’s invaluable. And then lastly, it keeps me on my toes, and it keeps me fresh. I have them read all the great books, and I have to reread them every year to make sure I’m on top of it. There are always new things you learn. And so, again, I love the investment business, so being able to dive in in a class, and again, it has impacted my view in terms of investing, in terms of looking at investing from the long-run perspective.
Benz: Are there any lessons from market history that you think are especially relevant today?
Bondurant: Well, one that I would highlight is that when you look at the history of investing, it really is very much the history of speculations and booms and busts, and they kind of are consistent over the course of time and I imagine they’re going to continue to be. There’s a great book Devil Take the Hindmost from Edward Chancellor that I have my class read but walks through all of the bubbles from Tulip Mania, the South Sea bubbles to rail roads to the roaring 20s to the Nifty 50 to the Japan asset bubble, more recently the internet bubble and the housing bubble.
And so, while I would say that every market cycle is different and it’s kind of important there’s a good reason why. So, if you go to Vegas and you go to the different casinos, in the craps table and the blackjack table there’s a certain number of outcomes that are possible because you get 52 cards in the deck and two dice with six numbers on them. And so, you can actually predict the odds very nicely in future returns. But in the real world, it’s always changing, it’s always evolving. There are no parameters that are fixed. And so, when we try in general to make models that will help us in forecasting, there’s a great line somebody had that said, all models are wrong but some of them are useful. And so, I think it’s really important to understand when you’re looking at models of forecasting future that they’re all imperfect but they’re as kind of good as we can do if they’re good models to help you in terms of the expected returns.
And the last thing in terms of, to your relevant question of how does it affect things now, I would say that in a lot ways I think this current AI-driven market shows a lot of signs, classic signs of a bubble. You’ve got obviously a rapid price increase that always happens in bubbles. You’ve got all sorts of investors doing speculative things. And you only have to look at the inflows into leveraged long ETFs, cryptocurrencies, SPACs again, and so on, to kind of see that and high-beta stocks outperforming. There’s easy access to capital. There’s so much money, billions and billions obviously being spent on AI, so there’s tons of that. There are tons of media hype and public enthusiasm. This is what happens. We’ve got intense media coverage and social media buzz, and so on. And then valuation metrics aren’t so relevant. If you look at the S&P 500, all of the valuation, historical valuation metrics are extremely expensive and obviously driven by the biggest names in AI.
And then, there tends to be this new paradigm thinking it’s different this time, the old rules don’t apply and a certain overconfidence in new technologies and business models. All that leads to eventual unwinding. So, I would be cautious in terms of the current S&P 500 and US markets.
And the last thing I’ll just say is that for personal investors, never underestimate the power of compounding and the benefits of cost averaging, dollar-cost averaging. So, you’ve got lot of guests talking about that, but Einstein said compound interest is the greatest mathematical discovery of all time, and I think he’s right. So, people need to be well aware of that now and in the future.
Arnott: So, in an environment where there is a lot of speculation and potential bubbles in the market, how should investors respond? Is there an argument for pulling back an equity exposure or just trying to be more cautious about not getting caught up in fear of missing out and things like that?
Bondurant: So, I’ll answer it a couple of different ways. The modeling I do when markets are overvalued, it says, yes that you ought to be reducing your allocation to overvalued equities in favor of more bonds and bills. But I would tell you that this market, you guys know, is very unusual. So, there’s a large part of the market that is reasonably valued or attractively valued. So, we’re believers in looking at the markets globally and markets outside of the US are reasonably priced in their equities. Value tends to be underpriced and attractive in a lot of areas. In the US, small caps are, we think, very attractive. And so, we can find places and we have our clients in equities that are reasonably priced that we think will get a historical return. So, that’s the way we’re addressing that.
Arnott: So, we wanted to transition into the whole topic of mean reversion, and this is a key concept that you incorporate in your practice, and you’ve also written about why you think the idea that market returns follow a random walk is flawed. When and how did you first start getting interested in this topic?
Bondurant: I think I read Siegel’s Stocks for the Long Run about 25 maybe 30 years ago, and it always kind of stuck with me that there seemed to be a lot of logic in that that basically if you hold stocks for long periods of time not only do they do well because they’ve got higher expected returns but they’re really not nearly as risky as generally perceived. And so, that’s always something that I’ve had, and I generally followed in personal investing. But then when I got to doing my own investment advisory and really trying to figure out how to advise people in a really rigorous and intelligent way, it was quite frustrating for me. I was convinced of the stocks’ mean revert—there’s lots of really good academic literature out there. I’d note Thaler and Siegel in like 1998 put out a study about how in a one-year time frame volatility of stocks are kind of 18% in bonds, 10-year Treasury is about 5% or 6%, but over a 20-year holding period they really have very similar levels of that.
And then, Shiller did similar work way back when on this whole notion that stocks are much more volatile than the underlying values, and he looked at dividend-paying. But basically, again, there’s a lot of literature out there. So, I was very convinced that this is a real thing, and I was encouraging clients to do it. But I didn’t have a systematic way to go about trying to incorporate mean reversion into financial planning and portfolios. So, I went about looking up all the old data and Shiller’s got a site that goes back to 1881 for monthly returns in the US for stocks, bonds, and bills, and I had a friend who is much smarter than I am. I’m not a financial engineer but had experience in bootstrap methodologies for using Monte Carlos, which enabled long-term holding periods to incorporate mean reversion.
So, the first conclusion was that it was very clear looking at the expected returns that stocks do mean revert over time and very powerful. And then, we wanted to figure out, well, at different asset allocations and different withdrawal rates how would that impact people over the course of time, and we got pretty far down the path and then we got wind of what GMO, Jeremy Grantham’s firm, was doing identifying the same anomaly and they’ve created something called Nebo. But they have developed a formula that takes historical returns and incorporates them into a mean-reversion simulation through Monte Carlos. And so, we took that formula and we were able to do a whole lot of simulations comparing random walk with mean reversion and getting very robust outcomes in terms of understanding probabilities of not going bust, probabilities of ending assets and sizes of losses, drawdowns. And so, those are really, really powerful findings we think, and so that’s kind of the evolution of the paper.
Benz: You write that there are both fundamental and behavioral reasons why stocks mean revert. Can you expand on what some of those reasons are?
Bondurant: Yeah. So, fundamentally, there’s a lot of forces that force companies and markets toward mean reversion. So basically, companies or industries that come up with new better solutions or most often new technologies that they can take advantage of, they get unusually high profits and very high valuations because of that. But the reality is, when that happens, it attracts a lot of capital into the marketplace and you get a lot of competitors and ultimately, if there’s an oligopoly or monopoly and you’ve got regulatory potential things that happen that basically cause profits and companies’ fundamentals to revert the mean over the course of time and the same thing goes on the other side. So, if you’ve got an area or industry that becomes very unprofitable, a lot of players just leave the field, there’s no more capital coming in until you get a level where the players that are left are actually quite profitable and they’re running good businesses, and so again their fundamentals mean revert back to that.
But I think the more important thing really is the behavioral. And I think, again, people just get really excited about the new technologies in particular. Again, this is the whole list of things that I kind of talked at the beginning about speculations and prices get way out of whack and well above fair value and eventually we get that corrected and prices move back to fair value and oftentimes they’ll overreact and get to times when you’re pricing things well below fair value and, again, that will mean-revert over the course of time as well. So, number one, I would just say mean reversion is in the numbers and so I think there’s ways to explain it, considering fundamental and behavioral rules, but I think there’s no denying the numbers.
Arnott: So how long does that process usually take if stock valuations are either overvalued or undervalued, how long does it usually take for things to self-correct and get back to the mean?
Bondurant: That’s the million-dollar question, right? So, the answer is, a long time. So, it’s different every time. All that said, we use about a seven-year mean-reversion time frame. That’s I think a reasonably good average, but you need to know that that is a point among what is no doubt going to be a wider variation.
Benz: You referenced Robert Shiller earlier, and we wanted to follow up on the cyclically adjusted P/E ratio. We’ve seen some research suggesting that the CAPE can be a useful directional indicator, but there have also been times when CAPEs have remained elevated for an extended period such as during the 1990s and more recently. So, how do you avoid getting false positives about the market’s valuation level?
Bondurant: It’s a good question. Basically, we like the CAPE because it looks back 10 years, so it does take into consideration different parts of the cycle. And so, we think that is a solid way to look at earnings. Over the course of long periods of time, it’s been a very good indicator, and not only in the US, but internationally and that’s really we find probably the best long-term indicator.
One other thing I would just worth noting on the CAPE earnings is that they also—one of the reasons we like it is they use reported earnings. And so, right now, the trend obviously, as you know, is it’s gone to pro forma earnings. I think 95% of companies in the S&P use pro forma earnings and essentially, they overestimate their earnings because all the one-time losses they kind of write off. And so, we think it’s actually a more robust number using reported earnings because in aggregate the S&P 500 has lots of write-offs every year that should be incorporated.
So, back to your question: There’s a couple of things that are worth noting. Right now, I think the CAPE is like 38 times and historically, it’s been at 16 times on average going back to 1881. We would note that there is, over the course of time, a consistent increase in the CAPE P/Es and that’s because economies and markets and perhaps regulatory regimes become more stable and therefore people are willing to pay more. And so, we think there’s some logic to that. So, when we were running our models on valuation, I think we’ve used a 20 P/E and undervalued 25%, overvalued another 25% on the other side.
But to answer your question, again, it’s another million-dollar question. So, is it different this time? Are the profit margins that we’ve seen in recent years because of the increase in technology as a percentage of the profitability of the S&P, does that mean it’s different this time? And the answer is, maybe a little bit, but historically, there’s all sorts of reasons and history shows that it’s different this time really doesn’t play out. And so, we think it’s as good a metric as there is, and so, again, if we can find CAPEs for different parts of the market that are reasonably priced in that kind of 16 times level, we’re pretty comfortable, and we don’t really want to take a risk at the 38 times, because history shows when CAPE valuations are high, you get lower returns; when CAPE valuations are low, you get higher returns, enormously consistent over time and we’re not sure that’s a good reason to think it’s different this time.
Arnott: So, it sounds like you would argue that even if we don’t go back to an average CAPE of 16 times, stocks are probably still pretty overvalued in the US at the moment?
Bondurant: That’s the way we look at it, yes, in the S&P 500.
Arnott: And on the fixed-income side, you also talk about bonds’ tendency to mean avert rather than mean revert. Can you unpack what that means?
Bondurant: Yeah, it’s a really interesting phenomenon. So, again, when you go back and look at the data, bonds show increased volatility relative to the standard random-walk methodologies would indicate. And so, the question is, OK, well, why is that? And there’s a couple of reasons. One, stocks, when you have unexpected returns, their future returns have a negative correlation. So, when stocks do really well, future returns don’t do as well; when the stocks do really badly, future returns are better. So, they’re negatively correlated. Interestingly enough, when bonds have unexpected returns, really largely due to inflation, their future returns are positively correlated. So, that’s increasing the volatility. And basically, historical periods of inflation tend to have persistency.
So basically, the idea there is that bonds are more risky than you think. And another way to look at that is to say there are periods of massive volatility within bonds. So, the 10-year in the two-year period, in the ’21-’22 time frame lost 30% in real terms. And bonds, they don’t have the possibility of kind of these exponentially large returns on the rebound that equities do. And so, when you lose that much money in real terms, it’s very hard to make it back up, and that’s because there’s quite a bit of volatility. So, another point worth making.
Benz: You earlier referenced trying to figure out whether you had enough to retire and how that sent you down the road of investigating various aspects of retirement readiness. So maybe you can talk about why standard Monte Carlo-based retirement software, which is based on the random-walk assumptions, why that leads to withdrawal rates that tend to be overly conservative in your view?
Bondurant: So, I’ll step back. I think it was 1952 that Harry Markowitz basically came out with his modern portfolio theory. And I teach a class on it. It’s seminal in the history of investing. He basically kind of said, hey, people haven’t systematically thought about diversification and here is a way to measure it and incorporate it in future expected returns, just really powerful. And the mean variance optimization that he used basically said that they incorporated a random walk.
And so, let me talk a little bit about what the heck is a random walk versus mean version. So, if you think about a coin toss and you flip a coin five times, it comes up heads five times in a row. On the sixth toss, what are the odds on it coming up heads or tails? So, the answer is 50-50. And that’s because past returns don’t have any influence on the next flip. And so that means that the next flip is random. And so that essentially is a very good use of risk measurement for coin flipping using a random walk. That said, it really does not properly reflect the way equities returns have been, and I think will continue to be because it doesn’t reflect mean reversion. So, the idea there is that if, again, stocks go down by 50%, historic return data says after they’re down 50%, they’re going to return more than normal until they get back to kind of fair value similarly. If they go up by 50%, on a future basis, they’re likely to return less. And so, there’s a mean reversion component to that if you were to, in the way mean variance optimization works, is they say, if the market is down by 50%, the average return of stocks over 100 years is somewhat like 10%. So, if you use 10%, they’ll say after 50%, you’re no more or less likely to have a 10% return. And so, you’ve got the possibilities of much larger tails. So, after you have big moves, they can continue to go in that direction because there’s no influence of mean reversion.
And so, what ends up happening is that when standard modeling takes place and you kind of say, well, what are the chances of going bust in a given a financial model, they’ll use random walk. And what they’ll show there is that high-equity portfolios have this huge potential spread. And therefore, you’re more likely to go bust. And so, the answer for an advisor looking at that is to say, well, I’m going to use less equities, or you tell your client to have lower withdrawal rates. And so, if you use less equities, you’re going to get lower returns and it’s suboptimal. And actually, if you use mean reversion, very often you’ll find that the existing withdrawal levels are just fine and have a very manageable chance of going bust. And so basically the argument that we believe is, if you don’t incorporate mean reversion, if you just use random walk, you’re going to get flawed results.
Arnott: So, it sounds like if you do incorporate mean reversion, you would generally end up with a higher equity allocation during retirement. Does that higher stock exposure lead to more sequence of returns risk? And how do you mitigate that risk?
Bondurant: Yes. So, I don’t want to be at all flippant about high-equity portfolios and bear markets. I think currently there’s less concern than there should be about that. So, the answer is yes—if you retire and have a high-equity portfolio in the very early periods of retirement years, you get a major bear market, then your chances of going bust go up very substantially. So how do we address that? A couple of ways. Most importantly, is for our clients, we make sure everybody has got three years of either cash or visible income coming in for spending needs. And so, it’s very interesting when you look back at the history of bear markets going back to the ‘20s, the average time it takes to go from a bottom of a bear market to a new high is 3.3 years. And so, if you end up kind of having three years of cash, what you’re able to do is not sell stocks during bear markets to replenish your cash levels. And if you have three years, you’re in a pretty good position. And frankly, we are more interested in just making sure that clients don’t sell during bear markets. And so, the time it takes to go from a bottom of a bear market to not being in a bear market as opposed to a new high is something like 1.7 years. So, we think three years is a pretty good cushion and there’s a behavioral benefit to that as well. So that’s the way we look at the sequential return risk.
And the other part of it, I would just say, the numbers are the numbers—there could be negative periods in the bond market and that sort of thing, too. You lose real value. And so, we think our models showing different asset allocations kind of give you a real sense, including the times when you get hit early with bear markets.
Benz: What’s a typical safe withdrawal rate if you assume a 30-year retirement period and incorporate mean reversion for the return paths?
Bondurant: So, first off, I just start by saying that it’s really important that people that are really planning for retirement have their own financial plan and it’s customized to their world and a few thoughts there. One, if you’ve got assets that are in IRAs, you’re going to get RMDs, and the taxes are going to be a big influence. If you’ve got big capital gains, when you sell them, you’re going to have big tax effects. So, one size doesn’t fit all. The other part of it that I’m careful about in terms of individuals is, a lot of people are quite comfortable with lower withdrawal rates. We can talk more about that, and I do want to talk more about that in the kind of Brian Courtney view of the world in terms of wealth and happiness.
Everybody is a little bit different is all in terms of what the correct withdrawal rate is. But I would say that to answer your question—long-winded—as a rule of thumb, we find that in the work that we did, an 85/15 equity portfolio and a 5% withdrawal rate leads to, I think it was a 7% chance of going bust, which is pretty reasonable. So anyway, that’s kind of a decent rule of thumb from our vantage point.
Arnott: So, you mentioned the body of academic research that does support the notion of mean reversion, but at the same time, I think almost every retirement software provider still is based on random-walk assumptions. Why do you think that is?
Bondurant: It’s a really good question, and I’m hopeful over time it will change. But I think there’s a bunch of things. One, a lot of this software is driven by institutional investors, and they tend to be much more short-term in orientation. Quants in a lot of ways, as you know, derive trading in the marketplace. And so, mean reversion doesn’t come into consideration in their investment time horizons. And then, your traditional active managers, they’re trying to outperform every year. And so, if they have three bad years, they’ll tend to get fired. So, they don’t have the luxury of saying, let’s look at this for the long run, and it’s a seven-plus year thing. And, by the way, mean variance optimization is really easy to use, and everybody uses it and it’s the standard and it’s all good. So, I think there’s just a lot of inertia.
The last thing I would tell you is that I think it’s very hard for a lot of the traditional financial advisory firms, brokerages, and so on to say that we’ve been telling you 60/40 is the optimal portfolio for the long time, but actually, it turns out that we weren’t looking at things right, and so, you should move to 85/15. That’s a very hard thing to do. And so, it’s been interesting, the guys at GMO that are promoting using this mean reversion, they have found that the area where they’re getting interest is registered investment advisories, and that’s largely because they’re their own bosses and they don’t have a forced methodology from the top. But there’s starting to be, I think, a decent amount of conversation in the marketplace about trying to figure out how to incorporate mean version. And I do think that things will evolve because it makes a big difference in terms of what kind of financial plan and what kind of portfolio construction that people have. So, I think it’s important enough where it will happen over the course of time.
Benz: Do you think there’s an industry bias toward underspending because, obviously, advisors don’t want to risk having their clients run out, but also, a motivation to preserve the assets that generate fees? Do you think that that might be feeding into this tendency to lean into the strategies that encourage underspending?
Bondurant: Sure. So, look, advisors are incented to keep the business. That’s the incentive. And so, the times when they’re likely to get fired are if things don’t go well with their financial plan. So, there’s an incentive, a couple-fold. One, to keep people spending less. And so, there’s not a risk of going bust or reduced risk. And then, the other part is they’re also incented to kind of promote the 60/40 in lower equity portfolio because there’s less drawdown risk. And so, in the short term, you’re not going to have to face your client and say, gee, you’re down by 50%. You do 60/40. You say, we’ve participated in the upside nicely, and then when the market goes down, we protect it. So, those are, I think, behavioral reasons why I think advisors do the things they do, including encouraging lower spending.
Arnott: You mentioned the Nebo software platform from GMO, which does incorporate mean reversion. How does that work at a practical level? Does the software use current market valuations to set return assumptions and then also incorporate some sort of time period where future returns revert to the mean?
Bondurant: Yeah. So, it’s a really sophisticated system that works really well. So, we start with the basic cash flow modeling from, and we use eMoney because it’s very granular. And again, if you’re forecasting 20-30 years out, you better get good data in there. And so, what we then do is take those cash flows and put them into the Nebo models and they’re able to come back and kind of say, here’s the probabilities of going bust, here’s the likely return given various asset allocations, here’s the likely drawdown that your clients are going to have to accept.
And then in terms of the inputs, it’s very modular. So, we put our own inputs in terms of the expected returns for the different investments, be it stocks, bonds or bills and or frankly, they’re also very flexible. We can use private equity and that sort of thing, and we can put different expected returns, correlations, and volatilities in there, but we may want to get to that later. But the point of the matter is, what we do is say, hey, we think this category, say small-cap international value is undervalued. So, we’re going to get excess returns for a seven-year period. And then, we’re going to get the normal returns, and it does that for all of the different inputs. So, yes, exactly what you said, there’s a seven-year reversion period and then, you just have normal returns after that.
Benz: Another topic you discussed in the paper is why standard risk tolerance questionnaires don’t always do a good job of measuring clients’ actual risk tolerance. So, what is a better way for advisors to get a handle on how much risk their clients can tolerate?
Bondurant: Really good question and challenging. So, the way we try and do things is say, let’s try and provide an optimal portfolio that provides the most utility to investors, our clients for balancing their goals of not wanting to run out of money and probably being able to live well and hopefully give away some money at end of life generationally or to charities.
So, we try and assume that they are—when we create this “optimal portfolio” for them that they are very rational. And so, we spend a lot of time upfront explaining what it is we do, why we do it, the empirical backing for it and trying to get a lot of buy-in upfront. Part of that is we tell them you are likely to have—bear markets are going to happen, and you’re going to have significant drawdowns. And so, when they do happen, you kind of say, this is what we talked about and we can run a new model from there in terms of what are your chances of going bust and so on. So, I think mostly that’s the most important thing that you get them comfortable that kind of like, this all makes sense and I’m going with it.
All that said, there’s some counterintuitive parts of that. So, for a lot, and our work in the paper shows this for investors, they’re more likely to go bust with portfolios that have low levels of stocks than they are with higher level of stocks. And a lot of that is simply because the power of compounding equities over time and then, again, the volatility and chances of actually losing money in bonds. So, in the end, if you look at the traditional models for measuring risk tolerance or questionnaires, they’re kind of tilted toward how scared are you of drawdowns and how much risk are you willing to take? And so, I think it’s really important that you have a robust questionnaire.
We use a modification to the CFAs. I think the Morningstar one is really good as well. And you try and get a sense of what the sophistication of the investor is, whether or not they actually sold stocks if they were around in 2000 for the internet bubble or the financial crisis or in ’20 with the pandemic. And so, you get more of a real sense of that. And then we ask them, does it make sense to you that you’re willing to take meaningful losses if it gives you a better chance of achieving your goals? So, I would just say in the industry, I’m very concerned that the industry outsources their portfolio management to the risk tolerance questionnaire and inevitably the risk tolerance—and I think there’s quite a bit of studies in this that come back saying most people are in the middle. They want to take some risk and get returns, but they don’t want to take too much risk and be concerned about losing it all, because advisors don’t want to get sued. They say, OK, we’ll give you 60/40 portfolio, or some modification of that if they’ve got a higher risk tolerance or lower risk tolerance. And frankly, that’s in our view, a suboptimal way to give advice to people, and you’re not really best serving your clients by that sort of methodology.
Arnott: It’s kind of counterintuitive that you could actually end up with higher probability of running out of assets if you’re overly heavy on the fixed-income side.
Bondurant: And it’s very clear in the data. And so, it’s really remarkable. And just again, using historical data. So, it’s very counterintuitive. And it’s another reason why it’s perhaps harder to accept, broadly, because we’re all very conditioned toward high-equity portfolios give you a better chance of blowing them and not making it. So, anyway, it is counterintuitive.
Benz: So, we also wanted to ask you a lighter-weight question. You’re still an avid tennis player, and you previously served as president of the American Platform Tennis Association. I think a lot of people, and probably our listeners, are familiar with pickleball but might be less familiar with platform tennis. Can you give us a quick primer on what platform tennis is and what kind of people might enjoy picking that up as a sport?
Bondurant: Platform tennis is awesome. So, it basically was invented in New York in the late 1920s before indoor tennis and tennis players in the winter were trying to get some exercise. And so, they essentially put a net on a back porch so the balls wouldn’t go by, and they started playing off the net and playing off the screens that stopped the balls from going into the snow and that sort of thing. So, you ended up with this game that’s kind of a mini tennis, if you will. It’s about a third or quarter of the size of a tennis court with these chicken wire screens around it so you can play off the screens. And most importantly, this is a sport designed for winter warriors. So, there’s heaters underneath and if there’s snow and ice, everybody goes out and plays. And it’s one of the very few things you can do in the outdoors in winter. And literally in Chicago, there’s an enormous popularity in terms of league and there’s 10,000 people playing league paddle in Chicago. And it’s enormously popular really in all northern climes essentially.
And the other wonderful addition to the sport has been what we call these “tasma” huts. But basically, there are mini sports bars next to the paddle court. So, you finish and everybody kind of goes in and drinks beer and socializes and that’s part of the game. So, it’s a fabulous sport that way and just a lot of fun for folks in the winter.
Benz: Scott, it sounds like you have a pretty busy life with teaching, running your own advisory firm and playing both tennis and platform tennis. Do you have any plans to retire yourself?
Bondurant: So, you spend a lot of time on this question of happiness in retirement, right? And I have too, and again, Brian Portnoy, I think, The Geometry of Wealth, is a fabulous way of looking at—well, number one, this whole distinction between rich and wealthy. So, the whole idea of chasing more money and becoming rich or being rich and chasing more money is the hedonic treadmill and not great. But what you want to be is wealthy and you basically want to do stuff that you really enjoy and have a lot of autonomy and get a really good sense of community and hopefully you’re doing something that is helping society more broadly. And so, I kind of think I’ve been able to do this. I’ve got my own company, so I’m doing my thing. I take a very long view in terms of investment horizons, and I appreciate being on The Long View podcast because I think it’s very applicable in terms of the mean reversion and how that works.
So, I’m not looking to figure out what’s going on in the markets every day and trying to react to that. So, it’s, I think, quite manageable and I really love it. I love playing paddle. I play a lot of—actually still playing some competitive senior tennis, and I love teaching. So, I’m happy to continue to do that and I think I can do it for a long time. So, we’ll see.
Arnott: Well, thank you so much for joining us. It’s been great talking with you, and you definitely gave us a lot of food for thought.
Bondurant: Thank you, and I appreciate you inviting me on. I’m an enormous fan of this podcast and your book on retirement planning. So, I’m honored to have been a guest. So, thank you very much.
Benz: Thank you so much, Scott.
Arnott: 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 could follow me on social media at Amy Arnott on LinkedIn
Benz: And @Christine_Benz on X or Christine Benz on LinkedIn.
Arnott: George Castady is our engineer for the podcast and Kari Greczek produces the show notes each week.
Finally, we’d love to get your feedback. If you have a comment or a guest idea, please email us at TheLongView@Morningstar.com. Until next time, thanks for joining us.
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