Mamdouh Medhat, PhD, is a Research Director and Vice President at Dimensional Fund Advisors. Previously, he was an assistant professor of finance at Cass Business School in London, UK. His research has been published in Review of Financial Studies, Journal of Financial Econometrics, and Financial Analysts Journal, among other outlets. He holds a PhD in finance from Copenhagen Business School, a BSc and MSc in mathematics-economics from University of Copenhagen, and has held visiting scholar positions at Stanford and Princeton in the US.
In this episode, we’re joined by Mamdouh Medhat, VP and Senior Researcher at Dimensional Fund Advisors, for an exceptionally deep, exceptionally nerdy exploration of factor investing—focusing on profitability, value, defensive equity, and the persistent misunderstandings that surround them. Mamdouh walks us through his retrospective paper (co-authored with Robert Novy-Marx) on the profitability premium, why profitability subsumes a wide range of quality metrics, and why it dramatically clarifies how we should think about defensive/low-volatility strategies. He also explains the role of profitability in value’s US underperformance since 2007, why price-to-book remains a remarkably effective valuation metric, and how Dimensional incorporates these insights into portfolio construction. In the second half of the conversation, we shift to private markets. Mamdouh unpacks Dimensional’s research on buyouts, venture capital, private credit, and private real estate—revealing what percentage of the global investable universe these funds actually represent, how to benchmark them properly, how much dispersion exists across managers, how fair-value accounting changed the game post-2007, and why many perceived diversification benefits are actually just return smoothing.
Key Points From This Episode:
(0:04) Intro to Mamdouh Medhat and why his research fits the Rational Reminder “nerdy happy place.”
(1:32) The story behind Mamdouh’s retrospective paper with Robert Novy-Marx and the impact of the original profitability research on academia and practice.
(5:36) Three things the paper examines: quality investing, defensive/low-risk strategies, and value—unified through profitability.
(6:55) Why none of the 15 major academic and practitioner quality metrics add explanatory power beyond profitability.
(8:18) How spanning tests show profitability explains quality, but quality does not explain profitability.
(12:24) Quality measures largely load on profitability—they’re noisier versions of the same thing.
(13:14) The link between quality metrics and fundamental momentum, especially for QMJ and quarterly ROE.
(15:18) Practical implications: profitability is a parsimonious, more efficient way to capture the “quality” dimension.
(16:30) Defensive equity through the profitability lens—why high profitability predicts low volatility.
(18:58) Why long-only low-volatility strategies produce zero five-factor alpha—and why a simple high-profitability/low-investment portfolio plus T-bills beats them.
(22:14) Alternative value metrics (EBITDA/EV, intangible-adjusted book-to-market, etc.) don’t outperform price-to-book when profitability is accounted for.
(24:57) Many “improved” value metrics simply rotate in profitability exposure, not better value information.
(26:17) Roughly half of US value’s post-2007 underperformance is explained by its negative correlation with profitability.
(28:42) Industry tilts (e.g., energy/financials vs. tech/healthcare) drive much of value’s volatility—not its long-term return.
(30:33) The theoretical case for combining clean valuation (price-to-book) with clean expected cash flow (profitability).
(33:36) Academic implications: models must jointly explain value and profitability—and their negative correlation.
(35:09) Practitioner implications: parsimony—use clear valuation and cash-flow measures, limit excessive complexity.
(36:53) How Dimensional measures profitability: operating profitability (revenue – COGS – SG&A – interest) scaled by book equity.
(41:09) Why tilting toward or away from countries based on aggregate characteristics rarely adds value—premiums come from stocks, not countries.
(42:57) Industry-level tilts show similar patterns—industry momentum exists but is impractical due to massive turnover.
(46:15) How Dimensional handles country and industry weights: sort within countries, then apply sector caps.
(48:27) Private markets: private funds make up roughly 10% of the global investable universe—not 25–100% as sometimes claimed.
(50:53) Benchmark choice for private funds is crucial—S&P 500 is not appropriate for buyouts or VCs.
(52:00) Using KSPME (public-market equivalent), buyouts and VCs match small-cap value/growth benchmarks; private credit matches high yield; private real estate underperforms listed real estate.
(55:50) Factor exposures post-2007 explain 70–80% of private-fund return variation due to fair-value accounting.
(1:00:48) Wide dispersion in private-fund performance—top 5% double or triple capital; bottom 5% lose half.
(1:03:49) Little evidence of manager persistence—manager selection must rely on due diligence, not past vintages.
(1:08:24) No strong time trend in private-fund outperformance, but correlations with public markets have increased.
(1:09:13) Many diversification benefits historically attributed to private assets were actually illiquidity-driven smoothing.
(1:12:25) Rising demand and democratization likely reduce expected returns in private markets—exclusivity is fading.
Read The Transcript:
Ben Felix: This is the Rational Reminder Podcast, a weekly reality check on sensible investing and financial decision-making from two Canadians. We are hosted by me, Benjamin Felix, Chief Investment Officer and Cameron Passmore, Chief Executive Officer at PWL Capital.
Cameron Passmore: Welcome to episode 384 and Ben, I was taking down notes during this conversation, which was an incredible conversation. It's kind of like, I think I called it the great trilogy of listeners, right? It might be even more than three factors going on here, but to talk about factors, global diversification, private investments, from a great, super nerdy, super technical academic, who is a great communicator. It's like this is the happy place, I think, for so many of our listeners. This was a phenomenal conversation.
Ben Felix: Yeah, nice to do a nerdy episode. It feels like it's been a while.
Cameron Passmore: It's been a while and it was such....because it's talked about questions that we get all the time and our team gets all the time from clients, especially when we got into the part at the back end around private investments and the research that he's done. I guess this week is Mamdouh Medhat, who is a researcher and investment manager and VP and senior researcher at Dimensional Fund Advisors in London. People know that we do a lot of work with Dimensional here.
With that, Ben, why don't you kind of tell a background story and go from there?
Ben Felix: The background story is that Mamdouh wrote a very interesting paper with Robert Novy-Marx, who of course we've had on this podcast, about profitability. They wrote what they called a "profitability retrospective." Professor Novy-Marx wrote his paper on profitability, I believe, in 2013.
It had, as Mamdouh talks about during the podcast, it had a huge impact on academia. It was added to the Fama and French: The Five-Factor Model, which was like, that's a huge thing. That's the main workhorse model in academic finance, like the benchmark model.
It influenced Dimensional's investment strategies. They incorporated profitability into their portfolios, the way that they sort and weight stocks. So, have other asset managers all around the world.
They wrote this paper, looking back since that original paper was published, what have we learned about profitability since its initial publication? They look at a whole bunch of interesting questions. They look at quality metrics.
They look at defensive low beta stocks. They look at the performance of value and they ask how profitability relates to all of those things. We talked about that in the first part of the episode.
That was co-authored with Mamdouh and Robert Novy-Marx. Mamdouh has other awesome research. We also asked him about that.
We talked about industry and country tilts, like whether it makes sense to tilt toward or away from a country based on its aggregate characteristics. That was an interesting discussion. Then we also talked about he has a really interesting paper looking at private market fund performance.
He looked at private equity, credit and real estate and did really deep analysis on the returns of each asset class, but also look at its public market factor exposures. When you invest in buyouts, what public market factor exposures are you actually getting by doing that? We've talked about private markets for quite a while, back into the episode.
Yeah, really interesting conversation. Profitability, I don't know, for the average index fund investor, maybe the profitability discussion is not as exciting. For anyone that's tilting toward factors, it's really important.
Profitability is incorporated into a ton of factor models. It's incorporated into a ton of investment products. How to think about it, how to measure it does really matter.
Hopefully that part of the discussion is interesting for everyone.
Cameron Passmore: As I said, he's currently the VP and senior researcher at Dimensional Fund Advisors, has a PhD in financial economics from Copenhagen Business School. Pretty cool. Very cool, yeah.
Ben Felix: He was previously an academic doing academic research. He's now arrived at Dimensional, still doing academic research, but also doing more practitioner oriented research, obviously being at Dimensional. Research that they're using, research that they're publishing for their clients and other people to use.
That would be like the private markets papers that they get a lot of questions about private markets. Should Dimensional be in that space? Should I, Dimensional's client be in that space?
That's why they would do that type of research. Anyway, we covered lots of ground, super interesting conversation. Like you said earlier Cameron, Mamdouh is a great communicator.
Cameron Passmore: Okay, with that, let's go to our conversation with Mamdouh Medhat.
Ben Felix: Mamdouh Medhat, welcome to the Rational Reminder Podcast.
Mamdouh Medhat: Thank you very much. Happy to be here.
Cameron Passmore: Indeed.
Ben Felix: Very excited to be talking to you. To kick it off with the first question, can you talk about why it was important for you and Professor Novy Marks to write a retrospective paper on the profitability premium?
Mamdouh Medhat: It's really without a doubt that Robert's 2013 paper had quite a bit of impact on the academic literature. For instance, Eugene Fama and Kenneth French added a profitability factors to their five factor model. That's a pretty big deal.
We also have billions of dollars now in quality funds and they don't agree on much, but they do agree on profitability. It's reasonable to say that it had some impact. We felt like it's due time to look back at the literature, figure out what we've learned and really the point is to try and connect a bunch of themes that seem disconnected, but that can really be brought together by thinking about profitability.
Ben Felix: We are going to dive into each one in a bit of depth, but can you just summarize what have we learned about profitability since its initial publication?
Mamdouh Medhat: So, in the paper, we could have looked at more things, but we specifically looked at three things. We looked at quality investing in all its shapes and guises and what we figured out is that profitability really does a striking job explaining quality investing. Whether it's the quality metrics that have been suggested by academics, the factors suggested by academics, or the ones that are typically applied in practice, profitability does a really good job explaining those.
It also has really strong power pricing defensive equity strategies, and that is quite interesting because you wouldn't expect defensive to be particularly related to profitability a priori, but it does a really good job pricing defensive equity strategies across several different ways of constructing those. Then the final thing we look at in the paper is value investing because one of the main points of Robert's 2013 paper was that profitability is the other side of value. We've seen value have a pronounced period of underperformance in the US since 2007, and so we try and shed some light on that.
How much can profitability explain and what's left off?
Cameron Passmore: Let's go deeper on that, go deeper on quality. How well does profitability explain the returns of portfolios that are actually formed on quality?
Mamdouh Medhat: In a nutshell, basically none of the quality metrics that we consider in the paper add in terms of being related to returns on top of profitability. They don't agree on much, many of these quality metrics, they come in many different shapes and they target different aspects, and you tend to see a lot of composites being used in the literature. It can sometimes be difficult to figure out exactly what the different strategies are doing, but irrespective of which one you look at, they really don't add anything on top of profitability.
The converse is false, so profitability tends to add something in terms of returns, in terms of being related to returns on top of quality. If you were a quality investor, there would be some information in adding profitability. If you're a profitability tilted investor, there's really not much evidence that adding quality will add anything.
Ben Felix: Oh man, that's interesting, and that's true over a whole bunch of different metrics, quality metrics.
Mamdouh Medhat: It is, and some are trying to capture aspects of the company that you would perhaps intuitively think of as related to profitability, and some not at all.
Ben Felix: In the paper, how do you quantify that explanatory power? When we say that profitability explains the returns of quality, but not the inverse, how is that quantified?
Mamdouh Medhat: We go to the literature and we do what people typically do in the empirical asset pricing literature. We start by looking at just average returns. Take your favorite quality metric, it can even be a composite, sort stocks on that metric, identify the ones with a high value for it, the ones with a low value for it, and then look at their average returns and take the spread, and then figure out is there a statistically reliable and economically sizable spread.
And somewhat surprisingly, the vast majority of the quality metrics that are popular out there, they don't actually produce a spread in returns. Some don't produce a spread at all, and some produce a little bit of a spread, but it's not statistically reliable. So already there, there is a bit of a question mark around the notion of quality.
What they do produce is three-factor alphas. So when you look at the difference in return, so you construct the typical academic long-short quality factor, then what you see is that they do tend to produce alphas relative to the Fama-French three-factor model. And that's because most of them end up with a large cap growth tilt.
So sort of the opposite of what the model would predict should have high average returns. And so you end up with large three-factor alphas. But then the three-factor model was extended exactly to include profitability because profitability tends to capture variation in returns beyond what is captured by the market, the size factor and the value factor.
And once you adjust for profitability, you see that none of these quality metrics produce any kind of alpha. This is whether you include the other factors in the five-factor model or whether you do it just relative to profitability by itself. And so the academics, they call this spanning tests.
You want to see if a quality strategy spanned by profitability and profitability spans all the quality metrics we look at. The converse is not true. None of the quality metrics we look at span profitability.
And how many different flavors of equality did you look at? So we did a fairly deep sort of dive into the literature. We also looked at what practitioners tend to use, and we ended up with a total of 15.
So, we have 12 metrics that are commonly used in the literature or by academics to try and capture sort of one particular notion of quality. So, it's things like the earnings of the company, the earnings volatility of the company, the leverage. You can think of a composite of those three that ends up being called the Q-score.
There's lots of distress-related measures. So, the Campbell et al. distress measure, the O-score.
And then there are even things that are related to defensive characteristics in there. And so we also look at a composite of all of these 12. That's 13 in total.
And then we add two more, which are quality definitions that come from, let's call them alternative factor models. So, there is the quality minus junk, QMJ, which is a fairly famous quality factor that's by Asnes Fersini and Peterson and is part of AQR's factor model. And then there is the quarterly return on equity or ROE.
That's due to Huzhu Zhang's Q-factor model, which is sort of, you know, one of the competing factor models out there in the literature. The last two are sort of interesting because of the complexity in the way that they're constructed and the difficulty of really interpreting what they do. So QMJ, for instance, is based on 28 different signals.
And many of those 28 different signals are known to produce large three-factor alphas. So it's not really that surprising that by putting them all together, sort of in a composite, you get something that has a very large three-factor alpha. The ROE, the quarterly version is kind of in a similar way.
It can be difficult to unpack exactly what it's doing.
Ben Felix: Incredible. The simple version of just profitability is able to explain whatever those things are doing.
Mamdouh Medhat: And that's because we try and break down exactly what are these different quality notions and these different quality factors trying to do. And most of them do have loadings on the other factors. So, as I said, they tend to be large-cap growth.
Some of them also have a little bit of a low investment loading, but really the factor that has the greatest explanatory power for all of the 15 that we consider is profitability. They all, to a certain extent, load on profitability. The one that loads the least is book leverage, which is one of the quality notions that have been suggested.
But otherwise, they all load heavily on profitability. They tend to take positions in profitability. And so they're basically noisy versions of profitability, if you will.
Ben Felix: What's the relationship between quality metrics and fundamental momentum?
Mamdouh Medhat: Yes. This is really something where it's the academic quality definitions that tend to have a position in earnings surprises. And so it's QMJ and the quarterly ROE.
We show in the paper that they tend to have mechanical tilts on earnings surprises or the signal that underlies post earnings announcement drift. So when you look at QMJ and the way that it's constructed, it depends positively on earnings growth and it depends negatively on earnings volatility. So the way that the literature has typically measured earnings surprises is by taking the year-over-year change in quarterly earnings and then divide that by the volatility of quarterly earnings to get what's called standardized unexpected earnings.
And when that is high, then you say that there is a positive earnings surprise. When it's low, then you have a negative earnings surprise. When you form a factor on those, you get what's called post-earnings announcement drift or fundamental momentum, as Robert called it in one of his earlier working papers.
Because of the construction of QMJ, it tends to load quite heavily on post-earnings announcement drift. So it's not a pure sort of low frequency profitability factor. It has a little bit of peed in it.
Now, ROE, the quarterly ROE that's part of the Q-factor model is even more so a peed factor because the way that it's constructed is that you don't take the return on equity from the annual statements. You take the return on equity from the latest quarterly earnings announcement. And that basically means that it's driven more by earnings surprises than by this low frequency type of profitability.
ROE, the quarterly version, is essentially just a dirty version of peed. And so because of this conflation, it can sometimes be a bit difficult to understand exactly what are these two factors trying to do. But they both have enough of a profitability exposure that you can still price them using just simple profitability.
Cameron Passmore: And what are the implications for investors if profitability completely subsumes the whole idea of quality investing?
Mamdouh Medhat: I think what we end up arguing in the paper is profitability is, by and large, all you need. If you want to capture this dimension of expected returns, which we have a theoretical underpinning for, and there's lots of evidence for it in the US and developed markets outside the US in emerging markets, then profitability is all you need. Quality gives you a kind of diluted exposure to profitability.
It's capturing some of the same things. It's not wrong in any way to do quality investing. But given that many investors think about making efficient use of their capital, you might as well trade profitability.
And one additional benefit of that is that it should lower your monitoring cost. All else equal, it is easier to monitor a portfolio that is based on one dimension of what quality is instead of a composite that's based on three or 28 different metrics. So there are some practical benefits as well of shifting the focus towards profitability instead of thinking of the broader notion of quality.
Ben Felix: How do defensive, low volatility, low beta, that whole idea, how do those types of investment strategies look through the profitability lens?
Mamdouh Medhat: Defensive equity is interesting because it's both a separate asset class, whether you look at the literature or you look in practice, but it's also often included as part of quality investing. It's often considered a quality dimension. QMJ has a dedicated betting against beta tilt, for instance.
It's right in there as part of the 28 signals. But a priori, you wouldn't think that there is any connection between defensive equity characteristics and profitability. So defensive equity is typically measured by, as I said, beta or by low volatility, either total volatility or idiosyncratic volatility doesn't really matter.
But what we show in the paper is that profitability is strongly related to these defensive characteristics. Actually, high profitability is the most important predictor of low volatility. And controlling for profitability valuations also become really important predictors of volatility.
So by having profitability in there, you can sort of decompose what are these defensive equity strategies trying to do. And so if you use the standard academic construction, just like the way we construct HML or the profitability factor, then you see that neither beta nor volatility produce a significant spread in returns, just like the other quality notions. They do produce large three-factor alphas, again, because they tend to load on large growth, but it's mostly driven by the short side.
It's mostly driven by these high risk stocks. It's really not a low risk anomaly, which it's also been called. It's really a high risk anomaly.
And it's because those high beta, high volatility stocks, they tend to look like small cap growth, low profitability companies. And so when you don't have a profitability factor in your model, you're not capturing that. And so you end up with the model not being able to explain the return.
Once you have a profitability factor in there, you see that the alpha goes away. There is also a bit of a link to the investment factor, kind of the fifth factor in the five-factor model, but it is sort of of slightly second order importance compared to profitability. If you start relaxing some of the standard construction methodologies that we've now been using since Fama and French 1993, then you can get slightly different results.
And some papers in the literature have that, but we try and stick fairly closely to the standard academic construction.
Ben Felix: You mentioned the importance of the short side there. One comment that we've heard on low volatility is that things change when we move from that sort of academic long short setting to a practical long only setting. I'm building a fund and it's a long only fund, an ETF or whatever, and I'm going to just buy stocks.
I'm not shorting anything. What does profitability say about long only low beta specifically?
Mamdouh Medhat: I've heard about this and there's a string of papers that are arguing for sort of long only defensive equity strategies. The thing is we show in the paper in a couple of different ways, both in the US and developed markets outside the US and in emerging markets that standard long only low beta and low volatility portfolios, they have zero five-factor alphas. They don't produce five-factor alphas.
The papers that are claiming otherwise, they typically deviate from just the standard methodology of forming portfolios. Even long only portfolios, they typically deviate by applying leverage or assuming that you're hedging out some kind of market exposure. That tends to change the playing field a little bit and it makes it difficult to evaluate.
If we're going to change the way we construct these things, then should you also change the factor model that you're using to evaluate them? You end up sort of down a rabbit hole. We try and stick fairly closely to the common standards.
What we can see is that your standard long only defensive equity portfolio looks like a simple portfolio constructed from high profitability stocks with low investment. High profitability, low investment, it's those two new factors in the five-factor model that are really closely related to what the long only defensive equity portfolios look like. In the paper, in one of the appendices, we do a very simple experiment.
We take an 80-20 portfolio. In the 80 part, you have high profitability, low investment stocks, just long only. In the 20 part, you put the rest of your money in T-bills.
You're not even 100% exposed to equities at this point. We show that that portfolio, it outperforms long only defensive equity strategies, whether they're formed on beta or on volatility. We also show that long only defensive equity strategies don't have an alpha relative to that simple 80-20 combination.
Again, you want to make efficient use of your capital. I can explain why high profitability and low investment is related to returns. I don't need to estimate either of those things because I can observe them directly from income statements and balance sheets.
It's fairly easy, and I'm not even 100% exposed to equities. I'm really trying to get that diversification that I get also from bonds. That performs, if anything, better than long only defensive equity.
Ben Felix: That is super interesting. You're lowering your beta slash volatility directly by reducing your exposure to stocks and then applying the same kind of factor tilts and you're getting to a similar place, but it's actually performing better than a portfolio formed on volatility or beta.
Cameron Passmore: Sometimes simplicity is just very nice. There's been a ton of alternative value metrics proposed to replace the apparently archaic price to book. How do these alternative value metrics hold up when you actually consider profitability?
Mamdouh Medhat: My boy, price to book has taken a lot of beatings lately, but just like quality, there are really many different ways of measuring value, but the difference is we tend to agree more on how to measure value compared to quality. When you think about valuation, it's all just a scaled version of price. It's all just a way to scale prices, and the idea is you want to figure out what do you scale prices by so that they're comparable in the cross section and over time.
Because of the proliferation of these alternative value metrics, we look at quite a few of them that have been proposed in the literature. There are some really popular recent ones like EBITDA to enterprise value, and then there's various versions of adjusted book to market where you adjust for intangibles, you adjust for an estimate of the value of internally developed intangibles, you adjust for goodwill, things like that. We ended up looking at 13 of them, and just like in the quality section of the paper, we also find that none of them produce any alpha relative to standard value and profitability.
Even though they have been promoted as in they produce a better value metric or they produce a better value premium, they don't actually add anything on top of standard value and profitability. The reason is something that the academics in a wonky way called a factor rotation problem, and it sounds super fancy, but what it actually is is that your improved performance on top of the standard factor is not because you found a better signal. It's because you have blended the traditional signal with other factor exposures.
It's through that rotation that you then get the better performance. In this case, all the improvement comes from rotating out of standard value and towards profitability. To really hammer that home, the case in point is that we look at a mechanical rotation in the paper.
We look at profits to price, that is literally profits to book times book to market. It's a mechanical rotation of the two. It is the alternative value metric that generates the largest spread in average returns of all the 13 that we consider, just the mechanical rotation.
It's also the one that produces the largest three-factor alpha of all the 13 that we consider. It just looks amazing. Relative to the five-factor model has no alpha whatsoever.
Again, if the point is to try and improve on value, the adjustments that we look at there don't really seem like they're doing anything else, but just tilting on profitability.
Ben Felix: You're really just adding profitability exposure. The other value metrics aren't better value metrics. They're just combining profitability with value.
Mamdouh Medhat: Which is great. Combining profitability with value is great. Robert argued for that in 2013.
We argue for that in the paper as well. Again, make efficient use of your capital. There are lots of positives to price to book.
It is persistent. It is nice in implementation. It leads to the lowest turnover.
It cleanly separates the valuation channel from the profitability channel. Again, no evidence that any of the other metrics, including the ones that adjust for intangibles, are adding anything on top of it.
Ben Felix: So, interesting because pre-profitability, these value metrics, maybe they were better than price to book because they were giving you profitability exposure. Now that we know about profitability, like you said, there's a more straightforward way to get there.
Mamdouh Medhat: Again, it's not wrong. Lots of index funds, quant managers, they use typically a composite of value metrics. Their argument is, well, we don't know which signal is going to work best, so we're going to diversify across signals.
Sure, it's not wrong, but there is a more efficient way to allocate your capital. There is a way to go directly to the source.
Ben Felix: Right. Once you know about profitability, that is true.
Mamdouh Medhat: Exactly.
Ben Felix: Prior to that, maybe we didn't know, so maybe it did make sense to diversify. You mentioned value's recent underperformance, particularly in the US earlier. How much of that is explained by profitability?
Mamdouh Medhat: The underperformance that we've seen for value is very much a US-centric phenomenon. It has not been to the same extent negative in developed markets outside the US, and it's even been positive in emerging markets over the same period. But in the US, you saw an underperformance for value since its latest peak, which was in 2007.
We argue that around half of that negative performance, around half of that underperformance, can be explained by profitability, or at least can be linked to profitability. We're not arguing for a causation here. This is all empirical asset pricing.
It's almost always about correlations. The thing is that what you had was that it was a period of strong underperformance for the standard value factor, but it was coupled with a period of strong outperformance for the profitability factor during that same period of time, and they were strongly negatively correlated. What we argue is that by just accounting for the negative correlation that standard value had to profitability, it accounts for a headwind of about half of that underperformance that you see over that sample.
The remainder, the remaining half of the underperformance, we provide some evidence that it has something to do with industry tilts. The standard value factor tends to take quite a bit of industry tilts beyond being short profitability. It's also betting on some industries.
We show in the paper that if you construct a standard value strategy, but you put in some sensible controls for that negative, that short profitability exposure, and also you try and rein in a bit its industry tilts, then you actually get something that looks a lot more reasonable. It has lower volatility, and it actually ends up having a small, but positive average return since 2007. It's not significant, but it is small and positive, which is a long way to go from the very negative one that you see for just unadjusted value.
Ben Felix: If you go and build a standard academic value portfolio, it's going to be tilted away from profitability. It's going to have some big industry tilts. If you go and adjust for those things or take those things away, take the profitability, negative profitability tilt away by adding profitability exposure to the value portfolio and constraining the industries, then all of a sudden that negative gap over that period shrinks is what I'm hearing.
Mamdouh Medhat: Yes. The negative profitability exposure, we understand why value stocks, they tend to be small and unprofitable. Growth stocks, they tend to be large and highly profitable.
There's a mechanical shorting of profitability in just the pure academic value strategy like that. When you also look at it, you tend to see that certain industries show up disproportionately often on the value side and on the growth side of a standard value strategy. On the value side, you tend to have things like energy and financials.
On the growth side, you tend to have tech and healthcare and things like that. What we show in the paper is that those industry tilts have driven more of value's volatility than it has driven its average returns. It accounts for a big portion of the volatility of the value premium, but over the long sample, it's actually been flat in terms of adding anything to its return.
If you manage those industry bets a bit in the construction of your value strategy, you tend to get something that is less volatile and also performs better, especially when you also control for profitability. There isn't a one-to-one mapping between what you do in the academic factor space and what you do in practice, but that's generally speaking what we try and do in dimensional with our value strategies. We also consider profitability when we construct value strategies and we also manage the industry and sector tilts so they don't run haywire and end up driving a lot of uncompensated risk, if you will.
Ben Felix: I think you mentioned earlier that profitability has a strong theoretical story. Can you talk about at a fundamental theoretical level, what makes the combination of traditional value like price book and profitability a sensible combination for explaining differences in returns?
Mamdouh Medhat: To me, the fundamental story really starts with this basic rule that we have in asset pricing that price is equal to expected discounted cash flows. The price of any investment today has to be my expectation of what I will get out of it in terms of cash flows and then I discount it back to present value using some discount factor, some notion of the cost of capital, if you will, that reflects the riskiness that reflects my expected return. If I observe a low price, then that can mean two things.
It can mean that the discount factor is high, which is what I want. That's a high expected return, but it could also mean that the expected cash flows are low. Prices can be low because of low expected cash flows or because of high discount rates.
I'm mostly interested in the latter. By controlling for profitability, what you end up with is making the price signal more informative because now if I compare two stocks that have the same profitability, that's my proxy for the expected cash flow channel. Then I know that the one with the lower price, oh, that's because of the discount rate channel.
Now I've made the price signal more informative. Coming to what we actually do in practice, book to market and profitability work really well together because book to market is the cleanest pure valuation measure that you can get. It has very little to do with profitability.
Book equity, yes, there is a mechanical link to pass earnings and things like that, but very little in terms of the actual profitability channel. It becomes a very clean valuation metric. Profitability, on the other hand, because we avoid scaling it by price or something like that, it becomes a very clean expected cash flow channel.
You have two measures that are cleanly separating these two effects and so they work really well. Beyond the academic jibber jabber, in practice, price to book and profitability work really well because they have hardly any overlap. Price to book tends to go for these small companies that don't have a lot of profits.
Operating profitability tends to go for large companies that have growth-like valuations. They sit on opposite sides of the market. That means that very little overlap, lots of diversification, lots of flexibility to try and blend them together in a sensible way.
In practice, they also work really well.
Ben Felix: Man, that makes a lot of sense. It's like instead of taking one of the other value metrics we talked about earlier that has some blend of value and profitability in one metric, these are two very distinct ways to measure the two different things. Then you can blend them together however you want, but you can be precise about it because there's such distinct metrics.
Mamdouh Medhat: Our portfolio managers here, they tend to talk about that they give them a lot of control. There's little overlap. They can clearly see why they're holding this particular stock due to the valuation measure and why they're holding this particular stock due to the profitability measure.
That's really nice in constructing portfolios and managing them. It matters in practice as well.
Cameron Passmore: What are the main academic implications of the findings documented in this paper?
Mamdouh Medhat: I think the paper for academics, first of all, it provides a unifying framework that connects a lot of seemingly unrelated things. It connects a lot of these different quality notions to basically just profitability. It connects the low risk anomaly, the defensive equity strategies.
It connects it also to profitability. It cleans up a little bit in the factor zoo and it provides a unifying framework for understanding many of these things. That's kind of empirically, but theoretically, it also has some implications because it basically says that you really need to think about value and profitability together, even at a theoretical level.
Lots of academics, they will write down theoretical models for why we should, for instance, see a value premium. This is one mechanism that will lead to a value premium. It could be related to capital investments or it could be related to macroeconomic risks or some combination.
They're trying to explain why you should see a value premium and derive some testable predictions from that. I think our paper is pointing towards that that's no longer enough. A model has to be able to produce a positive value premium.
Yes, but it should probably also be able to produce a positive profitability premium. Really importantly, those two should be negatively correlated within the model. If the model cannot produce those sort of empirical facts, then it's missing something.
Then that means that whatever you're getting out of it might be incomplete.
Cameron Passmore: Listening to this are many practitioners who I'm sure are wondering what are the main insights for them?
Mamdouh Medhat: I would say that I think this shows that parsimony goes a long way. You can be parsimonious about the way that you think about quality investing. You don't need a whole list of different quality metrics.
Profitability really is the one that tends to be the strongest and tends to explain them all. You don't need a separate consideration for defensive equity. That's what we talked about before.
Again, it shrinks what you need to think about. It brings it down to something more of a unifying framework. Many investors that I talk to, they're constrained in, let's say, how much tracking error they can take or how much active share they're willing to take in their asset allocations, how much they can deviate from the market.
They want to think about how to efficiently allocate, how to efficiently deviate from market weights. You just don't get a lot more efficient than just by having a clean cash flow channel, which is profitability, and a clean valuation channel, which is price to book, I would say. I'm in a lot of conversations right now with investors who are maybe looking at the size of the US and global markets and thinking, should I do something differently?
If I am going to hold the US at market weight, and given those really high valuations at the top, and given that concentration that we're seeing, maybe I should go for quality. Maybe I should at least get some quality for those high valuations. Well, I think our paper points towards, okay, first of all, how should you look at valuations and how should you try and identify quality for that valuation?
This is why I think profitability right now is very appealing, especially when you combine it with price to book.
Ben Felix: We've been talking about profitability. You mentioned it once, I think. We haven't talked much about how you actually measure profitability.
Can you talk about what metric Dimensional uses to implement profitability in portfolios? Maybe also why that metric instead of some of the alternatives?
Mamdouh Medhat: I've purposefully not defined what I mean by profitability, and that's because it's surprisingly robust. You get, by and large, the same results irrespective of which profitability measure we use. We do quite a bit of work to try and identify which one we should use.
We've done that here at the firm, and we also continue to do that in the paper. The one that we use in the firm and in the paper is operating profitability, which is you start with Robert's gross profitability from 2013, so revenues minus cost of goods sold. Then you subtract SG&A, and you also subtract interest expense, and you scale that by book equity.
The reason why you have these two extra elements, SG&A and interest expense, is because those two items account for differences in operating expenses across industries, and they also account for differences in leverage across industries. Those two things are important. It just makes the profitability measure comparable and easier to interpret.
In the literature, and this is something we review in the paper, there's talk about one particular alternative to operating profitability, which is called cash profitability. The idea is you take operating profitability just like I was talking about it before, but then you subtract one additional item, which is accruals. Accounting accruals are supposed to, it's an accounting tool to align the timing of expenses with their revenues.
You can book the expenses when the revenues come in, you can book the revenues when the expenses are occurred. It basically means your profitability metric becomes comparable across firms and over time. When you subtract that out, you get down to the actual cash that the company has at hand.
What are the actual cash flows that the company has? We compare those in the paper to try and figure out which one corresponds better to theory. We think that's probably the best way to differentiate between them.
Theory basically calls for a profitability measure that predicts future cash flows. This is why we use current profitability. We're not interested in the past in terms of what the company has done, we're interested in what is it expected to deliver in terms of cash flows in the future.
You should really think about the profitability measure that is the best predictor of future cash flows. Operating profitability, we show in the paper that it predicts its own future growth. Companies with high operating profitability today tend to grow faster in terms of operating profitability in the future, but it also predicts the growth in earnings.
They also tend to grow their earnings faster in the future, which we think is a desirable property for a profitability measure. Cash-based operating profitability is a lot less persistent. It actually exhibits mean reversion in the sense that companies that look high on cash profitability today, they actually tend to grow negatively or stagnate in terms of their cash profitability going forward.
It doesn't predict the growth in earnings or anything like that. This is true in the US. It's true outside the US and developed and in emerging markets over the shorter samples as well.
We're talking about earnings growth over long periods of time, five years, seven years, things like that. We think that's a pretty compelling argument for sticking with operating profitability.
Ben Felix: Not a huge, huge difference, but theoretically, you want something that's persistent and operating tends to be better than cash-based for that purpose.
Mamdouh Medhat: I would say so, yes. Also, one thing that we've observed in the data is that the accruals effect has gotten weaker over time. That's the only difference between them, really.
Generally speaking, the accruals effect is very much a US phenomenon. It's gotten a lot weaker post 2000. There's no theoretical argument for why it should get weaker, but that's what we see in the data.
The difference between them has also gone down in terms of their ability to predict returns. The accruals effect doesn't really seem to be adding that much over the last 20, 25 years.
Ben Felix: You mentioned earlier, you were just talking about the idea of people worrying about their US equity allocations right now based on where valuations are and the size of the US market and all that stuff. You got a paper that looks at that concept. Can you talk about the downsides of tilting toward or away from a country based on its aggregate characteristics, like in the US example, tilting away from the US because it looks expensive?
Mamdouh Medhat: A bit tongue in cheek, we call the paper few and far between. That's both because when you start sorting countries or industries on these factor characteristics, they're few and far between, but also the benefits are few and far between. You don't actually get that much from it.
The premiums have historically not been driven by countries. They've been driven by stocks within countries. When you sort countries on something like their valuation or their size or their profitability, you tend to see that the spreads kind of go in the right direction.
They have the right signs. You expect to see something like a size premium, a value premium, a profitability premium, maybe even a momentum premium, but they tend to be quite weak. They tend to produce unreliable spreads and returns and no alphas relative to the factors when you construct them from individual stocks.
This is why we don't really see much of a benefit there. The intuition is that there is some informational loss. There's a lot of cross-sectional variation in these characteristics.
By aggregating up into these little components that are countries, you lose a lot of that variation. You don't really get much of a benefit in terms of trying, over the long term at least, trying to tilt away from countries or industries based on these characteristics. There might be other reasons for tilting away from certain countries.
You might think about a home bias. I live in Europe. I might want a home bias in my portfolio for other reasons, but it would not be driven by, let's say, that I'm expecting a premium for that because Europe is on average smaller or cheaper than the US.
Cameron Passmore: I just want to dive into industries a bit more. I know you mentioned it, but what about over or underweighting industries based on their aggregate characteristics?
Mamdouh Medhat: The story is relatively consistent. You tend to see that these industries don't really drive the premiums. Again, the spreads go in the right direction, but they tend to be fairly weak.
The only nuance to that is when you look at industry momentum. There's a famous JF paper by Moskowitz and Grinblatt from 1999 that looks at industry momentum and shows that it's actually strongest at the one-month horizon, which is interesting because for individual stocks at the one-month horizon, you tend to see reversals. I've actually written a paper with Robert and some of my colleagues here at Dimensional on that.
But at the industry level, you actually tend to see momentum. That tends to be quite strong, and it is significant, and it does produce an alpha, but it just has 400% to 500% turnover per year. It's very difficult to capture in your standard practical portfolio.
This is why we reach somewhat the same conclusion with the footnote that industry momentum exists and tends to be quite strong in the US and outside.
Ben Felix: Other than the industry momentum piece and the previously mentioned results when we're saying that tilting toward industries and countries doesn't have much of an effect, how important to that finding is it that the investor is tilting at the security level?
Mamdouh Medhat: That is really the nail in the coffin in terms of trying to pursue these premiums at the country level or at the industry level. Because if you weren't aware of how these premiums behave at the individual stock level, you would again be just looking at sorting entities on characteristics that should be related to expected returns, and then maybe that's the only thing you can do. But again, that ignores that we know that these factors work really well at the stock level.
Once you know that, once you look at whether you're adding anything by taking country bets and industry bets on top of having the security level exposures, then you see that these country bets and industry bets don't add anything. They don't have any alpha. They're spanned by the security level factors, if you sort of want to talk in the wonky academic way.
One thing I really do want to mention also is that once you start aggregating individual stocks up into specific countries and industries, you have fewer building blocks to play around with. That means that it's quite difficult to take into account premium interactions. So imagine just doing a simple two-by-two, two groups on price-to-book, two groups on profitability.
Over that grid, you would want to emphasize the ones that have a low valuation but a high profitability, and you would want to underweight, de-emphasize the ones that have a high valuation and no profitability. Just a simple two-by-two grid can lead to very empty and under-diversified portfolios if you do that at the country level, and something very similar also happens at the industry level. There are a lot of drawbacks to sort of going away from just doing things at the security level.
Ben Felix: If you, for some reason, couldn't tilt at the individual security level, maybe there's something there trying to tilt at the country or industry level, but it's a lot noisier. You've probably got more idiosyncratic risk. Because we can tilt at the individual security level, it's like there's really no point in trying to tilt at the country or industry level.
Mamdouh Medhat: Completely agree, yes.
Cameron Passmore: So, how does Dimensional deal with country and industry weights?
Mamdouh Medhat: Based on that research, but also a lot of other work that we've done, we generally tend to sort within countries. We go within each country and then we sort on the characteristics that we care about, size, relative price, profitability, and then we aggregate up across countries.
We tend to generally hold countries at their market weight because these borders that we've drawn in the sand don't really tend to be something that differentiates high from low expected returns. We tend to hold things at market weight. There's also that whole discussion around, are some of these big companies truly based in one country, or are they really multinationals, or are they getting their revenues from many regions and so on?
The thing about doing things at the individual country level and then aggregating up is that you will naturally get some industry bets from that. Tilting towards value, as we talked about before, you tend to get more exposure in energy and financial. Tilting towards profitability, you get more exposure in tech and healthcare, things like that.
You will naturally, in the pursuit of the premiums, get some industry bets. If you try and neutralize those completely, then it tends to hamper your ability to capture these premiums because they want to go into a deviation at the sector level. Having a completely sector-neutral portfolio often requires a lot of turnover, a lot of trading, which is, again, inefficient use of capital.
On the other hand, letting them go without constraining the industry deviations at all will create some of that volatility and some of those concentrations that we talked about before. We balance that trade-off by introducing sector caps. We limit the amount that you can deviate from the market weight in that given sector when you are pursuing these premiums.
That can vary a bit from strategy to strategy, but we think that's a good way of having enough flexibility to capture the premiums, but not introducing concentration at the sector level.
Ben Felix: Last few questions here on private markets. You've got a really interesting paper looking at that. Can you talk about how much of the global investable universe in stocks, bonds, and real estate consists of private funds?
Mamdouh Medhat: Yes. So, we wrote a paper last year. We call it "Understanding Private Fund Performance" because that's basically what we tried to do.
We were getting a lot of questions about private markets and about private funds. We'd like to look at the data. We got some data on private market vintages going back to, I think, the earliest start is in the 80s for VCs and then buyouts, then private credit and private real estate somewhere in the mid-90s.
We have about 6,000 funds in the sample, so it's a pretty sizable data set. We try to understand different things that might be of interest to investors, but one thing that we did look at as a snapshot at the end of the sample was what's the size of the investable universe here. There's a lot of discussion around that.
What we found is that about 10% of the total global investable universe is in alternative funds. That includes private funds and hedge funds. You often hear a much larger number.
You often hear that private markets are much larger than public markets, but the thing is that not everything that is unlisted is investable. You cannot go and buy shares in every single private company. You can't take a stake in a private infrastructure or a private real estate project.
We really have to think about what's the AUM, what is actually investable today? Where can you go out and get some exposure to this in a fund? There, it turns out that it's about 10% of the total investable universe.
The remainder is about 45% in global equities and REITs and 45% in global fixed income. That market breakdown we think is a pretty decent starting point. If you are actually going to venture out into private investing, think about those market weights when you're allocating.
Ben Felix: We definitely see institutions and other advisors with like 25, 30% in alternatives. For what reason? If you wanted to say, well, hey, I'm going to build a market cap weighted exposure to private asset classes, it's going to be like 10%, not 30.
Mamdouh Medhat: Something in that ballpark, yes. That's at least how it looked at the end of last year, I think when we last updated those numbers. It's been fairly stable.
It hasn't moved around that much.
Cameron Passmore: We've even seen some this week, Ben, where they're 100% in private assets because public markets aren't working anymore. It's incredible. How important is benchmark choice when analyzing the performance of private funds?
Mamdouh Medhat: I would say that it's crucial. Based on the results in the paper, your choice of benchmark can completely change your conclusion. We often see private funds being benchmarked to style neutral indices, sort of market wide indices.
You can, for instance, think about U.S. buyout funds. Their favorite benchmark is the S&P 500, which granted has performed exceptionally well over the recent past. Generally speaking, you don't tend to see that U.S. buyouts look like the S&P 500. They are not this broad kind of relatively style neutral type of investment. They tend to take style exposure. They tend to take certain sector deviations compared to the market.
Those tend to not be constant from vintage to vintage. They tend to vary. I think you'd get an incomplete picture of opportunity cost if you just benchmark to something that is market wide.
The same is true in VCs, in private credit, and in private real estate.
Ben Felix: When you benchmark against a style appropriate benchmark, how do private funds tend to perform?
Mamdouh Medhat: This is where you need some specialized methods to be able to actually talk about relative performance in the private fund space. An internal rate of return, an IRR, is not just comparable to an annualized compound return. I know that you've had Ludovic Phalippou on the podcast.
He's brilliant. This is one of his pet peeves that he writes a lot about in his papers and in his book. All the issues with IRRs.
The main one right now, at least for me, is that it's not comparable to the annualized compound return of any index. You need some specialized methodology. The consensus in the literature is to look at something called the "Kaplan-Schoar Public Market Equivalent."
It's named after this really phenomenal paper in the JF by Steve Kaplan and Antoinette Schoar from 2005. It's a money multiple. It's a benchmark-adjusted money multiple.
It has several interpretations. You can sort of think about it as in you finance your capital calls by selling shares in the index or the benchmark and you reinvest any distributions back into the benchmark. That's one way to form.
You form a money multiple of that, then you get the KSPME. Another interpretation is that it is the present value of distributions and NAV relative to the present value of contributions, where the present value is calculated by using the benchmark's cumulative return as the discount rate. That last interpretation is really important because it links it to valuation theory and it means that the KSPME is a proper risk-adjusted performance measure.
It can also not be fooled by leverage. This is often one of the concerns with IRRs is that if you use leverage to pay out early, then you can sort of get a higher IRR early on. Because long out into the future, the cash flows matter less and less for the IRR, you can basically have a higher IRR by paying out early.
There are some ways to fool the IRR. That's not possible with the KSPME. This is the measure that we think of and that we think that people should use.
By using that, what you see is on average, buyouts and VCs, they look like they outperform the S&P 500. They have a KSPME above one on average, you know, going over the long sample relative to the S&P 500. But when you calculate that same KSPME relative to a small cap value index or even a small cap growth index that is tilted towards the highly profitable small cap growth ones, then you tend to see that they on average perform just about in line with that.
So your choice of benchmark can really change your conclusion around relative performance. For private credit, you see that, again, according to the KSPME, they on average outperform the Bloomberg US credit index, but they perform just about in line with the Bloomberg US high yield index. So on average, you've gotten the same return by being in US high bonds.
Private real estate tends to actually underperform the sector portfolio of listed real estate companies. So this is not REITs, but listed real estate companies, real estate operating companies, and they tend to underperform REITs based on the KSPME. So again, you can get wildly different conclusions depending on the benchmark.
What I want to stress here is that this is for the average fund. There's lots of dispersion around that. But for the average fund, you know, you can basically get the same performance by going into the public market.
Ben Felix: That's a pretty wild finding, isn't it?
Mamdouh Medhat: We find it quite striking as well.
Ben Felix: We've had people like Ludo that you mentioned on the podcast. It's not that surprising, I guess, because we've seen research like this before, but you guys looked at more asset classes, more data, I think, than a lot of other papers have looked at. That's pretty wild.
Mamdouh Medhat: Yeah, we're quite happy with that paper. It's about a year old, if not a little bit more, and we're still presenting it at conferences and for a lot of institutional investors. Yeah, it's been really useful sort of digging into the private markets, even though we don't, as a firm, invest in that.
Cameron Passmore: So, what do private assets look like through the lens of public market factor exposures?
Mamdouh Medhat: Yeah, I mean, this is kind of a natural analysis to do, right? Whenever you talk to anybody that likes to play with systematic factors or asset pricing models, then you want to figure out what's the exposure, right? When you look at private funds and you look at their periodic returns, often you can only get them quarterly.
And so the quarterly private fund returns, they tend to be a function of the NAV, of the N-A-V, and the N-A-V is an accounting appraisal of the ongoing investments, the ones that haven't actually been exited yet. And so because it's based on an accounting appraisal, they tend to not change that much and they tend to not change that often. And so what that leads to is lagging and famously smoothing.
So periodic private fund returns, they have an element of lagging and smoothing. And so that means that you need to adjust for that whenever you're measuring factor exposures, because they can screw up your factor regressions. They can screw up your factor exposures.
By the way, this is no different from when people started looking at real estate returns back in the 1970s. We have methodology to do that. It's really not rocket science, but it's important to do that.
Another caveat and important fact to mention is that we saw a structural shift in 2007, because there was a change in the accounting rules. And in particular, there was the adoption of fair value accounting. This happened in Europe and it also happened in the US.
And fair value accounting basically means that you as a private fund manager cannot get your books audited unless you can show that there's some mark-to-market accounting. You have to show somehow that you're marking your current investments to deals that have already happened or comparable investments from the public market. There needs to be something like that.
And so you asked me about what do the factor exposures of the private funds look like? Well, pre-2007, pre-fair value accounting, you mostly have negligible factor exposures, even when you adjust for lagging and smoothing. There's very little explained variation.
After the adoption of fair value accounting, you see that the factor exposures of these private funds in aggregate can explain up to 70 or 80% of the variation. It's really a material shift that we saw after the introduction of fair value accounting. And so if you look at the different asset classes, buyouts in aggregate, they mostly look like highly profitable companies, perhaps in the mid-cap space, something like that, which is kind of intuitive.
They tend to go for sort of your cash cow type of investment and in aggregate, they tend to look like that. And that explains north of 70% of the variation in quarterly buyout returns. VCs in aggregate, they tend to look like small cap growth, but high profitability.
So not these short end of the defensive equity strategies, they tend to be small growth, but high profitability. Private credit tends to look like high yield, and they tend to not take material term exposure. So they're not really differing that much on whether they take long duration, short duration.
They tend to look fairly market-like in their duration, but have high yields. And private real estate, it does correlate with REITs, and it also does correlate in aggregate with the public equity market, but not very much. Private real estate is the asset class that we have the hardest time explaining by public market factors.
It's the one that looks the most different, I would say, from the public market factors. Perhaps this is why it is somewhat popular as the quote-unquote diversifier. Do you have any idea why that is?
I just think the composition of what you have in private real estate funds is quite different from REITs in the sense that a lot of private real estate funds, they will get these real estate investments and just basically get the capital from them by renting them out. Let's say it's slightly different from what you see in real estate investment trusts. And so that basically means that while you have some connection, because they're part of the same economy and they're largely speaking investing in real estate, it's very different assets that they're actually holding.
Ben Felix: So we've been talking about average fund returns, which has been super interesting. So we've got PMEs pretty close to style appropriate public market benchmarks, which is super interesting, but does make sense. And then we have public market factor exposures that largely explain returns, aggregate returns, post-2007 once we have fair value accounting.
But again, those are average funds. In public markets, there's not huge dispersion between the worst and best cohorts of mutual funds, for example. Can you talk about how much dispersion there is in private market fund performance?
Mamdouh Medhat: I think there's a little bit of a wrinkle to what you just said around public sort of funds. Let me get to that in the end. But what we see in the paper for private funds is that there is actually quite a lot of dispersion.
So for instance, the top 5% in any of the four asset classes that we consider, they often double or triple your invested capital after management fees and carry. So the top 5% of funds there, they really have quite impressive returns. The bottom 5%, on the other hand, they tend to lose half of your money after fees, maybe even more than that.
So quite a lot of dispersion between the top performers and the bottom performers. And we see that in all the asset classes. The dispersion is widest in VCs and it's lowest in private credit, which is kind of what you would expect.
That's sort of quite intuitive. It's quite difficult to compare that to what you see in public markets because of survivorship bias. So in public markets, the funds that live for a considerable period of time tend to be the ones that haven't closed down.
Kind of a tautology, but funds closed down because of bad performance, because of outflows. The funds that do badly, they tend to not survive over long periods of time. I think over the past 20 years, when you look at US equity funds, so US domiciled equity funds, the survival rate, I think is only like 45% and the outperformance rate is close to 15% or something like that.
That survivorship bias means that it's difficult to compare that dispersion because private funds, they tend to be 5, 7, 10, maybe even longer now year investments. If you look at the dispersion that you've seen returns over relatively short periods of time, where you have less of that survivorship issue in public markets, you tend to see as much dispersion in the private space as you do in the public space.
Ben Felix: That is a really interesting point. There's a chart that I've seen that shows the increasing dispersion when you go from public to private markets and I always just took that as, yeah, that makes sense. I always accepted that as true, but what you just said also makes a ton of sense.
When you account for survivorship bias, you can actually have a lot more dispersion than it would look like if you just look at the sample of existing funds. So, you're saying that there's as much dispersion in public market fund performance as there is in private market fund performance.
Mamdouh Medhat: When you look at active public funds, I would say so. That would be my expectation. There is quite a bit of dispersion.
We recently were asked to do an analysis where we would look at exactly the dispersion in information ratios. Outperformance relative to tracking error for active funds and quite a bit of dispersion just over the last five and 10 years. It's a pretty common phenomenon that when you think about active management, traditional active management, which private funds are just in the illiquid space, you tend to see a lot of dispersion between top and bottom performing managers.
Cameron Passmore: And the obvious next question, Mamdouh, given this wide dispersion, what are the criteria to predict these successful managers?
Mamdouh Medhat: Manager selection is always difficult. The thing is that what people tend to think about is persistence. So, they tend to think about, "okay, I'm going to go for the GPs that had a stellar vintage because I expect them to have a stellar vintage next time as well."
The thing is that there isn't a lot of evidence of persistence in the literature. Lots of caveats around data here. The most complete data source that academics use is the data that was formerly managed by a company called Burgiss, and now it's been acquired by MSCI.
That's the data that we use as well. And using that data set, there really isn't a lot of evidence of dispersion. And I'm particularly thinking of a particular paper from 2023 by Harris, Jenkinson, Kaplan, and Stuckey.
It's called "Has Persistence Persisted?" And they basically find that there is no evidence of persistence in buyouts. There's a little bit of evidence of persistence in VCs, but that's kind of pre-2000.
Afterwards, you don't see a lot of that. We couldn't study persistence ourselves in this paper because of data issues. We don't actually have access to the individual funds or the GPs.
We only have access to vintage level data, and so we couldn't study it. But using the same data set and using fairly rigorous methodology, very rigorous actually, I would say that there isn't much evidence of that. So given that you don't have persistence, and given that there's wide dispersion, just like we talked about, you're basically back to due diligence.
That should be the main criteria for selecting managers. I co-authored this paper with Wei Dai, who you also had on the podcast called Systematically Evaluating Systematic Managers, where we came up with this framework for what criteria should you look at before you hire a manager and what criteria should you look at after hiring a manager. And these things extend beyond just systematic managers.
For any kind of manager really, before hiring them, you should look at their full investment proposition. What kind of research do they do? How do they construct their portfolios?
What is the process of going from idea generation to actual implementation? And then when you look at the track record, don't only focus on the track record maybe of the funds you're interested in or the style you're interested in. Look at their full track record.
Look at their survival in particular. Look at what is the percentage of funds they have that have outperformed their benchmark. Because if you pick that one fund that works well and the other ones don't, then that's not really giving you a good indication for being sustainable.
And then after you hire them, it's of course, the continuous monitoring. You have to keep assessing, are they doing what they should be doing? Are they sticking to the investment proposition or things in line with expectations?
That holds in public markets just as much as it does in private markets. But in private markets, of course, you have additional things like the illiquidity and they don't report as often and things like that.
Ben Felix: And then you have to keep revisiting it. I do some volunteering on an investment committee and investment committee meetings are always about three-year performance. And if things aren't going well, then you're reevaluating the manager.
And I think that's pretty typical.
Mamdouh Medhat: Yeah, it's tough. When you understand how much volatility there is in returns and how likely you are to see negative premiums over a three-year period, it really puts things in perspective. I always like to highlight Fama and French's volatility lessons paper from the FHA from a few years back.
That's a good read for anybody thinking that three years is enough to evaluate any kind of performance. It can be tough. And I understand manager selection is difficult, but you really have to put things in perspective.
One last thing around manager selection in private markets. It's always difficult to do manager selection, but the illiquidity and the way that private markets are set up actually exacerbates the manager selection problems because you can't hold the market. You can't make your asset allocation sort of relatively manager agnostic.
You can always do that in public markets, right? You can always go for something more systematic, something less dependent on which manager you choose. That's not possible in private markets.
You have to pick a GP or some GPs and so your opportunity cost can be a lot higher.
Ben Felix: Which makes going into private markets in the first place a big decision. You're forced to make that second decision about which manager to pick, which is a very difficult decision to make. How has private fund performance relative to public benchmarks, to appropriate public benchmarks like we talked about earlier, how has that trended over time?
Mamdouh Medhat: It's a good question. Again, because these vintages, they tend to last for anywhere between five or even seeing like 12 year vintages. Sometimes it can be difficult to discern a time trend.
I don't think that there is a significant time trend that has been trending sort of up or down, but what we have seen very clearly is higher correlations with public markets, especially after 2007. There's just a lot more alignment in terms of the returns that you get in aggregate from private funds and the returns that you get from the public market. I think that's important to take into account when you're thinking about an allocation going forward.
Cameron Passmore: I love this next question. It comes up all the time. How much of the apparent diversification benefits of private assets is just an artifact of illiquid asset return smoothing?
Mamdouh Medhat: Based on what we can see around that structural break in 2007, pre-2007, a lot of the diversification looked like it was overstated. It was because of the lack of fair value accounting across sort of a wide spectrum of these funds, the lagging and the smoothing really looked like, oh, these private asset classes are not entirely uncorrelated, but almost close to that when you compared them to public markets. This was pre-2007.
After 2007, and especially when you account for lagging and smoothing, we see these really substantially higher correlations and it allows us to, I think, more reliably quantify the diversification benefits. Yes, the sample shrinks and you also have the financial crisis as part of that sample, which tends to induce correlations by itself. We some robustness in the paper where you throw out the financial crisis and get similar results, but we can kind of quantify it.
This is where we find that, for instance, for buyouts, you can explain up to 80% of their returns just by their factor exposures. What's important here is that we can't explain 100% of that. We do some statistical testing to see if that explained variation includes 100% as part of the set of values that you can trust.
In most cases, we actually reject that you can explain 100%. That means that there are some diversification benefits. They're probably just not as high as you may have heard by looking at some of the marketing material and especially if you look at old vintages and old funds.
The reason why I think we won't be able to fully explain the returns that come here, even after fair value accounting and even after controlling for lagging and smoothing, is that private assets at a conceptual level, they should expand the opportunity set that you have as a public investor because you're adding things that are not available in the public market. They might be highly correlated with what you see in the public market, but they're actually not there. In a conceptual framework, if you think about the total market, which includes every single asset, you are actually getting slightly closer to that.
Again, there are lots of caveats around that and I don't want to overstate the diversification benefits given the high correlations we see in aggregate.
Ben Felix: Yeah, it makes sense. I think when we had Ludovic Phalippou on, if I remember the example correctly, he said "tech in Europe, if you really want to get a European tech exposure, you have to go into private markets." That may not be exactly the example, but conceptually.
It does make sense. If you're adding sector exposures that you can't really get in public markets, then you would expect some diversification benefit.
Mamdouh Medhat: Lots of institutional investors make that exact argument, right? That some exposures that they're looking for, they believe looking at the data, looking at what's available, that they can better get it through unlisted assets. They might have an easier time accessing that than your typical retail investor or your advisor.
Ben Felix: Yeah, it's getting easier for retail investors, which may not be a good thing. Actually, on that point, what effect do you think the increasing popularity of private investments and maybe also the increasing access to retail investors might have on their expected returns?
Mamdouh Medhat: This is one of the questions that I get asked. It's tough, but I can say this. When you bid up the price of anything, then all else equal, you should expect its returns to go down.
We've seen the democratization, if you want to call it that, of private investing. We've seen greater demand for private funds, whether it's in retirement sort of solutions, whether it's an institutional portfolio, whether it's in retail portfolios sometimes. That's removed some of the exclusivity that we see around private investment.
It's also put some pressures on GPs to deliver, despite their much higher AUM. We've seen indications, at least in the financial press recently, of vintages prolonging their lifetimes, exits being difficult to come by. We've seen continuation funds where an unlisted asset gets passed on to the next vintage, and maybe even the next vintage again.
So, there are some indications here that the asset class is trying to cope with this increased demand. Ultimately, I'm a data guy. I don't sort of forecast, but I would say, just take that into account.
You're not getting the same level of exclusivity that you tended to get before in private markets. There's a lot more demand. There's many more assets under management.
So, just be careful with that. Again, think about the market weight of that in your overall portfolio, if you're going to allocate.
Ben Felix: You did this paper. You obviously spent a lot of time crunching pretty cool-sounding data set that you had. Sounds like you get a lot of questions about private markets too. What would you say are the implications of your findings for someone considering an allocation to private assets?
Mamdouh Medhat: I try to stay away from being prescriptive on asset allocation, right? Because I think that that depends on so many variables, your risk tolerance, your horizon, what you actually want to achieve from this portfolio, and what else you're holding. But the findings indicate that you should really have this middle-of-the-road view of private assets.
They're neither a fantastic unicorn, but it's not just complete bogus. All of it. There are some positives and some negatives.
The negatives, I mean, we talked about the wide dispersion that you see in performance between the top and the bottom. And because you have to allocate to managers, you are exposed to that dispersion. It's difficult to get away from that.
It's difficult to make your allocation manager agnostic. Even secondary funds and so on will only be able to capture that many GPs and that many individual funds. And there's also another negative, which is on average, you don't actually see a premium relative to some of these styles.
The average fund performs in line with some of these styles that we know from public markets. The positives, there's some evidence of diversification benefits. I can get away from that.
But there are lots of caveats around the types of fees that you're paying and then the GP that you select. The GP that you select might just be very highly correlated with public markets, and you don't know that ex-ante. I would say, if you're willing to stomach those caveats that I just mentioned, and you want to expand your opportunity set, fine.
And you're willing to stomach the fees and the liquidity, and you might not have the liquidity exactly when you need it and all these things, then fine. But I would say, start with the market weight and be very clear about your expected liquidity demands and what your tolerance is for some of these investments maybe continuing beyond what you expect, because we are seeing longer and longer vintages right now in private funds.
Ben Felix: There's a huge idiosyncratic risk component too, right? With the manager selection, unless you have a huge amount of capital, you're not going to get private equity or private credit or whatever. You're going to get a GP.
Mamdouh Medhat: Yes, that's it. It's a tough one. There is talk in the literature as well around, basically, to get diversification in the private space, you need two types of diversification.
You need diversification within vintages. So, this is across managers, and that can be very difficult to get, especially as a retail investor or as an advisor, because they often have minimum capital requirements to be able to be part of a fund. And so, spreading that out across many different GPs can be difficult.
But you also need diversification across vintages, because one vintage might just be a bad apple. If you need that, then you need to keep allocating year after year to vintage after vintage, and to multiple managers. And so, you very quickly run out of capital.
Ben Felix: Or you do a continuation fund, which might have its own set of issues.
Mamdouh Medhat: There are, again, idiosyncratic issues with continuation funds as well. You don't really get away from some of the liquidity constraints and some of the redemption limits and things like that.
Ben Felix: And you might get adverse selection, I don't know, maybe. Or with the evergreen funds, I mean, the perpetual funds.
Mamdouh Medhat: Yeah. Again, this is the lack of exclusivity is that what is being offered right now to retail investors. Again, not to say that I know exactly what's going to happen, or I have looked at the individual managers, but these are all things that you need to take into account. Manager selection is difficult, irrespective of where you go.
Ben Felix: Very interesting. Very cool. Look at the data.
And like I said earlier, it's interesting how you guys looked at all the different private asset classes, not just one, which a lot of papers have done, but then taking the factor lens to private assets. Super, super interesting.
Cameron Passmore: This has been an incredible conversation, Mamdouh. We have one more question for you. How do you define success in your life?
Mamdouh Medhat: Venturing into philosophy now. I don't want to sound too cringe, but it's probably something like doing something I love professionally, but at the same time, being able to provide for myself and my family. I think marrying those two things together is probably some definition of success for me.
I love doing research. I did it as an academic before coming to dimensional, and now I feel really privileged being able to do it at a firm that appreciates that level of rigor and the deep dives into the data and the theory. Compared to academia, it's great to be able to see some impact in the real world.
One more aspect of success for me is to try and stay grounded and try and maintain some humility. I think in asset management in particular, there is a little bit of a tendency for successful people to exhibit some forms of overconfidence or say, oh, it's my way or the highway. If you don't agree with what I'm saying right now, then we have nothing to talk about.
That's not really useful. I try to stay humble. I try and keep an open mind, whether it's about wonky asset pricing research or it's more general.
I think that helps when you're out actually talking to real people about their investment decisions. Staying humble would also be part of success for me, I think.
Ben Felix: Great answer. Not cringe at all.
Mamdouh Medhat: Thank you. I appreciate that.
Cameron Passmore: Great answer and great interview. Thanks so much for joining us, Mamdouh.
Ben Felix: Thanks, Mamdouh.
Mamdouh Medhat: Pleasure. Thank you very much for having me.
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