Michael Mauboussin - Pattern Recognition and Public Markets (EP.370)

19 Feb 2024 · 50 min

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Podcast Summary: Michael Mauboussin - Pattern Recognition and Public Markets (EP.370)

Podcast Overview Title: Capital Allocators – Inside the Institutional Investment Industry Host: Ted Seides Guest: Michael Mauboussin, Head of Consilient Research at Counterpoint Global Episode Focus: Pattern recognition in investing and the evolution of public markets.

Key Concepts Discussed

  1. Pattern Recognition
  2. Definition: The ability to identify and interpret patterns in data or experiences.
  3. Importance: Critical in investment decision-making but can lead to flawed reasoning if misapplied.
  4. Intuitive Expertise:
  5. Combines intuition (unconscious understanding) with expertise (predictive capability).
  6. Differentiation between experienced individuals and true experts.

A. When Pattern Recognition Works

  • Stable Environments: Effective in predictable scenarios with clear cause-and-effect relationships (e.g., chess).
  • Examples: Chess masters can quickly identify advantageous positions.

B. When Pattern Recognition Fails

  • Complex Adaptive Systems: Markets are complex and unpredictable, where experts often struggle to make accurate predictions.
  • Representative Heuristics: Investors apply past experiences to new situations, risking flawed conclusions.
  1. Experience vs. Expertise
  2. Distinction:
  3. All experts have experience, but not all experienced individuals are experts.
  4. True experts possess predictive models that yield reliable results in their fields.
  1. Decision Aids and Investment Approaches
  2. Decision Aids in Investing:
  3. Quantitative vs. Qualitative Approaches:
  4. Quantitative investors leverage historical data to guide decisions.
  5. Fundamental investors may not harness available decision aids effectively.
  6. Base Rates: Using historical performance data to inform forecasts.
  7. Position Sizing: Establishing a structured approach to determine investment sizes.
  1. Manager Selection and Skill Assessments
  2. Identifying Skill vs. Luck:
  3. The challenge is determining whether past performance is due to skill or random chance.
  4. Importance of causal relationships in skillful performance.
  1. The Changing Nature of Public Markets
  2. Market Environment:
  3. A significant decrease in the number of public companies from historical peaks.
  4. Fewer IPOs and consistent M&A activity contribute to the decline.
  5. Expected Returns:
  6. Shift in public market expectations from low returns (3.2% real) to more normalized returns (mid-6% real).
  1. Wealth Creation in Public Markets
  2. Distribution of Returns:
  3. 60% of companies underperform treasury bills, while a small percentage generates vast wealth.
  4. The concept of "power laws" in both public and venture capital markets.

Key Takeaways

  • Investing Strategies:
  • Indexing is a sound strategy to capture overall market performance and mitigate risks.
  • Active managers should aim to identify potential future stars based on a probabilistic assessment.
  • Behavioral Insights:
  • Emotional biases and reliance on intuition can lead to poor investment decisions.
  • Investors must remain skeptical about their pattern recognition tendencies, especially in less stable environments.

Closing Thoughts from Michael Mauboussin

  • Curiosity and Rationality: Great investors are characterized by a desire to learn and adapt their views based on new information.
  • Staying the Course: Successful investing often involves enduring significant volatility and drawdowns.

Guest Background Michael Mauboussin is recognized for his analytical prowess in investment concepts and the application of academic research to the investment industry, particularly in relation to pattern recognition and market dynamics.

Additional Resources

  • For more insights and to access premium content, listeners are encouraged to visit [Capital Allocators](https://capitalallocators.com).

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This summary captures the essence of the episode and highlights the critical discussions on pattern recognition within investing and the evolving landscape of public markets.

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Transcript

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0:01Capital Allocators is brought to you by my friends at WCM Investment Management. To outperform the markets, you have to do something differently from others. In my 30-something years investing in managers, there may be no one I've come across who does that as clearly and as well as WCM. I've seen it up close as an investor in their international growth strategy for the last five years. WCM is a global equity investment manager majority owned by its employees. They believe that being based on the West Coast, away from the influence of Wall Street groupthink provides them with the freedom to live out their investment team's core values, think different, and get better.

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1:26This testimonial is being provided by Ted Seides and capital allocators who have been compensated a flat fee by WCM. This payment was made in connection with capital allocators testimonial and production of podcasts and does not depend on the success or level of business generated. The opinions expressed are solely those of capital allocators and may not reflect the opinions of others. Investing involves risk, including the possible loss of principle. Past performance is not indicative of future results. Please visit wcminvest.com for WCM's ADV and further information. Capital Allocators is also brought to you by Morningstar.

1:55What if data wasn't just a bunch of raw numbers, but a clear and decisive language to help connect investment strategies with long-term investor needs in a constantly evolving market landscape. Morningstar created that language, bringing order and utility to insight-rich data so you can prepare for your next opportunity, no matter the asset class or market. Visit wheredataspeaks.com to see what Morningstar data can do for you.

2:32Hello, I'm Ted Seides, and this is Capital Allocators. This show is an open exploration of the people and process behind capital allocation. Through conversations with leaders in the money game, we learn how these holders of the keys to the kingdom allocate their time and their capital. You can join our mailing list and access premium content at CapitalAllocators.com. All opinions expressed by TED and podcast guests are solely their own opinions and do not reflect the opinion of capital allocators or their firms. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.

3:11Clients of capital allocators or podcast guests may maintain positions in securities discussed on this podcast. My guest on today's show is Michael Mobison, head of conciliant research at CounterPoint Global, a$70 billion equity manager. Michael is renowned for his ability to articulate important investment concepts backed by academic research. His first of three prior conversations on the show is replayed in the feed. You can find the rest at CapitalAllocators.com. Our conversation explores Michael's most recent piece on pattern recognition, including when it works and when it doesn't. We then transition to discussing the changing nature of public markets, inspired by another of Michael's recent research reports entitled Birth, Death and Wealth Creation.

4:04Before we get going, our little experiment to engage with our mid-roll sponsor NetSuite by Oracle seems to be working. For those one percenters who actually listened to the last two weeks and hopped on netsuite.com slash allocators to download their KPI primer, we thank you for helping show proof of concept for our advertisers. For those who haven't yet done so, there's still time to join the one percenters. But please don't go to netsuite.com slash allocators while you're driving. And don't interrupt your meditation by going to netsuite.com slash allocators. Although I guess it would be a little weird to be listening to this while you're meditating anyway.

4:46And definitely don't go to netsuite.com slash allocators while you're running on a treadmill. But most other times, please do go to, you guessed it, netsuite.com slash allocators, so we can end this experiment of promoting our mid-roll to create proof of concept. Thanks so much for spreading the word about the mid-roll ads on Capital Allocators. Please enjoy my conversation with Michael Mobison. Michael, great to see you. Great to see you, Ted. I'd love to dive in on this recent work you did on pattern recognition. It's just something that's part of everyone's arsenal, but to really tear apart what you learned from doing the research on it.

5:29Yeah, absolutely. I mean, the first thing I'll say is that this is another one of these topics where people talk a lot about this in the investment world, and it's across the spectrum. venture capitalists will say this is important in public managers. And we also know that people have intuitions that are not always perfectly well guided. So pattern recognition felt like a really important topic to get into this. And as with most of these topics, the question really boils down to when is pattern recognition likely to be effective? And when is it likely to be ineffective? Or where should you be checking yourself?

6:00So a couple of ideas come to mind right away. You can almost frame this is the concept of intuitive expertise. So let's break that down. Intuition says you have a sense of what's going on without thinking about it consciously. That's what we're going to say is intuition. And then expertise is you're good at something and as a consequence, you can perform above the average. So you bring these two things together. Quality pattern recognition would be you can see it without thinking about it and it's going to deliver outcomes that are really good for you. The question is, where does intuitive expertise work?

6:33Now, one of the points I stress in this piece, and I think it's really important, I got this from a psychologist named Gregory Northcraft, this distinction between experience and expertise. I guess the way you could say it is all experts have experience, but not all experienced people are experts. And so what Northcraft says is an expert is someone who has a predictive model that works. And so the question is, Is that the case everywhere? So in some domains, you clearly have experts. If you have a problem with your plumbing at home, you bring in a plumber who is an expert and he or she is going to be able to fix your problem.

7:10By contrast, if you say, where will interest rates be in a year from now? What will the price of oil be? We know that there are a lot of people who are qualified to make predictions, but they may not be predictive in any important way. That's the interesting thing when you impose that on the investing world is to ask who are experts and who are people just with experience and just not to blur those two concepts. On that point of experts, there's all kinds of research that shows, particularly in markets and complex adaptive systems, that experts don't do very well in their predictions. So just as that starting point, before we dive into when that works in pattern recognition, how do you decide upfront what constitutes an expert and not just someone with experience?

7:54One of the ways to think about this, and this actually frames pretty much the whole report as well, is that you're likely to see expertise work or intuition work, candidly, is when you have environments that are relatively stable, where the relationships are linear, so cause and effect are quite clear. The canonical example of this, of course, is chess, which has been studied by cognitive psychologists for a very long time. If you show a chess master the chess board a game in progress, they are going to very quickly know which player has the advantage. They're going to very quickly look at either side of the board and tell you either an optimal or close to optimal move.

8:29And if you have three grandmasters lined up, they would pretty much tell you the same thing or something very similar. Those conditions are all very structured, stable, and linear. Now, if you came out and said, hey, instead of eight by eight, the board's going to be 12 by 12. And instead of the bishop moving this way or the queen moving that way, we're going to change how they all move, that chess expertise will go out the window. They're essentially back to square one. So what they've been able to do, chess masters, is they're essentially have learned deliberate practice. They've learned about this system in such a way that allows them to conceptualize the whole thing in a way that's very effective.

9:05Now, as you point out in things like markets, and again, there are different flavors of this across different components of the investment process, but you think about markets, that is the classic example of complex system. And so what we know, and this has been extraordinarily well-documented by folks like Phil Tetlock, psychologist, University of Pennsylvania, and others, is that, in quotation marks, expert forecasts. They tend not to be very good at it. In fact, typically, simple extrapolation algorithms do as well, or in some cases, a little bit better. The key to these experts is they're like the rest of us.

9:37They have a whole list of reasons why they almost got it right, and so on and so forth. And so they're still storytellers. One of the things, I think, Ted, when you get down to it, what happens is for most of us, what we do when we're thinking about this sense of pattern recognition is we all walk around with a little bit of a mental database based on your own experiences and what you've been exposed to. And when you see a new situation, what you're going to do is match it with your personal database. It's called the representative heuristics. You're going to basically say, this is like that based on my experience.

10:09And hence, this is what I expect to happen going forward. That's obviously a problem because your mental database is probably somewhat limited. And again, the causality components may not be perfectly clear. Those are some of the things to think about. And again, think about this as a continuum from one range where there are clear experts, people that agree on things to another range where people may have done this for a while or studied it in detail, but they really don't have very good predictive models. So macroeconomic forecasting, we know, is problematic. What about, call it microeconomic or at the business level.

10:45So you could think of it as private equity as opposed to public markets where maybe the behavior of the participants isn't as impactful. It's not as reflexive. Yeah, that's a great question. I think your intuition is right on this, which is it's going to be less applicable to macro because of the complexity of the problem you're taking on and probably more applicable to micro. One way to think about this is to say, when you look at, for example, corporate performance. And so we can get into the nuts and bolts like sales and sales growth rates and profit margins and return on capital patterns and all that good stuff.

11:17We have a large receptacle of corporate history on these things. And some of those distributions of performance are pretty well-behaved. They're not perfect, but they're pretty well-behaved. So if I said to you, what is the distribution of sales growth rates for companies with$5 billion of revenues in the technology industry? You could answer that question and it would look pretty good. And those could be things that could really guide you. So there are ways to get, I think, closer to understanding. And that's not really pattern recognition. Well, it is in the sense that you know what the base rate looks like, you're familiar with it.

11:48And then when you see a situation, you say, ah, this base rate applies here. This is going to help guide my thinking. So I do think it's going to be more effective on the micro level than it's going to be on the macro level, because it's just a much simpler problem to try to parse. When you talk about a model or the framework in developing intuitive expertise, you think about a model, you think about something quantitative. And I think a lot of people, pattern recognition, whether it's elements of stocks or even in this world, manager selection, it's very qualitative. How do you draw the distinction between the quantitative and the qualitative?

12:22It's also a very important question. And one of the things to think about is, do you have access to or do you use so-called decision aids? Let's take equities, for example, quantitative investors versus fundamental investors. Well, quantitative investors are using decision aids. So they're saying here, we're going to look at history and we're going to judge why certain stocks did better than what, for example, an asset pricing model would predict that would give us some sort of excess returns. We'd love to have an economic story to explain why that factor did particularly well. That's nice. But those are decision aids.

12:56And then once those decision aids have been set, you let the decision models themselves essentially run the portfolio. And you're overseeing and obviously doing more research to refine everything, but that's basically it. Many fundamental investors don't really use those decision aids or they don't use them as much as they possibly could. And so that's a fascinating area for us to explore, which is, are there decision aids at our fingertips that we're not accessing as fundamental investors? I think the answer to that is yeah. And by the way, the other thing I have to say is I tucked that comment in there that the quants should have a story as to why a factor is working.

13:29That's in a sense, moving over to the qualitative to some degree, and then the qualitative investors should use more decision aids. So as we evolve as an investment community, it's trying to take the best of both of these worlds to be the most intelligent as possible. What are some of those decision aids that fundamental investors maybe don't use, but should be readily accessible for them to use? One that, Ted, I think you and I have talked about this a fair bit over the time is the application of base rates. So thinking about historical performance and to the degree to which that can help inform your forecast, they're deviled in the details in terms of what are appropriate reference classes and so on and so forth.

14:05But that's, I think, a very rich mine for us to continue to explore. The other one I find fascinating, I don't really understand enough about why it doesn't happen, is position sizing. I think most fundamental investors have some sense of why their position sizes are what they are, but in a sense, they're not quantitative or they're not completely structured. So the question is, might you have a decision aid to say, I'm going to lay out in advance, here's how I'm going to think about sizing my positions. I'm going to provide the appropriate inputs to allow that to happen. I'm going to consider all the constraints that are relevant and to some degree, even let the model generate what I should be doing.

14:47And then if I want to deviate, I might want to. But starting with that decision aid, those would be two areas right off the top in fundamental investing, I think are really interesting. And I think that we can continue to make progress on. When you take it from the level of security selection to say manager selection, how do you apply the concept of where pattern recognition could, or maybe shouldn't work to that seat? The key here is to, I'm telling you, but you're the expert in this much more than I am. The key here is to sort out when you think about the past performance of others, what components are attributable to skill and what components are attributable to luck or something else.

15:23So this is the key. Skill is going to be all about causality. So the question is, can we identify certain features or performance that are persistent that we'd argue are causal? People like to dismiss performance data. And I understand a lot of limitations of performance data, but it's better than nothing to know what's happened before. But the key, can you break that down in such a way that allows you to get some of those skill signals? So that would be a decision aid. we've thought about what kinds of things are consistent with skillful people. We understand that skillful people have periods of underperformance, and then we try to apply those as we think about selecting managers.

15:59When you think about doing that assessment, so that's a quantitative assessment to some extent, where does the intuition about what might happen in the future come from when you think of the pattern recognition piece? We're all humans. So I sit down with you, Ted, and I go, oh, Ted looks great today. He's got beautiful ties, his presentation deck's really important. But perhaps it might be like, hey, Ted reminds me of that manager we invested in five years ago who was great. Or Ted gave me a funny vibe and reminds me of the manager that wasn't good and matching you with something that may or may not be relevant to what's actually going on today.

16:35When you say it's intuition, you're not thinking it through. This is a sensation I'm having, which is going to create a bias either in favor or perhaps against a particular manager. And that, by the way, happens all the time because you just walk around and you have feelings for how things are. It's true for us personally and it's true for us professionally. What are some of the ways you've seen where people use the label of pattern recognition or intuition, but the research would show you that there's no signal in that? I think this is very common. That's why I opened with a story. I was a junior analyst working for a senior analyst and came in and basically made a decision about a recommendation of a stock based on no analysis.

17:14But he knew a couple of things that were true for the most part historically, and these are imprinted on his own experience. And hence, he made this decision. So just to be clear, we all have these sensations. Now, the other thing that's really challenging about this basic concept is we remember when pattern recognition led us to a good outcome, even if it was luck. And we forget about when it led to a bad outcome, even if it was bad skill. That's the other thing is we're very selective as to when we remember when it works and when we remember when it doesn't. We all have these sensations when we walk around about these patterns.

17:50The whole goal of this report is not to say this doesn't apply, it does, but just be careful about applying it too broadly or too boldly. In a situation where, as you said, we're all out here feeling things, sensing things anyway, how could you go about improving or fine tuning how you're using your pattern recognition in the investment process? There's actually a chart. I think we only have one chart and report where we talk about the continuum of where this is likely to apply. And I think that's the way to think about it. Ted, even in the luck skill discussion, we have a luck skill continuum.

18:28It's the same basic concept. And so the first thing to think about is, as I have these sensations is where is my domain in this continuum for the applicability? If I'm in the domain where pattern recognition absolutely works, I'm playing chess, I'm good at it, absolutely. Rely on it, that's good, that's documented. But if I'm in, for example, macro forecasting, just flick on CNBC, someone will say, oh, this market is just like 1994. They're matching patterns. You just have to be much more skeptical about those kinds of things. Part of it is just knowing where you are on the terrain and thinking it through and then checking yourself and saying, I'm going to be more skeptical about my own sensations when it's in this less reliable realm.

19:09And I'll be more confident in my assessments when it's on more stable ground. What have you heard in feedback on this piece? Is there something like this? Is it fun to see how people respond? It's actually been very popular because everybody can relate to it. I ran into one very successful money manager, a great guy. And he says, oh yeah, I love that pattern recognition piece. And he goes, pattern recognition is the key to what I do. So I was like, well, the point of the report is that people overuse this potentially. And he goes, yeah, yeah, I know, I know, but it's still the key to what I do.

19:42It did maybe slow him down a little bit and get him to be more thoughtful about it. But that was very much part of the process. I heard from a sports executive, which was really interesting. And he had read it and he's like, yeah, this is huge for what we do as well. So again, they see a player, an athlete, or they're drafting somebody and they'll say, this reminds us of that. And it may or may not be grounded in causal drivers, but it's a feeling that they have about it. So he's like, we can make a lot of mistakes by finding these faulty causal relationships. And by the way, our minds are great at this.

20:16We create spin stories to create all this stuff all the time. That was also cool. Sports executives also seem to struggle with this exact thing because they have a profile of what players should look like or what has worked in the past. The key thing to underscore in all this is that we all have our little mental databases. We've all had experiences. We've all had wins and losses. We value those, right? They're important to us. And so we tend to think that they're going to apply more than they do. And we don't open the aperture and think about the much broader world. That sports example calls to mind Moneyball.

20:49Michael Luz's Moneyball came out with Billy Bean. So much of it was just that, that the scouts had said, oh, that's a short fat guy, he can't possibly be good. And the data would show, no, actually he's a good player. Why is it that even when people start to understand what domain they're in and where pattern recognition should apply, I say in the investing world where so much of it is maybe overused, what behaviorally prevents them from nevertheless creating that story? One thing I never knew about was a concept in cognitive psychology called acquiescing. And the idea, Ted, is that you examine a situation.

21:27You have a certain intuition about what you should do. You do the analysis, and the analysis says this intuition isn't correct. And you still do what your intuition tells you, right? One of the studies done by the researchers on this was actually about football, so we can stay on sports for a second. It was going forward on fourth down versus punting. And so they got a population of people. They knew enough about American football. They said, here's the situation. what do you think you should do? And about 40 % of people said, my gut tells me I should punt. And then they said, okay, well, here are the analytics and the analytics show that there's a substantial gain in win probability if you actually go for it.

22:06And people saw that and acknowledged it. And it turns out more than half of the people that saw the compelling analytics still decided to punt. So they acquiesced to this sensation. Now, in the real world, if you think about you're an NFL head coach, there are a lot of other things that come in besides pure analytics. You might say, gee, if I go for it and it doesn't work, I'm going to get beat up in the press tomorrow, or my owner's going to be mad at me or whatever it is. Fans might have a reaction. So there might be other variables that come into play, but there is this concept called acquiescing because we just, at the end of the day, we feel more comfortable going with our gut, going with the intuition.

22:43That is a thing. And I didn't know about that. It goes back to your money ball thing. It's saying we see this athlete that doesn't look like a good player. And we see this other person that looks like they're amazing and five tools and so on and so forth. And even though we think the short chubby guy is a better player, we're going to go with a five tool guy because it just looks better. I've actually had this professional experience as well. When you look at interviewing candidates, sometimes people just look better on paper and other people don't look as good in their resumes or whatever, but you can see that they're going to be more effective and people still say, I'm going to go with the credentials.

23:16I'd love to pivot the discussion. Just talk a little bit about public markets. With all the rush of private markets and the contraction in private markets, there hasn't been as much focus on it. And yet we now have a very, very different market environment. And we'd love to just open it up and get some of your thoughts. Yeah, I agree with that. I think it's interesting to contrast. Let's just do the beginning of 2024 versus the beginning of 2022. So obviously 2022 is a very challenging year in markets, both equity and credit. 2023, equities did well, credit, okay, flattish up a little bit. I'm going to use some numbers that are guided in part by equity risk premium numbers from Aswath, the motor in New York University, well-known guy.

23:55By the way, we've looked at Aswath's numbers over 60 years. They're pretty good actually in predicting 10-year returns. So January 1st, 2022, the expected return on equities was 5.8%, nominal. If you take out inflation expectations, which were a little over 2.5 % for the 10-year, the real equity expected return was 3.2%. Now, Ted, as you know, and many of the listeners know, real equity returns in the US have been something like 6 % to 7%. So you were a little better than half of historical returns. That's not good. And you can go right down the line in terms of high yield spreads and BBB spreads, and it just did not look very attractive.

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24:37So fast forward to January 1st, 2024, For expected equity returns are 8.5 % nominal. You take out the inflation expectations. Your real equity returns are from the mid-sixes, low to mid-sixes. Not great, not compelling, but a heck of a lot better than real 3 % or something like that. And again, going through high-yield spreads and BBB spreads and so on and so forth. So the basic idea is, I think, public markets went from appearing to have very low expected returns, got much higher at the end of 2022 and into early 2023. And obviously we had that rally, pretty substantial rally at the end of 2023 that brought it back down, but it's still a much, much more attractive environment.

25:16And I think we went from really way below expected return averages to something more closer to normal. So then you just take a step back and you say, okay, well, we're all here to try to satisfy future liabilities. So the question is, what expected returns do we need in our portfolios to allow us to do that? The two-year story is that it's a lot easier to do it today than it was 24 months ago. So the structure of public markets is a lot different than it's been over the past. And you wrote this paper on birth, death, and wealth creation in public markets. And I would love to talk through first, what's left after these changes over the last stretch of time?

25:55For some reason, I'm completely fascinated by this topic. And the stories will go back to mid-1970s. There were about 4 ,800 companies. the Wilshire 5000 was launched in 74, I believe. The 5000 was because there were 5000 public companies by the mid 1970s. In 1996, we reached the apex, which is a little over 7 ,300 companies. Today, we don't have the 2023 numbers yet, but it's going to be around 4 ,200. So we're not only way lower than the peak, we're lower than we were in the 1970s. Academics study this, they call it the listing gap. And they say, oh, given how big our economy is and our population is and so forth, that the listing gap is somewhere between 6 ,000 and 12 ,000 companies.

26:35In other words, if we had just continued at that pace, we would have 6 ,000 to 12 ,000 more public companies today than we actually do. So what the heck is going on here? It's basic math, which is companies get added in the listings and companies get pulled out. The main way companies get added are initial public offerings. And what we've seen is IPOs from 76 to 2000 averaged about 280 per year. They're a couple of years way above that. And since 2001, they're about 120. And by the way, the last couple of years have been horrible. 2022, we had 38 IPOs. This is data by Jay Ritter at University of Florida.

27:13Last year, 2023, 54 IPOs. I mean, you're talking about the levels that were great financial crisis, recession type of numbers. So just fewer companies are coming public. So why do companies get delisted? The number one answer is mergers and acquisitions. And notwithstanding that we've had fewer IPOs, M &A marched along. Obviously, it's cyclical, but it marched along. So the companies coming out has been pretty consistent. So that's led to this fewer number of public companies overall. But when you take a step back and say, oh, we went from that apex of 7 ,300 to 4 ,200, it turns out that the vast majority of those, about two-thirds of those, are actually micro cap.

27:51They're crammed into the bottom 2 % of market cap. the whole index. And so they're just not really big or super consequential companies, but it's addition by subtraction. So by getting rid of these super small companies, that means the average and median market cap of those companies that remain today are quite a bit bigger than they used to be. You're exactly right. And it's funny because we talk about historical multiples or historical returns or whatever. Again, it seems like we should be paying some attention to what the underlying companies were in our numbers. We often don't do that, certainly are not overt about it.

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29:18Learn more at srsaquium.com. That's S-R-S-A-C-Q-U-I-O-M.com. And now back to the show. On public companies exiting, you mentioned M &A. Private equity, obviously, there's more and more coming in, how much of a driver of the shrinking public companies has private equity world been? It was very quiet in the 1980s. It got rolling a little bit in the 1990s, but really it's in the 21st century. Since in the last 10 or 15 years, buyouts have been about 20 % of delisting of public companies. So for every 100 public companies that were delisted because of acquisition, a lot of it is strategic one buying the other, but one out of five was through a buyout.

30:05And It's an interesting question to ask, what do we think that's going to be going forward? Especially if these guys can exit some stuff, there's a lot of dry powder around for companies to do deals. As the public markets, I guess you could say, get more concentrated, and we certainly know that from the MAG7, but also this washout of microcaps. What have you seen over the years in how public markets have generated returns? It's interesting because venture capital is almost a completely shrunken time horizon of what happens in public markets. And this is another area I find fascinating. This is all work by Hendrik Bessenbinder at Arizona State University, professor of finance, and he's got these jaw-dropping statistics.

30:42He studied every public company that's been around since the 1920s. Again, since the CRISP database was established, there have been a little over 28 ,000 companies. And what he found is just under 60 % earned returns less than treasury bills on a monthly basis. So just take a moment to take that in. Of all the companies that have ever been public in the United States since the 1920s, let's call it a century just around it, almost 60 % have failed to earn treasury bill returns. And they destroyed an aggregate of$9 trillion of wealth by his calculation. Okay. So the other 40 % plus have obviously created value and they've created$64 trillion of wealth.

31:21So the aggregate wealth creation, and this is through 2022, we'll have to update for 2023, was$55 trillion. dollars. What's remarkable is if you distill that even one step further, the top 2 % of that 28 ,000 created 50 trillion of the 55 trillion in total. So just 2 % of all these companies are essentially 90 % of the total wealth creation. So again, it's worth taking a moment to let that settle in how profound that is and how extraordinary the skewness is. Now, there's some really good work on venture capital. There's a study of 30 ,000 venture capital deals. What is the distribution of returns?

32:01Hmm. Turns out 60 % of them don't earn their dollar back. And then again, this extraordinary right skew, this right tail that pulls up the returns for the full asset class. We're looking at a century for the public markets. The average holding for a venture capital deal historically has been about five or six years. It's happening in a very shortened universe, but it looks the same, interestingly, just sped up a lot. We associate power laws with venture, but if you let it play out in public markets, it's also the exact same power law. It's a power of compounding is the key insight. It's a completely fascinating thing.

32:40And by the way, not only did Best in Binder document this for the US, and my data I just shared with you is for the US, but in the third quarter of last year, they published a paper in the Financial Analyst Journal going around the world, 64 ,000 public companies developing developed markets, same basic pattern. At this point, I think we can say this seems to be a characteristic of public markets around the world. And if that's a characteristic, essentially, these are some of the rules by which we're going to have to play. The question is, how do we play? What are the implications as you get your head around the distribution of outcomes in public markets for the optimal way to think about participating in public markets?

33:22Boils down to two things, seems to me. One would be just cast a wide net. We said 60 % are going to be bad, 40 % are going to be good, 2 % are going to be spectacular, just index. And if you index, you're going to get the bad, but you're going to get the good and you're going to capture that skew. And that would be, again, if you take the public market equivalent returns for venture capital, it's obviously very, very cyclical, but it's been pretty good over very long periods of time. And this would be essentially saying you're going to own every venture fund. You're going to participate in all the goods and bads, but over time, it's going to do pretty well for you.

33:53So indexing would be answer number one. The second thing to say as a fundamental investor is, might I be able to identify those companies that are likely to do this over time? And it's a probabilistic assessment, but can I find those stars and put them in my portfolio and hope to get much better returns? So that would be the second approach. And so that also automatically then says, all right, well, what are the characteristics of those kinds of companies? Can we identify them? Again, a priori, pattern recognition, right? Going back to pattern recognition, are there things that we should be looking for?

34:23So that would be the second strategy. And I think that's what probably most active managers, fundamental managers are trying to do. The one thing I'll point out though, I guess to state the obvious is if you do find one or two or three of these superstar companies, they appreciate a lot and they get really big in your portfolio. And so you get in these interesting challenges with diversification rules. Berkshire Hathaway has, I think, a$320 billion public equity portfolio. It's obviously every quarter you can see it. Just an ooch under half of that portfolio is in one stock, and that's Apple. So it's very interesting.

34:58Now, obviously, Berkshire is big and diversified. There's lots of stuff going on, but it couldn't be a viable standalone fund because who's going to pay a fee to own half the portfolio in one stock, which happens to be the second largest company in the world and very liquid and so on and so forth. So that's another challenge is even if you can find these things, can you manage them within a portfolio setting that works? So that wealth creation, the 2 % of companies generating substantially all the profits is over multiple decades. If you shorten that to even five or 10 years, how did the dynamics change?

35:33You're making a really important point, and I call these data jaw-dropping, and they are, but I think it becomes much less dramatic if you shorten the time horizon. For example, you think about the leaderboard, the top 20 greatest wealth creators of all times, and it would include some suspects you would guess, which would be Apple and Microsoft and Amazon and Alphabet and so forth. But it also includes things you might not have guessed, which would be General Motors and General Electric and companies that have really struggled for a long time say, well, how the heck they get on that list? And the answer is they delivered spectacular returns in the 50s and 60s and 70s.

36:08As a consequence of that historical performance, they remain on the list. So one of the tricks here is that Besson Binder is translating these wealth numbers from percentages to dollars. And as you know, Ted, you and I, as investors, we care mostly about percentages, not dollars. Now, if you're CalPERS, you're super big, maybe you have to worry about dollars, but for the most of us, it's percentages. If you buy Apple, I'm going to round the numbers just to make it easy for myself. If you buy Apple, it's a$3 trillion market cap. It goes up 10%. That's$300 billion. But if you buy a$30 billion market cap, it goes up 10 to$3 billion.

36:40As a portfolio manager, the 10 % is 10%. But the wealth creation implications using this framework would be wildly different. So that would be the answer. So in a sense, what you're doing as an active manager is you're trying to find tomorrow's stars today, essentially. That would be, I think, the way to try to think about that. So find those companies with much more modest market caps that can deliver the percentages. And it's probably reasonable to have a three to five-year horizon or something like that. So look for those kinds of things, and I think you'd be fine. Anytime you think about the dollar wealth creation, you get back to the concept of compounding.

37:13Is there anything you looked at in even the multi-decade horizon that would start to indicate compounders, in air quotes, are the things to own as opposed to some value stock or something like that? The one thing I'll say, and we've alluded to this in a few pieces, but not written a ton about it, is since 2000, roughly speaking, it feels like the economics of the biggest companies have been almost economic law defying. By that, I mean two things. One is they've grown faster than what we would expect big companies to do, and they've sustained very high returns on capital that have really not been driven down in a way that's been very difficult to do.

38:00Now, there are lots of interesting theories as to why this could be the case, but the ROICs for the top quintile of public companies has distanced itself from the next quintile and sustained at a very high level. And that is wealth creation versus percentages, but I think that's a distinction that's really an important one to pay attention to. There's a book, it's provocative, I'm not sure it's right, it's called The New Goliaths by a professor named James Besson at Boston University. The premise is that large technology companies have invested substantially in proprietary software that has allowed them to both enjoy scale benefits, but also differentiation with their customers in a way that has not really happened in the past.

38:46So these are intangible investments. Many of these large companies now, they have data and developing software that is not diffusing throughout the economy. So it's not benefiting others. an interesting thesis to explore. Those are the big issues. We talk about Magnificent Seven, and obviously they're very different types of companies. And so throwing them all in one bucket may not be the right thing to do. But when you take a look at the economic profit, so return on capital, less cost capital spread times invested capital, the economic profit of the Magnificent Seven, it's actually larger than their market cap representation.

39:22I'm not justifying anything, but I'm just saying it's not without any foundation. These are really extraordinary businesses with very high returns and generating lots of cash. You touched earlier on the private markets. You're in the public markets. All the people you talk to are multi-asset class investors. Where are you assessing out what's happening in the private markets? The first question I always like to ask is, why have these asset classes historically drawn so much attention and realistically capital? And it goes back to really what we talked about a moment ago with probably the apex being the beginning of 2022 was the return profile public markets really seemed to degrade a lot since the great financial crisis.

40:06The asset class did well, but the prospective returns did quite poorly. And so I think a lot of investors are saying, I need to satisfy my liabilities. I need to generate certain types of returns. And as a consequence, I need to move out on the risk spectrum. Look, buyouts increase risk by adding financial leverage. Venture capital takes on risk by buying nascent companies with fairly high failure rates. So I think that was the main impetus for a lot of this. And the question is whether that has or has not reversed. Now, the other thing is we know buyouts in general, we've had a very gentle pattern of lower rates, which benefits these guys.

40:49benefits them in terms of financing costs, or perhaps important is exit multiples versus entry multiples. And by the way, I don't think there's a single buyout firm that assumes the exit multiples can be higher. They assume that's going to be flat or down, but if it's higher, that's great. So I think that's contributed to the returns. It's conceivable that easy component of it may not be around going forward. That's interesting. So just the return profile. The other thing that's interesting is to think about if you're in venture or buyout, the key is you run a fund and you have to buy, but you have to sell.

41:24And I think the buy part may not be that difficult, but the sell part seems to be a little bit of a bottleneck right now. We just mentioned the low levels of IPOs. Historically, IPOs were a very substantial mechanism, especially for venture, for exit. That's gone away. Now they're doing more strategics. We've seen a really substantial uptick in antitrust focus. Whether or not the Justice Department or FTC wins these cases, it creates friction and makes managers and executives, it creates uncertainty as to whether they want to do these things. The question to ask is, how can we open up these exits?

42:02Because before we can keep going, we have to get some of these exits done. So you're seeing things like continuation funds. And by the way, obviously the number one technique for buyouts to exit was selling to other buyout firms. Is that a musical chairs type of thing? So how long does that go on? So I think these are established, very thoughtful people in these. I think they're here to stay. The question is, when you think about what you need to do as an investor to satisfy objectives, how much do you need to do this? The last thing I'll mention, Ted, I think that you and I have talked about this as well.

42:33Often it's the case that investors are worse enemies themselves. and its inability to stay the course in particular things. And because venture and buyouts have essentially longer time horizons, whether people like it or not, they have to sit in their seats for a while. And that's actually, I think, probably been to their benefit versus their detriment. That's the other thing that's interesting is the time horizon component of all this is you don't need to do that. Maybe you could do that in public markets as well, but it instills a constraint that actually may ultimately be to the benefit of the beneficiaries.

43:08So to put color on that in the public markets, what does it take to own some of these, say, 2 % winners for the long term? Bessenbinder wrote a series of papers, and I would recommend people go check them out. If you go to SSRN, you'll find these papers, and they're accessible for the public to download. And he found a couple of things. One of the things that's not shocking to people would be they performed well. So they had good sales growth and profits and cash flows and so forth. So all the fundamental stuff was fine. The other thing he pointed out, technology companies, they're not overrepresented actually.

43:41And so this is something we forget about this a lot in life, but everything's a distribution. And so even if a right tail is fat, it doesn't mean the left tail isn't fat as well. So a lot of technology companies do well, but a lot of them fail. Same, we were talking about biotechnology, same thing. So you have to look at the fullness of the distribution. But I think the one that was most striking was that he documented that almost every one of these great compounders had massive drawdowns at some point. And it was common to have drawdowns at 75 % or more, but some were 80%, 85 % drawdowns. And to state the obvious, it shakes out all but the very hardiest of shareholders.

44:18You buy XYZ, you think it's great, you think it's going to be a compounder, and it goes down for a whole series of reasons. could be market generator, some specific stuff, goes down to 80%. That's just a hard one to overcome that and sit tight. And by the way, you're questioning yourself, your clients are questioning you, everybody thinks you're nuts and so on and so forth. So part of that is staying the course. Obviously, Apple, again, is a good example, a very valuable company. But if you follow and went public in the early 1980s, I mean, this thing was teetering on bankruptcy. The stock had done very poorly for a long time.

44:50People forget about where it came from to get to where it is today. It was great at the outset, and then it had a really, really dark period, and then this wealth creation phase since Steve Jobs 2.0. It's an amazing story. It's worth understanding the full history of Apple to understand this basic concept. Curious what else is tickling your research interests? A couple things. One is we are working a lot on a concept called increasing returns. If you're an investor, and again, this is macro or micro, you've heard of increasing returns. I mean, just to define a decreasing returns, what we learn in microeconomics, which is incremental unit of capital earns a lower return.

45:30So if Ted Lemonade Stand does really well, Michael Lemonade Stand comes and competes, and eventually we drive the returns down toward the cost of capital. By the way, that's the basic default. But we know that there are instances of increasing returns where returns on capital actually go up, market shares actually go up. They're sort of these winner take all markets. We're sitting down and just going through, there are actually multiple different flavors of increasing returns. Classic economies of scale, which you do learn. Alfred Marshall talked about this 150 years ago. Learning by doing. International trade is another area.

46:00Paul Krugman is now probably known as a New York Times pundit, but he won the Nobel Prize for his work on international trade. Path dependence and lock-in and network effects. This is the most common one in the investor world. And then finally, Paul Romer won the Nobel Prize in 2018 for his work on endogenous growth theory. So basically this idea, how do ideas contribute to a better world? How we can recombine physical assets. So increasing returns. The other one, Ted, that we're working on, I'm going to call 09 to 2021, the period of easy money. Central banks around the world, relatively low, Fed funds rate close to zero.

46:36And so people talk about easy money. The question is, what did public companies do in the face of easy money? And so you say, what would the predictions be? Well, the predictions be like, oh, we're going to take on leverage. Hey, we're going to invest more because we have a pecking order of projects. And so now the cost of capital is down so we can do more stuff and so on and so forth. What did companies actually do? None of that stuff. They actually didn't invest more. And so what capital structures got more conservative cash balances built up on balance sheets is excluding financial services companies like what the heck is going on?

47:08So it turns out it's really interesting that companies, they calculate their cost of capital, they know it, but they don't use it. For almost everything they do, they use a hurdle rate. And that hurdle rate's roughly 15%. So whether the cost of capital goes to six or eight or 10, doesn't make any difference, they're using 15%. Why do companies not do in quotes what they're supposed to do in theory? So that's another fun area. What a fascinating world we live in. Always something to work on, always something to think about. Great. Well, Michael, I want to ask you a couple of closing questions that I haven't asked you previously.

47:38And the first is in all your travels and talking to money managers, I'd love to hear some of your favorite lessons that you've learned from other people. The thing that almost always strikes me about the best managers that I know is I guess two aspects of it. One is they're always curious about the world and want to learn about the world. And so anytime you find someone in investing, who's not curious, you should ask the question as to what's going on. And then the second thing is, which is also, I think, extremely difficult to do is people are willing to see the world as it is. It's called epistemic rationality.

48:14So when new information comes in, they're able to update their views. And some of the investors I admire the most are people who are really good at that. And I think it's incredibly difficult because it requires a lot of mental nimbleness and a lot of mental energy, candidly. So those would be some of the lessons. I wrote a piece a number of years ago about the attributes of great investors. Those things certainly come out. What's one fact that most people don't know about you? Probably that I had a misspent youth playing sports and I played lacrosse in college. At the time I graduated, I was the leading goal scorer in the history of the school, which is a long, long time ago.

48:56That record's been broken many times since, but I don't think people think about me as a sports guy. They think about me as this propeller head guy, but that might be one. What's your biggest investment pet peeve? We live in a world where we have to simplify things to be able to understand the world, but simplification only works when you understand what's going on underneath the hood. I think Aswap the Motor's got this phrase where he says, people price companies and they don't value companies. And by that, I think he means you say this multiple used to be this, go back to this. This used to be 15 times EBITDA or 25 times earnings or whatever it is.

49:31So I always tell my students, you have to earn the right to use a multiple. In other words, you can demonstrate that you understand what the economic implications are. That always bothers me that people are very comfortable throwing around heuristics or multiples, for instance, without really understanding what it implies about the performance of the business. Which two people had the biggest impact on your professional life? One stands above all others, and that's Al Rappaport. I read his book in 1987. I can say confidently that was a complete world changer for me. Many things he talked about in that book remain at the core of what I do today.

50:10Al's in his 90s. He lives in San Diego. He's amazing. And I talked to him a couple times this week. I talked to him frequently. He just submitted another academic paper with some co-authors, which is a very cool paper. I hope it gets published. It's extraordinary. Just think about someone that age doing that high caliber work. It's great. So he would be one. Maybe the second one I would mention, and I say that I'm leaving off tons of people who have been really helpful along the way. But the other one I probably mentioned is Charlie Munger. I actually had the benefit of having lunch with him in June.

50:41So really feel fortunate to have seen him shortly before he died. But it was less like my personal interaction, but much more about his way of thinking about the world and this idea of mental models. I've been involved with the Santa Fe Institute for many years. I think that's been very reinforcing. But this idea that learning about the world, learning about different disciplines can help you be a more effective investor. It can help you be a more effective parent, partner, professional. I mean, it's just not just investor, but anything you're doing. And it's my natural inclination to think that way.

51:13But I think that whole reinforcing of that lesson has been very powerful. Michael, last one. What's the best advice you've ever received? It's funny. maybe can I flip that and say, what is the worst advice I ever got? Because this is something my mother told me and she was very dismayed when I told her I thought it was completely wrong. My mother always complained that I focused too much on what I was good at and didn't do enough of what I was bad at. And she said, you should work on your weaknesses versus continuing to build on your strengths. Now, on some level, what she's saying is correct.

51:46In other words, you have to be able to do certain things to get around life. But what I firmly believe today is that identifying people's strengths and letting them do what they're good at is much, much more value creating than having them work on their weaknesses. But even sports coaches for teams or managers of people, your job essentially is to make the people who you work with as effective as they possibly can be. So you're putting them in situations where they can win. And that's almost always going to be figuring out what they're good at and letting them do more of what they're good at and figuring out what they're bad at and having them do less of what they're bad at.

52:22Good advice would be to figure out what you're good at because you're more likely to be able to add value doing something you're good at. Michael, thanks so much for joining me again. Always so much fun. Thanks, Ted. My pleasure. Thanks for listening to the show. To learn more, hop on our website at capitalallocators.com where you can join our mailing list, access past shows, learn about our gatherings, and sign up for premium content, including podcast transcripts, my investment portfolio, and a lot more. Have a good one, and see you next time.

From the publisher

Michael Mauboussin is the Head of Consilient Research at Counterpoint Global, a $70 billion equity manager. Michael is renowned for his ability to articulate important investment concepts backed by academic research. His first of three prior conversations on the show is replayed in the feed. You can find the rest at capitalallocators.com.


Our conversation explores Michael’s most recent piece on pattern recognition, including when it works and when it doesn’t. We then transition to discussing the changing nature of public markets, inspired by another of Michael’s recent research reports entitled Birth, Death, and Wealth Creation.


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