Intro

Patrick: [00:01:21] My guest this week is Dr. Ben Hunt, the Chief Investment Strategist at Salient and the author of the extremely popular website, The Epsilon Theory. I've always enjoyed Ben's writing style, particularly his use of farm and animal-based analogies to describe market phenomenon. In this conversation, we discuss his recent post, The Three-Body Problem, why growth has been beating value, and why a strategy that he calls profound agnosticism, a sort of take on risk parity, may be the most appropriate investment strategy in what he views as a very uncertain world. Please enjoy our conversation.

Views on Investing

Ben: [00:01:54] It's an old problem in physics, and it refers to Henri Poincaré who was one of the geniuses of mathematics of the last millennium. It was the 1800s, and what he was trying to figure out is this ... Well, back up a second. What's he's interested in ... This is going to be your connection to bees and, actually, I think, to humans and investing and to your original question of what's your fundamental idea about investing. He was wrestling with this question of algorithms, which is a formula, which is a process that you can apply over and over again, where you put in your values and you turn the crank of the algorithm and it gives you an answer. What I will tell you is that whether it's human beings, the way we construct our societies and our markets, whether it's bees and the way they construct their hives and the way that they search for honey or search for pollen and make nectar and make honey, they're all dominated by these algorithms, which are just our simple formulas for making sense of the world. In my experience, that whether you're a value investor or a growth investor or a momentum guy or a quant or whatever you are, in the back of your head, you've got a collection of algorithms for how you make sense of the investing world.

What Poincaré was trying to wrestle with was one of these classic problems of geometry. He was a geometry nut. It's related to physics, and the three-body problem really takes a problem from physics, which is to imagine you're in outer space and you've got three planets or stars or what have you, three massive bodies, and you know exactly how they interact with each other, gravity, and you know the laws of gravity. That's all that acts here. There's no drag from atmosphere. It's just a pure calculation of here's where these different three objects are, you know exactly how gravity works. Now, the question is what's the formula, what's the algorithm to say where those three bodies will be 10 minutes from now or an hour from now? Again, you know everything about these three bodies. You know exactly where they are, you know how fast they're moving, and you know how they act on each other.

What Poincaré proved was that, and this is crazy talk, there is no algorithm. There is no algorithm. There is no formula that you can plug in all those starting conditions and turn the crank and say, 10 minutes later or 10 years later, here's where all those objects will be. It does not exist. That's such a hard thing to wrap one's head around. If you see pictures of it, you can kind of imagine it. But what it's really saying is that these are ... We can use a term like a chaotic system or the like, which is kind of right, maybe not quite right but, for our purposes, right enough. So the three-body problem is simply that when you've got these three entities, there is no formula because the starting conditions, you can really end up anywhere. It's not governed by a two-body problem, where you've got something orbiting around something else. They keep messing each other up, three bodies in space, such that there is no formula to ever predict this.

So what's really hard about that for humans is to think, "Well, wait a second, actually, three-body or multi-body problems are the rule in space, not the exception. What if that's true for some of our human systems also, like investing?" What does it mean that a new star appeared with massive gravity in the form of central banks buying 20 trillion dollars worth of stuff out of thin air? It's what Jim Grant likes to call about the Swiss National Bank, out of the thin Alpine air. It's such a great phrase. What does that mean for our traditional algorithms and views of what makes a stock work? What the three-body problem tells us is that it changes it forever and it changes it in a way that I know you'd like to say that, oh, things will go back to normal, where quality matters or value matters under these conditions, where algorithms work again.

But the scientific fact is that ain't so or it might not be so, and we need to understand, well, what does that mean for all of our fundamental views of investing if, in fact, the existence of central banks buying, this isn't going to go away? You can't unring that bell. It's not like they're going to say, "Oops, sorry, let's sell everything and, oh, gosh, we're never going to do that again." No, this is a new gravitational body in our world of investment, for better or for worse, and to wax and to wane, forever and ever, amen. So what that means is that I think we have to question all of our fundamental views about investing. The big question is, well, where does that end up? Is there a fundamental view of investment? I come up with something that, again, is kind of really hard to wrap your head around. It's what I call profound agnosticism, profound doubt that any of our algorithms, any of our fundamental views, are true with a capital T in other than, I'll say, kind of exceptional local circumstances.

Patrick: [00:07:44] Maybe we can talk very specifically about some reflections of this idea in this post-financial crisis period. One of the charts you put in there was the growth of the S&P 500 Index, and then at the bottom, in sort of a flat line, a long-short portfolio of quality metrics basically showing that higher quality in that same timeframe relative to low quality, so no beta in there, is flat. High quality has not outperformed. Maybe one of your foundational principles is higher quality businesses should deliver better returns over the longer term.

Ben: [00:08:15] I think that's all of ours. It's probably the most pervasive fundamental view of investment, I think, that all of us have.

Patrick: [00:08:24] I would layer on there that, obviously, price is a key component always. It's always interesting to look at different kinds of investment factors, that's what we do-

Ben: [00:08:33] That's what we do. That's what we all do, yeah.

Patrick: [00:08:35] ... and understand the horizon over which they matter, right?

Ben: [00:08:38] Right.

Patrick: [00:08:38] So quality in the short run can often really not matter at all. In the long run, it has more of an impact. Valuations, for example, matter throughout. So the price you pay up front is a very good predictor of your future returns, but in this period, so that quality line is flat, if you did the same line for a long-short value versus growth portfolio, it's sharply negative. You've gotten killed, right-

Ben: [00:09:00] You've gotten killed.

Patrick: [00:09:00] ... because growth has crushed value. So I'm curious how you think about why that new gravitational body resulted in that outcome, why you think that has ... It hasn't given us a flat line, it's given us a sharply negative line. So why has value done so poorly relative to growth with this kind of framework in mind?

Ben: [00:09:18] Because the gravitational pull of central bank purchases has no impact on value and no impact on quality. In fact, if it has any sort of gravitational impact, it's got an inverse impact. Why? Because the purpose of these large scale asset prices is not to reward good companies or value-oriented investors or companies with intrinsic value. The entire purpose of large scale asset purchases ... And this isn't conspiracy theory stuff. This is what Bernanke and Yellen say. This is the canon. Large scale asset purchases are there to force all investors to go farther out on the risk reward curve than they would otherwise prefer to be.

So if you are a US Treasury investor, that's your thing, we're going to push up the price, to your point about the price you pay always matters, we're going to reduce the yield, we're going to push down your expected return on what you pay for those US Treasuries to force you to get into a riskier asset. Maybe it's mortgage-backed securities. For those people who are in mortgage-backed securities, we're going to force you into high yield. If you're in high yield, we're going to force you to go farther out their risk curve and get into equities. If you're in equities and you're doing safe stuff, we're going to try to force you into doing riskier stuff. It's pushing every investor farther out on that risk curve than they would otherwise prefer to be. Again, that's not conspiracy. That's not hidden. That is the overt, that is the written purpose of large scale asset purchases.

Patrick: [00:11:01] One of the things I don't understand is specifically this value growth spread. Now, obviously, you can go and do normal attribution and see the reasons why, and it's the FANG stocks and all the companies that people are talking about have been enormously successful. But you can make the argument that value companies are actually riskier in some cases than growth stocks that are distressed or whatever else. So I have trouble squaring that. I understand the pushing out on the risk curve, but I still have trouble squaring if you just were a US equity investor, this has been the longest in the tooth. This is always cyclical. Value underperforms for long periods of time sometimes. But this is about as long a period as we've seen. I'm just fascinated to hear what you think about that phenomenon.

Ben: [00:11:41] Here's exactly what I think. What I think is that secular growth, in a world where central banks are lifting all boats, secular growth is the rarest thing in the world. So that commands a premium. Why does growth command a premium over value? Because growth has been so rare in this world. Because the other side of the coin of central banks' monetary policy forcing greater risk in markets is to reduce risk-taking in the real economy. It reduces risk-taking in the real economy. This is exactly what we've seen with the mystery of productivity flat-lining and declining. This is exactly what we've seen with the mystery of companies not spending money on growth capex, on plant and equipment and wages to grow their efforts. Why would you? Why would you take that risk? You're IBM. Why would you take the risky action of investing in new plant and equipment, a new idea, a new business line when you can deliver earnings growth, when you can deliver return for your shareholders through purchasing revenue, stock buybacks, and increased dividends, through financial mechanics rather than real world risk-taking.

So in that sort of world, and you see it in our GDP numbers, not just in the US but globally, secular growth becomes incredibly rare, and so that's what becomes rewarded by the market. That's what I think explains that growth value dynamic. Again, it's all predicated on this notion that this huge gravitational force of 20 trillion dollars worth of propping up of markets, of buying financial assets. It don't care about any of this stuff, and so it has real world impacts of reducing risk-taking and putting a premium on secular growth and it has a real world impact, well, a financial world impact of value sucking wind for a long time now.

Patrick: [00:13:53] So the responsibility of every active manager is to adapt to changing conditions, and there's no excuse for not trying to figure things out. You're always trying to outperform.

Ben: [00:14:01] Yeah, but I'm going to push back a little bit on that a little bit because, yes, all investors, all professional investors, should be able to adapt to changing circumstances but what I also think is so critically important is that as a professional investor, you have to know yourself. You have to know what makes you tick. I'll mix a lot of metaphors here, but an investor can't change his or her stripes. I think it's almost always a mistake to do that. So instead of adapting, what I think you've seen in so many cases is investors who do know themselves. They say, "It's not working."

Patrick: [00:14:42] They have a lot of funds shredding down.

Ben: [00:14:44] That's exactly right. And I think that is the correct way to do it. I think that's the intellectually honest thing to do, that your stripes, your DNA as an investor aren't working at this time. This is, again, what gets me back to thinking about a different kind of DNA, which actually, I think, lends itself to a more systematic approach, this notion of profound agnosticism, which I think really finds its home in terms of how do you operationalize that with what goes under the name of risk parity but more broadly speaking is the notion of having a risk or volatility budget for the assets you take and having it programmed into your investment strategy that you're going to adjust your portfolio. You're going to adapt to your portfolio, not on your views of what algorithms you've got in the back of your head about what should work but, really, mechanistically based on, well, what does work, that as risk goes up in this asset class or that, you reduce your exposure.

As risk goes down, you're going to increase your exposure, so that you're taking this, again, what I call a profoundly agnostic approach of I don't know what asset classes are going to work, I don't what geographies are going to work because I don't trust the algorithms that I've had in my head about what factors work and what factors don't work. Instead, I'm going to take exposure to all of them, at least so long as they are truly diversified, that they're not just correlated one to one with each other but independently active factors, whether that's traditional investment factors or whether it's a geography or whether it's asset class. My answer is yes, I want all of them and I want to have a profoundly agnostic view of how I should weight them.

To me, I think that for an investor or an allocator who's looking to take what the world gives you, which is what we're looking for with beta, you want to take what the world gives you and you want to take it in as a cost effective way as you can ... I think that these risk balancing strategies of which risk parity is a specific form of that, I think that is how one copes with and adapts with the three-body problem, the fact that in this environment our old moorings don't seem to have a lot of purchase and may never again.

Patrick: [00:17:29] So let's take on risk parity a little bit.

Ben: [00:17:31] Sure.

Patrick: [00:17:31] An interesting concept. The common criticism would be, assuming you have the normal inputs that people do, stocks, bonds, commodities, maybe some other things, that effectively all risk parity is a levered-up bond portfolio-

Ben: [00:17:42] Levered bond portfolio, right.

Thoughts on Risk-Parity Strategies

Patrick: [00:17:44] That backtests really nicely because we've been in this secular interest rate environment and so you lever up bonds and the portfolio looks fantastic. But volatility regimes can change and then be different for a very long time. So how do you think about that typical criticism of the risk parity approach?

Ben: [00:18:01] Before I joined Salient, that was exactly my view. One of the first Epsilon Theory pieces I ever wrote was frankly a critique of risk parity strategies. It was right when the taper tantrum was occurring, which was a bad month. It was a bad time for risk parity strategies because, think about it, bonds tend to have a much lower volatility than stocks, so if you're trying to have an equal bucket of risk, you're going to have to lever up that bond bucket to get the same level of risk that you would historically have seen in your stock bucket or your commodity bucket or your credit bucket, your corporate credit bucket. I like to think of those as being the four buckets, right?

Patrick: [00:18:47] Sure, yeah.

Ben: [00:18:47] So we can think of rates or government bonds as being that one bucket, which is what really got hurt in the taper tantrum. You can think of equities as another bucket. You can think of corporate credit as a third and commodities as a fourth. Now, then, importantly, within each of those buckets, you also want to have that agnostic view in terms of geography. So in equities, you're looking at developed markets, emerging markets. You want to have diversification within their or markets that aren't strongly correlated to each other. It's turtles all the way down. You're divvying it. You're getting smaller buckets within each of those buckets. But your main point is that is the standard, I'll say, critique, and it's exactly the place where I started approaching this.

What I didn't appreciate until I got immersed in the managers who actually implement these sort of strategies, two things. First, these risk allocations are, I'll call them, barge-like as opposed to motorboat-like, meaning that the other criticism you typically hear is that, oh, well, if there's a down market, that the selling pressure from these funds as they seek to rebalance, well, that will just accentuate the crash down. It just makes these cyclical movements that much more accentuated. That would be true if they were rebalancing and making these big shifts intraday or on a daily basis. They're not. These are typically, like I say, barges that change these risk allocations to these large asset classes pretty slowly. But then that gets to that point that I had earlier, which was that, well, gosh, if you're allocated to bonds in a levered sort of way and you get something like the taper tantrum and those bond prices really go down, well, your portfolio's going to take a significant hit, and it does.

But, and this is the critical thing, there is the adjustment mechanism. These are dynamic portfolios, meaning that they don't do it on a daily basis, but they do adjust. If you were telling me that we're taking static allocations to stocks, bonds, commodities, and credit and that we're starting ... Okay, here's our allocation. Here's our static. We're going to have to lever up bonds to get the cover of Vol we need in this, and, by god, that's our allocation for the year or what have you. Yeah, that'd be a mess. But these barges, they move, and they can move quickly enough and carry enough weight so that if we take anything other than a monthly time horizon or the like, they really do adjust themselves quite well. So the dynamic nature of these strategies is what turned me around, at least, to see that, well, actually, still, for harvesting beta, these do an incredibly good job.

I'll take one more point at this. Your need to lever up that one bucket is accentuated if all you're doing is stocks and bonds. So to the degree that you are, a., looking at things like commodities, that you are looking at credit, that you're looking at, say, four buckets, to the degree that you're also, again, subdividing each of those buckets into different geographies and factors and classes, all of that really helps in terms of getting too levered or too weighted to any one thing that gets tagged at any one moment in time. Anyway, that was a long-winded answer to the fact that I know exactly where you're coming from and I used to share that view. But then getting immersed in it and finding, well, how actually dynamic these portfolios are, it really brought me around, particularly when combined with just the fact that since March of '09, so many of our algorithms, our foundational views on how one should invest, haven't worked. I think that taking this perspective of profound agnosticism, it does lend oneself, at least for the beta component of your portfolio, to thinking in terms of a risk-balanced or ... And risk parity is maybe an extreme version of that, a risk-balanced approach.

Patrick: [00:23:23] We were talking before we began recording about the very interesting structure of your firm, Salient, and how there's a clear focus on things like infrastructure, MLPs, et cetera, but teams deploying into niche asset classes. Given where you sit as the chief investment strategist at the top, I'm always really interested by how you interact with each of these individual teams, what you feel your guiding purpose is in those interactions. What are you trying to do for them or on their behalf, and how has that been working in relation to this kind of goofy world that we're living in?

Ben: [00:23:57] This goofy world. I love this question because what we've tried to do in the firm is an extension of what I just described about being profoundly agnostic about the world. What do I mean by that? What I mean is that I would say the typical structure of whether it's an asset manager or a hedge fund tends to be pretty hierarchical, pretty pyramidal, let's say, where at the top, you've got the chief investment officer or the head of the firm, the portfolio manager, the guy who has the view and the answer, who has in the back of his head a algorithm for what's going to make markets tick, either overall or in his sector or what have you. It's kind of the great man theory of investment, which I would argue has really dominated so much of our structure, the business structure of how this industry has come along.

What we've tried to do at Salient, I think, is very different. Instead of thinking of that pyramidal structure, to think instead of a hub and spoke, almost a wheel model, where there at the hub, not at the top of a pyramid, but there in the hub is someone like me who's actually managed money for a living who knows intimately how hard it is to manage other people's money but is there not to give you the answer with a capital T and a capital A but to be a coach, really, to be a counselor for navigating a world where your internal algorithms, you're the PM of this group or that group, they're not working as well. Our tendency is to flail out and say ... Oh, if I'm doing energy infrastructure, I think, "Well, I'm going to try to predict the price of oil, and that's going to tell me the answer for whether my company's going to do well."

So I'm there to say, "No, no, no, no. I don't know what the price of oil is going to be. You don't know what the price of oil is going to be. What you know how to do is how to analyze, bottoms up, the companies that you care about, and that's what you've told your investors you're going to do. I know how good you are at doing it. Let's go do it." It's really being a coach and trying to prevent the very natural tendency we have that when our process isn't working as well as we'd like to, we start flailing and we start looking for other answers with a capital A. This approach of profound agnosticism is to say, "No, no, no, we believe in you and your process. You've said what you're going to do. Now, what's do what you say."

Uncertainty and Behavioral Factors in Investing

Patrick: [00:26:49] You began, it sounds like, doing equity by equity long-short investing, and, certainly, from your writing, it seems now, not that you're not interested in the bottom up approach anymore, but certainly a lot of thinking top down, macro type stuff. What is that tension like for you today? Where do you think there's more value to be ... I'll use the word alpha or differentiated return from, let's say, just a passive ... If you could somehow create a passive global portfolio of all investable securities. So deviating from that, you're trying to earn better returns or a different kind of returns. Do you think top down factors are more useful, or do you still think that company by company, bond by bond analysis is the way to gain an edge?

Ben: [00:27:30] Yeah, I'm pausing in my answer because I have a different answer for me personally, for my company, and for our industry. And I'll-

Patrick: [00:27:38] I'd love to hear all three.

Ben: [00:27:39] Well, yeah, I'll try to go, but I'll go in kind of reverse order. I'll go for our world of financial advice, financial services first. What I would say to you there is that alpha is so rare. It's such a unicorn in public markets today that I think that less effort should be placed there and that more effort should be placed on proper risk allocation. Taking the right amount of risk is the most important decision that any investor, institution, or individual can make in our business, and I think that not enough attention in our industry goes to helping investors make that most crucial decision accurately and well, taking the right amount of risk.

Patrick: [00:28:29] Do you think that ... Just to pause there for a minute, you've also written a lot about the comparison of risk and uncertainty. I've spent a lot of time with some venture capital folks recently, and I think a lot of them would agree that it's the willingness to expose your portfolios and your dollars to uncertainty that results in these occasional enormous returns. I'm curious, as it relates back to the three-body problem, if getting your risk allocation right is the most important thing, but maybe there's the potential that we can't quantify or measure risk-

Ben: [00:28:58] We're in an uncertain world.

Patrick: [00:28:58] ... and the future's uncertain. How do you do that?

Ben: [00:29:00] Well, what I loved was you, in your introduction, that you said you were talking to venture capitalists. Venture capitalists, of course, operate in private markets as opposed to public markets. I absolutely believe, and this is how we're kind of making this transition from what I think in terms of our industry to the company I work for to me individually, we're kind of jumping to me individually here, there are absolutely still opportunities for private information, legal private information, hence alpha, in private markets. A venture capitalist can know their companies, the companies that they invest in. They can play a direct role in how those companies are managed. They can see at such a granular level what's really happening in that companies markets, its managements, and the like, all of which is now essentially illegal information to have about public companies. You cannot have that level of private information about public companies today.

So alpha is private information, period, full stop. That's why it's so rare in public markets today. It's still possible if you get a publicly available and yet not widely disseminated or widely appreciated information source. I think that source of alpha still absolutely exists in public markets, but it's very tiny. To your question about dealing with uncertainty and trying to overcome uncertainty through gaining private information, you're talking with venture capitalists because they can do that. It's why I think that in your search for alpha, that is best budgeted for your efforts in private markets as opposed to public markets.

Patrick: [00:30:53] What about taking into account the behavioral explanation for certain quantitative factors? I know Salient has alternative data strategies, so I'm sure you've thought quite a bit about this. We'll take last year as the example. The momentum factor ripped, right? It had a fantastic year. Momentum's always an interesting one because it seems so incredibly adaptive, so it doesn't care what's happening in central bank policy, it bought the FANGs early, and it's held the FANGs, so maybe there's a powerful way around this. I'm a subscriber to the behavioral reason versus the risk premium reason-

Ben: [00:31:23] I am, also.

Patrick: [00:31:24] ... for why these things work. So how do you think about those things as true and persistent alpha sources in public markets?

Ben: [00:31:29] I think yes. I believe that momentum or trend is a thing. That's the first thing you have to decide with something, do you think it's a thing or not? I do. I think it's a thing. Why is it a thing? Because of fear and greed. Because of human behavior. That makes it a constant in a way that is separate from the gravitational impulses of central banks or this player or that player in markets. I think that there's so many of these behavioral aspects that we now have technologies like natural language processing and the like to try to see for the first time. So I think the most promising aspects of alpha capture in public markets is looking for these behavioral aspect of markets. That is exactly where ... Again, we're talking now personally and a bit for our firm as well. Where does alpha exist in public markets? It's in these behavioral aspects, and we do have new technologies now that let us try to capture those and see those in ways that we couldn't before.

Patrick: [00:32:40] I always love this skin of the game question, which is to ask, so all of this considered ... We'll keep going on some other avenues, but this is a good point to ask the question. All of this considered, how do you personally think about your investments? What does your portfolio look like?

Ben: [00:32:54] Three things. It is private investments where I do believe that-

Patrick: [00:33:00] Interesting.

Ben: [00:33:01] ... interesting, smart people can still legally find private information. And, look, that can be something like ... That's why I own the house that I do, the farm that I've got, right? I've got private information about that, and that's a huge portion of my personal portfolio, is where I live and where I've chosen to raise my family and how I've chosen to raise the family. That's the biggest investment I've got, and I feel like I've got private information on that, legal private information, right?

Where I think that I won't have a big chunk of my personal portfolio is what we talked about, tapping the beta of global public markets. There, I'm not trying to kill it. I'm really not because I'm not going to. I'm not going to kill it. I don't believe that there is an algorithm that gives me the answer for where public markets are going to be a year from now. I really don't believe that that exists. What I do think I can do through a philosophy of profound agnosticism, through a risk balanced portfolio, is that I can efficiently, i.e., cheaply, harvest those global markets. That's all I'm trying to do, just to cheaply and efficiently take whatever the world is going to give me in those public markets.

The third piece, and this is less of a dollar investment and more of my time investment, I absolutely think there's an opportunity to find alpha in public markets by thinking differently and freshly about these behavioral aspects of individual market behavior.

Ben's Farm

Patrick: [00:34:41] Let's talk about the farm for a little while-

Ben: [00:34:43] Sure, sure, sure.

Patrick: [00:34:44] ... and the investing lessons that you've gleaned from your interactions with various different types of animals. So I'd love to start with bees. My son has a book, a really thick, fascinating book that's the history of bees, and I've just always been interested in bees as a species. So maybe tell us a little bit about what got you so interested in bees, and maybe pick a story to begin for how you find investing parallels in watching.

Ben: [00:35:05] First of all, we call it a farm. I've got to put it in air quotes. This is 44 acres in Fairfield County, Connecticut, so Grapes of Wrath material this ain't, right? If I have a bad day, I can go to the local, nice tavern and get my artisanal-

Patrick: [00:35:25] Craft beer?

Ben: [00:35:26] ... mescal, drown my sorrows, right, with a craft beer. So Grapes of Wrath material this isn't. But as part of this investment in my family and how my wife and I want to raise our children, I think there's so many lessons about life, frankly, that one learns from working with animals. I do mean working with animals because a lot of these animals, like bees or chickens, they're not pets. They pay rent. I love animals that pay the rent. So we've got bees, we've got chickens, we've got goats, we've got sheep, we've got horses, dogs. They are part of the family, right? But it's been a magical experience and an important investment for family and the like.

But to your point about taking lessons from this for investing, working with animals and the land and farms, it gives you such an appreciation for the cyclicality, certainly, of nature, as we see in markets with their cyclicality. Land, the nature, it's very forgiving, and so are markets. The thing you can't do is you just ... And this is one of the early lessons that I got as a cub investor in this business, is you always want to live to fight another day in investing because there always is another day. There always is another day. You never go all in with an investment, never, ever, never. You always want to protect your reputation because that's the thing you can't recreate. Once that teacup breaks, you can't glue it back together. It's still a broken teacup. So you never go all in with investments, and you always have to protect that reputation as your most precious thing.

That is exactly what it takes to be a successful, I'll say, farmer, anyone who works with the land. You see how markets and the land actually have so many of these similarities. I don't know. I've always had a knack for trying to communicate these observations, and I think that they're important observations, about markets but to express it in a way that connects with people. The real use case for what I write with Epsilon Theory, most of the audience are financial advisers, professional financial advisers. I think what I do that's useful for them is that it's not telling them something they don't know. I'm not there to educate people. That's a load of hooey. What I think I'm useful in doing is arming financial advisors with a way to communicate with their own clients, with a way to take these ideas like risk parity, which can seem so daunting and often is intentionally presented in a daunting way because the person presenting it to you wants to prove to you that they're smarter than you are.

They understand, well, if you look at implied vol on these asset classes, surely, you'll understand your Markowitz procedures require you to do X, Y, Z. It's just a lot of BS. The whole notion between risk parity and risk balancing, it's just an old idea. It's an old good idea with a little bit of a new twist because we've got some new tools we can apply to it. That's all it is. So what I find is that by communicating these ideas in terms of, "God, sheep are the weirdest animals in the world, and this is how they act and they behave," or, "Man, bees are geniuses in the uses of their algorithms," when I can couch it in those sort of terms, it arms people to have better conversations with their clients.

Frankly, it arms people to have better conversations with yourself. All of us, our real client when we're talking about investments, the person that we have the most difficult conversations with is not someone else, but it's always ourselves. It's always ourselves because we've got these ideas in our head, the algorithms, these theories of how markets should work, and it's so hard to think critically about yourself and to apply that same sort of rigor, intellectual rigor, to our own internal conversations. That's the hardest part about being an investor, but it's the most important, too, and that's what I think I try to accomplish by couching these stories about markets in terms of what I see in the animals.

Patrick: [00:39:55] There's this undercurrent of ... In the algorithm idea, specifically when you talked about bees and how bees can notice the day, basically past the solstice, that they need to ... I think it was past the solstice.

Ben: [00:40:07] Yep.

Patrick: [00:40:07] Or some point where they need to begin preparing for the winter by watching the sun, basically. They're able to calculate-

Ben: [00:40:13] They can measure the angle of the sun's rays. Isn't that wild?

Patrick: [00:40:20] It's amazing.

Ben: [00:40:21] It's amazing. But that's exactly what they do, and it goes beyond their preparations for winter. Actually, the bee's brain and its compound eye is designed to do two things really effectively. One, it sees motion very effectively. The compound eye is amazing for detecting motion, infinitesimal motion, and measuring it really precisely, so whether that's another insect, whether that's the wind, it captures motion really effective. The other thing it really does well is measure the angle of light. This is how the bee's nervous system and eye evolved, is to measure angles of light amazingly well. So that's what they use, actually, to navigate. They have pretty amazing memories. They have the famous bee dance that they use to communicate these observations to other bees.

But to go from the hive to the water source or from the water source to a clover patch, it's all calculated based on light angles. So kind of trivially, basically, for bees, because they can remember this, they've got a simple algorithm for when they need to start preparing for winter. That algorithm is the sun is at the highest point in the sky, northern hemisphere, on June 21st. So every day, every bee sees that angle of the sun, and if today the maximum angle that the sun achieves during the day is less than the maximum angle yesterday, we are now past the summer solstice, which means that summer is on the way out, so their entire repertoire of behaviors, hard-wired behaviors to start preparing the hive for winter, begins precisely on that day. It's crazy, but that's exactly what they do.

Patrick: [00:42:21] It strikes me as, in some ways, a risk mitigation strategy, that they don't overextend themselves. They start preparing at the right time.

Ben: [00:42:27] That's totally what they are. They're risk managers.

Patrick: [00:42:29] Yes.

Ben: [00:42:30] They're amazing risk managers, and they have other algorithms so that if you get an Indian summer where the summer lasts longer than you expect or if it comes earlier, they have ways to take account of temperature and wind patterns and stuff like that to adjust their trade because they're making a trade based on the summer solstice.

Patrick: [00:42:55] True.

Ben: [00:42:55] They manage the basis risk of that trade.

Patrick: [00:42:58] Can you define basis risk for anyone that's unfamiliar with that term?

Ben: [00:43:00] Oh, sure, sure, sure. That's what I've tried to do in the notes. Again, basis risk is one of these 10 dollar phrases that we use in investment.

Patrick: [00:43:07] It means something very simple, yeah.

Ben: [00:43:07] But it means something very simple, right? All basis risk means is you've got something that's related to something else.

Patrick: [00:43:15] A derivative.

Ben: [00:43:16] A derivative. So I really care about what we call the underlying thing, the price of the stock. But I think I've got in my mind that there's a relationship between the price of a stock and something else, a derivative, a signal. The basis is simply the strength of that relationship, and so basis risk means that, shoot, I thought that those things would move hand in hand or hand in glove, but, actually, it's a little bit tenuous. That means you're suffering basis risk there.

Patrick: [00:43:49] So in the case of the bees, it would be what they really care about is the temperature. They're using the change in light as the indicator of the change in temperature, but that relationship could be off year to year.

Ben: [00:44:00] Yeah, yeah. But it's not going to be off a lot. It's a manageable basis risk. In Connecticut, cold weather is going to start hitting. I would call it October. So you've got from June 22nd to October. Maybe the first freeze comes a week early. Maybe it comes a couple of weeks late. That's basis risk, whether it's a little bit early or a little bit late. But it's manageable basis risk. What we have in investment so much today is, a., unmanageable basis risk because it's basis uncertainty. We don't know if winter's coming or not. We don't know if quality's ever going to matter again. It's like the Game of Thrones, where winter can last for five years, where the weather patterns are so unpredictable. It'd be really hard for bees to survive in that environment, and that environment, that Game of Thrones weather environment, is what we as investors find ourselves in today.

Patrick: [00:45:00] I love this concept of ... I've been thinking quite a bit about risk in investing versus personal risk, and one question I love to ask is for the time or times that you feel as though you've been most personally at risk or put the most on the line, felt the most exposed, hopefully in a good way, with some decision that you've made, where sometimes taking big personal risks is better than taking huge portfolio risks ... I'm curious if you agree with that idea and then to hear an example of a time that you felt like you took a huge personal risk.

Ben: [00:45:31] So I'll give you two, and they're both connected with the business of investing, the business that we find ourselves in. The first would've been in September of 2008. I'm running my long-short fund. I would say we killed it in 2008. We're long-short. We're actually net long for the whole year, and we were up 20-something percent, 20-something percent in 2008. What we did ... So in September, I discovered credit default swaps. It was a broken instrument then. It's a broken instrument now. But when you're short something, which is being long credit default swap, you're shorting the credit of that company, being short is all about timing. If you're wrong on timing and you're short, you're just wrong. But if you're right on timing, well, you're right. I just nailed it. So the positions we had taken, where everyone else is in crisis around us, saying, "This is at a big long only shot," at our fund, we were absolutely killing it in September of '08.

I remember thinking one day ... It's what you do. You're thinking about, oh, what I made in the returns that day, if this happens, we're going to make this much more money. That's the calculus that all of us go through in our heads in this business because we had taken a big position, all this. That's when it kind of hit me, which is that, well, shit, if the whole system goes down, I'm not going to make any money, right? I'm not going to collect anything. This was, I think, why Warren Buffett famously sold all these naked puts on the S&P 500, right, which is that, well, if they don't bail me out, if they don't bail the system out, none of this is going to matter anyway. I remember what a chastening moment that was because I had gotten so wrapped up in the success we were having in our own fund, but stepping back to think that pressing that position here was essentially betting that the whole system would collapse, and if the system collapses, well, your fund doing well won't matter, right?

Patrick: [00:47:43] They were all out to lunch, yeah.

Ben: [00:47:45] It reminds me of people who want to own physical gold, which I understand the impulse. Hell, I have a farm, right? But if we're at a point where-

Patrick: [00:47:55] That matters.

Ben: [00:47:56] ... where physical gold matters, I don't want to have physical gold.

Patrick: [00:48:01] That's the least of our problems, yeah.

Ben: [00:48:01] I want to own bullets and seeds. That's what I want to own. So it was that moment that I talk about personal risk and rethinking the bigger picture of what we're all doing here. The other example I'll give, it seemed like a huge risk at the time, and that's the point where we gave all the money back in the fund because it just wasn't working. Again, we weren't losing money for the clients, and the way our business works, if you hit the ball down the middle of the fairway, you can do well in our business. But it wasn't honest. I thought I was taking an enormous personal risk by winding down a successful business.

In truth, it was the best thing I did, could've done. You give your money back to your clients before they lose money when you're honest with them and yourself that there's something not right here and it's not working. I often try to tell that story to people who are coming in the business with the like, and it gets back to this notion of your reputation is the one thing that if it gets broken, you can't put it back together again. So whatever risks you think you're taking to protect that, in the bigger scheme, it's not a risk at all, or it's certainly the smart play.

Patrick: [00:49:30] Come back to the farm for a minute and this idea of fingernail clean. I loved this analogy.

Ben: [00:49:36] Oh, thanks, thanks. Yeah, that comes from our chickens and our eggs. One thing you learn from living in the farm is that you see the way that you've lived in the past, things you've taken very much for granted, and you think, "That's not where meat comes from. It doesn't just appear magically in a"-

Patrick: [00:50:01] Cellophane-wrapped.

Ben: [00:50:02] ... "cellophane-wrapped little package all neatly with no blood." No, that's not really where meat comes from. Again, this is part of how I wanted to raise our kids, so that they really saw the underpinnings of our world, warts and all, right? I'm a meat-eater, but I own it. I think that's the most important thing about whether it's raising kids or how you interact with others, is do what you're going to do, but own it. Own it. That's true for so many things, but the example I wrote about to try to illustrate this example of how we've come to think of so many things as normal and natural when they're anything but is the notion of the egg.

What's a good egg? All of us or most, we buy our eggs at the supermarket, and it's wonderful. They come in these packages. They're all so clean, just scrubbed clean, and they're all nice and cool and refrigerated. So we think, "Well, that's what an egg should be. It should be just scrubbed clean and cool to the touch. Of course, that's what a good egg should be." That's absolutely untrue, absolutely untrue. A fresh egg, an egg that's laid that morning, it's got some dirt on it, and by dirt, I mean chicken poop on it, right? My kids do all the work, so they collect the eggs in the morning and bring them in, and there's nothing better than that fresh egg. There's nothing better. I don't have to describe all that to you.

But you don't refrigerate a fresh egg. You keep it out on the counter because you don't need to refrigerate a fresh egg. You also don't need to scrub and wash a fresh egg. In fact, you don't want to do that because when eggs are laid, they have a thin anti-microbial film on it called the bloom. This is how nature works. It's incredible. It's incredible because if the egg were to hatch, you don't want that baby chick coming out into some bacteria-infested shell that it comes through, you don't want the chick to be sick, so nature has that protection. They have an anti-bacterial film on freshly laid eggs. Well, if you scrub it, you wash it really clean, it washes that all off, and then you do have to refrigerate your egg because it can get bacteria and other stuff growing on it.

Now, I don't want chicken poop on the eggs I've got either, so what you do is maybe you get a little bit of wet on the most soiled areas, but you use your fingernail. You just get it fingernail clean. You scrape it off, and then you keep it on your kitchen counter, and it can stay that way for six weeks, and it's going to be the best egg you ever had. So why do we have refrigerated, scrubbed eggs? Because our eggs come to us from factories, from egg factories, where you have tens of thousands of chickens that are kept in their one square foot area and never move, never leave. The food comes to them, they lay the eggs there. There are horrible diseases and bacteria there. The temperatures for storing the eggs for transport later can get really high. So, yes, it is industrially required to scrub those eggs and get the dread diseases that you get in a protein factory off of them, and then it is required to refrigerate them because that's the only way you're going to keep them from decaying before they're delivered to the supermarket.

So this freshly scrubbed, cool to the touch egg, it's not there because that's what makes for a good egg. It's because that's what's required for mass-produced, industrially sufficient egg production. That was my example. You know what else is like that? ETFs. ETFs are an industrially necessary thing. They're convenient. It's like going to the supermarket, and I can buy my carton of wonderful eggs. But an exchange traded fund, to put a fund so that it can trade daily like this, this was not invented because it's good for investors. It's invented because it makes money for market participants, by God, right? And, again, I use ETFs all the time. They're wonderfully convenient, like I'll still go to the grocery store. The hens didn't lay enough, I got to buy some eggs. But we make this mistake all the time, whether we're thinking about our educational system or whether we're talking about our political system or whether we're thinking about our market system, where the things we take for granted and the things we think are natural and correct are merely industrially necessary.

Thoughts on Quality

Patrick: [00:54:57] Makes me think that perhaps one marker of the measure of potential quality is things that can't or don't scale, right?

Ben: [00:55:04] Amen, brother. That's right.

Patrick: [00:55:06] If it can't scale, doesn't mean it will be high quality, but maybe then it can be high quality. It makes me wonder back to our discussion on quality factor itself. I think about this a lot as a quant. We're not dealing, really, in individual securities. Of course, that's the unit of expression of a factor. But we have to build baskets of these factors, and people are more and more aware of these factors, and it's a really interesting question. Does awareness destroy alpha? In certain cases, I would argue the answer is no, it won't because of behavioral reasons-

Ben: [00:55:32] Because of behavioral reasons.

Patrick: [00:55:34] ... like we were talking about earlier. But quality is super interesting because, actually, I don't want to own high quality stocks because empirically they haven't done well, not just in this period but on a much longer term basis. I definitely don't want to own the absolute worst quality stocks. Those seem to do quite poorly. But the highest quality stocks, maybe there's no just simple one formula that can capture that. Maybe it really does require ... And I'm certainly not an advocate of fundamental discretionary stock picking. In fact, I think it's terrible. But perhaps to find the diamond in the rough and find the quality, it can't scale, it can't be a neat packaged ETF or a formula.

Ben: [00:56:08] You know, Patrick, what I think is a hallmark of I'll call it the modern economy, particularly when we're talking about publicly traded securities, is the need, the absolute requirement of these companies, to have enormous scale. I'm talking about our business, too. I'm talking about our business of financial services as well, that it's never been more crucial to have scale, scale for the sake of scale. It's the only way to survive, I think, in so many aspects, even of our business, certainly of asset management business. I think it's different ... To your point about quality not scaling, there are aspects where quality can be rewarded where it doesn't scale, like in a direct relationship with a client, right, where the financial advisor has a very good, close relationship, a quality relationship with their client. That doesn't scale. It means, typically, that that's not going to be a multi-billion dollar business, which means it's not going to be a publicly listed company, but it'd be a great private business.

This gets back to this notion of where, I think, alpha opportunities, quality opportunities are mostly in private market as opposed to public markets because you have to have scale to succeed in public markets today. Or, rather, it's just so difficult to survive if you're not. I think it absolutely goes hand in hand with this notion of quality, that quality, at least the way I ... Quality, like beauty, is in the eye of the beholder. So much of what I think about quality, and I think you're the same way, it doesn't scale, it doesn't scale.

Patrick: [00:57:47] It's a good business lesson, even in scaled-up business models or businesses, that just doing things that maybe only you can do because of the unique set of abilities or skillset, it seems like it's always a good bet. We tend to do things, we tend to be imitative species, right, do stuff because other people are doing it, just ape what other people are doing.

Ben: [00:58:07] It's a great algorithm.

Patrick: [00:58:08] Yeah, it's a great algorithm for basic survival, but not so great if you want to differentiate yourself-

Ben: [00:58:12] Correct.

Patrick: [00:58:13] ... in any interesting way in your business.

Ben: [00:58:15] Correct. That's exactly right.

Patrick: [00:58:17] So great advice on the idea of quality. I'm curious what in markets in general, can be anything, has you most interested, other than the three-body problem, which I know is of recent interest. What one other topic are you crunching through? We started the whole conversation with this love of puzzles. What is the current puzzle that has you most interested?

Ben: [00:58:37] Understanding the role of narrative, figuring out the rules, the computational rules, of narrative. This goes under that heading of natural language processing. Unfortunately, not unfortunately, I'll back up a second, there's another big buzz phrase in our business today, any business, artificial intelligence. I'm very excited about that, and I think there's a big opportunity here because I think it's largely misunderstood in our business. I'll put it this way. Artificial intelligence will not make your regressions run any faster, right? If your business model is to find these arbitrage opportunities that are increasingly vanishingly small, either in time or in price, behind this data series and that data series, this structured data series and that structured data series, artificial intelligence doesn't help you. Neither does machine learning or any of this other stuff.

When you take these vast computational arrays and you try to apply it, this gets back to the three-body problem, when you try to apply it to historical data to look for the answer or the pattern or the algorithm, you're going to be disappointed, you're not going to find it. But artificial intelligence, where it is useful is for ... Its usefulness is in looking at unstructured data like the ocean of words that we have, all the things that they're going to say on CNBC today, all the things that are going to be printed in the Wall Street Journal today.

Patrick: [01:00:05] Classifying stuff.

Ben: [01:00:06] Yeah. In game theory, we talk about missionaries, about a famous investor like Warren Buffett and how what he says, how that narrative or that story, how it rolls through markets, or how what Janet Yellen says or the latest fed governor comes out with, is how it rolls through markets. So what I'm saying where there's not trusting the algorithms in my head, I don't mean that there aren't patterns to the world and to nature. I think that we've got, with this new technology, through artificial intelligence and natural language processing, to actually start seeing some of these patterns we've waved our hands at in the past and actually start trying to understand them, not for crunching through structured data, not for finding patterns in historical price or volume or series, but for finding forward-looking behavioral patterns, again, that's where I think alpha can still exist, that's driven by the way we are impacted by, the way we are hardwired to respond to the things that people say and tell us.

I've written a good bit about this, but that's where I'm really excited about the new technologies that are available. It's like when Leeuwenhoek invented the microscope and put a slide of dirty river water underneath it for the first time and saw this whole new world, this microscopic world, or applying a telescope and looking at the sky. I think that what artificial intelligence does is it allows us to see these oceans that we swim in, oceans of words and communications, and lets us visualize that and make sense of it, really, for the first time. That's what I'm excited about.

Patrick: [01:01:57] Really fascinating.

Ben: [01:01:58] It's crazy stuff.

Patrick: [01:01:59] How personally do you approach trying to understand that problem? What are you optimizing for? What are you trying to come away from it with, a tool that you can use in the investing world, or just an understanding of the world? What's the outcome that you're after?

Ben: [01:02:12] Here's what I'm not after. What I'm not going to come up with is a formula. I'm not going to build a regression analysis of structured data out of this. That's not how it works. That's not how game theory works. It's frustrating. It's really frustrating about game theory because most games have multiple equilibria, multiple outcomes. If you give me a game and there are a couple of equilibria and you say, "Okay, what's going to be the outcome, what's going to happen in markets tomorrow," that's like I don't know. I know it's going to be one of these multiple equilibria, but I don't know which one. Game theory cannot tell you what the outcome's going to be. It's not part of the game.

But what it can tell you is how is the dynamics of the game. It is a tool for looking forward, not way forward, but tomorrow or next week, for understanding the human dynamics of a system. So it's never going to give me that I now have the answer where I'm going to invest in X, Y, and Z and in six months, I'll knock down these profits, right? It is going to make me a better investor, though, because I'm going to understand as it unfolds and I'm able to talk to my clients and the people I care about about the process and the dynamics of what's happening. Again, it's part of being adaptive and agnostic, but that doesn't mean that I'm going to just stick my head in the sand and say, "Well, I'm just going to give up." I do think we have these tools and these technologies to see how is it playing out and be adaptive enough to react to that.

Patrick: [01:03:55] So two closing questions for you.

Ben: [01:03:57] Yeah, sure.

Patrick: [01:03:57] The first is to ask what the most memorable individual day was on your farm.

Ben: [01:04:03] Frankly, it was a sad day because that is one of the things that you think you know intellectually that animals live and they die, but you don't know it emotionally. I wrote about it in a piece because it's similarly with our investments. We have a hard time saying goodbye to our investments, but investments have a life cycle also, and we've got to be not just intellectually aware of that but come to grips with that emotionally. So the hardest days on the farm are always when one of the animals dies. There have been a couple that have been particularly unpleasant because that's how life is. It's messy. But you have to own it and you have to deal with it and treat the animal as well in death as you did in life. So that's the hardest days on the farm and it's the hardest days in our investments also, but we've got to own it and we've got to come to grips when things don't work as well as when things do.

Patrick: [01:05:04] My closing question, which is the same question for everybody, is for the kindest thing that anyone's ever done for you.

Ben: [01:05:09] There are kind of easy answers like when my wife said yes. That was very kind of her. I can also think of things like the tough love kindnesses. But, frankly, I've had these ... I think we've all lived with these situations in both business and in our personal lives. Again, there are a couple of these moments. The kindest moments, the most human moments, are when you are forgiven. Those are very special and very human, and it's important, I think, to remember in our actions towards others, that that's the greatest grace we can give, is to forgive.

Patrick: [01:05:53] Fantastic. Great place to close. Such an interesting conversation on quality and all these great ways of looking at markets through analogy. This has been edifying. Thank you.

Ben: [01:06:04] Well, my pleasure. Any time. Thanks for having me.

Transcript by @ScarrottKalani