Transcript: Chris Cole on How to Build a 100-Year Portfolio
(Bloomberg) -- On this episode of Odd Lots, we speak to Chris Cole, the founder of Artemis Capital Management and a previous Odd Lots guest, about how investors can build a portfolio that outperforms for the very long-term. He walks us through his recent research, in which he recreates 100 years of the most popular financial engineering and portfolio structures to identify what works best. You can find the episode here. Transcripts have been lightly edited for clarity.
Tracy Alloway: Hello and welcome to another episode of Odd Lots podcast. I'm Tracy Alloway. My cohost, Joe Weisenthal, couldn't make it today.
So I'm looking at a chart of the S&P 500, and let's see, it's almost exactly one year post the big dip in the S&P 500 -- the massive crash that we saw back in March of 2020.
And of course we've seen risk assets come roaring back, but with the recovery in financial assets, we have a lot of questions over how long can this continue? Are we on the verge of some sort of big change in the market as central banks unleash fiscal stimulus and interest rates remain at or close to the zero bound? Are we going to get inflation? Are we going to get stagflation? Are we going to see perhaps even deflation -- is there anyway we're ever going to get out of this regime of low inflation?
And I think whenever we have these big turning points in markets, or when we have lots of people talking about the potential for big turning points in markets, we also have a lot of opinions on how those are going to go and what type of portfolio would perform best.
And of course, we've seen a lot of hand-wringing recently around growth versus value, 60/40. What happens when both bonds and stocks fall at the same time? So on today's show, we're going to be discussing exactly this idea. How do you build a portfolio that can withstand these regime changes and basically outperform over a really long time horizon?
And I'm excited to say our guest for this episode is Chris Cole, the founder of Artemis Capital. He's appeared on Odd Lots previously, digging deep into the low volatility regime of past years. It's great to have him back. He publishes some excellent research. Chris -- thanks for coming on the show again.
Chris Cole: Thanks Tracy. It's great to be back on the show.
Tracy: So I guess my first question is, the reason we're having this discussion is you published a paper called ‘The Allegory of the Hawk and Serpent: How to Grow and Protect Wealth for 100 Years.’ Now, most investors aren't really looking at their portfolios on a hundred year basis. What sparked your interest in that kind of time frame?
Chris: Well, what's really interesting about, in particular, the last 40 years is that there's a tremendous recency bias that market participants have. The last 40 years are incredibly comparative to overall history. And I believe as I'd make the case in the paper that recency bias is now a systemic risk. 91% of the price appreciation for a classic 60/40 portfolio over the last 93 years has come from just the 22 years between 1984 to 2007.
So what we've had is this incredible outperformance of both stocks and bonds that has been from a reinforcing cycle. And we use the allegory of the serpent for that. And that's been led by falling interest rates. Rates fell from 17% to 0%. There's been incredibly favorable demographics as baby boomers are coming to the workforce. Falling taxes -- taxes have fallen to near a hundred a year lows -- globalization, unprecedented monetary policy.
And we now have some of the highest levels of both government and corporate debt in American history. So as a result of this, there's been this incredible outperformance of stocks and bonds over the last 40 years. But the trillion dollar question that I think is important for any allocator is: is this repeatable?
And I actually believe that the factors that drove this generational boom in stocks and bonds are now reversing. We're now in a framework where debt’s at all-time highs, the middle class hasn't seen real wage growth since the 1970s and demographics are really poor and interest rates can't go any lower. So investors expecting the gains of the last 40 years are likely to be extremely disappointed.
And so to kind of understand what the next 20 years are going to look like, and how to build a portfolio that will last and can manage through this period of secular change, what I did is I went back through history and I recreated all of these financial engineering strategies using, I think, defensible assumptions over the last 93 years.
And we know that history doesn't repeat, but it rhymes. And my goal was to figure out what portfolio can consistently perform to every market cycle, whether it's secular growth, whether it's inflation, whether it's deflation. And I think I came up with some really interesting answers in my paper last year. In the follow-up paper that I just released that really answers the questions as to what type of portfolio really sustains wealth and capital appreciation and limits drawdowns. And the answer that I got is radically different from the type of portfolio that many institutions and retail investors are currently allocating to.
Tracy: I have so many questions already, but one that jumps out at me is going back a hundred years and trying to reconstruct modern portfolios against financial assets in the 1920s and 1930s, how exactly did you do that? Because I'm looking at the paper and, you know, you look at things like naked call selling -- how that performed during the Great Depression. I'm just curious how you recreated those portfolios?
Chris: Absolutely. So it's an incredible question. And I think one of the things that I think is very important to understand about this exercise is that we don't necessarily say that these portfolios are realized performance, but they're our best effort at understanding how a given financial engineering strategy might've performed in the past. And I think there's a lot of defensible assumptions.
First of all, we start with a wealth of observable data. There is data that we can gather off from history, namely the composite S&P 500 prices from stock data -- the top 500 companies in a given point in time. There's obviously gold data, interest rate data, there's data on commodities. And that's a starting point. And we use that data from the global financial database. From there, what we can do is we can construct some and replicate some basic strategies like risk parity, volatility-targeting a 60/40 portfolios.
Those are relatively easy to replicate using that kind of base level data. Now, Artemis is a long-volatility trading shop. What we do is we provide volatility -- long-volatility and defensive solutions -- for our investors. So one of the most popular type of strategies that has been employed by many institutions are volatility-overriding strategies, either for income or defensive purposes.
And what we wanted to do is to test these going back 93 years. Of course, how do you test a volatility strategy prior to the existence of the options market? And that's not necessarily something that's easy to do. So obviously you have to make some assumptions and it becomes a bit of an intellectual exercise, but I think the assumptions are very defensible, if one understands them.
What we first did is we took options data that exists from 1990 to the present, we're able to solve for a volatility surface. A volatility surface describes the pricing of volatility at various out-of-the money points, both for calls and puts. So that's the realized data. The real data that we have now -- we don't have that data going back, obviously, in the 1930s or the 1970s -- but we do have data on how equity markets performed. We can calculate, for example, realized volatility on 10-, 30-, 60-day timeframes. We can calculate the rolling performance and next drawdowns of equity markets over those times. So those are observable inputs. So using our arbitrage SVI vol surface that we solved for from real data, we then were able to run a multi-variable regression to actually look at the last 20, 30 years and fit a volatility surface based on observable market data. And then what we did is we used that fitted data to in essence, run various option trading strategies using a theoretical volatility fit.
And then we looked and compared that to some of the most popular CBOE vol indices, like the BuyWrite Index and the PutWrite index. And what we were able to do is we were able to replicate those indices using our theoretical volatility indications to generally over a 0.85 correlation and almost exacting performance. So from there, what we're able to do now that we have this kind of in-sample history, we were then able to apply that methodology to create a theoretical implied-volatility surface going all the way back to 1928.
Now there are limitations in this, because what you're naturally assuming is that the way market participants price volatility over the last 20, 30 years would be very similar to the way they would price it in the 1930s and the 1970s and the 1950s. Now we don't know that for certain, right? We don’t. We’re willing to make that assumption to in essence give ourselves a fairly realistic assessment on how strategies like put-writing or strategies like naked call-selling would perform during those time periods. And we received some very interesting results that really give allocators a sense on how these strategies would perform outside of the incredible regime of the last 40 years.
Tracy: Hmm. So you replicate the volatility performance from over a hundred years ago or 93 years ago, as you mentioned. You find that a technique that a lot of investors have been using in recent years to pump up returns -- again, going short volatility -- doesn't perform as well, or hasn't performed as well way back then. Why exactly did that happen? What were the market conditions in place that you think allowed for the under-performance of short vol?
Well, obviously over the many years I've been a critic of short volatility and that strategy. And I also believe that long volatility is one of the most under-allocated assets that is out there today. And I think this paper and some more analysis that we provided gives support to that. So I think saying short-volatility strategies -- and what I mean by that, these are strategies that sell put options, or maybe they sell call options, might be call overwriting, buy-write programs -- these are strategies that institutions have employed in many ways to generate excess yield over the last four years. They performed exceptionally well, largely because we've been in an environment that has emphasized stability. Every single time equity markets draw down central banks are able to respond, and that has produced an extremely mean-reverting environment. That's not always been the case.
And let me kind of explain why. Let's look at a period like the 1930s over the entire decade of the 1930s, volatility realized at about 40%. That's incredible. So vol was realizing 40% in 2008. Imagine 2008 for an entire decade. Obviously that does terrible things to a portfolio that's continuously selling optionality. Well, one of the surprise takeaways, and I think people would appreciate this after the last year. (You know, my original paper came out before the Covid crisis, and in many ways kind of predicted a lot of the problems that were experienced in the Covid crisis, and the reflation afterwards). Well, naked call-selling was among the worst strategies that we looked at. You would think naked puts-selling would be, or put-writing, would be the worst strategy. Naked call-selling was terrible.
Why was naked call-selling so bad? Well, if we go back to the Great Depression, we had these incredible drawdowns in equity markets. Central banks responded either by cutting rates, by implementing programs, or by devaluing gold. And you had equally insane rallies in the moment that were as violent as the drawdowns. So that's something I think that's so important for people to understand and really was foreshadowing the -- we had this huge drop in March, 2020, we had this big explosion in April. Well, if we look at the Great Depression, after a brutal three-year decline of 80%, the market rallied 72% in just 1.5 months in 1932. And that was after the signing of the Banking Act. In 1933, the market had an 88% rebound in just 4.5 months after Roosevelt devalued the dollar. So you have these violent rallies that occur during these periods of deflationary crises. And if you're selling call options into those violent rallies, you can clearly understand how bad that is.
If you're doing covered call overriding, it's clear how bad that is. Another period that was really violent was the 1970s, where you had this kind of right tail skew realization. Now, what does that mean? Prior to the devaluation of gold in 1971, markets used to trend. Equity markets used to trend. They were auto-correlated. And what that meant is that if yesterday was up, today was likely to be up, and the next day was likely to be up. So we reached this kind of secular peak in trending of equity markets in the 1970s. And after the devaluation of gold, which empowered central banks to be reactive, we began a multi-decade period where mean reversion ruled. And last year represented the highest peak in mean reversion. Another way of saying that is negative auto-correlation, but really it's like if yesterday was up today was likely to be down and vice versa.
The mean reversion and markets reached all-time highs in over a hundred years of history. In trending markets, that's really bad for short vol-selling strategies because volatility is comprised of, well naturally vol, you have the vega, that's the volatility, but there's another component to options, which is the gamma. And another way of saying gamma is the trend. So for the greater part of 70 years, option buyers would have profited from trend, but over the last 40 years mean reversion has ruled, and that's largely been connected to the decline in interest rates and the proactivity of central banks. So that's another reason why some of these short-volatility strategies dramatically underperformed and were incredible -- not only underperformed, I mean, resulted in complete and cataclysmic loss of capital for about 70 years.
Tracy: I’d love to get your take on how volatility strategies actually performed last year, like in March of 2020, and whether or not we've seen them build back up in the month since. Cause I think that might help us get a sense of the direction that we're going in.
Chris: There's a question as to what portfolio is most robust and how do you build a robust portfolio? And one of the conclusions that we had doing this 90-year study of history was that to achieve a portfolio that is optimal, what investors should do is that they should prioritize long-term correlations between asset classes over excess returns.
And so we devised a portfolio that's radically different than what many institutional portfolios have. What this portfolio does, is it diversifies assets based on market regimes and by market regime, I mean, regimes like inflation, deflation and growth. It diversifies assets based on market regime rather than asset classes. So 60/40 is an asset class diversification tool, or the other diversification tool used by many investors is trailing volatility and correlations. That's what's used by the strategies like risk parity.
So the portfolio that we really recommended to perform consistently over 93 years is obviously about 20%, 20-25%, equity, and about 20% high quality bonds. And this is where it gets interesting, 20% approximately gold and precious metals. And I'll call that fiat alternatives. Some people might actually put crypto in that today (obviously you can't test crypto going back). 20% trend and momentum strategies, these would be managed future strategies that profit off trends in commodities or currencies. And then finally, 20% long-volatility in defensive hedging.
What ends up happening in these different asset classes? Obviously, equities performed during periods of secular growth. Fixed income performs during periods of relatively stable inflation and in deflation. But you have a limit on fixed income at the zero bound, obviously. And that's occurred in the history before. It happened kind of in the 1930s. Now, long volatility and trend and momentum perform in periods of deflation, tremendous deflation and tremendous inflation. So deflation like the 1930s, inflation like the 1970s. And obviously fiat alternatives like precious metal perform in periods like the 1970s, where you have tremendous stagflation and negative real rates.
So you’re diversifying based on these market regimes. Now we published this paper at the beginning of last year. We didn't have the opportunity to see in the future about what would happen throughout 2020.
I released a new paper that talked about how this recommended portfolio performed through 2020. It was exceptional performance. Because as you know, 2020 was like an entire business cycle condensed into 12 months, the whole business cycle from January to March, that was like a 1930s deflation. Then from April to about August, we had this incredible fiat devaluation with $10 trillion in global stimulus and this speculative asset growth. That's almost similar to some kind of 1999 type of scenario. As we entered the winter time, we went into a reflationary scenario that began to resemble kind of the onset of stagflation in the late 1960s. That’s where you have struggles in interest rates, interest rates begin to rise outperform, but then you have this expectation of a re-steepening of the yield curve and commodities began to perform. I mean, lumber reached all time highs. Copper was exploding.
So if we look at how this portfolio that we call the Dragon Portfolio performed in that deflationary period in the first part of the year, well long-volatility strategies were the huge winners. They actually were able to fill the gap where equities drew down. So actually for this portfolio, that first quarter gained about 13%, where 60/40 portfolios and risk parity portfolios had huge underperformance because they were over-reliant on bonds as their defensive protection. So long-volatility strategies did so well and protected and helped you make money during that quarter. Then during the fiat devaluation and the kind of growth period after central banks stepped in, gold kind of took its turn performing in the summer, along with definitely crypto and other assets like that. And then we had a huge outperformance in equities. Then by the fall, we began to see gold begin to sell off, and we began to see equities continue their upward trajectory, fixed income began to sell off, but trending commodities, managed futures, began to outperform — profiting from the trends in commodities markets and the trends in these other asset classes.
So we had an entire business cycle condensed into 12 months. And at each point, these market regime diversification benefits of the portfolio became really clear. And this concept of this Dragon Portfolio that we introduced, actually would have returned close to 50% last year, where the 60/40 portfolio and risk parity portfolios only returned about 15% on average, with over three times the max drawdown. For people who read our paper, this should not be a surprise. It should not be a surprise. Because we saw this happen in previous market cycles. But you have to look at the periods outside the last 40 years to understand that.
Tracy: So I get the contention that if you're building a portfolio that's going to outperform in these big begime changes, then in 2020, when you basically had a compressed business cycle as you mentioned, it would do phenomenally well.
But most people would agree that 2020 was an extremely unusual year. And I think most investment professionals are probably more used to trying to pinpoint the regime changes as they come, rather than build a portfolio that's going to outperform all types of regimes for a hundred years, right? Their outlook is always going to be, you know, 10, 20, maybe 30 or 40 years, but it's going to be much, much shorter than what you're talking about.
So I guess my question is, how useful is this for your average financial advisor or investor? And how do you encourage people to think on a longer-term horizon, or think about diversification across regime changes, rather than just trying to pinpoint when a particular change is taking place in markets?
Chris: One of the phrases that we say is: ‘do not fear, do not predict, prepare.’ So if you are able to perfectly time and predict the regime changes -- and I tell you, I can't. But if you're able to do that, you're Stanley Druckenmiller, you're George Soros, and you should be a billionaire. Right? The average retail investor, the average institutional investor, is not able to necessarily time those changes perfectly. And even if they're able to do so, if you're managing a $50 billion portfolio if you're an institution, or a $10 billion portfolio if you’re an institution, it's difficult to tilt the portfolio. You have to choose some portfolio allocation. My point is that the average pension fund in America is approximately 70% equity-linked investments, and approximately 30% in fixed income and alternatives and cash.
What many of these institutions have done is they've crowded into assets like private equity, kind of pretending that they're diversifiers. But if you look at some of the Cambridge studies, private equity has tremendous correlation to business cycles. It’s not a diversifying asset by market regime. Now they jumped into VCs. It's the same thing. You jump into real estate. It's the same problem. These are all asset classes that are correlated to business cycles. They're long-GDP asset classes that are correlated to one growth regime. Now in the past, you could rely on fixed income to provide diversification, but when fixed income was at the zero bound, it fails to provide great diversification. And anyone who studies the 1930s would have seen that problem. When we recreated risk parity portfolios, which rely heavily on fixed income and lever the fixed income, there’s tremendous underperformance in the 1930s when rates were near the zero bound.
So these institutions are assuming the last 40 years will repeat. And they're in essence, levering equity-linked, or not levering, but they're relying on this expectation of equity-linked performance and these kind of false diversifiers to reach their 7.25% return targets. I think this is a huge mistake. Because if you remove the last 40 years, their performance is closer to 4-5% annualized. Abysmal. You know, using the allocations that they have. So if they're not able to meet that 7.25% return target, recency bias becomes a problem that all of us are going to pay for. And the reason being there's about $1.4 trillion U.S. state and local pension deficit right now. And that's assuming that they are able to hit the return targets. If there's underperformance, as one would expect given current valuations and the current correlation mix of their investments, you can expect that deficit to rise to anywhere between $3 trillion, all the way to up to $9 trillion.
So in the last stimulus bill, there was a lot of controversy over $85 billion in bailouts for union pensions. And I'm not going to get into the politics on that. But if you can imagine that there's a lot of tension over $85 billion, what's going to happen when the government's gonna need to step in and bail out PBOC and state and local pension systems to the tune of $3 trillion? It's a big problem. Recency bias is a big, big issue.
The problem with many of these institutions is they bought into the Sharpe Ratio myth. They look at these investments, be it private equity or these other investments. And they say, okay, what's the Sharpe Ratio? We want to put together all of these investments that have high Sharpe Ratios. Well, the Sharpe Ratio, if you go back and you read the original paper that William Sharpe wrote, you know, capital asset prices -- that was the paper that he wrote when he was 30 years old in 1964 -- it's clear that a Sharpe Ratio is not intended for components of the portfolio.
The Sharpe Ratio should only be used for the aggregated portfolio. You can't use a Sharpe Ratio to make judgments on individual managers. Well, why? Why is that? Well, I like to use this analogy to sports. When you're looking at evaluating players to add to your favorite sports team, if you're a general manager, you want to add players that help you win. That's what you want. Well, we all know -- whether your favorite sport is basketball or whether it's soccer -- there are players on mediocre to bad teams with gaudy statistics. And maybe they have really good scoring averages, or goals averages, but their statistics are padded and they don't help their team win. And the reason is maybe they don't play good defense, or maybe they dominate the ball. Maybe they have high turnovers and maybe they're not making hustle plays that help their team win.
So as a result of that, sports management has gotten really smart about selecting players and advanced statistics that measure how a player helps the team win. These are things like wins over replacement value and plus/minus ratios. But we have no metric for that in the investment industry. So what ends up happening is that these institutions buy into this myth of Sharpe Ratios and they keep layering on investments that have high Sharpe Ratios. But what ends up happening is that -- you can put together a bunch of investments that have high Sharpe Ratios but your portfolio will have lower risk-adjusted returns and higher drawdowns. It's really amazing. And the reason is that the Sharpe Ratio doesn't take into account the skew or the extreme right or left tails of the investment. It doesn't take into account the correlations of that investment versus the rest of your portfolio.
So what happens here is that people have bought into this myth that layering on top these managers that have high Sharpe Ratios will help them. And actually it’s hurting their portfolio. And likewise, just like in sports, there are some players with less impressive statistics. They may not look good on paper. Their point averages might be lower, but they're doing things like playing fast or helping the team win. And that shows up in the advanced metrics.
Tracy: Is this the Money Ball theory for market investment?
Chris: It is, it is. And we are working on a new paper that will develop -- this will come out on the next couple of weeks -- that actually develops some of these advanced metrics for institutions. But my point being is that investments like long volatility, which don't look that good on paper, you know, they don't have great Sharpe Ratios — but when you add them to your portfolio they push your portfolio out on the efficient frontier and result in a better risk-adjusted return for your portfolio. Assets like gold, commodity trading advisors and long-volatility in defensive hedging, even though they don't look that great on a Sharpe Ratio basis, you put them into the portfolio and they help your portfolio win. That's what you really care about.
And that’s what the average U.S. pension institution and the average retail investor fails, I think, to fully comprehend. And this is so important because this Sharpe Ratio problem is a social problem. Because if we don't fix this, we are all going to end up paying for it. So I really am passionate about this. I really believe this. I think the math proves this out. The history proves this out. And I think these are some of the most undervalued assets that one could put in the portfolio. Even though they may not look that good on paper, we need to stop evaluating the player and we need to start evaluating the team. That’s the secret to building better portfolios.
Tracy: So the idea is that the whole of the portfolio can be stronger than the individual components, right?
Chris: That's absolutely right. You know, in our paper, we talk about this idea where you can choose two assets. There are two assets that have high Sharpe Ratios, but they're highly correlated with one another. There's another asset that has a negative Sharpe Ratio, but it's anti-correlated. Actually you'd rather put together the negative Sharpe ratio that's anti-correlated with the one that has a positive Sharpe Ratio, than the two high Sharpe Ratio investments together. That results in a better portfolio. And it's just mathematically true. But for some reason, we don't seem to see this. But sports, oddly general managers like Daryl Morey, and, you know, the managers of the Warriors understand this principle as applied to sports, even though we're lacking it in investment.
Tracy: There's one other thing I wanted to ask you. You mentioned social value just then, and this idea that if we're going to solve things like massive underfunding of pensions then people should grasp this concept of building a sort of total portfolio versus just going after high returns in the short-term.
But there's one other thing that kind of caught my interest in the paper where you talk about the potential for wealth distribution in some regimes. So this idea that you can get a backlash to a market crash. You can get populist pressures on politicians that result in wealth redistribution, higher tax rates, things like that. And I think a lot of people would argue that maybe that's where we're heading now. How do you actually protect a portfolio from wealth redistribution? And what does your Dragon Portfolio do or perform in that scenario?
Chris: Well, it's a really interesting and complex question, and it really delves into a detailed analysis on the individual components. So people have to realize that how this ties into the last 40 years, because it's not just been rates that have been going lower, but it's been taxes that have been going over. So huge tax cuts starting in the 1980s. So that combination of low taxes, globalization, lower and lower taxes and lower interest rates has resulted in this huge buildup of debt and tremendous outperformance in equity-linked assets, private equity, and all these other long-GDP assets, we'll call them, because that's what they are.
Well, as we move to an income redistribution type of world, that presents a heavy, heavy burden on equities, on real estate, on private equity. That's a big issue for two reasons. First of all, you know, excessive regulation is going to cause problems in those asset classes. Obviously, if you're allocated 70% of those assets, that's a huge issue.
The second driver is that, look, we have the highest fiscal deficits since World War II and are likely only to be going up. We have unprecedented monetary policy and we have the highest levels of corporate debt in American history as a percentage to GDP. These are all true facts. So the problem with keeping interest rates so low is that it exacerbates the wealth divide. In addition to all of these extreme factors on excessive debt, we also have the highest income disparity in American history that is equalled only by the Great Depression, or prior to the Great Depression. This puts tremendous political pressure. You can't just keep rates low and do the Japan experiment because you're going to have a social explosion.
And, you know, I talked about this in the New York Times, I gave an article back in 2017 that was prior to my Ouroboros paper, where I talked about the blow up in vol. And I said, well, you know, if the Fed wants to suppress volatility indefinitely, well, that's something that — they can do that, but volatility can never be destroyed. It can only be transmuted in form and time. So if you're going to suppress asset price volatility using monetary policy, that volatility is going to come out in a different way. And that way is through social instability.
And I said: ‘Hey, my hedge fund, which seeks to protect against market crashes, now that's something I feel pretty confident about being able to protect against. But what you can never protect against is a social revolution. And I think we're starting to see the seeds of that. Of course, one of the ways that governments can stem that off is by fiscal stimulus, giving out money. But that’s stagflationary. That's tremendously stagflationary.
And so I think that's the route that maybe is the path of least resistance. You can destroy debt in two ways. You can default on the debt or you can print money, and you can do tremendous stimulus and you can inflate your way out of the debt. And we may be choosing the inflation route. Well, that's going to wreak havoc on the institutional portfolio because people don't realize this. In the 1970s, we were in a Great Depression on a real-adjusted basis. So the drawdown on a real basis after inflation was as bad as the drawdown in the 1930s. But the reason that people didn't feel it was that bad was because we were inflating away the problems. Of course, that led to rent control and all these other issues.
So if we go the inflation stagflation route, you're going to see equities destroyed on a real-adjusted basis. And you're going to see bonds obviously destroyed because rates go up. Well, guess what performs in that environment? If we go back to our Dragon Portfolio, well, at any point, the Dragon Portfolio has two out of the five thematic regime classes performing. So what performs really well in a stagflationary environment? Well, precious metals and gold. You had an 800% price increase in gold. We'd presume that crypto would be part of that, but there's a lot of questions on crypto. The other thing that performs extremely well is commodity trend and trending commodities. So the CTA portfolio performs really well. So those are strategies that take into account momentum. And so we're seeing that this year, where lumber prices are exploding to all-time highs. Copper's up. All of these commodities are trending tremendously higher.
So in this sense, if you have 40% of your portfolio allocated to assets that can play off of the stagflation, you're going to be okay, you're going to do well. That'll make up for your suffering stock and bond exposure. In addition, long volatility can make money in right tail events and stagflation as well. If we go into kind of a Japan environment, well that's a scenario where long volatility tends to outperform, and you have stability in fixed income and you kind of have stable returns in equities. So I think you can't necessarily… it goes back to the idea ‘don't predict, prepare,’ and thrive from change. And that's when we look at risks in terms of thematic baskets. Regardless of what regime you're in, at least two to three of the thematic baskets or market regime baskets are outperforming, and that saves your portfolio.
And that way you don't necessarily need to predict the winds of political change. All you need to do is to have a balanced allocation so you're prepared throughout any regime. I really stress that this is something that the investment allocation problem, the portfolio allocation problem, is a social problem. We have a window of opportunity. This is both on a pension systems, institutional investors, and for individual investors. There's a window of opportunity to create portfolios that are robust and are able to thrive through these different regimes. You know, my paper, ‘The Allegory of the Hawk and Serpent’ released last year in January, and the follow-up to that paper that chronicled how those ideas performed in 2020, that was just released, really make the case, and I think a very important case, that this is vital if you want to profit and be able to survive periods of secular change.
And this is not just an intellectual argument about portfolio construction. It is a social problem if we don't address this, because the pension underfunding is going to get massive and we are going to pay for that. Now we could pay for it through government bailouts, or we could pay for it through loss of purchasing power from inflation. But the asset allocation problem is a social problem. And I think institutions and retirees really need to strongly look at diversifiers that profit from left- and right-tail events, and profit from trend. And those are underlooked asset classes like long volatility, commodity trend and precious metals and fiat alternatives. And those are just as important as equities in the portfolio.
Tracy: Chris, what's the name of the new paper?
Chris: The new paper, which is available on our website, it's called ‘Rise of the Dragon.’ I love these metaphors and visuals. But I really encourage people to do deep dives into the papers because I think there's a lot of quantitative analytics. Everyone read the first paper. Not a lot of people actually read the appendix, which was twice as long as the paper and contained much more detailed quantitative notes. So both of those papers are available on our website, www.artemiscm.com. And I occasionally tweet about these themes too.
Tracy: Oh yeah. I'll mention your handle at the end of this. But in the meantime, everyone has some light weekend reading in the form of a 20-page appendix from your paper.
Chris: Sorry, a detailed 20-page appendix that talks about vol surface and risk-parity replication to the 1930s.
Tracy: All right Chris, it was lovely having you on as always. That was Chris Cole, the founder of Artemis Capital.
Chris: Thank you so much. Thank you, Tracy.
Tracy: So I I'm going to avoid recording a monologue here because I think it would be weird for me to talk to myself about what I found interesting in that conversation. But I do encourage you to take a look at Chris's paper. It's not very often that you get to see someone modeling volatility surfaces from the Great Depression, so whenever you get that chance, you should seize it. All right. This has been another episode of the Odd Lots podcast.
You can follow Chris Cole on Twitter at @vol_christopher
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