Trend Setters: Crypto, Crowding & Convexity, with Prof. Cam Harvey

Will crypto have a place in a multi-asset trend-following strategy? What’s the academic foundation for why trend-following works when so many other strategies have been proven wrong? And what can investors do to protect their portfolios from the ‘Achilles Heel’ of trend?

Campbell Harvey, Professor of Finance at Duke University, joins the podcast to discuss his ground-breaking research into trend-following and his latest books including Strategic Risk Management and DeFi and the Future of Finance.

Recording date: September 2022

Episode Transcript

Note: This transcription was generated using a combination of speech recognition software and human transcribers and may contain errors. As a part of this process, this transcript has also been edited for clarity.

Peter van Dooijeweert:

For today's episode, I'm pleased to be joined by Cam Harvey, professor of finance at Duke University. Cam, thanks for coming.

Prof. Campbell Harvey:

Great to be on the show.

Peter van Dooijeweert:

So you've written extensively on all kinds of financial topics. You've co-authored some of the papers we've been talking about in this series, including The Best Strategies for the Worst Crises and The Best Strategies for Inflationary Times. But you've also written a paper that says Be Skeptical of Asset Management Research. So, to kick off, I think it would be good just to have a conversation about why trend works and specifically why is this a genuine investment strategy from an academic standpoint and not some kind of passing fad or a factor that's been disproven.

Prof. Campbell Harvey:

So let me start by talking about this paper, the people should be skeptical about asset management research. And I've spent the last decade really calling out some research practices that are pretty questionable. I've detailed that 400 so-called factors have been published in academic journals. And to me, there's just like no way. There are 400 long, short factors that deliver significant alpha. It is a data mining expedition that people are on. And part of the message that I try to convey is that you need to have some economic foundation for a strategy. If it is purely a data mine strategy, the hurdle is so high for it to be real. So it's got to be some sort of foundation needs to make sense. And unfortunately, many of these strategies, they look good. They might be a story spun ex-post, after the fact, that might make a little bit of sense, but that's not the way to do research.

And unfortunately, many of these so-called strategies are not going to work in live trading. And there's plenty of academic research that shows after something is published, then the performance actually is degraded or goes away. There's a couple of papers that are out there that are really interesting on ETF launches. So, with the ETFs, and these are kind of the act of factor-based ETFs or Smart Beta ETFs, that if you look at the backtest, which are generally available, they look great. And then as soon as the application is filed to the SCC, the performance is flat. The access performance is nothing there. So this is not just an academic issue. This is also a practical issue.

Now, you asked about trend-following. And trend-following actually is interesting because there is an economic foundation for trend-following. And if you think about how it works where if markets are trending up, you're buying, and that's kind of replicating dynamically a long call option. And if markets are trending down, you're selling, and that's kind of replicating a long put option. So you put that together, you get a straddle like a payoff. And it's no surprise with the straddle that when times are very bad, the straddles going to pay off. And that's what I've demonstrated with my papers with many colleagues at Man including Otto that you mentioned that the trend strategy, given its economic foundation, should pay off in crises times. And that's exactly what we find. And the level of payoff, of course, varies through time. Nothing is perfect here, but it is consistent with the theory, and that is a big plus.

Peter van Dooijeweert:

Yeah, that's really interesting. We've been talking about creating convexity in some of our prior podcasts, but in a way where you're describing instead of buying a straddle, where you'd go to the market and buy convexity, we're actually using trend to create convexity. Does that sound about right?

Prof. Campbell Harvey:

That's exactly right. And this is really important for a portfolio to add some convexity because in bad times, at minimum, you want to do better than your competitors that don't have any convexity in their portfolio. And obviously, the downside is the most painful. And to have a program in place that reduces that downside is something really important. Indeed, it's the entire motivation of my book with Otto and Sandy, a Strategic Risk Management, where we argue that in designing the portfolio, you need to take into account the third moment, which is kind of the downside risk, not just the expected return and the variance, and to layer on at the beginning, not in an ad hoc way afterwards, but when you construct the portfolio, you inject certain strategies that have positive convexity. And in doing that, it reduces the downside exposure of the portfolio. Some people just layer on bolt-on strategies when markets start to get bad, and it's too late. You need to do this at the beginning and do it in a way that is efficient and affordable.

Peter van Dooijeweert:

Yeah, I think that's right, too. On our side, we frequently see tail hedgers come into the market around minus 10, 15% in equities when options are getting pretty expensive. And let's face it, the horse is kind of out of the barn. It's not quite at the glue factory yet, but things are getting expensive. And if you're going to be outsourcing that risk management to the options market for a price, well, that's probably not the most efficient way to run a portfolio. What you're describing to me is basically insourcing it, taking that responsibility and avoiding all that premium cost.

Prof. Campbell Harvey:

At the beginning, trend-following strategies have disability. And this is important. So I mentioned the economic foundations of trend-following, but there's also lots of historical evidence. And in our book, Strategic Risk Management, we had actually two out of sample episodes, one when we're writing the book and then the COVID-19, what happened, and we were able to actually do some out-of-sample validation. And the evidence is consistent with the basic foundational idea. So this is something that is important for your portfolio, and we applied in many different ways in this book, including rebalancing strategies and other things that are really super important for successful asset management.

Peter van Dooijeweert:

And so how important do you think that is, that trend is multi-asset?

Prof. Campbell Harvey:

What you don't want is a strategy that just works on one asset class because it could be lucky. So you tried the strategy on many different assets. It works great for one asset. Well, that could just be random. So, when you've got a strategy that's consistent and works across different asset classes, that tells you something about the viability of the strategy. Now, I said the mechanism might be a little different. So, when we say trend-following, that's a very general term. So there's many different ways to construct the actual trend, and that might need some fine-tuning depending upon the particular asset that you're applying to. So, for example, if the asset's highly volatile, you might need a slower strategy. And it depends upon auto-correlation properties and things like that to actually fine-tune to individual assets.

Peter van Dooijeweert:

So I think I'm going to come back to the speed issue in a second. In a way, I think this is somewhat related. So you mentioned 400 or however in many fake factors in the world. And they're full of asset managers or academics interjecting some hypothesis that they want to sell or broadly disseminate. And the narrative might sound good on paper, but what you've described is that these aren't truly factors that are not really investment strategies. So, I guess, what I'm curious is, are trend managers vulnerable to this? Are there things that trend managers are out there doing that you think might look a little suspicious or a little optimized?

Prof. Campbell Harvey:

Let me talk about the 400 factors first. And I mentioned that these factors are published in academic journals. And that most of these factors are not real. For example, the academic research doesn't take into account transactions costs. And as soon as you take that into account, you're going to haircut the alphas and potentially haircut them into negative territory. So most of these factors don't make any sense, they're purely academic, but many of them get packaged into ETFs. So a company might sell their strategies saying, "Oh, well, this was published in the Journal of Finance. Therefore, it must be high quality. But the publication, Journal of Finance, could be flawed on multiple dimensions. So one dimension is that it could be a data mining expedition. So the finding could be lucky. And the second dimension I just mentioned, the real-world transactions cost might not be taken into account.

So, for trend, there are situations where there could be overfitting. And let me give you an example of that. So suppose that you've got a very simple model where you're just looking at a lagged return. And you look back one week, and that doesn't work. Then, you look back two weeks, and that doesn't work that well. And then you go back one month and two months and three months, and finally, you get the best predictability using seven months and three days. Okay. So you try all of these possible lags. And you've got this great performance with seven months and three days. So as soon as you choose the best backtest, I can almost guarantee that that will fail and disappoint out-of-sample. So this is just an example of overfitting. So you never take the best backtest. And from an economic point of view, a lag of seven months and three days, that makes no sense, whatsoever.

Peter van Dooijeweert:

Right. It's random.

Prof. Campbell Harvey:

If you try all of these lookbacks, you're going to find something that looks really good, so it's better to have a discipline and maybe go 12 months or one month or three months, I'm okay with that. But to go through every single possible lookback and then just pick the best one, no, that's a bad way to do research. And unfortunately, we see this happening.

Peter van Dooijeweert:

So changing gears a bit. Yeah, you've been doing a little bit of work on crypto lately, speaking at conferences, writing books. Where do you see crypto fitting in the trend world, if it does at all?

Prof. Campbell Harvey:

So I've been working on crypto for the last eight years, so this is not a new thing for me. I've been teaching this for eight years. And yes, I recently published this book called DeFi and the Future of Finance. That looks at all of the potential advantages and also details the risks of this new space. So this is very young in this particular disruption, maybe one percent into this sort of change in the way that we think about finance. And opportunities present themselves in a situation like that. So these markets are not particularly efficient. And when you've got a market that's not particularly efficient, it is an ideal application for a trend type of model. So many asset managers are actually using, for example, the Bitcoin or Ethereum futures to apply trend models and capture a premium that's based upon that. So this technology offers a lot of possibilities here.

So right now, most asset managers are focused on trading like the futures, but now, we've got many different ways to get exposure including the physical. And it's interesting the way that this operates is not the way that we usually think of trading. So for example, a stock, you decide where you're going to list. Is it NYSE? Is it NASDAQ? In the crypto space, there are hundreds of exchanges and you can... Basically, it's not your choice. People will just start and exchange and list your token. There's so many possibilities here.

But let me give you one example that is kind of interesting and then it's the flash loan example. And let me go through this because it's, to me, one of the most fascinating aspects of this space. So a transaction in Ethereum can have many different steps. There are many different exchanges out there. And it's possible that the same token is trading at different prices on two different exchanges. So let's do some arbitrage, but we can do some arbitrage in a very strange way, in that, we don't have any capital, whatsoever. So this is the way the transaction works. Step one, I borrow some money. Step two, I take that money and buy the token on the exchange where the price is cheap. Step three, I'd sell the token on another exchange at a more expensive price. Step four, I pay back the loan. And step five, I keep the profit.

And this is arbitrage. And it turns out that this loan is uncollateralized. And it could be arbitrary size. You don't need to know who the counterparty is. It's got no duration and is risk-free. And how is it risk-free? Well, let me tell you that suppose it unravels a different way. You borrow the money. You buy the cheap token, but by the time you sell on the other exchange, the price drops, so you sell it for less, so you take a loss. Step four, you can't pay back the loan. And as a result, the whole transaction fails. You go back to the original state before you borrowed anything. So that's what I mean by zero duration. It's a fascinating mechanism of arbitrage in this space where effectively it means that these hundreds of decentralized exchanges are all linked together by arbitrage, which creates a giant network exchange. And again, there's so many possibilities here for applying simple strategies, whether it be arbitrary strategies or trend-following strategies in this giant new opportunity.

Peter van Dooijeweert:

I think, occasionally, trend will come under criticism because it's long equities in an uptrend. Are you particularly bothered by that, in a sense that trend being long equities is effectively adding risk to a portfolio?

Prof. Campbell Harvey:

I'm not bothered by it at all. Of course, you need to take the overall portfolio into account. So there's different ways to implement the trend. And it might be using many different assets. And some people actually prefer not to have that beta against equities, so there can be an implementation where you actually avoid that positive beta. And that actually can be customized. It delivers very similar properties except you don't have that beta. So it is a criticism that can be addressed. It just depends upon the preferences of the actual investor. If they want zero beta, fine, we can do that.

Peter van Dooijeweert:

Yeah, I guess, what I'm thinking of, as an equity investor, frequently, they cut risk into the uptrend to take profits and, in some ways, leave themselves under risks as markets recover or rallies extend beyond their own expectations. I suppose trend addresses that tendency to sell too early.

Prof. Campbell Harvey:

So that's true. So this obviously has to do with just active asset management, asset application that's tactical. This is a signal. So it is telling us something about the conditionally expected return. So given where we are, what is the expected return, and then you use that information in terms of your asset management. So I talked about rebalancing. I talked about just adding a trend into your portfolio to induce some positive convexity, but it's also possible just to use the trend signal as an asset allocation tool. So you're looking at your sector exposures. You've got trend models on sectors. It's telling you something about how sectors will do. There is a new area of research and academic finance that looks at factor momentum. So you might be having a multi-factor sort of portfolio. And the momentum signals, the time-series momentum, or trend signals could be very useful in dynamically adjusting your factor exposures to take into account the persistence in some of these factor returns. So trend enters the asset management problem in many, many different ways.

Peter van Dooijeweert:

I want to turn a bit to your work on "trend turning points." You've called this the Achilles Heel of trend investing. The problem that, in sudden shocks, trend is too slow to adjust and may reduce positions after a big gap move. Can you talk about that a bit?

Prof. Campbell Harvey:

That one of the big issues in trend-following is choosing the speed of the actual model. And let me explain what I mean by that. So, if you've got a model that's slow, which means it's looking back, let's say, a year, then if something happens in the recent data, it's going to be mixed together with the other 11 months. And you actually might miss a turning point as a result. Okay, so this is a disadvantage of the very slow. This is not reactive. And you might, let's say, continue to be long when the market starts to go down and miss that turning point.

On the other hand, if you've got a very fast system, let's say, a one-month system, then you might actually be tricked by just noise in the data. So you get a positive return, you go long, but that was just a random sort of observation. And the noise really reduces the profitability of the very fast system. So, I've thought about this for a very long time, and I guess the idea here is, can we design a system that dynamically changes the speed and that change depends upon economic conditions?

And I've got a paper that is forthcoming in the Journal of Financial Economics called Momentum Turning Points that actually make some progress on this particular issue, where what we do is we divide kind of the world into four different states. We look at two different speeds. So the fast speed is a one-month lookback. The slow speed is a 12-month lookback. And the four different states are a bull state, and that means that a past one-month return and the past 12-month return is positive. A bear state is kind of the opposite of that, the last month and last year is negative. And then, we look at two turning point states. So one turning point might be the long term or the 12-month return is a positive return. And then the one month is a negative, and that could be a correction. So we call that correction. And then the fourth state is kind of the opposite of that, where the 12 month of return is negative, but the one-month return is positive, and we call that rebound. So we can characterize every single return by these states. And what we notice is that, if you look at the month after the state is declared, that there is a massive separation between the realized returns after a bear state and the realized returns after the bull state. The difference is 15%.

And this is a real challenge to the current academic theories. The academic theories, say, if you are in a very tough economic environment, the expected return should be positive to compensate the investor for this really bad time. And our paper shows the opposite. So it's caused a lot of soul-searching in the economics profession. And again, it's forthcoming, but part of what we do in this paper is to try to design a strategy for these two turning point states, where you've got the correction and the rebound. And when you enter those states, you adjust the speed.

So I think I make some progress, and I think there's a lot more progress that could be made. The model that we present is a very simple model, but it does appear to be very promising. Indeed, it was interesting. We submitted the paper for review. And we tested the model from 1969 onwards. Basically, this is what we think, the reviewer says, "Well, they probably are showing the results from 1969 because it really works well. We've got data all the way back to 1926. Why didn't they show that?" So the reviewer collected the data going back to 1926 and applied our technique and found that it replicated. So basically, using the old data, it's an out-of-sample test that they did for us. And this is quite resilient and it looks good across many different assets.

So I think that this is... It was described to me, I remember one of the first meetings I had at Man AHL, and there was a discussion of speed, and I said something like, "Oh, well, you should have dynamic speed depending upon economic conditions." And everybody looked at me and smiled and said, "Well, Cam, I'm not sure you've realized, but that is the holy grail of trend-following." Well, anytime anybody says something like that to me, it gets my interest. So it's taken a while. I've made a little progress, and I think more progress can be made to improve the kind of reaction to trend-following strategies to turning points. Turning points are a real challenge for a trend strategy. That's where they usually fail. So anything that can reduce that failure rate is a good thing for investors.

Peter van Dooijeweert:

It's really interesting. As a practical manner though, I think investors are still stuck with more or less a binary choice between fast and slow. Maybe, there's some in the middle. Is there a continuum I should think of if I want more crisis protection or more return, some kind of simplified matrix for the people who haven't yet implemented trend and are trying to think about what it is that they want out of the strategy and what speed is the right one?

Prof. Campbell Harvey:

Well, my paper is available on SSRN right now, so anybody can grab it. Sorry, to be self-promoting here. And implement is very easy to actually do the implementation for this, so it's not a binary choice, fast or slow. You can have something that is switching through time in a very simple way. Again, you need to be careful here. And this is also documented in some of my papers with my main colleagues on crisis and the performance of different strategies.

So the crisis vary in terms of the actual behavior of asset returns in a crisis. So, for example, if you've got a crisis that is very fast, like October, 1987, that's a real challenge for regular trend-following strategies. And the only way to get that would be to have a very, very fast strategy. And the slow strategies could be very painful during that. So again, it's not that there's only one type of crisis. Some particular crises are very slow-moving train wrecks where others are very rapid. So, again, if you fine-tune the speed for one type of crisis, it might not be optimal for another type. And that's exactly the reason that you need to have dynamic speed.

Peter van Dooijeweert:

Yeah, I think that makes a lot of sense. If we look the last two crises, if we consider what we're in now in 2022, a crisis, this one is a very much a slow-moving train wreck, and COVID was really the exact opposite.

Prof. Campbell Harvey:

Yeah. In March of 2020, yes.

Peter van Dooijeweert:

So from here, I guess, I'm going to pop around a bit. And maybe, it'll seem a bit random in terms of questions, but one thing investment banks are often talking about is crowding. Emails go out from strategists that talk about crowding and try to predict what systematic strategies might do. Is this something you think is real? Is it imagined? Do they have it right? And maybe you just don't even look at it because it's sort of a garbage in, garbage out process that the banks are doing.

Prof. Campbell Harvey:

Oh, I definitely look at it. Indeed, I have a paper on crowding. Again, it's on SSRN. And it takes a completely different approach. It looks at asset managers, and it looks at the difference in performance of funds that have a single manager versus a team of managers. And the idea is that a single manager's only got so many ideas. And when funds come in, those ideas get crowded. Whereas with a team, and assuming the team is a diverse team, you've got many more ideas. And it's just less likely that you get crowding of those ideas. So I believe crowding is something that is very real. And indeed, if you think about a particular strategy, what could cause it to fail? So that's one way to step back and think about this.

So one thing we already talked about at the beginning, it could fail because it's been datamined and overfit in the backtest. And it was never going to work in the first place. It could fail because, and this is the second reason, you just encounter some bad luck. So it's a reasonable strategy. It's done well historically, but you're in a very bad luck run. Value's a good example of that where we've got 10 years of bad luck before it turns around. It could also fail because the world changes. So, there could be a structural reason that what worked in the past doesn't work in the future. And if you recognize that structural change, that is a reason to abandon the strategy or to reshape it.

And then the fourth reason is crowding. So this is a viable strategy. It's got a good economic foundation. It is done well historically. People jump into the strategy essentially bidding prices up, reducing expected returns to the point that the strategy doesn't look like it's working. Okay, so that's the fourth. Recent crowding is very real. We see this all the time in terms of asset management in particular where an asset manager might take on too much capital.

Peter van Dooijeweert:

I guess, by its nature, given that trend is trading multi-asset definitely less susceptible to some of those crowding issues, or at least it doesn't seem like it's been susceptible to those crowding issues. What do you think?

Prof. Campbell Harvey:

The sort of strategies that are often crowded are long/short factor like trend strategies. And a trend strategy obviously can be long or short. It does not have the same scale in terms of the amount of capacity as some other strategies. And I believe that it could become crowded. And indeed, what happens here is that let's say there's an uptrend. Everybody jumps into the uptrend. And the trade gets crowded. So what does that mean? Well, that means, as I said before, the price increases. So, effectively, the price goes beyond its fundamental value. So, what happens then? Well, there's another group of people that see that and that are basically playing the reversal.

So, with trend, there's this natural mechanism in there that once we exceed the fundamental value, somebody else is going to come in and actually cause a turning point. So I think that that's the main reason that we see this. But if it was unrestricted, then, of course, a trend could cause prices to, and some people criticize trend for this, to go well beyond fundamental value because people are just buying as the price goes up. But I don't buy that at all because there's another group of very smart traders out there that will detect if there's an overshoot or an undershoot and trade the other side. So I think that's the main reason. When I talk about these long/short factor returns, there's no mechanism like that. So I think that the trend strategy is somewhat resilient to the overcrowding.

Peter van Dooijeweert:

And I guess, a differentiating thing with trend is that we've got a lot of live track records across trend managers for a very long time, but with live tracks, you get sharp ratios. And I think something that people will occasionally mention is that sharp ratios on trend aren't particularly high over time. And I wonder if that's the best way to think about trend because when you talk about it from a strategic point of view, probably sharp ratio isn't the best way to approach it if it's a non-correlated asset with positive returns over time.

Prof. Campbell Harvey:

So this is a fundamental mistake that people make. And you see it sometimes where you look at the sharp ratios of different hedge funds styles. And you see some styles have a very high sharp ratio and other styles have a lower sharp ratio, and you might wonder, "Well, why doesn't everybody pile into the style that's got the higher sharp ratio?" Well, the reason is that the sharp ratio, the denominator is a very narrow measure of risk. And that's the volatility. And since we're talking about anything that's got convexity, well that goes beyond volatility. That has to do with the downside and the upside tails. So the reason that a hedge fund strategy might have a very high sharp ratio is that it's got negative skew, like big downside risk. And you have to compensate investors for taking that type of risk.

And then, there's other strategies that have lower sharp ratios. And people are still interested in those strategies because they've got positive skew or positive convexity, and that's valuable. So I think that you need to take that into account. You just can't look at sharp ratios. And indeed, I hear from many people, "Oh, well, trend-following strategies haven't done well for the last two years." And I said, "Well, what do you mean by that, haven't done well?" Well, they've underperformed the market. And they've got a minus two percent return last year and the year before minus three percent. And I'm thinking, "Well, that is the wrong way to look at it," because if you go back to the basic structure, this is a long straddle. And you get your protection in the downside. You participate in the upside. And then every so often, when there's not a lot of volatility, you pay the premium. And the premium might be a minus two percent or a minus three percent. And we pay premiums all the time for positive complexity. You pay the premium for fire insurance on your house.

So I think that it's the wrong way to look at it. Another way to look at it in terms of the academic side of this is that you might have a strategy with positive convexity. And often, we try to figure out what the alpha is of that strategy. And it's a big mistake just to say, "Okay, the strategy, here's the benchmark, market return. We estimate the beta, and there's some alpha." Well, that just doesn't work because that also assumes that the only things that matter are the expected return and variance. So instead they have a different benchmark, you would have, let's say, a market portfolio and then some options like that straddle. And when you put the straddles in and then estimate the alpha, there's a much different story. And that's the correct way to do it. If you're evaluating a non-linear strategy, then your benchmark has to have non-linearities in it also. So these are basic mistakes that people make.

And this goes back, many years, in my research program where I published a paper in the Journal of Finance in 2000 called Conditional Skewness in Asset Pricing Tests that basically made the case that we need to move away from the Markowitz Classic 1952 Nobel winning paper that showed the trade-off of expected return and variance. Indeed, Markowitz in the paper, on page 92, there's a footnote where he recognizes, and this is remarkable, in 1952, he recognizes that his framework doesn't work if there is a preference for positive complexity.

It's right there in the paper, yet people ignore that. And for decades, we've been doing the same thing, "Oh, what's the sharp ratio?" Well, that's just not good enough. The world is non-linear. The world is not normally distributed. And indeed, it's an exception to find something that's normally distributed. And on top of that, people have a preference for positively skewed outcomes. They do not like the negative skew or that negative convexity. And again, you can see it in the data. You can see it. As I said, different hedge fund styles have different sharp ratios. And the reason they're different is because of the convexity differences. So this is really important. And of course, kind of circling back, a trend strategy is a type of strategy that produces that positive convexity.

Peter van Dooijeweert:

Yeah, I guess, I can see the argument. If you want to have convexity, you pretty much want to pick assets with really high volume. And obviously, crypto's a pretty good place to go after it. I suppose on my takeaway from all of this is that, in the DeFi world, there's just a lot more work for asset managers to do to get themselves set up for all these exchanges and all these different forms of trading.

Prof. Campbell Harvey:

There is work to do, and custody is a big issue. And there's work to do, but there's opportunity. You need to take that into account also. So think of the world of the future, all assets will be tokenized. So stocks and bonds, commodities, everything tokenized. So what we think of as an ETF will be basically a token of tokens. And then, there'll be strategies that will be systematic. So you deploy a contract that has got a set rule or a number of rules that automatically executes and does active asset management within a contract. And it is a token also that you can buy. It provides so many opportunities in terms of the tokenization, in terms of efficiency.

In this space, when you execute trade, you also settle it. There's no delay. It's the same thing. And the cost of trading, already, we can see the decentralized exchanges have a cost of trading that's far less for the liquid tokens then so-called centralized exchanges like, for example, Coinbase or Binance. So there's a lot of opportunity in terms of efficiency. There's also a lot of opportunity in actually tokenizing assets that, right now, are relatively illiquid. And making them liquid, via tokenization, this allows the opportunity set for investors to expand. It allows investors to get a much more diversified portfolio. And that's all good news. So there will be benefits for investors. And this will provide very large opportunities for asset managers to actually figure out all of these new types of assets and how they fit into portfolios.

Peter van Dooijeweert:

So I can tell you're excited about crypto. And I probably have another million questions that we could keep going on, but it's not today's topic. So, if you're game, I'd say let's save that for the next podcast that we do?

Prof. Campbell Harvey:

That would be great.

Peter van Dooijeweert:

Thanks for joining us today, Cam, and we'll look forward to chatting a bit more about DeFi in an episode in the pretty near future. And thanks to everyone taking the time to listen to this episode of Trend Setters. We have a few more coming up with some pretty interesting topics, and we look forward to you joining us again.

Prof. Campbell Harvey:

Thank you for inviting me.