Some better known examples of cryptocurrency include Bitcoin and Ethereum. Cryptocurrency is a relatively new form of currency that is not widely understood, hardly regulated by the government, and does not exist in physical form. There are ways to track cryptocurrency transactions, but the more traditional ways of monitoring financial transactions like bank statements, ledgers, or bank account numbers will be of little use.
Another difficulty cryptocurrency poses in equitable distribution is determining the value of these digital assets. Cryptocurrencies are notoriously volatile and can gain and lose value rapidly on any given day. 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? 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? 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. 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. 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? 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 month lookback. And the four different states are a bull state, and that means that a past one-month return and the past 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 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. 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 onwards. Basically, this is what we think, the reviewer says, "Well, they probably are showing the results from because it really works well.
We've got data all the way back to Why didn't they show that? 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.
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? 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, , 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 , a crisis, this one is a very much a slow-moving train wreck, and COVID was really the exact opposite.
Campbell Harvey: Yeah. In March of , 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.
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? 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. 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.
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This is not to say that there is no risk involved, rather than it is a risk that can be managed by undertaking proper due diligence of clients. Regarding volatility, this is also an area that can be managed to reduce the risk to the payee, and place the burden of any volatility on the purchaser. A Stablecoin is a cryptocurrency much like Bitcoin or Ethereum but is fixed to a specific currency and typically backed by assets cash or other assets to ensure its stability.
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