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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?
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.
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. 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, 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? 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 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? Campbell Harvey: Let me talk about the 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. 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. 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? 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. 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? 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. It may be necessary for those accounts that are subject to these charges to make substantial trading profits to avoid depletion or exhaustion of their assets.
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| Places to buy crypto | 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. A bear state is kind of the opposite of that, the last link and last year is negative. So suppose that you've got a very simple model where you're just looking at a lagged return. So these are basic mistakes that people make. Do not take financial advice from a podcast or video. The problem that, in sudden shocks, trend is too slow to adjust and may reduce positions after a big gap move. |
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The platforms and apps give you more investing options and let you gain exposure to both traditional and emerging investment opportunities. You're able to view your entire investing portfolio with crypto asset management apps, which keeps you better informed and able to make investing and trading decisions based on real-time information across all of your investments.
Crypto asset management is the practice of tracking, buying, and selling assets that are tokenized through a blockchain to ensure you're getting the best portfolio performance possible. You can check each asset in your portfolio individually; however, crypto assets can come from many different places, so managing them can become quite challenging.
It's easiest to use a crypto asset management app that lets you track your traditional investments simultaneously. What Is a Crypto Asset? A crypto asset is a cryptocurrency or asset that has been tokenized, which is the transfer of an object's value to a blockchain. The tokens can be fractionalized for broader distribution of ownership, much like dividing ownership of an asset into shares—but these shares are digital.
Since each individual's situation is unique, a qualified professional should always be consulted before making any financial decisions. Investopedia makes no representations or warranties as to the accuracy or timeliness of the information contained herein. As of the date this article was written, the author does not own cryptocurrency. Article Sources Investopedia requires writers to use primary sources to support their work.
These include white papers, government data, original reporting, and interviews with industry experts. We also reference original research from other reputable publishers where appropriate. Your favorite athlete, musician, or actor may be tokenized in the future and become part of your crypto asset portfolio. For the entities behind these platforms, the incentive is also clear: where there is an opportunity, there will be a company leveraging it to make money.
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You can view your holdings' performance through charts and metrics, and the robo-advisor can help you identify opportunities to exchange assets for others that are performing better. Any tangible or intangible asset can be tokenized through a blockchain. For example, shares of a company, fine art, jewelry, intellectual property, and even real estate can become crypto assets with shares offered to investors through tokens. These new asset management platforms allow you to do this as well.
These tools offer an easier pathway to entry and success for everyday traders who don't have a deep and thorough understanding of the crypto asset sector. Crypto asset management is different from asset management only because it includes tokenized assets—any asset that has had value transferred to a blockchain. The platforms and apps give you more investing options and let you gain exposure to both traditional and emerging investment opportunities.
You're able to view your entire investing portfolio with crypto asset management apps, which keeps you better informed and able to make investing and trading decisions based on real-time information across all of your investments. Crypto asset management is the practice of tracking, buying, and selling assets that are tokenized through a blockchain to ensure you're getting the best portfolio performance possible.
You can check each asset in your portfolio individually; however, crypto assets can come from many different places, so managing them can become quite challenging. It's easiest to use a crypto asset management app that lets you track your traditional investments simultaneously.
What Is a Crypto Asset? A crypto asset is a cryptocurrency or asset that has been tokenized, which is the transfer of an object's value to a blockchain.
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