“The reason that we in the business is because we strongly believe that there is a need for investors to have this offset capability.”
We’re back with the second part of our conversation with the Head of Research at Rho Asset Management. In this episode we discuss the details of Managing Equity Curves, Trade Length in CTA systems and how Rho achieves to get the optimal position size when entering new trades.
Thank you for visiting, now let’s continue the interview with Tushar Chande.
In This Episode, You’ll Learn:
- HowRho creates for algorithms to decide the size of a new position
- The difference between the models in terms of trade length
- The trade frequency Rho Asset Management
- About the design philosophy in creating the profile of the Altius Program
“We live in the world of randomness.”
- Discussing the research cycle and the research reviews
- Major findings that led to the creation of the Altius Program
- Why trend following systems make money over time
- How the CTA strategies will overcome challenges in the future
- The main thing investors should take away as a benefit of investing with CTAs
“Leverage is a two edged sword so it’s very easy to get knicked.”
Sponsored by Swiss Financial Services and Saxo Bank:
Connect with Rho Asset Management:
Visit the Website: Rho Asset Management
E-Mail Rho Asset Management: firstname.lastname@example.org
Niels: You're listening to Top Traders Unplugged, episode number 006, where we continue our conversation with Tushar Chande, co-founder and head of research at Rho Asset Management. This episode is sponsored by Swiss Financial Services.
Introduction: Welcome back to Top Traders Unplugged, where the best traders in the world come to share their experiences, their successes, and their failures.
Let's rejoin the conversation with your host, veteran hedge fund manager, Niels Kaastrup-Larsen.
Tushar: Regardless of what our testing may have been, you can see and look at our real-time track record, and see that we have controlled our risk very well on a daily basis. As well as on a drawdown basis for the entire program.
Niels: And so once the position is on, I guess there are two schools of thought: some managers actively manage their position size, and some people leave them as is.
What's your preferred way of doing things, and why did you choose to go this route?
Tushar: Yes. Again, if you remember, Niels, when we started off, we said that we are trying to think like or capture the behavior of discretionary traders.
So if you're a discretionary trader, your typical style is you're a little hesitant to get in. You're slow to get in.
Once you get in, you may get in with a large size or a small size, based on your feeling about what the trade is going to do.
And then some discretionary traders will adjust their position on day-to-day, but most will just leave it alone.
So that's the same thing as trading with a very wide stop, and then suddenly something will happen in the environment. There'll be some news item like, for example, the events in the Ukraine this weekend.
And then you'd come in on Monday morning and sell all your positions. So then suddenly your stop that was very wide will instantly become a market order to exit. So our stops behave in a non-linear fashion. And we use many different exit strategies in combination, and they're different strategies and different systems. So that we are looking at things like market volatility, things like overall volatility, things like time and trade to decide whether our stops should be far away or close together.
Now, part of the reason we can do that is because we have a breaker-style system. That gives us a much better ability to manage open trade risk, for example, when we had the fantastic rallying Swiss franc sometime back.
If you are a moving average type manager, or if you're a manager that wants to control your risk in a narrow period, then what people do is they will, say, look at some period of time like 100 days, and measure the standard deviation of, say, 100 days.
And say that, "We don't want to increase our 100-day standard deviation or some other measure of volatility beyond a certain level."
So let's say that for simplicity, we'll say that we use 100-day standard deviation and we don't want that to exceed, say, 2%.
So if that 100-day standard deviation becomes 2.1%, then they will reduce their positions, so that the volatility will now gradually come back below 2%.
Now, this is a complicated process that adds to the cost, because it increases the frequency of trading. It also means that sometimes you're penalizing a position that's been profitable, and has a long way to run.
It also means that there's always some lag between the time that you decide, "Okay, I need to adjust my position," and what the market volatility actually does.
So in many ways, trying to actively manage your equity curve imposes an external exit condition on your models, which really can't be tested. Because you don't know what the volatility is going to be in the future.
So we feel that our approach is a much better and cleaner approach that can be more easily implemented. And clearly, our real-time track records show that we have controlled our risk in a reasonable way.
So we feel that our approach is more consistent with what's best in terms of returns versus risk. And also more accurately captures the behavior of discretionary traders. So it's a way for us to differentiate ourselves.
And it's also a lot simpler than trying to actively manage our equity curve. Niels: And of course, we are talking about the main trend following models in the overall program.
And just out of curiosity, would any of them use any stop-profit targets, or is it just stop-losses that you would use, even though of course a stop-loss could happen...and exit at a profitable trade?
Tushar: Yes. We do not use any profit targets. Now, this is one of the core principles of trend following.
If you study trend following and you look at distribution of the profit and loss from individual trades, what you see is that you have a distribution that is skewed to the right.
What that means is, you have one or two trades that are extremely profitable where you make virtually all of your money. And most of the trades are somewhat profitable, or somewhat unprofitable.
And they tend to cancel each other out, so the whole philosophy of trend following, it's profitable over the long term, because you are not in a rush to get out of your position.
So we like to say that we take our profits slowly, but cut our losses at once. So it's top scholar, if you will.
And given that philosophy which is central to the core principles of trend following, we feel that there is no particular reason for us to have a profit target, because that would be counter-intuitive.
Now obviously, if the markets are in a narrow trading range, or even a wide trading range, where they're trading between some upper bound and some lower bound, then obviously a profit target makes sense.
Because you're going to take a profit against this boundary for a long trade or a short trade. However, we know at some point there's going to be a breakout.
The market's going to start moving and have a fantastic trend, and that's what we want to capture. Which means that we need to put on as large a position as we can as quickly as we can, or as close as we can to the start of the trend.
And then carry it up or down as close to the critical endpoint of the trade as we can, and that's what we've tried to do, and that's why trend followers have been successful over the long run.
We feel philosophically there's little justification for using profit targets, because that's self-defeating, even though obviously it would be attractive during some months because of the market's trading in a narrow or wide trading range. Niels: And of course you mentioned something interesting. You're saying that what you're trying to achieve is to get the biggest possible position on for hopefully a very long term trend, but how do you distinguish? Do you use any kind of variable position sizing that allows you to do that? Or is everything fixed in advance with taking the same level of risk every time you get a signal?
Tushar: The answer's that we actually have a way to change the initial risk on a trade algorithmically, using fixed rules, based on what we see happening in the market.
So again, in our discussion about how we are trying to capture the trading strategy of discretionary traders, we have said that in some cases discretionary traders will feel very strongly about a trade.
Because, as we said, good example of a change in leadership in a country like Japan.
So you can change your...put on a much larger size. Or, for example, you knew that when the Fed decided to intervene or start QE2 after the famous speech by Dr. Bernanke at the Jackson Hole meeting.
You could use that as an input and put on a very position in equity market because you knew what was coming. It was telegraphed very clearly. We don't have the luxury of doing that. Because we use the same rules, and we want an even, automated process.
But we do have a process by which we don't have to take exactly the same risk of however many basis points per trade for eternity.
But we sometimes use what we call a "booster." For our trend following products, we can either go in one step, we can have 1-X risk and step it up to 2-X risk, or we can go in steps. So we could go with 1-X, 1.25-X, 1.5-X, and so forth.
Now, why is that relevant? Because unlike some other trend followers, we're not trying to add to positions, so our whole focus is trying to put on as large a position as we can, as close as we can to the initial point of the trade.
So that's why we spend a lot of time trying to come up with algorithms that allow us to automatically change the risk from say, 1-X to 2-X in some form. Either in a single step, or in multiple steps.
So that over time it does add significant value. As we know, we just have to get it right once or twice a year to make a significant difference over a long period of time. Niels: Now, in a sense, we've talked a little bit about the trend following side, three of your systems you mentioned are following this type of approach.
But of course in slightly different ways...but you also mentioned you had a third group that trades slightly differently.
Perhaps you could just summarize some of the points we talked about on the trend following systems, but put it into the context of this third group...that I guess, from what you've said, is what you use to try and protect against big events in the markets.
Tushar: Correct. Now, if you study trend following and if you have invested with trend follows, you know that one of the primary failings of trend followers is that they're not quick to recognize key turning points.
And that's by design, because we know that...
We have discussed that we want to put on as large a position as we can, and hold onto it as long as possible before we exit, in order to capture these very, very few megatrends that occur randomly in the markets.
Because we are positioned to capture the megatrends that go on forever, by definition we have to be able to sit back and absorb a lot of volatility.
A lot of countermoves to the underlying trend. Because we don't know where the counter-trend move is going to stop and continue, so that the trend will resume. That, we've addressed using our first two groups of systems.
Group three systems are really a counterweight to group one and group two...where we see that, yes, we know that some trends are going to go on forever, and we have that capability using groups one and groups two.
But we still need to have some buffer in the portfolio.
And we also have mentioned that one of the key design features, or one of the key reasons why investors or allocators would like to have CTAs in their portfolio is to be able to react and offer a positive offset when there are sell-offs in the equity markets.
Now, in the good old days, these sell-offs were very gentle and used to come on very slowly.
But now with automation and computerization, and Twitter, and all the tremendous speed at which information can be dispersed from one point to another, the rate at which markets respond to a perceived adverse event is much faster today than it was 10 years ago, or 15 years ago.
So that's the challenge we are trying to address with our group three systems.
Now, the other side of the coin is that we cannot have a system that's so sensitive that it's going to change positions for every little wiggle of the market.
So we need to obviously smooth out some of the market volatility. And yet somehow, be able to recognize that this change in the direction of the market is potentially more significant than some other change in the direction of the market.
So you can see that there's always some trade-offs involved in terms of how fast you want to react and so on and so forth.
But what we've done with group three is, we've primarily designed it to go short the equity markets and go long the bond markets, when you have a rapid reversal in the equity markets.
So we're trying to reinforce the primary reason for owning CTAs in the design of our group three systems.
Niels: Excellent, Tushar. And just out of curiosity, kind of give a benchmark idea about how different the models are in terms of their trade length. Just to give the listeners an insight to how long they're looking to be in a position for.
Tushar: Right. Our first two groups, which are trend following in nature, are trying to hold a position from, say, one month to three months.
And the group three systems are typically holding it shorter than one month, but we don't have any systems that are shorter than two weeks.
If you just use two weeks one month and one quarter as the rough benchmarks, then our medium-term system will hold a trade from, say, one month to one quarter.
Or a little bit more than one quarter, and then our group three system will hold it, say, approximately one month or maybe a little bit longer.
Niels: Let's shift gears a little bit, Tushar, away from the individual models...and just talk a little bit about how trading limitation takes place.
Maybe you could just run through a little bit about how many times you need to run your systems per day when it happens. And maybe also a little bit about how long it takes to run a whole program like this.
Tushar: Typically, we've said that we are an end-of-day trader. That means we only need to do it once a day, but what do you mean by once a day? Because we have many different time zones. We have some markets that are in the Pacific Rim.
So they have their own time zone. Then we have markets in Europe which have their own time zones, and then we have markets in the US which have their own time zones. So in effect, we have to run the system once a day in each of these time zones.
So even though we're running the system once, we're actually have to run it three times except, of course, on a holiday or the weekend, because we are catering to three different time zones.
Now typically, it takes somewhere maybe less than 30 minutes to run the system.
Sometimes it may only take 5 or 10 minutes to run the system because we have to download the data. It's totally organized and systematized, we just run it. It spits out the tickets and then we send the tickets on to the brokers, then you're done.
So some days it might take a bit longer, but typically we say less than 30 minutes, so somewhere between, say, 10 and 30 minutes should take care of just about everything you're likely to encounter during the year.
Niels: And I guess there will be days where your system doesn't even have to...it doesn't even trade when it is a medium-term trend-following system. Would that be right?
Tushar: Correct. We have about 800 to 1,000 round terms per million per year, so we are not...say There are many days where you may have no trades at all.
Typically we'd only have one or two trades a day, and on a busy day we may have four or five trades, so if we have, say, more than five trades and we're having a very busy day. And most of the time, we're doing zero and two trades.
Niels: Excellent. I also wanted to talk a little bit about risk management. I know you've touched upon on it on your talk about how the models work. But I just want you to try and maybe describe and define what you mean by risk, and how the program is taking that into account.
You mentioned the integrated risk management as being part of the design. So maybe you could speak a little bit about that.
And also how you report and inform investors with regard to risk, because obviously many investors are very focused on this particular point.
Tushar: Yes. Niels, we are very aware of risk. As CTAs, one of our primary responsibilities is to control risk. We have a lot of leverage, and the leverage is a two-edged sword. So it's very easy to get nicked, so what we've done is...because of the design of our systems, we are a breaker-style trader and so forth, as we have discussed. And we're putting on a position all-in or all-out, and a number of positions can expand or shrink.
What we've done is we've embedded our entire risk management into a single, integrated trading platform. But what you also have to recognize is that risk management is not strictly a number, like calculating via VAR...it also means being aware, fixing any trading errors. It also means submitting the orders on time, confirming the brokers have received our tickets and so forth.
So risk control is a very elaborate process that involves many different activities.
Making sure, for example, that our statements check out every day, that the out trades...the trades that are on the statement are also the trades that we should have based on what trading we've done. So to knock out and eliminate and fix trading errors, and so on.
So risk is in many different forms, but the simplest one that everyone thinks about is value-at-risk, which is typically a one-day event. And we certainly provide all that.
Because we have enough consulting with our outside accounting resource, which give you very elaborate reports, which give you detailed insights into all the positions, and what they've done today and this week and this month and this year, with all the value at risk, and so on and so forth.
All that information is available, and we'll give it to you free. And we'll update every 24 hours and it's provided by an outside third party, so there's no opportunity for us to play any mischief.
Now, what we have done is that, rather than worry about just value-at-risk which only gives you one day's worth of control, we're taking a much longer view. And we're trying to limit our overall peak-to-valley drawdown.
Now, I've already told you what our model is, our basic model is. That'd be, for a diversified trader like us, the drawdown risk is typically three to five times a monthly standard deviation, so part of our job is to make sure that we have weighed our risk, initial risk, so that our long-term standard deviation is around 5%, a little bit more, a little bit less.
Based on what's happening in the markets, and so our risk, multiply that by four, is about 20%, and we've done approximately that, a little bit more, a little bit less, depending on which particular time window you look at.
We've gone beyond merely looking at one day's risk in terms of the value-at-risk or so forth, but we also try to limit our total peak to valley drawdown risk. So that's quite different from what other people have done, and we've totally integrated this into our algorithmic process.
So it's not anything we need to think about. It's just automated.
Push a button and it takes care of itself seamlessly. But we've also gone beyond just looking at drawdown risk to also worry about avoiding trading errors, making sure that all the tickets go out on time, making sure we have the proper backups for data, and so on and so forth.
And our various trading records and software and servers, and all of the back office infrastructure needed to automate and deliver a very consistent execution process.
Niels: And just out of curiosity, correlations. A lot of people talk about correlations, and a lot of people put a lot of emphasis on that. Just very briefly, what kind of approach did you take to the point about correlations in your risk management?
Tushar: Typically, the problem with correlations is that correlations are not static. So if you look at the correlations from, say, one group to another group over yesterday, over the last ten days, over the last ten years, you get very different answers.
So we've try to avoid using correlation directly or explicitly in our day-to-day risk management.
Because it's too unstable, and also it doesn't fit with our problem, that we don't have the same number of positions every day.
Sometimes we can have a lot of positions, sometimes we have very few positions.
But of course, by saying that in our overall long-term test we want to adjust our initial risk to maintain a certain long-term standard deviation, in effect, we have smoothed out the correlations over the different sectors into a single number.
And it's smeared into a single number called our long-term standard deviation. We're not looking at day-to-day or week-to-week changes in correlations, because we think they're too unstable.
But we have implicitly absorbed and averaged out the correlations between sectors over a very long period of time.
Niels: Now, drawdowns is of course part of... The risk management is of course, trying to avoid too many drawdowns. But drawdowns are important, and it tells a little bit, I guess, about how the design of the program has been done. Maybe you could just recap for me.
I know you mentioned that the worst drawdown of the Altius Program occurred in February 2010, lasting 20 months. Perhaps you could tell me a little bit about how long a time it took to recover this drawdown, initially.
Tushar: We recovered in 18 months. And again, you have to think of this in the context in what is happening in the environment to go back to where we were. Was it a hot, very windy day on the golf course, or was it a very rainy day on the F1 course, or was it icy at Sochi?
Or very slushy for the skiers in Sochi, and so forth. You have to think of the time to recovery in terms of what is happening in the environment, what is happening in terms of the portfolio weights and the system design. But our recovery time was 18 months. Niels: I guess this is a little bit of a philosophical question. But the drawdown profile that you wanted to create with your design of the program, does that take into account the kind of investors that you want to attract?
Or is more a personal taste, meaning that some people have very clear ideas and opinions about what is the best way to trade the markets, both from performance and a drawdown point of view.
Or how did you go about ending up with the profile that the Altius Program has?
Tushar: We had a very clear design philosophy, as we said. In terms of why we are putting certain systems together, why we have different groups of systems. In terms of how we want to respond to the markets, and then also about the breaker style.
And how we've embedded all the risk management all into the overall design of the systems. It's hard to find a consensus on what's the best way to do this. Because clearly different people...someone may have a trading horizon of ten minutes and holding a position for two minutes is a very long time for them.
On the other hand, there could be some traders for whom holding a position for three months is too short for them.
Managers as well as investors come in many different types of preferences. We try to find a solution that we think is sustainable, that expresses our belief that you need to carry the trade as long as possible, you need smart exits, and you need smart entries.
You need to have smart risk management. You need to have smart initial risk allocations. So we've tried to be adaptable and flexible while using the same rules in all markets.
In many ways, these are difficult conditions that we somehow have to reconcile and trade off, and I thought we've done it pretty well.
Niels: Tushar, I wanted to jump to something different.
I wanted to jump to something that is very close to your heart. And that's the point of research. Maybe you could just talk a little bit about some of your general research cycle, and how you go about these research reviews.
Tushar: Again, that's a... Well, first of all, we do research all the time. We are constantly thinking about, "Is there a better way to do what we do?"
And of course, we have to account for what's happening in the markets, and there's always a question of, "Are you changing too soon, are you not changing quickly enough?"
And sometimes it's really difficult to answer these questions, because they're ambiguous.
As we've said at the very beginning, we live in the world of randomness. So it's not a cause-and-effect world, in other words.
Which means that just because you made a change doesn't mean that you're going to find out whether the change has been any good for quite some time. And conversely, you may not get any benefits from the change for a long time.
So making changes is a tricky business. My certain view is that we're doing research all the time. If we can find a significantly better way to do things, we're happy to make the change.
In general, in my experience, it's best to make a change when you're at a new equity high. And of course the trading conditions have been difficult.
So we've had to make some change in our responses based on the unique role that central banks have played in the last few years. Which probably won't be repeated for quite some time, hopefully.
I think it's a tricky question, but you always have to keep doing research, and you have to keep asking yourself whether this is the best time to do it.
But it really goes back to the basic design philosophy and the structure of the program. And I think if you're clear on that, then it's very easy to decide whether a particular change really adds value or not.
Niels: And if you look back since the inception of the Altius Program, is there anything you could point to where you say, "These were major research findings?" And perhaps what prompted you to discover what you discovered, and make the change?
Tushar: Certainly one of them was the major role of the central banks.
Because clearly, if you look at the data leading into the crack-up of Lehman, there's nothing in the data in the last 15, 20, 25 years to indicate the extraordinary role played by the central banks.
So they had a very specific effect on the bond markets and the equity markets that were really unprecedented. So that's clearly something that we had to come to grips with.
The second part of this whole equation becomes being able to characterize the environment.
Because part of the problem is that because the sectors were...or because the trends were so limited in terms of where they were occurring, you certainly...we had to find some way of quantifying the trend strength. And one of the research outputs was something we call the Rho Trend Barometer that allows us to set the context. So we can understand our performance, and the performance of the others, in terms of what's happening in our portfolio.
As we talked about the sports analogy, a certain golfer's swing doesn't change necessarily. But the high winds are going to change the way he has to play shots.
So the same way, we need to understand our system rules are preset.
They don't know what's happening in the environment as such, so we have to decide whether a predictable set of rules can be defeated by the environment due to what's happening.
Niels: How different is the Altius Program today, would you say, compared to when you started off six years ago?
Tushar: Not very different. As we said, we started off with three systems, and now we have six. Our core systems, the first three systems, continue in essentially the same form with a few minor tweaks.
And the new systems are consistent, and reinforce or fill in some holes that were left in the design by virtue of the first three systems.
But they're all breaker-style systems. They have the similar design philosophy in terms of entries or exits or trade sizing.
So in a way, we have not changed very much, and if you look at our live equity curve, versus the simulated equity curve from day one of the system that we are using, they look very, very similar.
But we've had to reduce our volatility in the markets, because of the extraordinary events of the recent past.
Niels: Of course investors always want firms to do research. And I don't know what your general view is.
Of course, I guess a lot of people also believe that the more Ph.Ds you have in your research team, the better your research is. But do you have any experience that you might share, and view on this particular topic?
Tushar: Well, if you just look at the track record of real-life CTAs, you'll find that you don't have to have a Ph.D on your staff in order to have great returns and having a boatload of Ph.Ds doesn't guarantee you that you're never going to have severe drawdowns. So having Ph.Ds is not a necessary, nor sufficient, condition for a great performance. Having said that, clearly Ph.Ds can add value, especially for larger managers. They have more issues to grapple with. And they need more different kinds of systems.
And like all individuals, certain individuals might be good at one type of problem and some other type of individual might be good at solving some other type of problems.
So it's really a case-by-case basis, and certainly the larger managers can make a stronger case for hiring a larger research staff.
Niels: Is there something, by the way, about maybe doing too much research? Can you over-think things?
Trend following has been around for three, four decades, and generally has been very successful.
Sure, we've had a spell now of three, four years where performance has not been great. It's not been a disaster either but is there something to say that you can over-think and do too much research, do you think?
Tushar: Well, a leading question, [laughter] Niels, but, yeah, it's certainly possible. It's certainly a possibility that you can make too many changes.
And as we've said throughout this conversation, we live in a world of randomness. So the mere fact that you have made a change in response or something in the recent environment doesn't mean that the change is going to produce positive effects forever into the future.
Because the market environment will change.
And the market for sure will create a new environment that defeats whatever changes you made in response to some previous state of the environment.
And you just have to look at the track record of the largest managers, which theoretically have the most number of Ph.Ds and the maximum brainpower, if you will, applied to the problem.
And there are numerous examples where CTAs or managers have blown up, or have drawdowns significantly greater than their record previously or their research might suggest.
Yeah, maybe in some cases you might be doing too much research. But you have to remember that the more things change, the more things stay the same.
Trend following requires a certain discipline, and a certain ability to absorb volatility, and ignore the day-to-day noise of the business.
Niels: And before we jump out of the research area, at its core, in your opinion, why do these systems make money? Is it because of economic events? Is it fundamental? Why do these trend following systems make money over time, do you think?
Tushar: Primarily because you have one or two really massively outlier trades that are hugely profitable.
And way more profitable than you could ever imagine, or you'd be led to believe or expect when the trade is initiated.
And that happens because there's some new information that changes the market, or that has not been properly factored into the market.
For example, think of the drought in California that's currently going on. This is the second year of a severe water shortage in California, so that's going to affect crops, it's going to affect the cost of feeding, say, animals and livestock.
And so cattle prices are going up, or hog prices are going up.
Now, no one knows when this drought will be resolved. We don't know how long the supply/demand imbalance will persist, and the market will adjust and consumers will adjust, and then eventually the price will find some equilibrium.
So the reason great trends occur is because there's some sort of change in the background or the environment for that particular trading instrument. And the market is not quick to recognize, or the driving forces drive the bus or drive the trend far longer than expected.
If you look at the Swiss franc, which had an incredible trend a few months ago or a few years ago, it went much farther much faster than anyone could've ever imagined, and this has happened over and over and over again, because that's how random this works.
So the basic philosophy of trend following is that we have to wait for these megatrends to occur.
And we can go through a period when there are few or evidently few or no megatrends, as we've had in the last few years.
Or you could have a period where something happens that causes a huge trend in the market. And the only way to catch it to be systematic and disciplined, and put down your position and sit tight and wait for the market to go wherever it goes, which often takes longer and travels further than anybody expects.
Niels: Absolutely. Tushar, before we wrap up today, the CTA industry has gone through a rough time, as we've alluded to a couple of times during this talk. How do you see it overcome these challenges, and what is it that will perhaps reignite the interest in these CTA strategies, in your opinion?
Tushar: Certainly the primary way to reignite interest would be a return to profitability by trend followers. That could happen when trends emerge, it could happen when there's a sharp reversal in the equity markets, and CTAs can reposition faster than other strategies, like as happened in 2008.
It could happen when people realize and recognize that the role of central banks in the marketplace will diminish. As it will eventually, if not tomorrow, maybe next year or the year after.
Certainly the emergence of interest will be driven by better performance by the CTAs industry as a whole, by a lack of performance in other key sectors such as equities and bonds.
And of course, emergence of great trends due to supply/demand disruptions, or due to one reason or another.
Niels: Now in the last few decades, we've seen an enormous growth in the number of CTAs. And therefore obviously, I guess investors can find it hard to distinguish one from another.
If you were just going to summarize the key points about Rho and the Rho Altius Program in particular, what would you say that is? What should investors take from this talk that you and I have had today?
Tushar: Precisely what we said at the very beginning, that the primary purpose of CTAs is to provide offsets to portfolios of equities and bonds. And typically, these offsets have costs if you were to buy or put...some other form of protection. However, because CTAs can be profitable, holding them is less onerous.
It doesn't cost as much as buying an outright insurance policy. So I think the primary reason why we need to have portfolio...a CTA in your portfolio is to give you offsets when the sustain declines in the equity markets and the bond markets.
And Rho has spent a lot of time and effort in designing systems that are geared specifically for responding to changes of the equity and bond markets over different time frames.
And we've demonstrated and proved that we can give positive offsets on time intervals ranging from one week to one month to six months to a year.
And then we have to recognize that investors by definition are going to long-equity and long-bond, so the strategy that they're using is long-equity and long-bond.
So what are the offsets of their strategy? It's the ability to go short, the ability to trade other instruments, to ability to react faster than the current strategies, and we deliver all of that in Rho.
So I think we need to make sure of course, that there's a high probability that Altius will make money. And we've done that when there are trends in the markets.
So the reason why we've designed the program is because we want to provide this positive offset.
The reason that we are in the business is because we strongly believe there is a need for investors to have this kind of offset capability, this sort of insurance or protection in their portfolio.
They need to be able to respond at high speed. They need to respond consistently, they need to do it in an unemotional and consistent way, so that you're not relying on the discretion of somebody.
You don't have to worry about somebody's finger freezing on the button, if you will.
Because we have a systematic and disciplined approach to delivering this offset performance for the investor when they really truly need it: when markets are moving rapidly, or they're moving in a confusing way.
Niels: Now, just to finish on a slightly different note, of course there are lots of listeners today who are not necessarily investing with CTAs but in fact are trying to become a CTA. What would you say it takes to become a great trader or CTA, in your opinion?
Tushar: First of all, you have to be an optimist in this business to survive.
You have to bring an original point of view, why you want to do it a certain way. And you need to be able to explain to people in a way that makes sense to them why you're doing what you're doing.
And I think with those three things, you should be able to be successful as a CTA.
Niels: Tushar, this has been a great conversation. I appreciate your openness, your willingness to share your insights and your views on your strategy and Rho as a firm, and the industry as a whole. So have a great afternoon. Thank you very much.
Tushar: Thank you, Niels. Have a great day. Ending: Ready to learn more about the world’s Top Traders? Go to TOPTRADERSUNPLUGGED.COM and signup to receive the full transcripts of the first ten episodes of the show, and visit the show notes where you can find useful links to other amazing resources. Thanks for listening and we'll see you on the next episode of Top Traders Unplugged.
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Date posted: 12 Jun 2014no comments