Welcome back to our conversation with Jason Gerlach of Sunrise Capital Partners.

In this interview we discuss the history of Sunrise Capital Partners, the current evolution in their financial product line and the inner workings of what it takes to make it as a successful trading organization in the ever changing market conditions.

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In This Episode, You’ll Learn:

• Exploring the design structure of the Sunrise Evolution program: Quadrant A, B, C and D and how they play together
• Detailed examples of how trading models can be different.
• How trade implementation works with complex systems like these
• About the Volume of trading the Sunrise Capital does each day
• Why the game of picking sectors is a fools errand
• Strategies for dealing with the emotional weight of drawdowns
• Sunrise Capital Management’s approach to research
• The traits and what it takes to become a great CTA

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Jason

Sure, so the way I like to explain Sunrise Evolution is, it's just that - an evolution. In other words, nothing we've done historically has been thrown out - everything we've done historically is still in Sunrise Evolution. But if you envision a piece of paper and you divide it into four quadrants, what we've done is we've taken our legacy approach, our long-term trend following approach and put it in one quadrant. And for explanatory purposes you can call that quadrant A. So in box A, quadrant A, we have a grouping of 50 medium to long-term trend following models that we think are highly evolved and quite good at what they do, but at the end of the day they are trend following models.

So in environments where trend following is not particularly effective, those models will probably not be particularly effective. We're happy to admit that. We're the first to say that there is no perfect system out there - every system has weaknesses. The key is, in our view, bringing together complementary systems that have strengths that cover for the weaknesses of other strategies, and that's really what Sunrise Evolution is all about. So in box A, quadrant A, we have our long-term trend following models - still working. They can have as much as, you know, nearly half the kind of risk in capital allocation at times, and other times, depending on where we are in the market cycle, they could have very little of the capital and risk allocation of what we're doing. It's all is going to depend on the environment we're in.

Quadrant B, we've installed a set of shorter-term, what we call Impulse Recognition Models or almost kind of trend or mean divergent models. In other words, these models look in shorter time frames, they're looking for particular patterns spin off of trends that we found to repeat themselves particularly in choppier market environments. And you know, the set of models in family B holds trade on an average of three days or so, versus quadrant A, our longer medium-term trend following models which can hold trades on average of 40 to 50 days. So very different models looking for different things, complementary. The correlation between those two families of models is in the point two range, so they're very different approaches.

Niels

True.

Jason

Quadrant C is another set of models; these are more reversionary in nature. They're not pure mean reversion, but they're looking for a different inefficiency in markets, they're shorter-term. Again, they hold in kind of the two to three day range as opposed to the several month range of system family A. They will look for a slightly different inefficiency and a slightly different pattern than those in B, and so the correlation between the models in box B and model C is quite low.

Niels

Sure.

Jason

And then lastly we have a fourth quadrant, D, where we've put something that's quite different; it's actually the genesis of our standalone equity models. And what it does is simply try and use a variety of trend following and pattern recognition approaches to capture bursts of beta when the US equity market is in a bull period. And you might say,"Boy, that's an odd thing to add to this," but when you assess it and look at how it correlates to the other three quadrants, again, the correlations are very low, and that's really what we like to see.

Niels

Sure.

Jason

vy R&D days in the, you know,: 2010

Niels

And do you allow all models to go long and short? Because of course, a lot of people may not be familiar with the fact that trend following as such, 90% of the profits over time are made from the long side, not the short side.

Jason

Yes.

Niels

I mean do you take that into account? I mean I could imagine that group D might not really be looking for short size, but I don't know this, but I mean . . .

Jason

Yep.

Niels

. . . do you look at that as well?

Jason

Sure. I mean first we tried with each quadrant; we try to build something that as a standalone, is as good as it can be, independent of everything else. And so in that lens, we think each of them are fairly robust on their own and they're allowed to do what they do. So system family A, long to medium-term trend following is a long/short, just like any, you know, solid trend following . . .

Niels

Sure.

Jason

markets - it had a fantastic: 2008

Niels

True.

Jason

But at the same time, you know, it can also do very well in long environments. Last year system family A was quite strong; it captured a lot of the long equity rally, the bull equity rally we saw last year. But it was also very good in gold and copper, which went in the opposite direction. System family B also can go long and short; it doesn't trade in as many markets as A because it requires a little bit more precise execution and there's some markets that we're not comfortable with in terms of liquidity and how they function.

Niels

Sure.

Jason

So it trades a more limited set of markets, but again, it can be long or short. Now, there could be a situation where a system A is very long in something of a system B is short because it's seen a shorter-term impulse pattern off of a trend, for example, that it finds to be a good opportunity. So there are plenty of occasions where A will be largely long in a market and B might have some short positions in the market. And that's okay, because our view is each of these models should be allowed to kind of be what they are going to be independently - we've built them all to run well independently. We've decided to marry them together after the fact because they complement each other very well and because we've come up with what we think is a very good systematic approach to balancing them, which we can get to. C can also be long or short; it trades a subset of the markets that A does, again for execution concern, liquidity issues, and just the fact that there are so markets with a pattern C is looking for we've just found don't exist. And conversely there are so markets that work very well with C that don't work as well with A or B; so that's, you know, again, treating each model family as its own entity first and understanding it in-depth and worrying about how it interacts with the other models as a secondary measure, is kind of the thinking there. And then D, long only.

Niels

Yeah.

Jason

So it can be long equity or a can be in cash - that is it, it does not take short positions at this time. It might down the road as we continue to evolve it, but right now we think it performs best when it is capturing beta from bull rallies in the US stock market and when it is sitting quietly in cash during periods when the US equity markets is stalled out or declining.

Niels

Hmm. Makes sense, I mean each group needs to, has a purpose in the portfolio and diversification is key, so I mean I think you've resolved that in a very unique and an interesting way.

Jason

We think so, and I think, again, what we like people to focus on when we presented them, and by the way, anyone who's hearing this podcast, you know, we're not going to have anywhere enough time for me to go into the kind of depth that I'm sure most investors want me to. You know, we welcome discussions where we can get more in-depth in this when people reach out to Sunrise individually. But for purposes of today, you know, what I want to emphasize if nothing else is, the fact that ultimately the key to all of this is low correlation. You know, I have in front of me a chart that summarizes why we think our approach makes sense. If you look at the correlation of system family A to system family B over the past 20 years, it is 0.26.

Niels

Mm.

Jason

Correlation between A and C over the past 20 years, 0.20. System family A to system family D over the past 20 years, 0.24. And those numbers repeat themselves; if you look at B's correlations, I'm looking at 0.26, I'm looking at negative 0.05, I'm looking at negative 0.10. System family C correlations, very similar. So in other words, these are different horses running different courses; they're doing different things, we don't expect them all to work at the same time, and in fact, most days some of these models are losing money, and that's okay with us.

Niels

Absolutely.

Jason

You don't want everything you're doing to make money at the same time too frequently because that means you have one trade on, that means you're really at risk for something terrible happening in your book. Taking it one step further, and I can share this slide with people who reach out to Sunrise later on, within each of these families of models A, B, C, and D, is some diversification. For example, in system family B, if you take the models and compare them to each other, what you'll find is that intra-family, the correlation is low - in fact, negative months. So when this system, system B is not performing well, the intra-family correlation of the models is .15 - very low. So again, it's all about making sure the things you are doing are complementary and not crowding trades and correlating in a way that a single market event can wipe your strategy out.

That's what this is all about, and this is really just a continuation of what we've always tried to do. Diversification's the only thing the market gives you for free and we have always tried to capture as much diversification as we can. Not simply by trading different sectors, which we do. Not simply by trading different markets, which we do. Not simply by trading different timeframes, which we do. But by bringing in different theories of logic, different techniques, different ideas and assumptions about how markets are going to behave and blending them together. Knowing that everyone is wrong once in a while, and some people are wrong a lot of the time. And just to make sure you have something in your portfolio that's right when something else is wrong.

Niels

True. No, I mean it makes perfect sense. What I'd love to do, I mean I know of course we could talk for four hours about these things, but what I'd like to try and do for the benefit of the audience is really to, just from a really basic, but in a kind of an overview, if you could just give a couple of examples of how models can be different. I mean take a traditional trend following model and talk maybe a little bit about what typically would trigger a trade and how you would get into that trade and how you would get out. Just a very, very basic overview, but then maybe an example of a model that is very different from that. Just to give people ...

Jason

Sure.

Niels

. . . a sense of all you've just described and how you've grouped it into different categories. But then actually taking a little bit of a dive into, you know, just a couple of them, to really spell it out. Because that's where we're always kind of attacked about, you know, it's a black box. No it's not. I know it's, you know, it's completely transparent to you, of course, but let's try and help everyone just by giving them an example of what this really means in practice, if you wouldn't mind.

Jason

Sure, I can do that in fairly crude terms; my partners can certainly do it much more elegantly than I ever could. But you know, again, I think the best way to look at it is to look at an example, so think about the gold market last year.

Niels

Sure.

Jason

know what happened in gold in: 2013

Niels

Sure.

Jason

. . . it collapsed fairly significantly. And it was pretty orderly for a while, but then it started to get choppy, and it was a tricky trade. We were able to find profits different ways using models A, B, and C. Now obviously, models D didn't participate in that - those are equity only. But let's look at that using A, B, and C.

Niels

Right.

Jason

So when gold really kind of hit its peak and then started to fall off the cliff and cascade downward sharply, model set A got very interested. As you know, a good trend following model isn't necessarily gonna predict the top or a bottom of a market, but it will notice a market change off of a top or a bottom of the market, even using moving averages, or breakouts, or some other technique. The technique really isn't that important. You know, spotting a trench is not that difficult - most, you know, good C.T.A.s know how to do that.

Niels

Sure.

Jason

The question is how you manage your position sizes once you've spotted that opportunity and how you trade in and out of it. So think of that gold move. You know, right after the price collapsed the start of the tumble downward, our models A got very interested, and a lot of those model started to pile into that trade. Now we look at long-term trend following a little differently than a lot of our peers; our view is that the best opportunity to capture profits in a trend trade is early . . .

Niels

Okay.

Jason

. . . as opposed to late.

Niels

Mm-hmm.

Jason

So as soon as that gold price started to tumble down, model A got active and as I mentioned, there are 50 different kind of models in that family, they all started to watch gold very closely. And one would come on, and then maybe another one would come on, and maybe three, and they started to kind of pile in as that trend got stronger and stronger. But they piled in more towards the early part of that move, so that, you know, we had our risk. We like to have our risk on and the long-term trend following as early in the trend as possible, because we think that is the point where the trend is most likely to continue.

Niels

Yeah.

Jason

As trends kind of extend themselves longer and longer the risk of reversal becomes stronger, so you don't want to have massive positions on later in the trend because that one event code wipe you out. So we kind of piled into that trade early and inlets are intelligent long-term trend finding models in system family A do what they do. Some come off in profit targets, some come off in you know, fairly tight trailing stop . . .

Niels

Sure.

Jason

. . . scenarios. Some traces didn't pan out and came up altogether and money stops because there was some volatility, but ultimately, every one of these 50 trades had a different kind of destiny to it, but being long-term, hung onto that gold movement for quite some time.

Niels

Sure.

Jason

And carried it down almost probably to its bottom, and then probably, you know, gave back on a little one of reversal off of the bottom. But ultimately captured a good part of that move downward, and stayed in, it largely stayed in, okay? So that's a system family A - long-term trend following, average whole period 40 day type period. You know, kind of holding on, eating some of the volatility, you know, giving traders if you white knuckle days, 'cause that's what long-term trend following is.

Now, set all that aside, so what is system family B doing? Initially it did nothing, right? Gold just started moving down within a fairly orderly way, and B wasn't that interested.

Niels

Sure.

Jason

But then all of a sudden there was some interruptions in that downward trend. Think of the gold trend, you know, coming down and then all of a sudden for whatever reason, the price spikes back down towards for little bit. And then spikes back down towards that trend again. Those little impulses are what B is looking for, all right? And all they're looking for is to capture a certain piece of that move, and I can detail that in meetings with investors later, I won't get into canopy mechanics of it. But they're looking only for a small little portion of kind the these, what we call impulses off trends.

Niels

Mm-mm.

Jason

And that's exactly what they did, so every time there is one of those impulses happening, system family B would get in; sometimes long, sometimes short, it depends on when and where that impulse happened. But it will kind of grab a piece of that impulse, hopefully take some profits, and then be out within two with three days, okay?

Niels

Right.

Jason

And this is happening all along the fall of gold prices over the broader picture, okay? So a very different inefficiency going on there, B was only interested in that - it would come in, and then get out, it would come in, and then get out. Whereas, all along system family A is in that trade . . .

Niels

Sure.

Jason

. . . short gold, okay? Now, what about system family C? After gold, you know, hit its bottom for the year in the started to creep back up, C got interested . . .

Niels

Sure.

Jason

. . . okay? Now, the market at that point was moving in a different direction, it was starting to move upward bit, and it was still doing it in kind of a choppy way, such that it will go up a bit, and then retrace, and then go up a bit, and then retrace, and then go up a bit, and then retrace. And those are the retracement were of interest to system family C.

Niels

Sure.

Jason

Okay? Just that little retracement piece. It wasn't buying into a long-term upward trend in gold, but it was buying into the fact that we were seeing some retracement, we were seeing prices start to wander off from what we thought was the likely path of gold. And so C was buying little pieces of that - going in, and coming out, going in, and coming out, and finding profits in its own way. Meanwhile, system family A at that point was probably largely skilled out because, you know, the price had probably hit rock bottom, we're starting to climb up a bit more, and so a lot of the profits have been taken, and it was mostly shaken out. So ultimately, if you look at our book, we were probably making money and some of the models on any given day in gold, and losing model on any given day, and then the next day maybe it reversed.

But you had three different families of models eating different parts of the gold move up and down. And if I could, you know, so it to you graphically it will make a lot more sense, but I think that's the best way to think about it, is to take an actual example and retrace what your models did. They did it very different things, they did it over very different timeframes, they were looking for very different patterns and inefficiencies. And they did it in a way that was complementary such that, if you look back at last year, we made a lot of money trading gold.

Niels

No, it's a great explanation, so I think that's very helpful. And the other thing I think people probably don't realize is that, I'm assuming that's when you do have the models encased, they're actually not taking particularly big risks every time they get in and out.

Jason

No. No, no, no, no, no, absolutely, that's a very good point. We believe in diversification by timeframe, by technique and all these other lake layers because you never wanna have too much riding on any one outcome and small position sizes are key to that. Going back to system family A, as I mentioned, 50 models and there; no single one of those models is going to take more than, you know, perhaps 15 or 20 basis points of risk.

Obviously, the size will vary on the move were looking at . . .

Niels

Sure.

Jason

. . . volatility and other factors. But yes, very small pieces with each trade. And that way if a few go wrong, and they always do, you're not to unduly hurt by any single one of them, so absolutely.

Niels

Sure.

Jason

All of these families consist of multiple models that take things in fairly small pieces.

Niels

And do the models operate completely independently, or do you have some kind of overall risk matrix stat can limit the risk that they take? Or are they completely autonomous?

Jason

There are some overlay limits to the whole system, the whole . . . I would say think of the piece of paper we do earlier with the four quadrants.

Niels

Sure.

Jason

Around that piece of paper, draw a dotted line, that dotted line is essentially an algorithm that weights capital and the risk we're willing to take amongst the different models.

Niels

Okay, okay.

Jason

So there are budgets, each model gets a budgets on a given day of how much risk it can take overall, how much capital it can allocate. It has the flexibility to allocate or not; in other words, we can tell system family A you can take up to X percent of risk today . . .

Niels

Sure.

Jason

. . . if the opportunity's there. If there are no trends, and system family A will be quiet, but at least we'll have the opportunity to do that. But that is all governed algorithmically outside of the four corners of the paper - think of it as kind of our capital allocation out the rim. And it's a powerful tool, and let me give you just some examples so you understand, if you look back over the last 20 years, what you'll see is the amount of capital and risk that each system family is allocated very significantly.

For example, system family A has had a maximum risk allocation of as much as 22% in this little as 10% over the last 20 years. System family B has had as much risk allocation as 24% and as low as 7%. System family C has been as high as 36% and a slow as 14% of risk. In system family D has been as high as 39% and as 17% of our risk, so that's quite a bit of variance. It's not going to change it lots day today, but over the course of months and years, they can vary quite a bit, and that is a function of the markets we're in. Which system is showing the most volatility, you know?

Which system is showing the most, kind of inner market correlation? As things correlate, as things start to become more volatile, capital starts to move away from them, and vice versa.

Niels

Sure. And did you expect in the long run that each of the four groups will contribute approximately the same amount of performance to the overall product? Or are they sort of geared to do also little bit different things in that respect?

Jason

It's hard to know what’s going to happen in the future; if you look back historically, they've all done a pretty nice job.

Niels

Okay.

Jason

They've all been significant contributors to profits, both in real-time and in simulation. You know, we built them all with the expectation they will all make money over the long term. They all have very compelling compounded annual growth expectations . . .

Niels

Hmm.

Jason

. . . Some are higher than others. The highest compounded annual growth expectation is of the whole system . . .

Niels

Sure.

Jason

Just . . .

Niels

. . . we have?

Jason

. . . under 10, just under 10.

Niels

Wow.

Jason

What we've done basically is cut our volatility in half.

Niels

Sure.

Jason

In the old days when we were doing just system family A, you know, we saw volatility certainly on average around 15, but you know, obviously spiking higher at times. You know, we've had a few drawdowns in the high teens and even one around 20. The drawdowns for evolution we expect to have are half of that. Are half of that. And that is a function of, you know, again, this fact that we have four complementary systems, all with weaknesses, admittedly, but weaknesses that we believe are compensated for by the strengths of each of the other systems. So to give you some context, the best year we've seen in the last 20 system family A isn't up 21%.

Niels

Mm-hmm.

Jason

The worst year, negative four. Okay? System family B, the best year 23, the worst year - flat. System family C the best year only 11, the worst year, positive one. That's actually a more robust system in terms of delivering positive outcomes.

Niels

For sure.

Jason

System family D, the best year of 13, down 2. And on average over the last 20 years, system family A has been at six, system family B has been about seven and a half, system family C has been about five and a half, and system family D has been about four - this is as they're blended algorithmically.

Niels

Sure.

Jason

So what you could see is, as you when you blend them you reduce downside, you certainly take a little bit off the of sight of all of them, but what you get is a steady, what we think, you know, return profile.

Niels

Yeah, no absolutely. Jason, I want to shift gear and little bit because another thing that I think is important just for people to get a better understanding of it is also trade implementation . . .

Jason

Mm-hmm.

Niels

. . . because clearly running multiple systems and, you know, having a lot of computerized algorithms going on all the time, people might find it, you know, to be quite a daunting task to implement. But of course, that's another thing that you have thought through and resolved. So how does it work with all these things going on at the same time? How do you actually implement all the trades?

Jason

e in, you know, modern times,: 2013

Niels

And does the signal happen intraday, or is it end of day calculations and that implementation during the day? Or how often do you actually sample the markets in order to catch the late whether there's a signal or not?

Jason

Those are done on a daily basis at the end of the day, and the next day is kind of set up, a queue is set up with all the possible positions that might come on.

Niels

Okay.

Jason

And then they are implemented as they hit in a given trading day.

Niels

Mm.

Jason

We are looking at something that will starts to move kind of into a more intraday basis, but for now, we found that begin better results doing things as we do them. And as I mentioned, everything's automated, it is police very closely by Chris Stanton and his staff, they watch things, and we certainly have the option to, for example change and I'll correct them if we see a trade coming on and there's something going on in the market that makes us think that we can get a better execution using a slightly modified fill algorithm.

Niels

Sure.

Jason

So I wouldn't call that a manual override, but what that basically is Chris is watching things, he sees trades coming on, he sees that it's gonna use this type of execution out with them, and he says, "Away to second. This is happening right now. I want to switch it to this kind of algorithm . . ."

Niels

Sure.

Jason

we implemented this in early: 2013

Niels

That's always good. And how many trades do you do in a day on average you think?

Jason

ery loud. Last year did about: 1400

Niels

Okay, okay.

Jason

Which is about four X but we used to do one were simply doing system family A and our long-term trend following approach. So we've certainly increased the amount of trading we do, but we think the benefits outweigh the costs, and in fact the cost of doing on this have come down. It's kind of ironic, but the more volume we've done, the more leverage we've had with our partners to actually get prices that we think are more than reasonable and certainly better than market. And so I think our investors have benefited handsomely, despite the fact that we're trading more.

Niels

Yeah. Clearly, you know, we've talked a little bit about the risk management. It seems clear to me that one of the key focuses in evolution has been, you know, how do we improve the drawdown downside, and that is, of course, part of the overall risk management. But you know, CTA industry as a whole have had, at least in my experience, you know, a slightly different drawdown profile in the last couple of years. I mean we've seen managers that have been around for decades suddenly experienced drawdowns that were significantly larger and what they've seen before. And what's your kind of take on that and without giving anything away? How have you dealt with that and put it managed to improve, at least conceptually, what's you're doing clearly in evolution which is, you know, so much better than the traditional trend follower?

Jason

different. It had a very good: 2008

Niels

Sure.

Jason

And then in: 2009

And I also think that we are quite evolved and systematic as to how we scale into and out of that risk. You know, breaking trades up into very small pieces, giving them each their own kind of destiny from a statistical standpoint, aggressively using profit targets, using a wide range of different trailing stops on what we do, we think it gives us a different profile. And that's, I think, probably why we perform differently, and why I think we've, you know, managed to not be terrible in long-term trend following for the most part in the last five years, even though it's been very hard to do. We certainly haven't been great, and we certainly haven't been thrilled with, you know, just long-term trend following component of our models over the last five years, and I don't think any trend follower has been, 'cause it's been very hard, as you mentioned.

Niels

Sure.

Jason

tarted to unfold in the early: 2000

Niels

Sure.

Jason

You know, this government intervention we're seeing is changing the way markets work. Now we may go back to an environment where trend following works beautifully, but we don't know when that’s going to happen, so in addition to making that your trend following models the best they can be as we believe we have, we've also said, "You need to start using some other techniques, and you need to blend them in in a way that they complement, rather than cripple your trend following.

Niels

Yeah.

Jason

ns in each of the years since: 2008

Niels

Yeah.

Jason

. . . using the evolution approach. And I don't think there's a single trend following model on earth that can say that.

Niels

No, I think you're right about that. I mean it does sounds different, and it does sound that is, a lot of it is based on the concept of models rather than the weighting of sectors, which in my opinion, has been more of a lucky punch because we know people who've been overweighed in equities and fixed income . . .

Jason

Yeah.

Niels

. . . for that matter, just by default have done well, but it doesn't tell us anything about whether their models are better than anyone else's.

Jason

Agree with you 100%. We've had this discussion internally, we've had this exact same discussion, which is the game of picking sectors is a fool's errand. It's luck, it's happenstance; I mean, yeah, we've just came off one of the greatest bond trades in history, one of the greatest, you know, trends ever. But if that didn't happen how would everyone's results look? Very different, very different. Are we going to sit around here and try and guess where the next trade of lifetime is going to be? Or are we going to build something that's agnostic to those kind of factors that you really can't predict?

Niels

Sure.

Jason

And that was the path we chose. We have always believed in allocating roughly/equally to every sector, and allocating roughly/equally to most markets within those sectors, because we don't know which market's gonna pop in a given year. We don't know which sectors going to pop in a given year, but what we do know through science and math and statistical study is, what kind of inefficiencies and patterns will proliferate themselves in those markets. And so our bet is that if we're focused on finding a wide range of patterns and inefficiencies across all the markets, we are going to end up beating the people who are trying to pick the winning sectors and markets all the time.

Niels

Sure.

Jason

Now time will prove whether we're right or not, but that's the bet we're making.

Niels

Sure.

Jason

I agree with you 100%. Some people, you have no idea how good their model saw because they simply over weighted the right sector or the right market.

Niels

Yeah, no, that's true. Lots more things that we could talk about in terms of drawdowns, but I wanna move on, but before we do so, I want to ask a slightly question that I think maybe not so much for the professional investors listening to this, but for a lot of people who aspire to be the next sunrise and getting into this industry. And that is, you know, we've certainly chosen an industry that emotionally puts us at stress since CTAs, since strategies that we run very often find themselves in some kind of a drawdown.

Jason

Oh, goodness, yes.

Niels

So I was just curious to know how you balance that emotionally to, you know, always be, you know, somewhat away from a new equity high and dealing with that. And the roller coaster of the way performance is distributed in our industry. Do you have any sort of good advice of people out there?

Jason

Ugh, it is very, very hard. I mean I respect the heck out of my partner Rick and our other two founders, Jack and Gary, tremendously. And I think I respect them for a lot of reasons, but I think the biggest reason I respect them as best they were able to kind of weather with the phenomenon you detailed, for 30 years.

Niels

Yep.

Jason

Thirty plus years, 'cause it is hard. There's no feeling like being in a drawdown. You feel bad for yourself because you've obviously built these models and they aren't working at this particular time, you so bad for yourself because you've obviously got a lot of your own money invested in the strategy and you're losing money at that point. Then you feel the worst for your investors; your friends, your family, trusted industry confidants and others who've entrusted you with their capital, and at a particular time you're losing money for them. And that's very, very hard to reconcile. If you're passionate, you care about what you do, you are going to feel bad when that happens. So the challenge, and the advice that I give is, you have to find outlets, you have to find ways to manage that. You need partners, for example. Oftentimes when you have partners in your business it's rare you'll all be down at the same time.

You know, the way our partnership works, there will be times when one of us is more down than the other and we pick each other up. That's very, very important. Doing this alone, I don't know how people can do it, honestly.

Niels

Yeah.

Jason

I would say to the extent that you want to carry forward in this business, build a team around you of people who can complement you and lift you up when you're down and vice versa. The other thing is, you know, don't panic. If you've done the work going into it, you've built robust models, you've done the work ahead of time, and you trust what you've built, you have to know that drawdowns are part of what you do. Every single investment strategy has drawdowns, whether it's a CTA strategy, whether it's a stock picking strategy, you know, whether it's an index, a simple index, 40 Act Fund, there are drawdowns, they happen. And so you know, you have to just trust that you've done the work and you've done what you need to do, and that you will come through it.

Now, trust doesn't mean blindly, you know, looking off into space and just assuming it'll fix itself; when you're in a drawdown you need to watch it, you need to learn from it, and you need to try and see if there's anything endemic in that drawdown that might allow you to improve your systems in the future. We have a saying around Sunrise which is, "Never let a drawdown go to waste." So when it happens look at it, study it. It might reveal to you a very simple change that you might that isn’t going to prevent drawdowns in the future, I don't think you can do that, but might prevent the kind of drawdown you're seeing from happening again.

Niels

Sure.

Jason

So that will be the third piece is, learn. So partners, you know, have faith in what you're doing and understand that drawdowns are part of the process, but three, learn from them.

Niels

Sure. And actually that's a good segue into the next area that I just want to touch upon because I know we've already gone beyond the hour and I really appreciate your time here. But I want to touch a little bit about research because clearly research is something that really drives, you know, your firm. And maybe you could just, again for the audience, describe a little bit about, you know, was the typical research cycle in your opinion. And I think you're definitely right about, you learn from what happens in a drawdown and that might give you ideas to future research, but just describe in general how you approach research.

Jason

Well, to us, research is a constant . . . you know, it's tricky in this business, a lot of people, they want to see innovation, but they don't wanna see change, and they're very, you know, they sound like the same word but they're not.

Niels

Hmm.

Jason

You know, people look at you and they'll say, "Well, what's your R&D what are you doing? Oh, that sounds exciting, that sounds exciting." But when they actually focus on your models, they wanna see some consistency, they wanna see that you've kind of been doing the same thing for a long time otherwise they don't trust it. So it's a razor's edge to walk, but our view has been, like I said earlier, if you were standing still, you're falling behind.

Niels

Hmm.

Jason

So we are constantly looking every day, are the results we're getting in the markets matching our assumptions? Are they matching how we've simulated this thing? If not, why? Are there areas we can improve? Are there things we can change? Are there assumptions we've made that are just flat out wrong. Always questioning what we're doing, always looking at that. And so that process in and of itself is often a source of ideas for how we can improve things. Is watching what you're doing compared to how you expected to behave, but secondarily to that, you always, at least at Sunrise, we always have a queue of different ideas that just people find interesting.

Niels

Hmm.

Jason

You know, so and so will say, "You know, I think we can make a lot of money doing X, Y, Z in the following five markets and I’m going to go ahead and look into that." "Great, you know, Mr. A, you go and do that and then bring us back a report next week and we'll see what it looks like," you know. On the queue there will be an ideal, a simple concept of . . . I think, you know, that if we, you know, moved all of the stops in these particular markets in this direction, it would cut volatility by X. Okay, well, you know, person B, go look at that question and then come back and report. So it's kind of busy dual track, you're kind of watching what you do and comparing on a day-to-day basis, and then on the other side you're working on projects of interest to the smart people you've hired and entrusted to work on things.

And where the next great innovations going to come from, you don't know. It could come from one of those projects on the queue, although, you know, honestly, when you have a queue of 10 things you're working on, ultimately maybe only one of them turns into something of significance . . .

Niels

Sure.

Jason

. . . but that's okay, that's a process. Or the next great innovation might come from that other process which I mentioned, which is just simply comparing and contrasting assumptions to what's actually happening on a day in and day out basis. One of the best innovations we had in the last two years was Chris Stanton, my partner, watching our systems work and seeing something that was happening over and over again, and going back to Rick and saying, "Rick, as I execute this thing, I'm telling you, this is happening over and over again. If we make this slight change, I think we are going to see material improvement in what we do." And sure enough, that's exactly what happened.

Niels

Interesting, yeah. I guess, I mean research is about finding all the things that doesn't work before you find the things that work, and you know. . .

Jason

Absolutely.

Niels

. . . and so that's what it's all about. And it's funny, I saw just this week, actually, there is a fun article in the Hedge Fund Journal written by, I think it was the SEB Asset Select head of that firm, about the 10 fallacies of picking a manager. And of course, one of them was, you know, the number of PhDs in a firm, does that they actually have any correlation to the success of the firm? And so you know, there's a lot of interesting views about research and you know, number of research staff and so on and so forth.

Jason

Well absolutely.

Niels

Yeah.

Jason

You know, as a smaller manager like us, I mean I think you probably see that every day, which is you'll meet with some people and they'll say, "Well, your team's a little small. How do you compete against David Harding and his dozens of PhDs?" And my answer's always, "Look at the results."

Niels

Yeah.

Jason

I mean last year we outperformed David Harding, does that mean we're smarter than David Harding? Not necessarily. Does that mean we're smarter than a room full of PhDs? Not necessarily. But certainly it undermines the notion that if you have lots of PhDs, you are, by default a better manager because the disparity end result.

Niels

And you know what, I think that we probably all in our industry have a PhD, because if we just term it, passion, hunger, and drive . . .I mean I think that's what it's all about.

Jason

I like that, I like that. There, now I'm a PhD . . .

Niels

Exactly.

Jason

. . . because I had passion, hunger and drive. Yeah, I mean, look, I would never disparage a PhD, there are some brilliant PhDs and there's probably brilliant PhDs working in this space, who've come up with some brilliant investment ideas. But to me, to use that as kind of the sole basis, or a large part of the basis for making your investment decisions makes no sense. Never has.

Niels

Jason, I want to finish off just by going a little bit off track because, I mean you've been so generous with your insights and your time for but I think it's important that, you know, you also perhaps share some of your experience, more maybe as an advisory to people who want to, you know, join this industry and also for people just generally to learn from, so. I mean in your opinion, what are some of the traits that you need to have to be or become a great CTA? What does it take in your opinion?

Jason

You have to be passionate about it. If you do not love doing this, if you do not love finance, if you do not love markets, if you do not get a thrill out of beating the markets, it is not the space for you. It's too hard to do without passion.

Niels

True.

Jason

Passion's the only thing that can carry you through the hard times, the drawdowns, you know, the inevitable moment where a client decides to withdraw their money from your fund, you know. It's just too hard to do without passion, so you've got to bring that. Assuming you have that, then I think, you know, I think the traits that make anyone successful at anything. You know, hard work, diligence, curiosity, you know, continually reading and studying others in the industry to learn. You know, working well with others, building, as I mentioned, having teammates and partners. Have building a network of people around you to support you whether they're actually your employees or partners, whether they're outsource providers. Either one. You know, that's absolutely critical, you know, being detail oriented, and you know, ridiculously attentive to the little things, is critical to success in this business. And honestly, hard work. Just working really hard and putting yourself in a position to get lucky.

Niels

Yeah.

Jason

Because if anyone in this business says that look doesn't play a role in their success, they are complete denial. As you know, markets can do very strange things at very strange times for reasons that no one could've ever predicted. And if you happen to have something that captures that effectively and generates a return off of that at the right time, and an investor notices that, and then based on that, they allocate you $100 million, There's a little luck involved in that. Now you can't get to that moment without all the other things I mentioned and the hard work, but ultimately you need to get a little lucky. But as a great man once said, "The harder I work, the luckier I get."

As another great philosopher, Vince Lombardi, who was the legendary coach of the Green Bay Packers American football team said, you know, "Gentlemen," and I'm paraphrasing, he said in a speech to his team, "Gentlemen, we will strive every day for perfection here with the Green Bay Packers. Not because we can ever achieve perfection, because that's impossible. But knowing that in pursuing perfection, we will always achieve excellence." And you know, that's really what I try and instill in my team is, let's try and be perfect today, knowing that we can't be, but knowing that if we work on being perfect, the likely outcome is going to be excellence. And I think that's what's kept Sunrise going for 34 years, and I'm hoping that's what will allow us to work, well, for another 34 years.

Niels

Sure. And of course, I mean, you know, as CTAs, we're also entrepreneurs and being an entrepreneur it's, you know, there are many failures before there are successes. And I was just wondering whether you could share, you know, a failure in your opinion that you've been through and what you learned from it. Because we all, you know, we evolve as well as business people and not just from a research point of view, but that could be just something that you could think of that you say, "Wow, you know, had I known that today, I would've done it differently."

Jason

Oh, boy, I mean, I fail every day. Do you know, I think failure is an absolute necessity to success in any business, particularly our business. My view with decisions is, every decision I make I have to first get comfortable with the fact that it could be the wrong decision, but be confident enough to make it anyway. And know that the outcome of the decision could be a failure, but be confident enough to make it anyway. I think that is a critical trait for any entrepreneur and anyone in our field. And in our business, in particular at Sunrise, where have we failed?

billion by the early: 2000

Niels

Sure.

Jason

more of the assets. Now maybe: 2008

ade, which is what we did, in: 2008

Niels

Yeah, that's hard

Jason

nt out of business because of: 2008

There's maybe a conversation with an employee I should've had, or a conversation I had with that employee that, you know, in retrospect I should've worded differently. You know, there's just any number of things that can go wrong in a business. You know, the measure of a business is how well it owns its mistakes, fixes its mistakes, and carries forward wiser and smarter so that it doesn't make those mistakes again.

Niels

Absolutely, absolutely. And what does a perfect day look like in your life? What inspires you, Jason, coming into work every day?

Jason

The perfect day, well we had a lot of perfect days here in the San Diego area.

Niels

Yeah, I can imagine.

Jason

Certainly from a weather perspective. No, I mean a perfect day for me is, obviously starts with all the things that have nothing to do with my business and that's, you know, my personal life. Obviously if my kids are smiling and happy when it drop them off at school, and they're excited about their lives and the possibilities ahead for them, you know, that's a big part of it. Obviously, I wish the same for my wife every day, and you know, my loved ones and friends. I take a lot of joy in the success and happiness of other people.

So I start there, but then when I actually get to work, the perfect day, you know, my employees are all as cited to be at work and enjoy themselves and you know, doing good work and make any difference for our investors and their different roles. Markets are moving in a direction favorable to us and maybe we're up, you know, 150 or 200 basis points for the day. Certainly that always puts a bounce in my step.

Niels

Sure.

Jason

And then, you know, the day ends with, you know, Sovereign Wealth Funds XYZ calling and saying, "You know, we've been tracking you guys for three years and we love what we're seeing and we'd like to allocate half $1 billion next Monday." That would be a good day . . .

Niels

[laughs]

Jason

. . . you know? That will be a very, very good day, I'd have to say. And you know, I'll leave it at that.

Niels

No, I think that is actually a good way to leave it. But before we finish, maybe you could tell some of our listeners where they can best reach out to you and learn more about Sunrise.

Jason

-: 8911

-: 8911

Niels

Jason, I think you have certainly done, you know, yourself justice in terms of laying out the great things that you do at Sunrise, and I thoroughly enjoyed our conversation today, and I really appreciate the openness and the willingness, you know, to share your insights and views of your strategy in the firm, and the industry as a whole. And of course our listeners will be able to find, you know, in the show notes more about Sunrise, and I hope we can connect at a later date and see where you are and all the great work you do.

Jason

Absolutely, well, Niels, thank you so much for putting this forum together and I think it's a great idea and I appreciate you. As I mentioned, getting information out into critical to the success of our industry, and you know, as a part of this industry, I want to see it grow, I want all of our firms to grow and succeed, and you're obviously going to do a lot to make that happen and I can't thank you enough for giving me this platform and for your kind words. And I look forward to collaborating with you again soon in the future.

Niels

Great stuff, Jason. Take care and all the best.

Jason

Take care, Niels. Bye.

Niels

Ciao, bye.

Ending

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