Volatility is the most important part of our strategy | with Nigol Koulajian

imagine spending an hour with the world's greatest trainers imagine learning from their experiences their successes and their failures imagine no more welcome to top traders unplugged the place where you can learn from the best hedge fund managers in the world so you can take your manager due diligence or investment career to the next level before we begin today's conversation remember to keep two things in mind all the discussion will have about investment performance is about the past and past performance does not guarantee or even infer anything about future performance also understand that there's a significant risk of financial loss with all investment strategies and you need to request and understand the specific risks from the investment manager about their products before you make investment decisions here's your host veteran hedge fund manager Niels Castro Larson you're listening to top traders on plot thanks so much for tuning in today I know how valuable your time is so I appreciate you spending some of it here with me today now on today's show I'm talking to Nikol kalarjian founder and CEO of quest partners originally of a minion background Nikol spent part of his childhood in Lebanon but by the age of 16 he moved to the u.s. to escape the wars around him the uncertainty he had to live through as a child has clearly influenced his approach to life and later on the way he designed his trading strategy in a very personal and detailed conversation we discussed some of the major risks that Nikol sees in how investors are perceiving skill and alpha with some of the large hedge funds today what he does to differentiate himself from these risks and how daily meditation helps him see the world with clarity which allows him to focus on continuing to build a solution-oriented alternative investment firm and for those of you who are new to the show I just want to let you know that you can find all of the show notes including a full transcript of today's episode on the top traders on plot comm website now let's get on with part 1 of my conversation I hope you will enjoy it [Music] nikohl thank you so much for being with us today I really appreciate it thank you nails now as I was preparing for our conversation today I noticed a few really interesting things that I'm sure we'll have a chance to discuss today but here are some of my initial thoughts one is the fact that you have a long and may I say very solid track record in your original program but the program itself is a bit different to many of your peers which I found fascinating also you seem to have diversified your business into other product types such as an equity program both hedged and long only as well as a tracker index where you seek to deliver the returns of your CTA peers and I would be tempted to say that your firm seems to be looking to find solutions for investors rather than being a purist in one particular strategy and finally I noticed that you actually started on the other side of the table so to speak namely as an allocator or investor into hedge funds and CTA so that in itself is of course an interesting way into what you do today so I'm really excited about all these possible topics that we can talk about today but of course before we go into too much details about your company and where you are today I would really like if you could take us all the way back to the beginning telling us your story and what led you to take this path and also feel free to go back as far as you want then and share how you met your business partner pole and and I'll be open here and say I'm gonna let you pronounce his surname because I don't think I'm gonna do that justice it's a policy on yonce so thank you it's a Polish name actually okay great thanks Niels so in terms of background I guess I'll go way all the way to the beginning I guess I was born in Lebanon of Armenian descent okay so I grew up you know in Middle Eastern culture but also within an Armenian family so a lot of tradition and that sort of thing and this somehow came into play later in terms of the way we actually with the way we look at the world the stability of the world etc so we'll cover we'll go back to this soon enough fantastic at age 16 I came to the States and you know due to the war in Lebanon and becoming a little too intense and went to college at Notre Dame to study Electrical Engineering okay from there came back to New York and went into consulting I worked for Anderson consulting and at Anderson I spent most of my time working at Salomon Brothers totally by chance okay and from the engineering background we went into more financial type of application of programming and modeling for mortgage-backed security you know prepayment models and that sort of thing completely by chance sure so I became more and more interested in finance of course I was you know working downtown New York and you know Wall Street it was very impressive at the time so I studied the release you know doing a lot of study on my own I started programming on my own start designing models in 1991 okay and from there decided to get a more formal education in finance so went to Columbia Business School mm-hmm during business school I was mainly spending my time on programming and designing models and all kind of things like that and when you say models I mean just just to put things in context I mean what kind of models were they at the very very early beginning you know fondly enough way early at the beginning before looking at for a trend following I looked at volatility breakout models which means much much more short-term day trading holding the trade three to four days per trade that sort of thing and it's only later on that I actually the you know started developing trends following models I mean it sounded funny but I used to read the a newspaper called the investors business daily that was very kind of giving investors a lot of tools to evaluate different stocks using different measuring techniques some fundamental some technical and obviously the fundamental factors were difficult to test but the technicals I it was pretty easy to test and to program so I got the data I started you know that developing a testing platform and it's it's due to that newspaper read that I went into you know technical trading and designing models interests oh yes yeah yeah so for vault breakout actually was pretty early on I guess at the time I was you know Monroe trout and eventually Toby kraebel and that sort of names that started using it but yeah I wasn't he yeah so this it started you know during business school I was interacting with all the professors at Columbia and you know Columbia is more directed towards equity and fundamental you know investing and that sort of thing and I was going around with my models and showing you this little professors and nobody was getting very excited it was great that way during this during business school I also worked at the Deutsche Bank and did you know value at risk for for the US Bank so over the summer and into the second year so that was also still in the early 90s at the time value at risk was relatively you know novel sure so it came out of Business School in 94 I had models ready to go to start the CTA okay short term models long term models you name it I had already a platform to track keeps track of trades download data all kind of things yeah after business school I still spent rather than getting quickly going into the industry I spent another six months actually still working on developing models so it was a 100 percent of my time was still you know dedicated to you know to research at the time sure finally in at the end of 94 I by chance I got in contact with Victor Tiger who at the time was running a risk or hedge fund okay he was looking for a trader I guess he was impressed by my background and he told me this and I what you're doing is very interesting I like it but I need a trader so why don't you join me as a trader and we'll do your stuff down the line once there's time for it okay so from looking at from going in from a place where I was looking to write to raise money to start the CTA next thing you know I'm I'm trading in a risk of hedge funds yeah designing hedges for all kind of you know positions all over the world and trying to reduce overall market risk and try to really isolate specific factors within the stocks that we were actually holding sure and of course you know we were trading a risk or globally and really there was never any time to to focus on the CTA strategies yeah say eventually I moved on but it was a very very interesting experience for me because I was so used to trading in the direction of the price action and in this fund we were actually trading very very fundamentally driven very value driven sometimes in illiquid positions where we would based on our buying where I was actually influencing the price I mean even in a small fund at the time so it was from a psychological perspective it kind of like erased everything I knew about how to trade sure and I realized that there's a there's other ways it really broadened my horizons pretty substantially interesting and sounds like great experience to get exposed to early on definitely especially that I wasn't looking for it yeah I thought I was I was sure I knew what I needed to do and it was like a straight-line path and somehow the the detour was very useful yeah I have to say yeah so after that I joined the Western capital western capital at the time was a marketing firm and they were looking potentially they were interested in starting a CTA and to raise money for you know a start-up for the CTA that I was looking to start and I joined with that intent and as we were preparing somehow by chance the opportunity came to start the Fonda fund okay and so I said oh yeah that shouldn't take too long at the time the industry was much simpler and due diligence was not as sophisticated and in-depth as it is today sure so we started a couple of founder found that allocated to all kind of strategies including CTAs mm-hmm and I ran those fund of funds for a couple of years until I realized that I was getting sidetracked again and left and started my own firm initially as a founder fund right and then in in nineteen nine a partner joined me okay and we started a CTA called Enterprise Asset Management okay yeah so so a few detours along the way a few detours but I would say I'm very glad I went through those yeah sometimes not getting what you want is the best thing you can get because the long term you know there's things that are very very useful that you're we don't even realize that we don't know no I couldn't agree more so here we are we're running a city and a founder for 99 2000 2001 2001 my partner and I decided to split sure and I started quest okay Paul had joined me in 1999 at Enterprise and we came in and he decided to you know follow me here at Quest fantastic and we hire the the head trader of HL at the time Neil Hanover okay and off we go so our assets rose I mean when we started the quest we had about at Enterprise we started about two three million of you know private capital sure and at Quest we started with about 25 million and our assets slowly grew to a first peak which was around like 650 million in assets around seven okay in 2003 we actually signed an agreement to give 90% capacity rights to all our strategies to the fonder fond of a large city a very well known city a and they allocated about you know five hundred and fifty million to us at a peak okay and we just basically ran money for them from 2003 to 2010 Wow interesting very unusual and but very interesting indeed and and maybe you can sort of you know as a sort of a lead on to to that just I know we're gonna talk a lot about more about the details but just takes over being a jump and say so how does the business look today what kind of programs do you run and and sort of roughly what what the AUM is as well today we're managing about seven hundred and sixty million okay of that there's about 80 million in our traditional CTA strategies right then we have the the replicator has about 80 million as well okay and then the rest is in a hedge product so equity hedge and fixed income hedge I think equity hedges around four hundred and fifty million and fixed income hedges around 150 million okay and and in in total how many different strategies or programs do you do run we're offering the original program which is the benchmark CTA strategy the qti which is the replicator equity hedge fixed income hedge and equity long so I guess about six different strategies right and if you notice that most of the assets that we have today are actually in the new strategies yes which was kind of out of necessity you we had to develop them because in in 2010 that large fund a fund had to redeem due to their own complications okay with it very well for them but as they redeemed and we're here we are in an environment where people are deallocating from CTAs yeah and we said okay we need to raise money quickly and what can we do and realize that it was a question of fee is a question of more like the relationship between CTA s and the equity markets were not as clearly defined as they could be etc etc so we define these you know different programs along the way but we'll get into that yeah definitely no it's it's it's it's super interesting actually very very interesting now normally I would go on and ask you a little bit about your business sort of how its structured but before we do that today I'd like to I'd like to go a little bit of a different route I want to talk more of a broad question which i think is really important to many investors and perhaps managers may not be focusing us on this enough and that's really what people when they look at track records may be perceiving as the value they get when they look at a manager but in reality it may not be you know anything to do with skill or alpha some people might say that you know we've seen a lot of sort of style drift and you know because deep down you know it's often rooted in the managers that over time they changed their strategy and their style and I think maybe the last few years we've seen quite a big you know shift in in in in that sense so I'd love to talk to hear your opinion about this and and way where you see the dangers may lie of an increased correlation between the you know so-called alternative strategies that are designed to protect investors against traditional investment you know when they have trouble and and you know the traditional asset classes themselves sure a great question actually and I think that there's some pretty interesting angles on this especially looking at it from a CTA perspective so so first I I mean I will I will cover the city industry and I then I will take it more broadly within I think you know within the from the universe sure so today the CTA industry can be replicated with a very simple model such as ten day to 100 day simple moving average crossover strategy so the most basic strategy you can imagine you apply it equally to you know four sectors without any other optimization you have you already have something which is 70% correlated to the city industry which is outperforming what you know three to five percent a year due to fees right so about ten years ago the city industry was looked at as a big pool of skill-based returns today it is more and more apparent that at least 90% of those returns are really very easily replicatable and the techniques of replication or if they are broadly available as a step number one so starting with that saying that the industry can actually be replicated so easily now if you start looking at the at the offshoots of this industry so if you look at the the managers that have raised money in the recent years and they have grown from you know into the 20 billions and tens of billions and five billions etc etc you will see that they actually have exposure to certain factors and most commonly the factors that they are exposed to have been risk on strategy so strategies that are correlated to the equity markets in one way or another right so over the years we wrote three research features that actually explore the specific and not not looking at the city industry as one broad basket but saying specifically what are the factor drifts that could actually affect the returns of city is and so I would list those factors I'll go over them and know quickly and give you give you an opportunity to see what kind of impact these factors gonna have so suppose you're investing in the S&P 500 and you see a mutual fund which is down which is up let's say 20% more than the S&P you say wow the manager is a superstar we say well he's actually invested in micro they've done very well this year so at this stage considering the transparency has changed the view of the city industry it's important for the investors to go one step beyond and become aware of the sub factors and their impact because these these sub factors that have been performing very well have very different characteristics during equity Corrections as we will cover okay so let's look at them quickly I would say the first factor is a sector optimization it means CTAs have been allocating more and more to fixed income because fixed income has performed very very well in the last 20 years sure now it happens to be that fixed income is also the most liquid sector and therefore as it is as they're growing even weather even if there was not a research driven optimization the the allocation issue and the liquidity issue force them to allocate more to fixed income hmm so allocating more to fixed income could actually improve your Sharpe ratio over the last 15 years by 40 percent so if you look at a basic model the 10100 which you will take as the benchmark for the city industry and you increase the fixed income sector allocation your Sharpe ratio would go up by 40 percent which is you know they're already very very substantial sure so that's factor number one now as a sidenote fixed income has been negatively correlated to stocks which means during equity Corrections it's provided the substantial returns to CTAs I would say I would say the majority of returns that CTAs have generated in the last 15 years during equity Corrections have come as a result of their fixed income exposure yeah okay so that's a factor number one so sector optimization and there's a there's a potential for substantial improvement in Sharpe ratio due to an increase in allocation to fixed income factor number two is long versus short it means if you look at the returns of CTAs over the last 20 years you will see that over 90 scent of the returns of a basic CTA strategy come from the long side it means long trades sure yeah short short trades have made almost no money a couple of periods in you know around ninety four or one two or three very small returns and then oh seven 209 some some pretty good returns there but overall shorts are almost flat yeah now effectively this means that the CT industry is not so different than the buy-and-hold just you know a general you know running a buy and hold strategy on the same basket or the same portfolio that CTA strayed and this is very surprising to most you're saying all these trading models etc etc are really minutely different than the buy and hold on the same markets mm-hmm so now so we're saying it the Long's if you traded the Long's only instead of fighting Long's and shorts you would have improved your Sharpe ratio by eighty percent over the last fifteen years okay now again assume you're a large CTA and here you have a higher transaction cost you to do to you know do to your size and shorts are basically at this stage a straight line down due to the higher transaction cost you'd say you would say why am i trading shorts let me focus more and more on the lungs so again at this as you grow in size this factor is something which is imposed on you sure and it's improved your Sharpe ratio by 80% so we see again the potential for self reinforcing feedback loop where the managers that are growing in size have are forced allocate to factors which have done very well yeah and I would I would I would argue completely by chance sure so the third factor is the timeframe over the years and we've shown this in our research pieces over the years CPAs have had to increase the time frame that they use for their models now you can use 10 100 it used to be in the 80s and 90s that 10 day to 40 days simple moving average was the classical trend following strategy and eventually in the 2000s the first first decade of the new century it became 10 100 and today if you look at it if you do a factor analysis you will see that CTS are trading time frames as long as ten day to five hundred day moving average so much more long term here we're talking where the average days per trade used to be 10 15 mm-hmm now we're talking a hundred days a hundred fifty days per trade if you don't count if you don't count for all sure sure sure okay now just to give you an idea of how how valuable it would have been to trade this ten five hundred to begin with the equity curve of ten five hundred has not even flatlined in the last five years such as the rest of the CTA industry yeah so you look at 10 100 or ten five ten forty you see the last since 2009 City you know those models have not made any money 10500 is in a straight line up without any interruption sure so if you optimize the portfolio and add 10 500 you will have the potential to improve your Sharpe ratio by 70% instead of 0.5 and we're talking you know point eight and that's alright at that sort of level which takes you to this to a superstar level immediately yeah so these are already three factors which have improved your operation by 40 percent 80 percent and 70 percent and there's no skill involved yet well I'll let you go on I've got some some observations of course but I'll let you go on okay so a third factor that we talk about in our research pieces is the fact that CT is make money during trends but the biggest factor that explains their returns and the value added aspect of the returns is the fact that they make money when the volatility is expanding so when you put on a trade you know a trend following trade you're making money because of the trend but you're really benefitting well mainly when the Raval is expanding as the trend starts sure once the trend is established your returns start to correlate to the fight to to equities and they start to call it the factor to fixed income there's no more alpha relative to the traditional portfolio's okay okay so now over the years we show that city is this character of benefiting from the volatility expansion is called positive skew basically when you have a surprise it tends to be positive on the returns sure and the opposite negative skew is when you know and this is a surprise it tends to be more negative so now CTA is over the years in order to generate higher Sharpe ratios have taken profits faster and bought the corrections within the trends more and more very very substantial first this improves your Sharpe ratio but also it makes you less capable of benefiting during volatility expansions okay and can you explain that a little bit if just just take a few steps back and then and explain that a and I'm not sure I fully understood what you put you meant by a boat to drawdown okay so let's say you're running a simple trend following models such as 10 100 yeah and you size your position a based on volatility sure now the saying is the most profitable trades for CTAs are the trades that were entered when the volatility was low and then expanded once the trade was in place mm-hmm so now the danger of that is that after a trend is well-established the volatility could have doubled versus when you put the trade on sure and now your risk in that specific market is twice as what it used to be from a var perspective sure all things being you know equal that you haven't moved your stop and so on and so forth the stops for the typical trend following models are pretty far away it is after a trend it could be two weeks three weeks away from you sure so if you replicate the industry I would say stops are irrelevant it means you don't have to have stops in the market you have you have trailing stuff in if the moving averages cross sure you trade but you can trademark on our clothes you can trade once a week you can trade once a months as a matter of fact sure and your performance would not be very different sure no sorry so so what we're saying is trend followers classically used to benefit from the acceleration in the so price is moving 1% a day and then the trend is established now this market is like a really hot market and now the trade that market is moving 3% a day mm-hm the typical CTA in North Clark classically you're gonna say this is a real trend and I'm gonna be exposed and I'm gonna be I have much higher exposure to this market the vol has expanded and typically the vol expands when equities are going down sure so when the vol is expanding CTAs are expected to generate high returns yeah now let's say CTA is decided that they don't want a risk as much when the vol is expanding and they want to wait if they want to make money more consistently what they would do is they would reduce their position size when the vol of markets expanded within the trade and when the market also is retraced so you're in an uptrend yeah the vol expanded slightly you're gonna reduce your position and want when the vol compressors which means during a correction they would they would add add to their positions sure so they're in an uptrend picking bottoms and selling rallies sure the benefit of that is that they have they now have a constant volatility portfolio it means they look at their var and they're expecting 15% annual you know annual volatility every day sure the downside of this is that you you've lost your positive skew you've lost your ability to generate high returns during equity Corrections now this is a basic so the factor number four I'm gonna get you know just mean reversion broadly mean reversion you means you're selling rallies within the trend and buying the dips within the trend now I want to take this one level beyond and go into the specific example of buying the dips and just in the financials it means you can very is a very easily user model which buys the SNP if it's down three days in a row so and because you're expecting it rally same thing in fixed income now you know we can potentially make this model available for listeners through podcast but just to give you a sense of this so if you bought the SNP if it's down three days in a row yeah and you exited when you make so sorry I'm gonna get a little technical but that's fine that's fine too will will will I'll stop you if it gets too technical but again step I say it's a pretty simple model so you buy the S&P on the third down clothes yeah you take a profit if it makes half a daily range so you bought it at the close if it goes up half a daily range sure you take profits and you at the end so the average true range over what period are you looking at over fifty days let's see not very relevant let's say over fifty days and you have a stoploss two daily ranges away okay this model over the last fifteen years have had a Sharpe ratio of about 1.2 hmm even during equity Corrections this model has you can't even tell that or 7208 happened sure so there's in today's world which is driven by central bank providing a central bank's providing liquidity to because markets are driving the economy rather than the other way around sure there's been a preponderance of mean reverting strategies in financial markets with again I'm saying Sharpe ratios over one and it would be not any scale this is a model which I'll you know I'll provide to to the listeners sure ncts are more and more including such strategies which are buying the dips in financials as a way to generate alpha sure okay so again I want you the numbers are you know really well but this type of strategy improves the Sharpe ratio of the typical trend following model by a hundred and thirty percent yeah substantial that's a very very substantial so I'm just giving you you know the potential things that CPAs are introducing them into their portfolios that are substantially changing their characteristics with no scale which investors have to be aware of if they want to really pick up the real at the CTA with real skill that yeah yeah no it's it's very very important it's very very important this there are three more factors okay that we say City as I've introduced one is a fixed long equity exposure mmm-hmm sure so if you look at the rolling correlation of the d top which are the you know the fifty percent the largest city is that control fifty percent of the assets of the city industry yeah you look at the you pick up that you take out there trend-following components through a factor decomposition the residual returns rolling correlation to the SNP today is seventy percent okay so we're not saying that they're trading equities and they're making money on equities going up we're saying they have fixed long positions in equities yeah very different now adding a long position in the SNP just to keep it simple over the last fifteen years would have improved your Sharpe ratio by 20 percent mmm-hmm and again going from a Sharpe ratio of 0.5 to 0.6 takes you from average to you know almost superstore I would say at point seven you're like superstar level sure so I mean this is how dramatic these factors are yeah now same-same factor is if you short the VIX so instead of going along the S&P you can short the VIX for volatility which has the same effect as being short puts on the you know is lying exactly then you could actually have improved your Sharpe ratio by 90 percent okay then you have the typical carry models yeah which improve your Sharpe ratio by 18 percent if you optimize them and then you have now today your cities are adding credit type strategies including by exposure to you know credit swaps and that sort of thing and those would improve your Sharpe ratio but about 80% mmm okay so here we're saying there's seven factors are absolutely not skill based which are highly correlated to the B top which means over over time you can see that the B top is steadily increasing its correlation to those fattest seven factors mmm and all all seven of those factors are things which reduce the ability of CTAs to hedge equity Corrections sure so see TAS are getting better and better by introducing these factors as on a standalone basis but they're becoming worse and worse at hedging equity Corrections and investors are haven't picked up those factors out of the CTA returns yet and they're therefore focusing on the CTA's that have the most of these factors hmm okay very very interesting incredibly useful and very very insightful and and and and people should really pay attention even though it might sound very technical but but this is important stuff but let me and I you know I have the same observations maybe not so eloquently sort of described as as you do but but I I agree with the overall conclusion of what you're saying now did the question here and and this is interesting and it's particularly interesting and very topical because David Harding from Winton was on CNBC I think yesterday or the day before in an interview where he was obviously being asked about you know equities and so on and so forth and and he kept stressing the point that they may not be a hitch if equities go down they may be but they may not be and and so from from from the way you describe things and I sensed that you and correct me if I'm wrong that you are saying that a lot of these managers that are doing these new things are doing it to compensate for size would that be a fair statement or do you think they're doing it not really because of size necessarily but simply because of trying to introduce some more stability in their returns I would say both and the fact that they don't have four to make a choice between one and the other is even a stronger or an accelerator for these optimizations hmm so sighs definitely you would have to make these optimizations but also these optimizations have worked extremely well and are working better and better as time goes on in the last five years shorting the VIX is like you know it has a Sharpe ratio of again over one just a simple simple strategy so it's it's numerical and it's sighs yeah I mean obviously I would love to have David Harding come on the podcast and and discuss this issue instead of us trying to discuss it you know because obviously there is some reference to people like Wynton who've been very very successful stabilize the return reduce the volatility and and and now coming out saying that they may not be a hedge when when equity markets go down and so on so forth but but let me be devil's advocate here even though I agree with what you're saying and that is we say it's not due to skill but what if the skill is that they actually introduce these strategies at a time where they would be Fenn official for their returns but it's not to say that they won't deal you know deleverage or de-emphasize these strategies later on at a time where maybe the trend following strategy should have more weight because we know and we have to accept perhaps with your firm as one of the few exceptions but we have to accept that for trend following strategies it's been a really difficult and tough environment and that obviously goes back to the fact that volatility has been decreasing and generally trend following strategies make money from the expansion of volatility so I just wonder I mean some of these firms they have 150 PhDs and they have to do something and if maybe they actually came up with this you know observation saying listen we should you know increase something you know risk exposure to these type of strategies because as long as the environment is as it is and we're not detecting any changes in the data they will help I don't know I'm just putting it out there okay a great question let me give you my angle on a sure so first they're broadly speaking I would say people have or it's been a basically predicting factor turnover has been something that I want to say less than 1% of fun my phantom and fund managers have been able to do like it's not something which is broadly done sure nobody even even the the macro guys you know the Lewis Bacon's etc at the time where we read truly exceptional managers today cannot tell you where the returns are gonna be what factor if you can actually predict what sector or where the returns are gonna be you you you know your Sharpe ratio would be 3 and above sure now going back to the CTA so you're saying is it possible that these PhDs have find a way to tell you when you should exit a risk on trading and go into risk of trading well I would say it's very highly you know it's very unlikely the reason I say that is because we've had an example already of an equity correction after these factors were introduced into Z TAS so these factors really started peeking into the large city is around 2003 right 2004 2005 when the wall went really low interest rates went to real low and the carry trades became highly highly contributory to a CTE portfolio and then here comes oh seven two oh nine so you'd say did these PhDs were able to get out of the risk on trading and go into trend following at the right time and if you look at the returns of these large cities or the B top oh seven 209 and relative to the replicator that has a better of one to them so you replicate the beat up you know with a certain you know what is better etc you see that all seven 209 the replicator over the two-year period made about 96 percent in return where the beat up was of about 17 percent so I'm saying that the DC TAS that introduced these these new styles into their portfolio underperformed the replicator by 80 percent an absolute return yeah over a two year period saying all the same volatility between the two the better of one absolutely so if you look at the way the beat up had performed relative to the replicator before it has about 75 percent correlation and they're completely in line now so I would say that they already was a one major test and they failed majorly right at a period which was really critical so now they're telling you we're not gonna give you a head you really don't care now today we're a hedge fund we're not a CTA anymore that's it that's a fair choice but investors should know what they're getting yeah no I agree any as I said I think it's really super important and it's something I I probably think is is is something that you have not only thought a lot about clearly and but but but also has sort of shaped the way you do things which I'm sure will we'll get to very shortly but I want to go back and and talk a little bit about you know you have these six strategies you have you know almost 800 million dollars in the management how big a team does it take to to to run a business like that and and how is it structured and how how do you balance between what to do in-house and what to do you know by the means of outsource you know providers we can cover that this is one point I'd like to make about those factors sure and it's that the hedge fund industry like a lot of these factors can be understood correctly within the CTA space and within the hedge fund space if people measure risk by measuring tail risk rather than measuring volatility or better to the stock market so most hedge funds and most CTA is today are generating numerical alpha to their benchmarks but the ones that are generating numerical alpha are doing so because they're taking more tail risk than their peers right not because of skill sure and that's something we we went into our second research piece oh and CTS are doing the same thing they're converting positive skew for negative skew with higher Sharpe ratio so this is something very very important so alpha does not equal scale alpha equals skill sometimes okay so good so going back to your question in terms of how can you run six strategies you know what kind of infrastructure is needed sure I would say the most important thing to run you know if a firm like ours a you know infrastructure is needed but the most important is to have a clarity of direction in order and a clarity of purpose if you're trying to create a product that does everything it's never in the infrastructure is it never gonna be enough yeah so so the most important thing is to start with a clear mind in terms of what you're trying to offer and to stick to that no matter what the market conditions are now infrastructure wise because what we do is based on automation right so we're saying as a starting premise for CTAs we're saying the human mind is not equipped to make ideal decisions in the financial markets because the financial markets are there to compensate for what feels good not so basically when you want you put on a trade that feels good you typically lose money and the way you in a way have to be detached from what feels good from what everybody is thinking and yet you know you have to be contrarian and you have to put on a trade that nobody wants and the human mind is not designed to make such decisions it's designed to think as part of the crowd yeah so now we're saying we're gonna rely on models we're gonna we're gonna decide we're gonna program the models and tell them what to do but then we have to trust them 100% now that gives you when you understand how distant you already need to be from the models it gives you the ability to run with a relatively small team a lot of different strategies because once they're programmed as long as they're not over optimized you can give them life and let them live yeah you don't have to be thinking about them every day whether I need to reoptimize the factors etc etc so it's a question of you know you have a certain degree of mental space we believe that that mental space is most well spent during the research and the design of the strategy but then if the strategy over optimize then it's gonna take too much mental space to maintain and therefore that's not a place that we go sure okay so now infrastructure wise today more and more things are becoming commoditized yeah you know whether it's the accounting the execution etc etc now for us we're pretty good at programming etc etc so we've actually kept in-house or the order management system order execution system obviously the trading strategies we do everything in-house so we don't outsource so it's a lot of the admin the back-office and all that can easily be outsourced and today it's a commodity and the price spread between different providers is really minimal so I would say it's like a it's like a no-brainer over time it's now that the replication is coming into play I would say that you're going to see that some strategies are going to become commodity as well you're going to have five different firms offering you know what used to be a let's say a small cap mutual fund you know in the CTA word is going to be the sub strategies are going to become available exactly the same way through different fine as the different providers etc so the industry is very mature compared to where it used to be twenty years ago and ten years ago and five years ago even but with nine people were able to I would say out of the infrastructure that we have we're here we can serve as clients we don't have that much of an infrastructure in terms of on the asset raising side sure because we focused on servicing one client for seven years today we have about five clients our account for you know ninety nine percent of our assets so we're much more focused on client servicing reporting and that sort of thing rather than asset raising and and as such out say our infrastructure is you know pretty tight sure sure but also still you know it allows you still to grow as long as you sort of keep that focused because as you say automation takes care of the day-to-day stuff so so very interesting now looking at the track record and I think sort of probably focus if I may focus on the original program we may talk a little bit about some of the other stuff but but if we focus on the original program because it has the longest track record going back to sort of May 2000 1999 when you start it and and all the way through to now and when people nowadays look at track records they tend to believe that okay this is what I'm gonna get going forward but but that's obviously not true because most programs go through if I you know evolutions and changes over time but how would you say people should look at your track record when they look at it and has it as it you know is it very different does it does the program look very different today than it did when you started and and when was these big changes when did they occur so here again is it what's really important is to know relative to the benchmark what bets we've made what factors will we've exposed ourselves to over time and whether our philosophy has changed over time nobody can predict the returns but if they can know the sub factors that they're exposed to typically they're pretty happy because they can build the portfolio in the way that they want and I think that's really critical sure so what we've done over the years in the original program we've had the philosophy of we've always we're looking first to be you know a trend follower it means you know we're looking to correlate to the city indices right but we also want to generate a lot of alpha so we've generated about seven percent of annual alpha to the beat op per year since inception now philosophically that philosophy has not changed so our alpha is there so we're not looking to be a replicator in the order program we're looking to generate alpha and we want our alpha to come from positively scale positively skewed sources of returns which which means we don't want to be bottom picking equities and with the sharpish of 1.2 sure we don't want to be allocating more to fixed income we don't want to be going long equities we don't wanna be shorting Vic's we don't want any carry we don't want any credit spreads sure okay so now how those concepts have been expressed over time has evolved substantially so so broadly speaking something which is really important and like I would say critical for the firm's survival is that today nine sources of Alpha out of ten or negatively skewed it means there they involve mean reversion they revolve being expose exposed to risk on top of trade and we disqualified those immediately at the at the forefront okay so now you're left with very few research ideas but those are typically things that are more difficult to exploit yeah due to transaction costs but we're pretty good at that at controlling that and that's how we've generated this alpha now the benefit of positively skewed sources of alpha is that they are more stable when the market regime changes mm-hmm so if you tell me you know I have a strategy which is gonna buy the S&P when it's down three days in a row because the SMP never goes down even two days in a row now I mean it's great but if there's any slight shift in market regime then it's a very very large vulnerability so the track record of the original program the philosophy has not changed but the way we've expressed this long volatility or long tails type of bias that we have it has changed over time and I'll give you examples first basic strategy we're not looking to go into first our days per trade is about seven days per trade okay where the typical CTA today is probably around 30 days per trade the reason we pick the shorter timeframes is because that's where we can generate alpha and that's where you have the most positive skew so the most if you want to benefit from tale events so larger increases involved seven days per trade on average is where you have the most expansion interval okay interesting when you go to you know 30 days and now people are going to sixty days and that's so the larger CK's are of course trading more long term the more long-term you go the less positive skew you generate okay so we're looking for vault action and volley expansion happens when you cannot predict when but you can predict the size of it based on its long-term average so typically you you would look at the volatility of the market in the recent past and compared to a longer term past and if the recent volatility is much lower than the long-term volatility you would say this is there's been some sort of vault compression now from this type of environment a trend following signal in many different time frames is going to give you much more positive skew and much more alpha then the typical was going to typically today in the industry which is to instead buy the dips to the fall has expanded let me trade let me put on the mean reversion trade so we're still looking for the vault compression although the vault keeps going down and it's becoming more and more difficult because of all the central banks basically constantly providing liquidity but even when the low in the low fall environment we're able to generate returns because the vol has spikes right so the markets are pushed very far away from equilibrium and they're doing and they're there with very low vault but when something goes wrong then the vault expands much more than he used to so even last year when the vol was extremely low and coming down we were able to be up in the original program but you know 16 percent or so because they were for small spikes in volatility if you look at the VIX for example and in those four periods that's when we made about four percent in each and ended up 16 percent on the year okay so now the track record over time if all expansion is measured very differently today than what he used to be when we started when we started you could look at the four day volatility compared to 250 duvall Attila T if the four day volatility was less than the 50 day you would say okay I'm gonna take whatever this signal or that signal or that sort of thing right today we're looking at things in a much more complex way for example we measure volatility differently when the market is making new lows and new highs than when it's in the middle of the range right okay there are a lot of people who are protecting price levels mainly central banks and other you know option sellers and that sort of thing so when a central bank sees you know for example dollar yen go below a certain level it's gonna intervene and it wants to intervene in a way that minimizes its cost which means it has to trade as much as possible as quickly as possible and to create a dramatic reversal so if you measure the volatility at the tails independently of the volatility in the middle of the range you have a lot of information about the character of the market that's an example of going from a basic volatility measure to something which is much more pattern based and much more relevant in today's world would you say that volatility is almost more important for you than price and when you know in identifying you know trends absolutely a price as I said trends which are so the typical I would say the typical approach it seems from a replication in today's world you have a trend following model everybody starts with that and they say now how can I improve it I want to pick the trades typically what they do I want to pick the trades that have the highest Sharpe ratio as you do that you're looking for those clean trends those clean trends don't have all expansion in them and they're highly correlated to the stock market they're highly coordinated to fixed income sure for us we're not looking for a trend we're looking for the correct volatility setup we're looking to generate alpha and in it needs to happen very quickly in time that's only available if the vault accelerates so yes volatility is the most important criteria behind our setup okay very very interesting now tell me a little bit about how you then constructed the the original program so to speak in terms of the kinds of models you use and and and maybe a little bit about you know I'm sure they're not all the same timeframe but but how how have you sort of from an overall point of view constructed to the program so in terms of where we are today we're trading anywhere from all the way from day trades to some you know long-term models that can stay in a trade forever but but about seventy five eighty percent of what we do it's time constrained which means after a certain amount of time we exit the trade no matter what okay because the Alpha is not there anymore and we expect other CPAs to be there and therefore we say there's no point being here the trade is not as valuable yeah so the concepts that we use or utilized across you know again multiple time frames the key is measuring volatility but volatility is not the right term really okay so markets have memory it means you look at a trend following trades and this is not the random walk there's a serial correlation which is way over zero and then the market stops from the trend and it goes into a negative serial correlation type of environment so it's not that the market overall shows zero autocorrelation day to day but it's actually positive sometimes and negative sometimes now what is the impact of that on volatility if you're measuring volatility in a trend like from a from a var perspective the ball can go to zero if you go into credit market where you're getting paid a certain amount of money every day the vol is zero yeah now the risk is definitely not zero no okay so conceptually our philosophy is that investors are not pricing tail risk correctly because they're using volatility as a measure of risk and they were over allocating to markets that have a extreme tail risk being confused thinking that they're getting skill so we want to maximize the exposure to that that exposure happens in the shortened time frames for example by looking at the volatility at certain times of the day which is highly predictable terms of the future so okay let's say you just take a concrete example let's say if you can just destroy and and visualize it really fall for the listeners so I will go back to a model which has you know compare the 10-day volatility as measured let's say compare the ten-day arrange to the 50-day arranged when the 10-day arrange is small in size relative to the 50-day range then take trend-following signals right okay that's one example now it's not really the range I mean we're looking at volatility from a statistical way it means we're looking volatility should be normal and when there is something which is missing in that normality we're looking for it to come back with a vengeance to for the market to become stable again so if the market only goes up you would expect this a sharp correction if it goes up without noise you would expect the correction to be much larger than if it goes up with noise sure okay so you want the you're looking for the pattern of volatility to be symmetrical and healthy and so we're looking I give you the example from an absolute level you know ten-day range versus 50 range but instead of looking at the range we're looking at the cooler characteristics of volatility I give you the example of the volatility at the ranges versus the volatility in the middle of the range right anytime you have big differentials in different aspects of volatility there's going to be something which is going to rebalance the market so so so if I understand you correctly what you're saying is that first you have some kind of volatility filter that determines what kind of model you want to trade the models are I mean that you can the models again you can replicate with moving averages typically most of our trades are based on stops so again it's not channel breakout but it's based on stops and it's different than so you need a trigger I mean it's fine to say that you know if one volatility level is below another didn't do trend falling but you still need to train following trick or some somehow so so the trigger you're gonna be okay if you use moving averages you're gonna be okay if you use channel breakout okay so so why don't we afford to for you to have a complete model I would say only trade when the vault has compressed then trade attend a channel breakout okay finish that's one model yeah now the way we do it we're using different entry levels yeah then the channel breaker but this would give you something which is very different than what the market is doing you would add a time stop to that trend following model even though it's trend following and usually turn following you know implies that you let the the trade run you would actually apply a time stop as well absolutely because we're looking to maximize the amount of alpha per unit of better beta is easier to get so we don't have an issue with that so we're looking to maximize the alpha we say depending on the setup so let's say we're looking for a vault compression where we're looking for the market to correct within a long term uptrend and the vault to compress expecting a continuation of the trend with a vol expansion that's the type of trade where you're going to generate some alpha for two or three days and very quickly transformers are gonna catch up with you okay so you have to say exit to after three days for example so now on the other side let's say the market is in an uptrend and the vol has acted in a very this is an extreme amount of randomness in the market so randomness okay let's say this the serial correlation of the market has in creative after a big upfront and now you're expecting that if a correction comes it's going to be very it's going to be very aggressive okay so now that type of market if that correction happens it's gonna you can stay in a trade much longer because it's gonna take trend followers two weeks to catch up with you so and therefore for two weeks you're generating alpha so every setup condition has embedded in it a time after which the better on average switches sorry the alpha switches to better do you trade these same or you know I guess I have many questions actually it's very very interesting firstly I wanted to to ask you how many different models would you say that you run and enters each model get applied to to all the markets in the portfolio that's very very important yes based on the stability of market regimes in today's world within the CTA space I would say that not applying models to all markets is a guaranteed over optimization within the CTA space in today's market regime I emphasize so I believe that yes I mean if your money I mean if you hire okay you gotta be really careful about designing models that work on individual markets sure and you gotta say a lot of prayers when you use them okay so yes we apply models it to to everything in the portfolio now in terms of how we build the original program we have a but call it five different concepts which are applied in two or more different time frames okay they're exactly the same concept okay one could be the skew of the market one could be the volatility compression one could be the volatility at the extremes versus the middle of the day that sort of thing sure so it's the same concept at the entry conditions or and the timeframes are different and these things are concepts which survive you know a cross market and survive across time frames as well fascinating now this is this is complex stuff I mean for you to explain this you know this is not you know a lot of people will will have difficulties in in sort of getting the head around these type of concepts because it's a little bit more than traditional trend-following you buy when the price move up and you sell when the price move down and so on so forth but what I wanted to ask you because you mentioned in the beginning of our conversation that your upbringing in Lebanon actually influenced the way you design your systems so now I want to try and bridge that gap between these very complex models that you just described and your upbringing in an inherently unstable environment how does that work [Music] thanks for the interesting question well you see we see the world based on our memory of what it's supposed to be like it means most people see the world based on their own filters of reality not based on how it is right okay so I have filters people who grow up thanks for listening to top traders unplugged if you feel you learned something of value from today's episode the best way to stay updated is to go on over to iTunes and subscribe to the show so that you'll be sure to get all the new episodes as they're released we have some amazing guests lined up for you and to insure our show continues to grow please leave us an honest rating and review on iTunes it only takes a minute and it's the best way to show us you love the podcast we'll see you next time on top traders unplugged [Music] [Applause] [Music]

Channel: Top Traders Unplugged

Views: 2,162

Rating: 4.8947368 out of 5

Keywords: Nigol Koulajian, Quest Partners, trading, risk, top traders unplugged, investing, top investors, how to invest, investment strategies, top trading, top traders, money, investing interviews, successful traders, how to be a top trader, best traders, hedgefund, better trading, how to trade, analytics, managed futures, future of investing, investing strategies, investing 2018, investment advice, investment challenges, investing podcast, systematic trading

Id: Y2KjhOs_uFQ

Length: 69min 56sec (4196 seconds)

Published: Fri Jan 13 2017