ReSolve Riffs with Rob Carver on Smart Portfolios and the Evolution of Systematic Trading

this podcast is brought to you by the resolve long horizon investing master class a 10-part evergreen podcast series where adam butler mike philbrick and rodrigo gordio of resolve asset management global explore an advanced investment framework specifically designed to steward quasi permanent capital with humility and balance from the science of decision making to all weather portfolio construction to the value of diversified alpha and tail protection this series provides a comprehensive capital management roadmap to improve outcomes for wealthy individuals advisors family offices and institutions managing less than 10 billion dollars to listen to the series or read the transcripts on demand please visit investresolve.com forward slash masterclass alternatively you can find it on your favorite podcast player by searching for resolve dash master class all right well welcome everybody yeah i see rod's got his frappuccino cappuccino ready to go i've got my because we're supposed to be drinking no i i've got i've got a johnny carson coffee you'll notice that there is no steam coming out of that coffee cup all right [Laughter] i feel like i'm in a very strange you didn't know johnny carson his coffee cup he always had a coffee cup that thing was filled with whiskey every day that's why there was never any steam out of the old coffee cup in the johnny carson show oh you learn something every day there you go okay let's let's introduce our guest um rob carver welcome to resolve riffs thanks so much for coming on and um i note that you are broadcasting from your man shed right you um you you had a great series where you sort of showed um pictures in stages as you uh as you went along you you sort of built that on your own or at least had something meaningful to do with the construction of that right yeah so i built the base myself and the the thing comes essentially is a load of pieces of wood that you have to kind of hammer together so i did all that and then i had to kind of do all the finishing and the the painting and all that kind of stuff so yeah but so probably two solid weeks of work i'd say for someone who was competent it would have been a week but for me it was two weeks also doing everything by yourself is is no it's it says recommends two people but yeah is it like an ikea shed where you get the parts and the assembly instructions um it's similar-ish except it's not from ikea and you you don't end up with like 78 sort of little screws left at the end and you've no idea where the hell they go so um fortunately i i didn't know if in that situation that's kind of neat but it's great and you showed the view and you you look out and onto this sort of garden this bucolic scene which um you know i'm sure is inspirational and calming as you're sitting coding and and riding your makeshift peloton in the background yeah it's it's quite nice the the only downside is is we have a lot of squirrels in our garden and um just you know i'm kind of deep into a function trying to debug it and like really concentrating hard and then this little rascal will kind of run across my roof and it sounds like someone playing the drums so that that's the only distraction i realize this is like a real first world problem now um because uh a lot of a lot of people over the last year or so obviously working from home brings massive distractions but but i'm fortunate that i built this in the summer of 2019 so when it came to last year and and i found suddenly that my my children were home the entire time uh there's a good 150 feet uh between me and them um at all times uh so uh now they're back at school it's uh it's less of a problem but certainly last year that having this this little refuge was was very very nice for me saving probably life-saving for them as well to be honest um yes i bet agreed so rob i think it's useful just everybody realize this is for entertainment purposes so take neither any construction advice nor any investment advice from any of the scallywags on this call and uh and just uh leave it at that but go ahead yeah and i just wanted to sort of introduce rob or maybe rob you can kind of give us a little bit of your backstory um i'm sure you know i always find it funny bringing guests onto this show who are much better known than we are and then you know assuming that people don't know the guests and someone know us but but i do think it's useful for you to um give us a little bit of your backstory and then i want to talk some about your books specifically smart portfolios and systematic trading we may get into leveraged trading um a little later on but yeah maybe just your your career trajectory to get us started um i'm not sure i'm better known than you guys if i am that that's a serious crime because uh you know you're out your offer is extremely good and um you know it's it i think it's generally accepted that most people who are trying to sell a fund and also putting out research that the quality of the research is not very much not very good at all right because it's purely it's often purely a sales exercise and i'm not going to name names um but it's fair to say that you guys are in a very small group of people who you know have a trying to sell a product but also putting out research out there this is extremely high quality so but anyway um enough enough mutual appreciation i'm sure people didn't didn't sign up for that uh did you say mutual information well we're really getting into it um okay um where was that oh yeah so me okay so um i've been trading probably for 20 over 20 years now um professionally um i started trading um on the on the sell side for an investment bank in 2002 i did a couple of years with them trading exotic interest rate derivatives and then i spent a couple of years in economic research and then i got a job with ahl which is a large systematic quant cta based in london um i did a couple of things for them i i started off with a new a new kind of product which was basically a systematic kind of global tactical asset allocation type product which is very different from what they've been known for which is kind of trend following mainly um so did that for a few years and then there was a business restructuring and i was sort of promoted kind of i guess sort of a bit up and a bit sideways so diagonally um to to be um had a fixed income um so that was running all the fixed income risk um so you know bond futures interest rate futures um interest rate swaps credit default swaps mortgage bonds you know you name it all that stuff uh and i did that until 2013 um and then decided um i had enough of working for other people um and i was in a fortunate position where i didn't have to anymore so um i left them and um the last few years i've done various things so as you as you've said i've written a few books in three books um two basically on trading one on investing although there is a bit of investing stuff in in the training books as well um all about doing things systematically so using systems using methodologies so you know none of this kind of looking at charts and looking for you know inverted vipers or any of this stuff and or uh you know any of this kind of stuff pure purely raw spaces of course yeah which um so none of that stuff but so stuff that the way i like to describe it it can be coded up but it doesn't necessarily have to be coded up and in fact my long only portfolio is is run using rules but not without any automation i just do the trading manually myself and it's quite low frequency so that's that's fine i probably trade that once a month um and then i've got a systematic futures portfolio that's a pure fully automated kind of you know um the trade's just futures um so so yeah so i obviously do my own trading although um mostly i'm not trading mostly i'm writing code or doing research and all this this stuff that leads to ultimately the the computer doing the the training for me mostly apart from this you know once a month rebalancing exercise um i do a bit of uh teaching university um as we were talking about i did a lecture this morning which is why i i'm dressed relatively smartly for me my normal pandemic kind of work where is is obviously a lot a lot more casual like most people um and yeah so so that that's what i've been doing for the last few years that's fantastic so actually um if you'll indulge me i'd love to to get a sense for when you arrived at the conclusion that systematic thinking was the right approach to markets because you know i think i i certainly came to markets with very much a discretionary view and i try to sort of be figuring out the macro um dynamics and trading off off those themes and it took you know a couple of frying pans to the face before i i realized that systematic thinking was really the only a coherent approach to complex uh systems but how did you walk that journey um yeah it's a weird one because um so my first exposure to the industry was when i was still at university and in my penultimate year summer i did an internship actually at ahl although i didn't subsequently go on working for them after i graduated which is another story entirely so so that was my first exposure to the systematic industry that was actually my first job effectively in finance you know completely in finance although i had done a bit of pa trading before that with a very small amount of capital that i had um as a poor university student um so i i kind of that was my first exposure to too and that that seemed like a very logical way of doing things um and at the same time i read a book by a guy called thomas bask or the predictors i don't know if you've come across it i've mentioned it a couple of times in interviews before and i'm surprised by how few people have come across this book but um it's probably it's a non-fiction book and it's a book about a hedge fund called the prediction company which is subsequently bought by o'connor and then by ubs um run by doing farmer who's one of the kind of and i know some other people he's the most famous who's one of the um the kind of leaders into the chaos theory and he now he's now uh teaching at oxford actually um but i read that book and and it was a very very well written book and very interesting and it made it sound like a you know a kind of um i don't know it's something about when when you're training systematically it's it's less um it's more structured and rigorous but but it's somehow cooler to someone like me anyway and more fun because you're not having to the kind of thinking that you're doing is completely different from the kind of thinking you're doing when you're making trading decisions so when i was working in a bank i did not enjoy that at all and i was working actually in a relatively difficult part of the bank in terms of the fact that we weren't just buying and selling say spot fx we know we're pricing complex derivatives so it's quite it's quite a mentally challenging job but it was an intellectually stimulating job so it was like the best description i've given um is that imagine that you've been told to sell sudoku puzzles while 12 fat guys are yelling at you that that's what it's like to me that's what working on a trading floor was was like um and some of them weren't fat actually but but you know the physically intimidating people right um so so that that was that so i had this kind of brief exposure to this really nice interesting fun industry called systemic trading and i had two years of hell in an investment bank doing the discretionary trading and then you know a couple years later i i got back into systematic trading and and it was it was just like i mean at this point i was i guess um how was i i was probably uh i was like 30 years old or something like that 32 i think i was 32 years old and it was just like this this is this i felt like coming home it's like right this is this is the industry or what i want to do this suits my skill set you know i can i not understand why most people choose to trade within a discretionary fashion because it's something that's superficially appealing and and interesting and cooler and let's face it no one's going to make any films about people working doing our job you know there's gonna you can't really see you're like i don't know um tom cruise like like leaning over a computer and going in that tom cruise voice like you know guys i think the algo is broken um you know it's not there's no drama there there there really isn't um it's it's not sexy it's not cool it's not interesting but to me it's actually it is it is cool it is interesting it is sexy it's intellectually interesting and and and yeah so for me it wasn't one of the you you sometimes hear of people who start trading in a discretionary fashion and gradually come to realize that systematic is better and i guess adam that that's kind of maybe your story you hear that a lot um but but um for me it was more a case of actually you know it was just a kind of blinding only obvious this is the way you should do it now the more interesting thing actually is while i was working at ahl if you look at my personal account training um which obviously was limited by compliance restrictions and that kind of stuff it was extraordinarily unsystematic um it was a my you know my individual personal portfolio was a complete mess um and that's because i i had two problems firstly i didn't really have time to kind of think about that i was worried about my day job which was obviously more important but also i was exposed to this constant flow of market news and ideas and i'd be like oh that wow there's this stuff on the street about this stock or i'll buy some of that or some of this or some of that so i actually probably and i ended up massively over trading my own personal portfolio and it wasn't until i actually retired i kind of sat down and looked at the spreadsheet with something like 120 tickers on it and and actually then began to piece together a way of trading this systematically using ironically all the skills and knowledge i already had from from my day job so that's an interesting back backstory when you talked about the 25 traders yelling at you while trying to do the sudoku uh method was you you mentioned that you didn't quite like that even though it is i would imagine it's more like um the mathematical problem is kind of a solvable problem but you have a lot of emotions involved in the trading of that to the market is that what you disliked about it the fact that it was emotional um i mean there wasn't really time to think like it was the ironic you had to think kind of a certain way and do a certain thing which is thing better to think very quickly under pressure to solve problems that were sometimes quite complex um often the the the thing i actually found fun was the fact that okay you've got 10 minutes to price this trade there's no way you it's it's a new kind of trade we've never priced it before there is no way you can get a quant to go away in a room for six months and come out and then with a french accent because all the quantum employer french say you know well uh you know this is the most elegant perfect optimal solution with all the factors considered you you had 10 minutes and all you could really generally do was glue together two different spreadsheets and price something that was probably 99 correct and i hope that you hadn't missed something that would end up blowing up the bank because of some unforeseen risk so actually i found that quite fun but it wasn't really um it wasn't really giving you time to actually think deeply about i didn't feel i was actually developing any trading skills really to be honest with you um because there wasn't the time to actually think about the markets about then i think at a more strategic level it just wasn't there at all um so yeah no i mean i this this is the thing everyone the ironic thing is a lot of people want to work on trading floors and investment banks right i mean maybe not so much now it's not such an attractive job but certainly 20 years ago it was a job that you know 30 of my graduating class wanted to do um and i was the only i was the guy doing it and i actually hated it yeah and now that you're trading your pa account and systematize it is all the emotion gone or are you still doing it are you still feeling the feel okay that that comment deserves sharing keanu reeves isn't going to star in the covariance matrix okay that's magical brilliant actually if any hollywood producers are watching this this this youtube channel i'm sure there are probably a couple there is a there is a very good book by um called the fear index which is actually about a guy who works in a systematic fund unbelievably and it actually is a very good book it's a real kind of thriller exciting thriller and so there is a film there potentially in that book if someone wants to make that and i am available as a script consultant at my normal race of course oh dude i want you as a leading man yeah uh perhaps not i don't think i think it'll be as marketable i still think maybe tom cruise or keanu reeves would would do better and back to your question uh which i haven't forgotten um emotion and trading i mean it's still true actually i mean even yesterday i was re rebalancing my portfolio and um i'd actually i actually made a mistake so a couple of days ago i bought too much of a particular fund and i went i went and i was still because because it's just after the end of the uk tax year so i have to do a bit more rebalancing than normal and uh i went and looked at i bought so much this i really ought to sell this down and and then you know put it into the fund it was supposed to go in to begin with and i've got some other stuff to do as well so it wasn't you know like correcting one mistake and i was and i looked at that and the price of this of the the etf had dropped by like two cents you know um for a loss that as a percentage of my net worth is insignificant could i pull the trigger you know i could i take that that 300 pound less which which also actually in a way is a good thing because it was a taxable account so it was a tax loss you know so actually it wasn't wasn't all that bad um and i was actually like and it wasn't it was about half an hour of me sitting there going maybe it'll go up maybe i'll just go up a little bit and i'll be able to to to close it and and after half an hour i kind of got a copy of one of my own books and i beat myself over the head with it and tonight so i was sensible pull the trigger close the tray took the tiny loss did the rebalance correctly but so yeah even even i after all these years still you know the emotion's still there it's very hard to get i thought your head looked a little flatter today yeah pulling on that's it's the economy uh pulling on that thread just one more time i i know there's a chapter in your book where you talk about having thought systematically about purchasing stocks when you're in a panic and and you talk specifically about oh wait i would love to to hear that story again um and share it with the audience and i and then the follow-up to that is how'd you do in the covet crisis so the the the story is basically that it was actually uh q109 um and um it felt to me like we were at the bottom of you know we were at the bottom and i thought well where where is the best place to put my money in the uk banking shares because they've been just massively beaten down as you would expect in a in a you know in a debt and leverage crisis of course um and i i barkley and also i i like understand banks as a business and i can read the balance sheets and stuff whereas if you were to ask me to do that with insurance company i would struggle so um i thought well i'll buy shares in a few banks the biggest share would have been in barclays because i felt that was in the best position and as it turned out you know they raised money privately rather from the government so that that subsequently was obviously the case um so the point was the point to make here is yeah it was a discretionary training decision but actually it was a good one the problem is that when it actually came to pulling the trigger i panicked um and i my timing was perfect i probably was within like three percent of the bottom something like that i i bought the moment some really really bad gdp numbers have come out in the uk just astonishingly about gdp numbers and the market tanked as a whole the banking share sold off uh i thought well this is all this is not new news you know this doesn't change my thesis i'm gonna i should go ahead and buy these things i just could not force myself to buy in the size of trade that i'd originally intended to trade i just cut everything in in by 90 and i bought one tenth of the position i intended to take um and yeah that those shares probably went up by some like 350 in six months so the reason i include that in my book is to show that even if you make good trading decisions and i'm not i'm you know by any means am i someone who makes those kinds of decisions all the time absolutely not um you you can you still need some kind of system in place to manage your risk and manage your position size um it doesn't matter who you know if you're some kind of genius trader i still believe that that is where you should use systems um you know most and i saw but most people probably shouldn't should use systems for everything including deciding when to buy and sell but if you do if you do think you can make those discretionary calls you should still use have a system in place um to to size them so so that's a really good point and a point that i think is um something that we we could talk about a bit more jason just cepheiic has a wonderful framework uh sort of a four uh four-legged chair if you will where you know you're harnessing beta so uh you know and then and then you've got some prediction that you might want to do some tilts um and you've got a bit of a protection side if you want to do some tail hedging and then lastly there's opportunistic and and this weird opportunistic bucket is really quite hard because opportunistic can be as you you encountered a particular situation where you had a fairly high degree of confidence that in fact there's a number of things that the models are actually not aware of your models only no inputs to the models are and you as a pm or over overseeing your own own portfolio do have a wider more pervasive view of the horizon so i mean how do you can we delve into how you do that either you know with a particular sector that looks particularly good i mean this was a banking sector in the middle of a crisis great you could look at you know tobacco stocks in 1999 you could look at uranium stocks today after a decade of you know just being absolutely obliterated um like how do you how do you work that in i mean there's different ways of doing it and actually i think another sort of paradigm that's quite nice and is is um often people say with you you know financial advisors say you should reserve five percent of your money for for just gambling um so you you for me i think it's okay to to say right most of my money is going to be run in this very structured and rigorous way for most people that probably means passive you know a diversified portfolio of passive etfs yeah and if you've got enough money maybe some stocks and maybe you want to go beyond that into tactical asset allocation and momentum and the stuff i do fine um but the the point is that it's our human instinct firstly for two things firstly it's our human instinct to have a bit of fun i mean you know and to be interested in things and i said earlier when i was working um you know in the industry there's this constant stream of ideas and stuff coming in so effectively my entire portfolio consisted of this sort of discretionary fun kind of interesting stuff i wouldn't even go as far as to call it opportunistic that that's dignifying it with a name it does not deserve that that implies you know a level of skill and rigor that was not there with the possible exception of the trade we just talked about um so i i think um that um it makes a lot of sense to to say actually yeah most of the time i'm going to run my money in this particular way but i'm going to reserve a certain proportion my risk capital for things that just come up um and they may be things that are outright gambles uh in which case it should probably be quite a small amount of money um maybe five percent maybe one percent whatever your risk appetite is whatever you can afford to lose it should be money you can afford to lose um or and um you know it's i mean there's sort of a link here between that and the kind of nasental of sort of barbell idea isn't there you know where you you preserve a proportion of your money for kind of out of the money options you think are cheap effectively um and then um on and the the um it could also be thing opportunities things that could come up and that depends very much on on firstly on on how much interest and time you're willing to spend on that as a bucket so it might be that you just keep five percent of your risk capital aside and every year or so something comes up or it might be that you're spending more time on this and actually it's a quarter of your risk capital that that's fine um but the the main point is firstly it should be a strict portion of risk capital you shouldn't just suddenly sell everything and go and buy you know um game stop because it's in that's the fun idea that's crossed your desk today um and the second thing is that even within that that special bucket you should still be applying some kind of system in terms of risk management position management so so take a allocate a particular portion step one you've got an opportunistic bucket you're stating that explicitly you uh put a percentage aside that you're comfortable with i suppose you'd also be able to rebalance that you know back to the your you know so your balance between opportunistic and sort of the other systematic buckets that you might have yeah i mean it might not be a fixed percentage it might so actually let me go back to the unanswered question about covid because actually that's a really good example because with coded it wasn't so much the case that i had oh that i've got 10 of my capital here i'm gonna i'm gonna put that into a covered specific trade what it was was actually i have my normal kind of portfolio rebalancing process and but i wanted on top of that pulling the lever mainly between equities and bonds although also individually within asset classes as well i wanted to override that to some extent to reflect the fact that i had basically had an opinion on the likely shape of market movements so my main kind of input into my sort of long only look ass allocation bonds equities is a 12-month momentum signal i knew that would be way too slow to to reflect what was i could actually see happening like coming in coming in at me at 100 miles an hour so the way the way i describe my discretionary trading ability is every 10 years i can i'm i've and this has happened twice so it's you know probably not statistically significant but maybe every 10 years if there's something really big happening i'm i'm i seem to have twice now been able to catch the top in the bottom of that actually three times if you count the tech the tech boom in 2000 although actually i had no money then to trade with so you know i couldn't couldn't commit to that decision so what i did last year was in february i started pulling the handle on my reducing my basically reducing my risk reallocating from you know risky assets like equities towards you know us treasuries and doing that in advance of my tactical asset allocation model so i i basically let's say my tactical application model was was going from let's say an 80 20 portfolio and was gradually moving down i was accelerating that but within limits so i wasn't just you know oh my god the world's going to win i'm going to sell all my stocks now i didn't i didn't do that i set myself a a kind of a a limit so it wasn't really it wasn't a proportion of my capital that was my opportunistic bucket it was a proportion of my risk allocation decision and then on march the 23rd i had this really strong feeling that that we were going to go back up again that we were at the bottom i think i was one day out so again i accelerated my i then i then switched the risk back into equities and again this was going against what my model would have wanted to do because my model was using 12-month momentum that was still showing you know a completely different signal but again i didn't i didn't go you know i didn't pull the handle and go from zero to 100 in one day it was a gradual gradual location but it was just it was just speeding up what the model would have done anyway so it was it was it was a you know essentially taking some of my system taking that away from the system allocating that to a discretionary risk management decision but basically otherwise following all the rules of my system and the difference between that an 09 was i felt very confident in what i was doing because i knew that i was still because i was doing it in a size that i knew that the worst case scenario is was if i complete you know i'd make completely the wrong decision was it wasn't going to be the end of the world i'd underperform what my system would have done anyway if i'd made the wrong decision but it wouldn't have been dramatic you know conversely of course i didn't end up making a lot extra you know so i probably made an extra two percent last year from that little discretion allocation based on my whole portfolio size which you know is better than a kick in the teeth um but but you know but it could have been a two percent loss which i would have been okay with right and it reflects the humility of you're making decisions overlaying your model that are probabilistic not deterministic where oh the world's going to end or oh the world's going to be okay so i'm switching 100 back and forth but it's a bit of a dimmer switch maybe overlay where you're saying i'm going to impart some of my experience expertise and judgment through the execution of my of my models probably would make sense to to have a bit of a checklist i'll bet you that that checklist for you is just it's in your bones because you have so much experience with respect to some expertise in judgment and having done these calculations so many times you're very aware of where your models will have some blind spots um is there any obvious check list points that you kind of go through mentally in your head when you see these opportunities that allow you to come to these conclusions or is that again i i feel like i'm dressing up like two two lucky lucky strikes as a you know i don't i you know it's like of course it's gonna go wrong at some point but if you have yeah yeah yeah you know no no i mean another three times yeah no this is this is something that's so rare for me that that i i don't really feel like i can tell you yes you know these are the facts i looked at when making this decision it is and i hate i mean my reputation's ruined after this podcast right that's why it's live yeah yeah this this particular decision was was literally intuition and i could tell you the things i was looking at but it was the same thing everybody else was looking at what made me decide that the the price was going to go from the then on 23rd of march whenever it was and you know you can check twitter i did tweet and say this is the bottom in my opinion um the the um what what the specific thing was i was looking at the maybe think this is the bottom like i couldn't tell you i honestly couldn't tell you and and you know i i think the the main lesson from this story isn't that rob is some kind of genius picker of mark at tops and bottoms and this is the secret checklist that he uses to pick them it's actually sometimes you your intuition can be a powerful thing but you've got to use it within the confines of a system and if you do that that means that you can make it make a decision knowing that your your downside is is limited to a degree you're comfortable with if you're wrong and that is the main difference for me between 09 and 2020. rob would you mind just to save your reputation a little bit like you're putting huge guard rails around your intuition so i think that that's um well said right a lot of people should take away from that you know much more systematic and very small uh intuition with a very high confidence in your intuition which leads to a very small adjustment in the systematic portfolio so i think that's a a great point sorry adam over to you no all good i just was wondering if rob because you did work in the the opm industry for many years um and so i'm just wondering if you could sort of comment on the intuitive or opportunistic decision-making process uh through the prism of of institutional fund management right like if you're in you know let's not use ahl because you you work there but if you're if you're a large systematic institution and having worked at those institutions you know what did you observe in terms of that type of decision making and and what is your general thinking on the responsibility of the portfolio manager to you know to to be able to observe that there are things happening that the systems aren't aware of but also operate within the the limits of the offering memoranda and those types of things any any thoughts there yeah it's a good point because you know what i just did if i'd done that at hl i would have been fired and right and rightly so you know um the the discretionary levers that that you would be pulling would be more things like how much should we allocate to this model should we turn this model on or off um you know should we trade this market in what size a lot of those decisions will have quantitative underpinnings but at the end of the day it's often a human decision to actually call the final numbers and and the i think actually this is where potentially um i it's dif it's more difficult in the institutional environment because there's a whole different set of pressures with other people's money that you don't have with with your own and things like for example year ends take on an importance that they don't really have for me so you know wanting to get a good calendar year performance i mean certainly and this is well known not giving her any secrets but when i was working in an investment bank if we were working on calendar years if it got to november and you'd had a good year you took no risk i mean you basically like hedged everything like just completely hedged everything and if a client came along with with you know you tried to win no no client business at all that would result in you taking any risk because you wanted to you've made your budget plus five percent that you're going to get paid this only downside from there you just you just sit on that um and even in a systematic business um there is potentially the pressure there because you know if you've got looking at you you've got obviously year-end numbers are in the report and there are things like awards which you know rightly or wrongly have some currency as well and they're often based on calendar year performance so there would potentially be the temptation to to reduce risk towards your end potentially um and there are ways you can do that without actually you know you can do it by there are ways that could you could do that potentially and and you're kind of in that big gray area between fully sourced systematic and never touching anything and fully discretionary and there is a big gray area there and i think it's misleading to pretend that pms don't have a degree of discretionary control they can't control the position size they can't do what i just did with my own money but they can control a lot of levers that can result in things happening and the would of the buyers be the case um so i will i will tell you um a story about somebody i know um and they were working in a fund and i won't reveal the fund or the person's name or the name of the person they were speaking to but but basically um they had a position on um and the the someone came over and they're having a conversation about this position and and if this person said well you know i think you should um i think you should cut the risk on that position and and the guys well that's reasonable to what level and the guy said until it's a short um now to me that's crossing the line i mean the moment the moment it's one thing to say oh this this the model doesn't know about a particular risk therefore this position is too big you know we should reduce the size of this position the moment you're changing the sign of the position you've you've moved from risk management to basically you know discretionary trading it through the through the back door so there's there's a clear line for me but before that there's probably probably a big gray area now um the the other thing is that um it's very difficult if let's say something's coming and everyone knows it let's take an example let's take um the u.s election last year or brexit some there's some big event in the calendar that's coming now if you take no action you don't make any overrides to your system at all you just let the system do its thing which is the pure thing you should do and you then lose money your clients are going to come back to you and say you had a fiduciary duty to look after my money it was clear this was going to happen um you shouldn't you know you shouldn't have let this happen now for me yeah so for me that's that's like for me that's like we'll hang on a second you're employed you're basically paying us to put money into a system um that means we shouldn't be doing this now of course if our funded made money through brexit no one would have been complaining everyone would have been happy and we'd be patted on the back and we'd get we'd get awards and stuff so so there's that level of pressure as well and even if clients don't say that to you i think there's always that feeling in the back of your head and perhaps there should be feeling in the back of your head that this is somebody else's money and you're going to be more risk-averse potentially and i don't mean just in terms of purely like economic utility of like choosing where your risk targets should be i mean your behavior will be more risk averse than if it was your potentially your own money and you didn't have that extra set of pressures very that's really explaining to somebody else is always a challenging dimension and i think also the fund management industry positions your role as you write an offering memorandum and your responsibility is to execute on the what you set out in the in the offering memorandum right but from the investors perspective while they technically bought a strategy that is described in the offering memorandum the implicit purchase is you know i want you to make me money right so it's not i want you to run this strategy it's i want you to use your expertise to my benefit right and and so there's a there's an implicit sort of conflict agency conflict that is actually explicitly set out in the regulations of the investment business that um that i think is not not widely recognized or discussed um now you've seen and it's different from say the relationship saying a bank you know working on a training desk because if you're working on a trading desk in a bank and you say make unexpected profits you're going to be called up like they're going to be like saying what the hell are you doing you've exceeded your risk limits that that you know they're they're they're big and that's because there's a much much more well firstly the contractual relationship is stronger obviously because they're paying you and you know and so on they have a lot of control of visibility over what you're doing um but but also because there is that mutual understanding of why you're there and there's a very it's a very very clear relationship i agree i think with with the fund management thing it's much more difficult it's like do this but don't lose money okay well what if those things are in conflict you know and they sometimes are what do i do you know i i can't predict the future i don't know whether with the right which option i should choose and i i you know i'm always going to be taking a risk that i will lose you money if i do exactly what i should be doing so yeah i'm in completely agreement with that that reminds me of ted's comment of be the same but different yeah ted sadies was on last week at capital allocators and one of the quotes from one of the portfolio managers is clients want you you us to be the same but different yeah don't drift out of your style bucket but make more money than the other guys yeah yeah so ted there was one thing you mentioned on the discretionary side for systematic managers and that was that a lot of the decisions are made on whether a system should be turned on or off or a market should be taken off the roster or on the roster i'm curious to know what that means from a discretionary perspective do they use data on it or do they is it more like gut based from an investment community perspective because i've seen it in blackrock i've seen it in ahl at least i've heard stories about all that i'm curious to know what you've your experience has been i mean my my opinion is is anytime if you're working in a systematic business and you're making a decision and you're having to make qualitative judgments you need to systematize that right you should not be making so i in a good in a good shop and and um i'm probably allowed to say this i think ahl is one of the better shots in this respect um you you the first time you make a decision it might be very qualitative but if that's the decision you're going to have to make again you should probably systematize it and i'll give you an example we were very worried um in in sort of 2011 about interest rates getting very very low oh how naive we were um and we we were particularly worried about this weird phenomenon in kind of obscure parts of europe like switzerland where interest rates actually were getting very close to zero and possibly even negative and we thought well how this this clearly is i mean negative interest rates does i mean what is this magical mystery word this makes no sense again how naive we were um but um we so the first we had to make a judgment about whether to stop trading um i think it was the euroswiss libel future which was approaching a price of 100 which meant the rate was about to go below zero so we we were like we had these kind of long discussions and in the end actually i built and and i built a model basically which um automatically reduced our position as interest rates approached zero in a particular market and we had long debates about whether that limit should win a where where that threshold should be whether it should be different for each market because obviously in japan they'd have very low interest rates for much longer period than in say the u.s um you know i made the i decide that it should be kind of fixed for all countries um which is simpler as well as avoiding overfitting and actually in retrospect was the right decision because you know we've seen interest rates come down in the us as well to historically low levels um but then that was the important point here is that was now a system that was it wasn't necessarily a piece of code we didn't have to systematize it in that way but it meant that if anyone had any discussion about whether we should turn a market off or reduce a position because of interest rate interest rates being very low then we would say well we have this procedure in place so if a client asked about it we say we have this procedure in place we have a system in place so it's no longer you'd no longer got a load of guys sitting around a table trying to make a discretionary decision which could be subject to biases and so on and so forth so you try and you try and systematize it so you know there i can think of many other examples so for example markets where volatility got very low which has a couple of effects firstly it means your leverage increases because we're volatility sizing our positions secondly it means generally trading costs increase because the the you know the market's getting less volatile but the tick size not getting any smaller so you're still paying the same spread risk adjusted that spread is now much bigger and more so it's a more costly market to trade so you'd want to get out of those markets again we you know the first time we had big kind of decisions about it then we put in place a system saying right the moment the volatility drops below this point then then we start cutting our position and we wait for it to go up to this level before we put it back in again so so that that's how you can do it i mean obviously they're always going to be decisions coming up that are you know non-systematic and you know so they often relate to things that are not necessarily very market specific like things like credit risk you know like do you maybe there's a market you could only trade with one prop we had this issue there was a market we we could only trade through mf global there were rumors on the street that mf global were in trouble mr corzine was getting a bit a bit candy with the gambling chips on on the casino table um so we had to stop trading that market and that you know there was that was not a quantitative decision that was purely like this is a very specific business risk because these guys are going down that's a great answer thank you amazing really so lots of fantastic insights there totally how i was wondering how much of the um the thinking that informs your strategies today um was pulled very directly sort of from the the general framework um that you were using at ahl right like um and and or sort of if that's a harder question to answer for um you know disclosure reasons then maybe how has your thinking evolved um since since leaving ahl and what sort of thinking are you applying today that that maybe um you weren't applying back then and and how do you you know do you have a framework for how to think about how your thinking should evolve over time in in sort of a in a way that reduces the amount of bias that you're bringing to the table yeah um so in terms of my actual pure trading system my features trading system is not dissimilar from what ahl were running certainly when i left the main the main differences are there there's no kind of proprietary signals in there it's all stuff that is very well known on the street pretty much um um the the um the the sort of um the long only stuff is obviously um quite different in their in obviously it's long only so you're doing a completely different you know you it's a different kind of setup um the the commonalities are obviously around the idea of full making forecasts i think one one thing that is in my system that a lot of people really find surprising is this idea of forecasts a risk-adjusted forecast of return future returns which is something that's very much straight out of the ahl system and very similar ctas um so the idea is that you don't just take binary long short positions you know you're continuously adjusting your position depending on how strong that trend or a carry signal is um and the the advantage of doing that that way is it means you can use the same kind of thinking in a long only system as well so that's how i do my you know my my asset allocation for example long short that we were talking about earlier when i'm not ignoring it of course because of covert um so the the kind of basic kind of theoretical building blocks you know are you know things that i i guess you could say i learned at ahl rather than so the direct copies i think the main the main difference is that i've tried to become much more rigorous in thinking about things like um i guess uncertainty in terms of back tests and so on and so forth so um interestingly um so when i decided to leave ahl we the kind of conversation was well can you hang around for a bit as a bit of a handover but on the other hand um you know we don't really you there's not really anything for you to do so obviously there's a handover that takes certain amount of time you know this you want clients to be handed over and make sure the clients are happy and make it clear this is not a kind of you know this is this is a sort of very planned and gradual transition and make sure it's all nice and smooth but actually in terms of what you're actually going to do you know what do you want to do so so i i i came up with this this project that was was basically about um doing conditional fitting so normally when we fit our models we look at all of history and treat it all equally but one big question that was that was still around in 2013 was actually how will ctas do when interest rates rise because a lot of the historic cta performance had been has been in the bot in bonds and actually just being long bond futures or long interest rate futures was a one-way bet um and a lot of the the the you know the kind of raw shark ratio was was was basically coming from the fact that there was an asset that was going up we were long it and that that was that was all there was to it what happens when that asset starts going down can you can you or should you fit your systems differently um and um i think adam actually last time we spoke i i did a blog article that basically did a similar kind of piece of research and we talked about that a bit so i won't go into too much detail about it but the key point about this project was it really brought home to me um how when people made very confident statements like well obviously if interest rates are going to rise then you know trading fixed income and with momentum is going to do badly um you should be doing something else instead if you actually look to the data it was so on it was so much uncertainty around around say let's say let's fit a model that works when interest rates are falling let's fit another model when interest rates are rising yeah there was some difference between them and there were the patterns you would expect but they weren't that different and they weren't that different because there was still so much uncertainty there um that these very confident predictions people were making were wrong so what's always amused me is that people in the systematic industry including myself many many times um spend most of their lives you know working with with statistics and data and having a much better understanding of statistical uncertainty than the average person will then ver so to make effectively make a point forecast and say i'm 100 confident that you should turn off this model because interest rates are going to rise for example um so i the main difference in my my thinking and i think this has been helped a lot by the fact that i dare to say i don't have to speak to clients anymore um and i don't have to deal with that pressure the opm pressure that we've been talking about is allow me to think much more clearly and honestly about how little i know and to build um my my everything around my systems to reflect that so you know a simple example is that if you're using something like um say a 12-month momentum to allocate between equities and bonds to tilt your you know your your um allocation away from a strategic allocation of say 60-40 and there are people out there who've written books saying you should go you know zero to 100 and back again right not naming any names but if you actually if you actually look at the amount the amount of confidence and uncertainty that that momentum signal gives you it's nowhere near enough that you should be going from zero to a hundred um you know you might be going at a push to be going from say 60 40 be going to 40 60 or 80 20 but no further you know and that's a really simple example of how you you should bring that uncertainty of i call it the uncertainty of the past everyone is about the uncertainty in the future but actually there's a lot of uncertainty in the past because we we measure we measure things and we measure parameters and we we test things but actually you know you do simple exercises like monte carlos or more sophisticated things and you can see there's actually huge amount of uncertainty in terms of what happened in the past and you can you should use that otherwise you know why are we here as systematic investors and systematic traders of course you should use that information but you've got to understand the limits of it and and how far you can actually push yourself towards doing something very dramatic um bearing it like turning off a model or like pushing your equity allocation to 100 bearing in mind that yeah not the future's uncertain with that's a given but the past is also uncertain as well and the you know the covert trade is a perfect example of that because you know i i made two percent on on what was basically an absolutely perfect almost perfect market call i think i got the i was two weeks late on the sale and i was one day early on the buy and it made a difference two percent of my portfolio because the amount of different the amount of things i changed reflected the fact that that i really have you know a lot of uncertainty about what's going to happen in the future but i had a lot of uncertainty also about how confident i could be about the predictions i was making that resonates really really deeply with um with the learning trajectory i think that we've all experienced and you know if you want if you want to get a sense of what this looks like at home um and you've got you know even some simple trading systems and we've run some simple examples and i know you have too raw but just try to for example take five trend following systems and every year based on the historical performance of the entire trading history up to that point try to use any sort of statistical tools to determine which ones you should emphasize or de-emphasize going forward and you know you'll just see that it is almost impossible right there's at the at the extreme limits you know very very short term or very very long term you might be able to sort of identify where some of the soft boundaries might be on on those but you know you give up so much in diversification by trying to concentrate in in the right one and the information you have to make those decisions is just so weak that the the realization currently dominates the precision um and i know you brought that to bear in your hand crafting process which which um we we really like but i wanna i wanna also dig into something because you kind of glossed over this in your um in your last comments which were great but this whole idea of fitting a model right like say say more about that because there's there's so much that goes into that that actually has a lot of discretion in in how you think about fitting models so how do you think about so let's say you've got i think you mentioned a 12-month momentum model or something or pick one doesn't doesn't really matter but some sort of signal how do you think about the model fitting process there i mean one one thing that is interesting is that i think people have the impression that model fitting is something that should be automated right i mean the whole kind of machine learning sort of drive is is that that you know really you should just be able to press a button and and and the parameters come out at the bottom and and it's all good and i'm i'm very much um against that um so one one thing i am one thing the first thing to say i guess is ideally you should keep your model away from the data for as long as possible um so the moment you you actually the you know there's the most tempting thing in the world is to i've got a new idea i've got some data i'm going to test it and see if i've made money or not that is literally the last thing you should do because the moment you've done that you're you're going to be opening yourself up to what i call uh implicit fitting which is basically where you you you've got an account curve it doesn't look good you say oh i won't do that um well you've you basically made a fitting decision but you've not made a fitting decision in an in a controlled way you've done it in an uncontrolled wave and it's an effectively an in-sample thing so you want to push that decision off as far into the future as possible so the first thing i do actually is focus more on the behavior of the model rather than on the the profitability so i'll be looking at things like does this model capture the effect i want it to capture are the trading costs reasonable um do does the holding period reflect the the sort of effect that i'm trying to capture so for example if i've got a model that's training every day but it's looking for six months trends well that suggests that some kind of smoothing would be in order before you know before continuing um and what i will try what i will do is one of two things what i can either if appropriate i'll use data that's completely random to make those kinds of decisions um and it may be not completely random but for example i might actually create data that has trends in it like six month trends up and down plus some noise um and it's completely unrealistic but it can allow me to answer the question well does this catch a six month trends because look here's a six month trend does it does it is it capturing that trend yes or no you know so that's you know that that's the basic thing is it's very easy to to construct something that doesn't do what you expect to do um the other thing if i can't do that it's not always possible i'll you i will use real data but i will limit some of myself to a single market and i won't be looking at profitability i will be looking at i might take a five-year snapshot of the single market um and i'll just just look at um you know the buying and selling behavior the holding period and and sort of make sure that that's doing what i expected to do um now the then what i want to do is to um once i'm happy with the model's behavior i then want to see whether it's any good or not but i won't i will won't test the the thing independently i'll just drop it into basically a massive portfolio optimization with all the other signals i've got and what that will do is is it will then effectively allocate some risk capital that to that model and obviously if it's a good model then the risk capital will be high at the end of the back-tested period and if it's if it's low then then obviously it's a poor model and i if it's below a certain threshold there's not really any point implementing it because i mean if you've got say 50 models and average of 2 risk capital well if one of them ends up with 0.01 because it's so shockingly poor then there's no there's no point in implementing it and you you're not going to be achieving anything you just be writing code for the sake of it now the important thing is that that that decision as to how much risk capital put into something is not based on me looking at an account curve it's based on a fitting process which is controlled and that means i can do two things firstly i can make sure that it's robust and it's it's allowing for the amount of uncertainty in in the data which as i've talked about that's much bigger than you think it is and secondly it can be a purely backward looking thing with no in-sample information polluting it now the reason i can do this is because my models are all structured effectively as linear weighted combinations so you know it's a very i've got lots of lego building blocks i i combine them in linear weights using a robust portfolio optimization process and that means that the the the actual resulting behavior can actually be quite complicated complex but the in this it's made up of things that individually are quite simple so none of my individual models are complicated they're all pretty simple and actually if you follow my blog there's a lot of things where i'm posting something saying oh look this is interesting but it's going to make my model too complicated so i'm not going to bother doing it you know so it's like i i i'm constantly spending i've spent a lot my research time looking at things and deciding that no that this is not actually it makes a small but insignificant difference to my performance that's not worth the complexity that it adds so that that's kind of the the way i'm thinking i i will only add something if it if it does if it adds something to my performance if if the if there's a good thesis behind it so it's not just a pure data mining exercise that's pulled something out that seems to work and i have no idea why i believe that can work but you need to be much much better at machine learning than i am and it also needs to be something that i can implement in a relatively simple way without making my system for example horrifically non-linear so walk me through the optimization a little bit because i think i know um a little bit about what goes on right i think you've got a regularization step where you are um sort of taking each year independently and then your what you want to do is sort of maximize the average performance relative to the dispersion of the performance from year from like calendar year to calendar year i think was one of the the techniques that you were applying or did i miss the expression on my face thing yeah maybe you've got me mixed up with somebody else yeah no yeah it's funny because i've got this second hand because we one of our internal um uh quants was using your or a a strategy that was informed by your systematic uh investing book when when he first joined us and so he sort of walked us through the handcrafting that he had been doing and that's what i that's what i i took from that so yeah um so the this handcrafting is um so what what i noticed when i was when i actually watched how people actually fitted portfolios this was a kind of uh an anthropological observation um everyone said that they fitted portfolios using some kind of fancy optimization process and i remember very clearly the first time i watched somebody actually do this um there's a guy called simon simon if you're listening or watching hi simon i hope you won't mind me using your name in the story but simon who was a much more experienced researcher than me we implemented a new model but simon was the guy on the team that was responsible for allocating the portfolio weights to different instruments within our model so so he that was his job so he he sat down and and around this optimization of course it came out with you know like 20 instruments and two had a weight to 10 the rest was zero as you might expect um and um i i sort of like looked surprised and simon said it's okay rob optimization is an art not science go and get yourself a cup of coffee and when you come back all will be well i came back and there were this beautiful set of weights and what simon had done was basically added constraints to the portfolio until the weights looked like he thought what they should look like and there's no disrespect to simon specifically because i know loads of people who do do exactly the same thing and probably still do it now and i've done it myself um now i thought well this is crazy why don't we just if we're going to just put informa in portfolio weights that we think look right well let's just do that but let's try and do that in a robust way so if you actually have intuition about how portfolio should be optimized well the first thing you do generally as a human being is you you put things into groups so you say well i'm going to put all my bonds over here and all my equities over here and i'm going to decide my top-down risk allocation that's the first decision i have to make and the nice thing about that is is from a mathematical point of view by separating that into effectively three problems a kind of how much in a how much in b and then how much within am within b those problems individually are much easier than the problem of doing a and b jointly um so effectively you end up with a hierarchical approach and this is quite commonly used so there's the hrp hierarchical risk parity which is the the kind of the more sort of high-end version of what i do i guess you could say coming from more a mathematical background rather than me with my anthropological kind of observation um so the first thing you do is cluster um and then within each of those clusters obviously you you work down until you're at the point where you have a cluster small enough that you can allocate within within that cluster in a robust way and actually i used to use clusters of three and now i'm down to clusters of two um and basically you allocate uh in within a cluster you give 50 risk weight to each asset because that's the optimal portfolio weight if volatilities are the same and shot ratios are the same everything should get 50 50. um the the the next step then is to um to say well actually if you do that unless you've got a very you're very lucky you're going to have bits of your portfolio where you're not going to have you may you you don't have equal risk rates when you should have equal risk rates so for example suppose you've got the same number of bonds and equities but your equity markets are much more diversifying than your bond markets then you'll have less risk effectively than you should do um between those two asset classes so there's a correction i do which is called the diversification multiplier and it's just the the inverse of the um the weight the sort of outer product of the portfolio weights and basically if you've got two assets that are uncorrelated then their diversification multiplier is going to be 1.41 which is square root of two if you've got two assets that are perfectly correlated diversification multiplier will be one so you multiply all your weights by these numbers and that that effectively then gives you equal risk allocations uh all the things being equal um and then and then you can then apply a couple of overlays and and one one is basically short ratios to say well actually i've got information about sharp ratio so i'm going to use that to tilt the weights and it is tilting it's not like we said it's not going zero to 100 it's tilting um and basically um to do that i use um a kind of a little bit of maths which relates to the sampling uncertainty of the sharp ratio um paper by andrew lowe about 25 years ago that introduces this quite simple formula and i use that to to to reflect both the if two assets have different sharp ratios but say i've only got a year of data those weights are not going to move at all but if those two assets have got very big differences sharp ratios and also if they're uncorrelated which means that difference is more significant theoretically and i've got 50 years of data well it's quite plausible that can make quite a big difference in the weights um and this this is all now this is all being done in a kind of assumption that all assets have equal volatility which in my futures world is is correct but in my long only world is not correct because i don't use leverage so then i'd apply a fairly simple stage which maps from risk weights to cash weights so that that's that's kind of where and it's evolved over the last few years actually the methodology um i made it more robust i've i've changed a few things like just making it two assets and made a few simplifications but it and it's not that dissimilar from the hrp but but there's a there's a few twists in there yeah it's sort of like robust risk parity times the probabilistic sharp ratio yeah exactly yeah so in a theory so one one thing um one lesson i like to talk a lot about is is about uncertainty because um people have different views on uncertainty in markets i was having a debate on twitter with with some trend followers because i'm i'm kind of at this we're in this weird position where i'm on the edge of the trend following community and they invite me to talk on their podcasts and stuff but we we disagree on a lot of things um and they they make this statement oh you know well the good thing about trent long is you're not making predictions which i disagree with but that's another discussion um and um this you know jerry parker who who's um you know this kind of big cta guy came on and said well you know you know rob um i won't try a news accent um the the you know the reason why we don't make predictions unless you do the reason why we don't make predictions is because you know markets are very uncertain yes they are uncertain but importantly there are different degrees of uncertainty and if you think about the three statistics you need when doing portfolio optimization which is mean standard deviation and correlation actually let's change that and say sharp ratio standard deviation and correlation um standard deviations are very predictable relatively speaking if you regress say next month's standard deviation or last month you get an r squared of about 0.25 which in finance is an amazing r squared for a regression anyone who's listening knows about r squares and regressions that's really that means that's very very predictable indeed so that that that means you should you should while things like risk parity are a good idea is because actually volatility is quite predictable and that's even without doing fancy like using you know implied option volvo vix as a secondary indicator or using a fancy garbage model or anything like that correlations are a little bit less predictable and that's why using this hierarchical structure is a good idea because that's that brings in robustness and means you don't do things like if you have two assets say that are very highly correlated normally um you know portfolio optimization will do crazy things with those normally because it's not accounting for the fact that the you know those correlations aren't always going to be that level and when they break down you know that's when you end up often with people losing a lot of money um and the thing that's least unpredictable of all this is sharp ratio which kind of makes sense right because if you can predict shot ratio you'll be extremely wealthy predicting standard deviation just means you'll you're less likely to go bust it won't make you rich sadly predicting short ratio is very hard and that means any predictions you have about sharp ratios should should not be affecting your your portfolio as much you shouldn't be putting as much um into them which is why i have this probabilistic layer in there as you say so basically what my thing does is assume i can predict standard deviations perfectly because i do i do risk a straight mapping from cache weights to wrist weights sorry the other way around assuming i can predict them perfectly it assumes there is some difficulty putting correlation so i have got a robust structure in there to deal with that and then i assume that predicting shot ratios is really hard so i have a full kind of layer of probabilistic uncertainty in there to to to reflect that so it's reflecting those three degrees of uncertainty whereas you know if you just take a naive mark of it's out of the box portfolio optimization it doesn't know anything about uncertainty it takes all the forecasts point forecasts assume there's no uncertainty in them at all so it's it's a more nuanced approach well yeah i mean we're we're obviously huge supporters of um that type of probabilistic based optimization and robust optimization i was curious whether you've run any well i suspect you have what what what do you observe when you run your optimization on so this is at the total portfolio level that includes your probabilistic sharp estimates have you have you walked that process forward and observed how the relationship between your estimated probabilistic sharp ratio weights and the realized probabilistic sharp ratio weights and and do you observe that those relationships are meaningfully persistent over time i haven't exactly done that one thing i have done is um with with all three of these kinds of statistics i've i've done um you know things like saying well how does the r squared change with how much data with how much time you've got and things like that um what what i have done for example is say let's take momentum i've said well as a way of illustrating actually the uncertainty so you if you had no information about anything you'd basically just use unconditional estimates and in fact you probably for short ratios you wouldn't even bother using them um but you basically you know if you if you get some kind of unconditional distribution for short ratios it has some certain shape if you've got a a really good indicator let's say that splits into i think you know high or low states for momentum say something like that you still end up with distributions and they do it and they overlap a little bit um and that overlaps telling you actually you know you don't pull the panel completely that this is this is about tilting your portfolio depending on the on the on the short ratio um but it it's there is some me there is there is some information there because obviously there's no information that the two conditional distributions will sit on top of each other and you won't be able to see any difference between them so i've done that um the the the difficulty with doing what you describe is there is so much noise that you know you if you're doing really really well you can you can you can make us you know your predictability in sharp ratios goes from an r squared of like .03 which is just noise to like 0.07 you know it's still a very very you're still not doing a great job of predicting sharp ratios but you only have to do a little bit better than than the unconditional noise to to do to have quite a decent portfolio return so no i haven't done that it would be an interesting thing to do though but i suspect it would be quite hard to to see much there i mean the the statistics would be meaningful but but um you know i'm not sure that the the there would be enough of a pattern there to make it a compelling pitch to look at well yeah i mean i think we've we've sort of concluded that that actually is the hardest and most rewarding effort in finding two things go together right yeah right absolutely um and i mean it is just shockingly hard i mean it and um we don't really use um the the linear model so much anymore but even if just using like really constraining the degrees of freedom in your models and and and having a small number of models for each sort of call it feature family right so you've got a few carry models and you've got a few seasonality models and you've got a few trends slash momentum models and you're gonna and you're gonna use 90 of the data and derive a probabilistically weighted expectation of future sharp ratio for the for the remaining 10 um and use a variety of different objectives or you know target metrics to determine your optimal weights and even using 90 percent of your data and some of this features data as obviously you know goes back to the sort of mid 70s um to make those predictions the r squareds are just vanishingly cl you know small delta between between 0.5 and and the realization right and so it ends up you've got it you've got to use you've got to sort of drill deeper into the the for example stuff like the the stability of the models through time and then you got to have like nested versions of of these validation procedures in order to figure out the right balance between you know trying to be precise versus trying to um be robust and like this is an almost endless uh rabbit hole right that so so i think the lesson is unless you want to spend all your time on this problem which again admittedly is the most rewarding problem in finance being extremely humble about your ability to make strong uh tilts in that dimension is probably your best course of action i mean this is what i find interesting because i think a lot of people um pay attention to things that they ignore things that are easy like it's easy to predict standard deviations which means for example that risk and we've talked about this before you know risk parity should kind of be your starting point um and people dismiss that because they they say things like oh we shouldn't invest in risk parity because you know you that means you'll be putting too much of your portfolio in bonds and everyone knows bonds are going to do badly which is basically making a prop you know a point forecast with no no knowledge or understanding are probably at all so they're they're they're saying you you shouldn't you should ignore this very predictable thing with your standard deviations while simultaneously saying that there is you know a very large predictability in sharp ratios because everybody knows that this is what's going to happen to bond and equity prices and i mean yeah sure bonds look expensive now but they've looked they looked expensive eight years ago but risk parity's done okay so you know um i'm not saying that risk parity is necessarily a great a great buy right now but the the point is that the the the structure of thinking that i like to use based on evidence around predictability leads you to really question a lot of the things that people say about about these in things flight right like the dunning-kruger effect there's one where somebody comes into the market they see a nice little trigger and they're like i'm going to do 100 of that so i'm either 100 in equities as you said earlier or 100 in bonds or 100 commodities as and as you dig deeper you're like oh i didn't know that that's not going to work out for me every time maybe i shouldn't do 100 and you go from 100 trying to predict the the future prices are sharp ratios to saying well hold on a second what's easy you go down to correlations then you go down to volatility and then you realize okay we should actually start with risperity start with that base case because that's the one we have most confidence in and then slowly build and tilt um after that so it just takes a it takes a decade a lifetime and a professional lifetime to go from being 100 certain to not being certain about anything at all and using that main probabilistic method and the other the other thing that that's kind of related not exactly the same in terms of things that are easy is risk premier like doesn't require any skill to collect risk premier um maybe requires a certain amount you know a certain amount of operational things you've got to do and to understand you know and you've got to maybe do things like short stocks which you can't do it very easily a lot of these things you do three etfs now anyway but but just collecting risk premium i mean people say to me you know what what's your your skill i'm like skill i'm not sure i've got any skill i collect a bunch of risk premier i try and do it in such a way that in a robust way which means i'm not making silly mistakes like paying too much for execution or overfitting my models but you know a large percentage of my returns is is just collecting risk premium there may be a tiny amount of of something special in there but it's a much smaller proportion than most people realize and i think i think the other thing that's missed is the idea or the understanding that when you've taken those lower building blocks that you have higher confidence in and you've built a maximally diversified portfolio when you tilt the portfolio you are likely increasing the standard deviation of the portfolio and have to have a you have to be more right because you've imparted this tilt and that tilt takes away from the diversification so the least likely thing that you believe you can predict ie sharp ratio you have to have a really high confidence in that because you're giving up the other two layers and i i think that's just absolutely lost people don't understand this is kind of the core idea of you start with risk parity and once you understand this maximally diversified portfolio and you lever it up to whatever risk that you can tolerate when you step away from that you are embedding a pre you're embedding a prediction of a sharp ratio which you have much lower confidence in in than the other components of inputs for the portfolio and it's a higher hurdle than people think that's right i think people think they they're layering returns right they think okay i start with my base care and then i'm going to layer this return but what's actually happening is you're taking away the rebalance premium when we talk talk a lot and so you're adding and diversity return with less yeah the diversity which leads to the rebalance program you're taken away from this and what you're adding needs to be the hurdle is what you're taking away so you're not layering anything you're losing something and you'd better be getting something really big to do better than the basis yes i mean it goes back it goes back to the very first discussion we had where i said well like you take some of your portfolio and you use that for say opportunistic stuff what have you well the the the the kind of hurdle rate on that it's not zero it's what you would have made is what you expect to make on what you've already got um so so um you you need to be very convinced as you say you need to be very convinced that this is absolutely the right thing to do and that's why and that that knowledge that that this is quite diff what i'm about to do is gonna be quite hard to get right is what should make you say well i'm not gonna put 100 of my portfolio into this crazy thing i'm going to put this much in because then then because i know i'm pretty confident this is a good idea i'm confident enough i'm going to do some of this but i'm i'm i know my knees arrogant enough to think that i can i can take 100 of my portfolio i'm going to limit it to 5 or 10 percent or whatever um and the i mean there's a lot of jargon in the in the investment industry that that kind of is is confusing misleading and i mean you guys are old enough to remember this idea of core and satellite which which um kind of gone out of fashion now but there was a lit there was some kind of that that actually i found quite a helpful way of of thinking about the world like basically you start with this and what you we can have and you obviously what we start with we have a big debate about and then you do a bit of this on top and again we can have a bit of a bit of a debate about that but it it's not it's a different approach from as you say that the idea of of layering things up i mean i remember a discussion i had with somebody during the last market crash and the guy said well you know i'm i'm i'm no idea what's going to happen so i've gone 100 into cash i'm like well that's a huge decision you're making a huge you're actually making an expression in what's going on you have actually made a much bigger decision a much bigger statement about what you think is going to happen than i have going from going for i think i went going to say 50 cash or whatever i'd you know another time and then you said if instead of cash you should buy 100 bitcoin right well instead of this is the point i was trying to make earlier on when you were talking about those subtle changes you had made in the portfolio in march 2020 right they were subtle and and they absolutely understood the trade-offs that we're talking about now and they understood that you understood the humility with which you need to think about those outside tilts and that that's i think that that point can't be emphasized enough i think as we yeah so i i guess we've kind of we've got you've got the systems trading that embodies the idea of uncertainty in this way we've described and and then the kind of as i said that the main evolution in my thinking has been the fact that i now apply the same level of understanding of uncertainty that to the kind of meta meta part which is actually in terms of how i design the system and how i back test it so it's not just that i have a system that embodies the fact that the world is uncertainty and there's different levels of uncertainty and and you you know you you go with the things you can predict the most but i also also similarly you know trying more so than when i was working for ahl actually my only if i've had any evolution it's that i apply that skepticism and uncertainty to the actual process of constructing these things as well in in a much better greater way i'd say hopefully when you went over sort of asset class inclusion um and we didn't dig into that too deeply but i wouldn't mind doing that now in the context of rodrigo mentioned the evil word bitcoin but how you know it's a trillion dollar asset class now but maybe that's a good example or a bad example but how long or how do you think about asset class inclusion at the you know sort of the first level and when it comes to new assets that might impart some different exposure to some other risk that you would be hedging by buying that particular asset how do you think about inclusion when does something qualify what how long does it take because you you talked about you know having something only as a year history well how do you know anything about that asset class so you can talk a little bit about that the asset class inclusion side and what are the sort of um hurdles that have to be overcome for inclusion i mean it's a difficult one and and um because actually my my portfolio is relatively simple in that it effectively only has three asset classes in it it's got bonds equities and then it's got this futures trend following account which i kind of treat as an asset um and because that account has um within it say commodities um it's so it's kind of picking up things that other people might want to get through for example buying commodity etfs which you know is the fashion 10 years ago less so now perhaps um so there are things that aren't in my portfolio like for example there's no things like private equity on in my portfolio um things like i mean generally speaking there are quite a few asset classes that i would not mind accessing but the the um the the sort of costs of doing so um which could be something as simple as doing it through a closed-end fund they tend to have quite high charges so one of the things i talk about a lot of my smart portfolios book was this very difficult thing where there are quite a few things that look good but actually to read to a retail investor unless you've got a lot of money and you do able to do things like directly invest in say 10 private equity funds which you know to get diversification because just putting your money into one private equity fund for me would be just too risky um so you need quite large amounts of money to be at the stage where you can write 10 checks to private equity funds so you then okay what do i do i'll go by the etf route well it's great the etf routes available now but then i look you look at the you know the annual the aers and these things and and they're the the kind of tens or even over 100 basis points and and you think well is am i really going to generate enough marginal improvement to my portfolio to justify that and the other thing is that i i think that there is um a case to be made that if you do have some let's say you knew nothing about private equity or nothing about anything at all well then and you can afford to then yeah absolutely have as many in theory i mean we can get into details about things like bitcoin but in theory have have all these things in your portfolio have a bit of commercial property have a bit of private equity have a bit of this have a bit of that by all means that is theoretically the best thing to do but i'm in a slightly different position in that i know quite a lot about this weird asset class called futures futures trading um and i can i don't have to i can invest in that without paying any fees um i'd have to pay some guy two and twenty to invest in that i can get it i can get it for the marginal cost of my time which works out to less than two and twenty um and especially when the system's running and just running by itself right and and i probably i can't if i look at my performance over say seven years which isn't really long enough to be statistically significant but long enough to kind of start making a judgment i'm kind of ahead of the benchmarks but a bit behind the top qual you know i'm sort of the bottom edge of the top quartile say so it's not what it makes more sense for me to on a marginal basis put my kind of alternative asset allocation if you like into that rather than to to sort of you know put ten thousand dollars in a private equity etf for example or something like that now if i had a lot more money than i do then maybe i would consider investing directly in private equity but it doesn't make any sense for me at the moment um in terms of history i i don't think that's that important because okay um the nice thing about the the process i've just identified to you is okay volatility so i need a month's worth of data since we've been training for a month that's enough for me to predict it's volatility so i'm i'm happy and in fact even even then like you could probably if you said to me what's the volatility of eternity of this turkish equity etf i'm going to guess about 30 because it's an emerging market etf equity etf and i may be wrong i'm not going to be wrong by by a factor of two though um i might be wrong by it maybe between 20 and 40. maybe it's a bit higher because turkey's quite interesting at the moment but i'm not going to be wrong enough that the position is going to be way out of line so so you know and because of diversification because that turkish etf is going to be ultimately a small part of my portfolio there's less kind of idiosyncratic risk in making the decision and putting it in with only with and potentially getting that wrong so actually that doesn't bother me and the nice thing about the the the sort of probabilistic sharp ratio idea is is actually it accounts for the fact there's not much data automatically it says well you've got to you've only got a month of data well that the fact that this this thing may have done amazingly well in that month is is kind of nice but but actually he's only going to tilt the allocation by a fraction of a basis point you know because it's statistically meaningless um and then for correlation i mean you know you i tend to find that about correlation predictability peaks at around six months say but ish you mean a bit longer is nice a bit longer but sure does not make much difference so but again you know you can probably guess roughly what the correlation of that thing is going to be with everything else as well i mean you know it could be quite correlated with other emerging market equities you know maybe a bit correlated with turkish bonds i mean you know so you you don't need very precise this is one of the the kind of things that that's a bit weird you don't really need very precise estimates of these things because your portfolio process should be robust enough that actually it's not sensitive i'd be worried i'd be really worried actually if if i had my turkish etf with one month of data the allocation was here if after two months the allocation was here i'd be like well there's something wrong with your system there's something wrong with the way you're allocating risk rob i'd love to um i'd love to get because we're we're closing in on an hour and a half and i'd love to leave and leave listeners with something with there's lots of practical stuff here but but something really concrete and i'm just curious um if you were not i mean you obviously have the ability to trade your own accounts and you and you have been doing so for many years and you've been using strategies that you that you fundamentally believe in if you did not have the ability to trade your own accounts how would you think about setting setting criteria for decision making for allocating to other um investors or or firms or funds or whatever that um you know would would raise the probability of success for you yeah i mean so in this hypothetical world do i still have all the knowledge skills and experience i've currently got let's assume you do but you just don't you know for some reason you don't want to run your own account yeah i mean i i probably um i'd only go with fun i mean this is the interesting thing because actually my decision-making process would probably be quite qualitative um because um i because of my limit for example if you showed me a manager with a great track record over say five years well the the statistical part my brain is going yeah that's good but it's probably not statistically significant okay so you know you you know i i would potentially if you showed me a manager who is supposed to be doing something and actually his correlation the benchmark was 0.2 i i'd be uh oh that's a bit suspicious there's a lot of style drift there you men there's something a bit strange going on here if you showed me a manager who shot ratio was exceptional i wouldn't even consider him because most likely probability is the next bernie madoff may he rest in peace wherever he's gone up or down um um the the the so but so there is there will be a limited amount of kind of quantitative information that that would be you know in that decision but actually less than you might think it would be much more i would you know if i would probably only invest with in in funds whose business model i understood um you know and um and where i had a high degree of empathy with what they were doing and that potentially would actually mean potentially a less diversified portfolio i than maybe you would expect because actually i probably still have quite a big allocation to ctas even though you know because i understand that that business and and i can read a prospectus and talk to people and and get that qualitative information and and i don't see the point of not using the skills and knowledge and understanding i have of that say um but i would be less likely to to invest in a in a discretionary long only equity manager because hey it's not a space i understand and b i'm more skeptical about whether they're really adding value beyond their fees that i couldn't just pick up through buying into the risk premium value premium things like that so so yeah it's not a very kind of these are the equations of calculations i actually think it aligns with what i believe investors do anyway which is it you align with you align with managers that align with your values and i think the reason that works is because if you understand if you have similar values and you understand that process better that you're more likely to stick to those managers through the thick and the thin because there's a shared there's a shared belief system there um and so yeah it totally makes sense to me that you would be more geared towards ctas and tactical asset allocation or like you know core and explore right and then from there it's just the pedigree and the reputation of the managers that um that that you end up interviewing and value investors do the same thing and momentum investors do the same thing and they call each other out and say valley investing is garbage you know at the end of the day i think the majority of in single investor alpha is their stick to witness and yeah and that's really most of it but don't don't take from this that um because a lot i think a lot of the problem with with people is is that they are and that's naturally like a sales thing like if you work in the cta industry of course you're going to say to people you should have a higher allocation to ctas and you know and tread falling is the best strategy and all this kind of stuff um but but you know you should have a diversified portfolio of of risk premier and and you know that that means you should still have allocations to equities and bonds just allocation to value you should still have allocation to to momentum yes but also to things like carry and some negative skew stuff that that's going to be a nice kind of counterbalance to the positive skew of momentum and and um yeah i think i think that the main difference between me now and me in this imaginary world where maybe i've retired and i can't be bothered to run the futures portfolio anymore is is is that the asset allocation is probably going to not look that different um but but um it may be a actually more diversified because i may you know be a bit more adventurous and have a few different types of funds um to replace that kind of allocation to futures now which i've obviously you know wouldn't exist in the future so um so yeah i wouldn't i wouldn't just do a straight swap and sell you know sorry sorry to my former employers but i wouldn't just get rid of that and buy ahl um you know i think i think it would be it would be an opportunity to diversify that a bit more definitely nice excellent thank you all right well we're we're over an hour and a half in and and rob it's getting late what is it like you're sneaking up on five there now on a friday afternoon yeah it's getting up to beer o'clock definitely yeah yeah sorry sorry to you guys who are like you know a bit earlier in the working week working day still but yeah well we do that to people on the west coast too right so we often have guests on the web so we're drinking and it's still close to me in their time and they're they're envious so it's it's the same anyway that this has been absolutely magnificent i'm really grateful for your time and your usual candor uh and humility and and um i'm sure everybody learned a lot and hopefully we can do this again sometime definitely it's been a lot of fun thanks rob thank you very much thanks guys yeah this episode is brought to you by resolve asset management inc separately managed accounts available for u.s and canadian investors while diversification is often discussed it is important that it actually be delivered through the suite of resolve global mandates offered at varying risk levels we aim to strike the balance between global diversification appropriate risk balance and directional alpha our portfolios are designed to safeguard and profit across many economic regimes including periods of negative growth shocks or unexpected rising inflation periods in which in our view the traditional 60 40 portfolios may fail to deliver adequate returns for investors resolve to improve your portfolio click on the link in the description to reach out to a representative and assess which resolve mandate is right for you [Music] you

Channel: ReSolve Asset Management

Views: 1,052

Rating: 5 out of 5

Keywords: straddle, adaptive asset allocation, alpha, asset allocation, asset management, diversification, equity momentum, evidence based investing, factor investing, financial plans, global equity, hedge fund, liquid alternative, machine learning, managed futures, momentum, mutual funds, portfolio management, portfolio optimization, quant, quant investing, risk parity, security selection, systematic investing, tactical asset allocation, trend following, wealth management

Id: zRA_ZSO2ZmQ

Length: 98min 20sec (5900 seconds)

Published: Fri Apr 16 2021