Chris Schindler’s journey really took him all the way down the rabbit hole. He joined the Asset Liability Group at Ontario Teacher’s Pension Plan in 2000 and soon became one of the founding members of the newly formed Tactical Asset Allocation Group.
Most of his 18 years at Teachers’ were spent exploring and developing quantitative tools and strategies to optimize portfolio allocations. An early insight regarding the importance of maximizing investment breadth (or unique independent return drivers) drove his research towards the world of CTAs and Risk Parity, eventually becoming one of the pioneers in Alternative Risk Premia (ARP).
There’s a good reason why this one runs a bit longer than previous episodes. We discuss the recent explosion in index-investing (“there’s no such thing as passive in this world”), Alternative Risk Premia (and why returns have suffered so much in recent years) and Chris’ new project: a truly uncorrelated alternative to ARP.
Please enjoy this incredibly insightful conversation between Chris, Adam and Rodrigo.
Chris Schindler, CFA
Chris Schindler was responsible for a variety of roles during his 18 years at Ontario Teachers’ Pension Plan. He started in the Research and Economics team where he worked on the Asset Liability Model before transitioning to the asset management side of the firm as a founding member of the newly formed Tactical Asset Allocation group.
Over the next 12 years, he was responsible for researching and managing a wide variety of systematic programs as head of the Global Systematic Investing group. Notable programs included an internal CTA, risk parity portfolio, alternative risk premium program, quantitative cash equities and the enhanced beta strategy, all of which were launched between 2005 and 2007, as well as a large number of pure alpha strategies (models not highly correlated to traditional alternative risk premiums). He and his team were also responsible for evaluating and hiring external managers in the systematic space.
In 2016 Chris joined the newly formed Portfolio Construction Group (PCG) as head of Asset Allocation and Portfolio Management. PCG was responsible for making recommendations to the CIO on Total Fund asset allocation decisions, including risk factor balance, asset class composition and weightings, FX hedging policy, tail mitigation strategies and constrained resource allocation. Chris is in the process of setting up a relative value systematic futures hedge fund with a planned launch date of Q1 2020.
Rodrigo G: 00:00:06 Welcome to Gestalt University, hosted by the team of ReSolve Asset Management, where evidence inspires confidence. This podcast will dig deep to uncover investment truths and life hacks you won’t find in the mainstream media, covering topics that appeal to left brain robots, right brain poets, and everyone in between, all with the goal of helping you reach excellence. Welcome to the journey.
Speaker 2: 00:00:28 Mike Philbrick, Adam Butler, Rodrigo Gordillo, and Jason Russell are principals of ReSolve Asset Management. Due to industry regulations, they will not discuss any of ReSolve’s funds on this podcast. All opinions expressed by the principals are solely their own opinion and do not express the opinion of ReSolve Asset Management. This podcast is for information purposes only and should not be relied upon as a basis for investment decisions. For more information, visit investresolve.com.
Adam B.: 00:00:54 Hello and welcome to ReSolve’s Gestalt U podcast. I think you’re really, going to enjoy today’s guest. His name is Chris Schindler. Spent the last decade or more at Ontario Teachers’ Pension Plan, responsible for a wide variety of roles over his actually 18-year career there. Started in research and economics, worked on their asset liability model before moving to the asset management side, and was one of the first members of the tactical asset allocation group over there.
Adam B.: 00:01:30 And over the subsequent 12 years he was responsible for researching and managing a wide variety of systematic programs. Notable programs including internal CTA, risk parity, risk premia, quantitative cash equities, enhanced data. He was well ahead of the curve on this. A lot of these strategies launched in ’05, ’06, ’07. And as we survey, and have conversations with institutions, big institutions, intermediate institutions, et cetera. At the moment, many of these really just got their all premia divisions really rocking over the last two or three years or so. So, way ahead of the curve on that.
Adam B.: 00:02:15 Chris is in the process of scoping out the launch of a new fund of ARP type strategies which have basically zero correlation to most of the popular all premia strategies, which is pretty exciting. And we go really deep into the weeds on how to think about all premia, how to best build all premia strategies, how to stabilize signals, how to build portfolios, how to put different all premia strategies together. In general, this is a fairly technical discussion at times, but I think if you can sort of bear with us that there are an enormous number of really valuable ideas, concepts, lessons, experiences that fall out of this.
Adam B.: 00:03:07 And so, without further ado I bring you Chris Schindler, also co-hosted by my colleague Rodrigo Gordillo. I hope you enjoy. Chris, you’ve got a really interesting background, you’ve got experience that, I think, a lot of listeners are going to be really keen to tune into. And so, there’s a ton of different directions we can go, and I just want to make sure we cover as much interesting ground as possible in the time we’ve got.
Adam B.: 00:03:35 And as Rodrigo said, I think a natural place to start is kind of your education. I mean, did you go through school? Were you thinking I want to be a portfolio manager, were you thinking I want to run my own fund, I want to work for an institution? Take me through sort of where you were in school, what you did in school, and then the first few years post-graduation.
Chris Schindler: 00:03:58 Okay. This is going to be a bit sideways then. So, I went to U of T. And I studied actuarial science. And that was more of an accident than anything, because I started off in math and philosophy, and was looking for something a little bit more practical, but I didn’t know what I was doing. And literally I started flipping through the course selection. Aboriginal Studies didn’t appeal to me, the very next thing was actuarial science, and it was like, “Well, what’s that?” Because I didn’t know what it was. And it was computer science, and finance, and economics, and statistics. “Oh, that sounds good.” Done.
Chris Schindler: 00:04:25 That’s how I started. Really what I was doing at university was rowing. So, I was trying out for the national team, and so my big passion at that time was rowing, graduated from school, started working at Tillinghast Towers Perrin … as an actuary. A friend of mine was at Teachers’, and said, hey, they’re looking to hire someone in the asset liability group.
Rodrigo G.: 00:04:41 What year was that?
Chris Schindler: 00:04:41 That was 2000, right before the bubble burst actually. And then there was this giant short internally. So, I started working there, and everyone was short the NASDAQ, and short equities, and bleeding, and scared, and then it all went right. So, that was a good start. And so, I worked there for about two or three years in this group called research and economics where I worked on the asset liability model.
Adam B.: 00:05:00 So, say more about that because I think a lot of our listeners are going to maybe not have as much experience with the way that some institutions operate, especially pensions. So, a few minutes on that might be helpful.
Chris Schindler: 00:05:12 Sure. So, ultimately a pension’s job is to pay liabilities, which is a series of cash flows out into the future. And of course, the cash flows are affected by a whole bunch of demographics, and mortality assumptions, but ultimately the asset liability model had two or three purposes. The main one being fitting the asset mix to the liability stream in some way, and your goal, I guess, is to maximize returns while minimizing slippage. And so, the optimization is not really in classic mean variance space, your source of risk is actually this liability stream, and that’s what you’re trying to minimize the risk to.
Chris Schindler: 00:05:42 And so, I would say the thinking in the industry has evolved massively in the last 15-20 years, because you see a whole bunch of what’s now called LDI, which is Liability Driven Investing. And you’ll see a bunch of portfolios that are broken into the RDI and the LDI where you have a return driven and liability driven investing framework. So, did that work for two, three years, and I guess around 2003-2004 that department got split up, and it got split between a risk group, and what was it? Really like a very small department that just started with four or five people that they decided to call a Tactical Asset Allocation.
Chris Schindler: 00:06:11 And my very first job was to try and build a Tactical Asset Allocation, TTA model, and Teachers’ had … Just to rewind for a second, Teachers’ had had a group called Quantitative Investing in the 90s, QI, that did equity quant. But it was an end of month, once a month GARP and value equity quant process. And that got rolled into this group in around 2003-2004 as well. There was a little bit of history of vol trading out of this group.
Chris Schindler: 00:06:37 There’s a bit of history around some relative global macro stuff, but mostly in terms of systematic and in terms of global macro it didn’t really exist yet in this department, started with it. And my first job, as I said, was to build a TTA model, and I went through all the dealer research, and I can’t remember now, but Credit Suisse had an investment clock, and it was five or six, and at the end of the day I built a TTA model.
Adam B.: 00:06:57 This is a macro model?
Chris Schindler: 00:06:58 It was a macro model. It took about six months. I think I had a Sharpe ratio of like a 1.8 or something like that, and I didn’t really know what I was doing, but I knew enough to know that as I presented it to my boss and said, “It looks great. I would never put a penny of my own money in this.” It is data mined beyond belief. In global macro, here’s the problem, there’s about five calls you have to get right over the last 10-15 years, and if you get four to five right, it looks great, if you get three to five right, hmm, if you get two to five right, it’s a disaster, and it’s just too low breadth.
Chris Schindler: 00:07:24 You’ve got stocks, you’ve got bonds, you’ve got commodities, you’ve got to get 87 right with equities, you’ve got to get the bond really right, maybe you pay attention to 93, you’ve got to get 2000. And at the end of the day, very low breadth. But here’s the other thing I’ve been working on, a CTA. So, at the same time I said I think this is a better way to do it. We thought about it a little bit.
Rodrigo G.: 00:07:43 So, that’s a traditional CTA versus the macro space you’re looking at traditional trend.
Chris Schindler: 00:07:47 I’m slightly playing around with dates here a little, because while I was building this global macro … And this is very, cool thing about teachers at the time. My boss at the time gave me, I’m going to say a year and a half of just time to just think things through, and research, and two of us sat down, and here’s the weird part. You said when I was in university, did I want to do investing? I didn’t even know what it was, even when I did actuarial science.
Chris Schindler: 00:08:09 The investment part of it is so assumed away, you’ll spend 100 hours talking about the subtle variations around the liabilities, and the assumptions you make around the mortality rates, and they go … And discount rates assumed to be six percent.
Rodrigo G.: 00:08:20 Yeah.
Chris Schindler: 00:08:21 And you’re like, “Well, that’s the first, and second, and third order source of risk of this whole thing, is that six percent.” And kind of knew what a stock was, I kind of knew what a bond was but not really.
Chris Schindler: 00:08:28 And within this year and a half, just a bunch of guys who had never done this before, just came at it sideways, not really any clue what we’re doing, and just started thinking about things. And that year and a half was so formative, because in that year and a half, we literally establish our entire model building philosophy that stuck with us for the last 15 years.
Rodrigo G.: 00:08:44 And what year was that?
Chris Schindler: 00:08:46 That would be 2003-2004. We’d spend some time working on a CTA. Even you say, “Well, how do you build a CTA?” And it’s like, “Well, you look up a little bit of the research.” And you say, “Well, you can do moving average crossover, you can do breakouts, you can do regression, slow progression line, zero correlation.” And went, all right, so, we built those models. And we kind of got to the … Here’s what we did and here’s what every model builder does, when they first start thinking, “We’re going to try and build the most complicated, awesome thing we can. It is going to be super sophisticated, super active.”
Chris Schindler: 00:09:10 And we went to a couple of conferences and one of the guys there, this is now 2004 one of the guys at the conference, I remember distinctly had this back test that had a Sharpe ratio of like, I don’t know, three. And that hadn’t done very well at a sample. We were like, “Don’t worry, that’s just noise.” And he’s like, but of course you have different moving averages for soy meal than you do for gold than you do for “10 years” than you do… Because of course you do because they’re different assets. And we went, “Oh yeah, of course you do.”
Chris Schindler: 00:09:31 And so, we did the same thing that everyone does is we built every asset and every moving average. And we tested them all, and we pick the winners. We made this giant dynamic process. Oh, we had something super complicated and awesome, which is we’re going to package these assets. And so, we thought about how we can package the assets to build better trainers and better universe, and now you’ve got packages of twos and threes and weighting schemes, very complicated, very sophisticated, looked great, had a Sharpe ratio of 1.6 in the back test.
Chris Schindler: 00:09:53 And really what we were thinking about at the time was how can we tell a goal was active to study and the stuff we … All these active calls and all this weight, how can we tell like how much value we’ve added from all our awesomeness? And so, we’re going to build the most basic CTA we can because we’ve got what’s passive?
Rodrigo G.: 00:10:07 What’s the benchmark for you in there?
Chris Schindler: 00:10:07 What’s a passive, what’s a benchmark? And so we went, “Okay, I’m going to build the most passive CTI I can. And this by the way, was the thought process that has led to everything we ever did from that point forward. It’s like, “Well what’s passive?” Nothing’s passive. There’s no passive in this world. But the real question then is like how-
Rodrigo G.: 00:10:19 Amen brother.
Chris Schindler: 00:10:19 Yeah. Well then the question is like, well, how can you get as passive as possible? If someone gives you two black boxes to invest in, and you don’t know what any of them are. Well, then I guess passive is putting 50 cents in each because it makes the least amount of decisions. It puts the least amount of pressure on your decisions, as far as the future goes. The S&P is not passive. The S&P is an actively created index of 500 names that are managed and it’s got some momentum in it, and it’s not the full universe and you always have to decide your universe.
Chris Schindler: 00:10:45 There’s lots of decisions you have to make, but the question is how passive can you get away with? This is where we started to think, “Well, what’s passive?” Well, clearly if we test 50 assets and 45 do great and 4 do poorly, we throw away the five or the four or five that did badly well that’s an active decision. That’s the assumption that that’s going to persist and I guess passive means investing in everything you have. I was like, “Okay, that seems pretty easy. We’re going to invest in every asset.” So, your universe is like, “Well, what do we have? What’s liquid? Let’s just invest in all of it. Let’s just test it.”
Chris Schindler: 00:11:09 And then the next thing is, well, what models do you use? We built five models. Whenever you have five things, one’s the best and one’s the worst and you kind of wish you go with the best one. Really? Why don’t we just use all five? Because that seems to me like the least amount of decisions. And who’s really to say who’s going to do well or poorly going forward. Then you go, what parameters do you use? It’s like, “I guess we should just use all of them.” So, we went, “Oh, instead of saying gold is a 45 and crude’s a 52, and it’s constantly changing, there’s turnover. It’s like, all right, let’s just use everything. 20, 40, 60, 80, 100, 120.” And we did that.
Chris Schindler: 00:11:39 We thought, “Well, that doesn’t feel right.” Because the 20 and the 40 they share half their data, and they’re 50% correlated. And the 180 and the 200 they share 90% of their data and they’re very correlated. So, if you do that, you’re over-weighting the backend because you’re putting more into these correlated things. “Oh man, we got to think about this differently.” How do you solve that? I was like, “I guess you got to geometrically grow your lookbacks and we want 25, 75 something or is it 5,102 let’s do those combinations and so it was like passive.
Chris Schindler: 00:12:04 It was about making the least amount of decisions about the future. But then the next one, this is the really, really big one was what about risk? And we had this conference in Montreal and I literally heard, I can’t remember who one of your other podcasts talk about this and ever, I actually had this exact conversation with AQR in 2007 and then they published it about six months later and I called him. Man that’s my exact conversation with you. And say, well what can we say?
Rodrigo G.: 00:12:25 Exactly.
Chris Schindler: 00:12:26 It goes like this, when you’re trying to predict the future, you have to make at least three calls always. You have no choice at all. You have to predict return, you have to predict correlations and you have to predict risk. And the vast majority of active managers that they’re focused on the return side, and I’m going to say like the problem is those are all active calls for sure. You’re making calls about the future and obviously your portfolio is going to be dependent on those calls. And the question really comes back to like, well if those are active, what’s the passive? Shouldn’t you make no calls? And there is a one over end solution. But the problem with one over end is that assets do have different natural native volatilities. You just can’t get away from the fact that a Eurodollar futures contract has one 50th the volatility of the crude oil. And so, you put $1 in each of those, you’re putting 50 times as much risk into crude. That’s an active call. So to get passive you have to have an active risk call. There’s just no way around it. You have to predict risk in some way some form.
Adam B.: 00:13:18 You’ve also got to have an active view on covariance, right? Or some kind of relationship.
Chris Schindler: 00:13:23 You do. And so, I’m going to start with risk and say you have to predict volatility. And how do you do that is a really interesting question because here’s the next thing. It’s like – an actuarial scientist has this decision. You’re taught more data points is better. Now if I have a thousand data points, I’m clearly going to have a better measure of the distribution than if I have 50 or, if I have 2000 that’s even better by the square root of 2000 over a thousand. And as we were doing this work, the guy sitting right next to us was trading volatility.
Chris Schindler: 00:13:48 He was trading various swaps and I remember him sitting explaining to me and he goes, well, like the strike could be 16, it could be 32, it moves all over the place. I went, what do you mean volatility? Like literally, what do you mean volatility moves all over the place? And he’s like, no, like look, it could be here, it can be here, it could be here. And I immediately thought, well, okay, but then how do we have a risk system which says in the tail is a very small chance of getting past two Sigma, very, small chance of getting past three Sigma. But wait a Volcan double isn’t that three Sigma now one and a half and easily breachable and you went, Oh my God, what does that mean? There’s a lot of implications around non constant volatility. The big ones being it makes for fat tails in the distribution.
Chris Schindler: 00:14:23 It makes it far more likely to breach your outcomes. But the other piece was once we started to work through this, was like, well, we got to predict volatility, but it’s a constantly changing thing. And you kind of turn your mind completely because you’re no longer trying to describe a distribution. You’re trying to figure out what distribution you’re currently in, and that actually means you cannot look back a thousand days. You’ve got to look back the shortest you can possibly get away with looking to best predict tomorrow and if you’ve got daily resolution data, you can’t use two days worth. Maybe it’s 25 or 30 and it turns out that that little bit of research, obviously that makes a big difference.
Chris Schindler: 00:14:55 Our CTA, this very short term risk targeting, we’ve evolved that thinking a ton since then to make it a little bit better, but really that’s the essence of it going, okay, well you’ve got it now you’ve got your assets, you’re predicting risk like actually surprisingly effectively well. And now I can put an equal unit of risk into my career and into my 10 years and into my equities and you go, that looks pretty good actually, and it turns out if you just stop there, you’ve got a pretty good CTA.
Rodrigo G.:: 00:15:17 What’s crazy is everything that you described we aligned to fully. That’s basically everything.
Adam B.: 00:15:24 Oh, its like a corporeal phase shifting happening here. We’re, all merging into one body and brain. It’s just, it’s a shocking phenomenon we’re observing here.
Rodrigo G.: 00:15:33 What’s crazy today though is we sat down with a large fund of funds, the just as CTAs and as we’re describing all of this and we ask what does everybody else use for waiting for the waiting scheme and it’s like conviction weighting, average true range, a few may do inverse utility. And then in terms of different markets having different unique parameter sets that they handpick, they’re all doing that still.
Chris Schindler.: 00:15:59 Terrible.
Rodrigo G.: 00:15:59 Imagine this becomes now the benchmark against which you compare every CTA that comes to you.
Chris Schindler: 00:16:05 Yeah. So, you’re absolutely right. So, not only like, so we built this-
Rodrigo G.: 00:16:07 What a benchmark.
Chris Schindler: 00:16:08 Very robust and then here’s the crazy part. So, we built the simplest thing we can build and yes, we took correlations into and I’m going to talk a little bit about a robust portfolio construction there because before we started the CTA and into some of the TA work we were doing, the other thing you do is you learn, of course you should do mean variance optimization and very quickly realized that does not work at all. And I’m going to say like I totally respect what Mark Chrisman was saying. I’m going to push back on a couple points, but you do get massive corner point solutions and corner point solutions are probably not a problem. If you trust your statistics, if you say Canada and US are going to be 98% correlated and you go it doesn’t matter if my weight’s a hundred percent Canada, a hundred percent US because it’s kind of the same thing.
Chris Schindler: 00:16:47 What you’re really missing is that there’s a massive parameter risk in that which is, or an assumption risk A, what happens if that breaks, but B correlation, you can have two things that are 98% correlated with completely different drifts. Look at Japan versus us. No, not 98% correlated, but like from 1988 to 2006, those guys are pretty highly correlated equity markets that went in completely different directions or US versus Europe in the last four or five years and realize that if you put 100% in one and zero and zero in the other or vice versa, you can very, different outcomes. The result of this is that mean various optimizers are extremely unstable to your inputs, especially expected return. I kind of came up with this like, well how do you soften that? My approach was to sprinkle white noise on the returns and I call it like, so if I have a process and I run the optimizer and it goes a hundred percent Canada, I’m like, okay, if you just sprinkle some white noise on it and run it again, it might go a hundred percent US.
Chris Schindler: 00:17:37 Subtle changes in some of the inputs it can really flip the weights and you go, okay, do it again and again. And then the average, all of those, Oh 60% of the time is Canada and 40% US and you go, that’s actually a pretty robust solution that statistically survives that white noise. Now if you throw enough white noise on it will equal weight them. So, it’s a process and I called it my blur optimizer. And so, that’s how I started and this was Excel back in 2004 I was like, okay, this is how I’m going to like handle this corner point solution. And that’s what I use on my CTA for a little bit. It turns out, didn’t matter too much because CTAs are kind of nicely balanced as it is anyways between stocks, bonds and people would go and commodities, but commodities is like three or four asset classes.
Chris Schindler: 00:18:11 It’s like saying and stocks and bonds in effect. So, and we built the CTA and going full circle now. We built the CTA. It was pretty nicely done. It was very balanced, it was very safe. And we looked at a Sharpe ratio. It was also 1.6, well I guess we’ll go with a simple one. And we threw all that first work away and we said, here, this is our CTA. So, that’s what I presented to my boss and I went, here’s the other thing. Well what’s a CTA? Kind of looks like an asset mix to me. It’s got some stocks, it’s got some bonds, it’s got some commodities, it’s got some FX, it’s pretty balanced. And it crushes from a confidence perspective, that silly TAA thing I gave you before, which I’d never put a penny of my own money.
Chris Schindler: 00:18:47 And I was like, that should be our TAA model. And then you kind of go, okay, so now the other thing about those, the CTA is as long short and you can always do a mix, a long only risk balanced or the CTA and you’re now your long and out, so, there’s combinations of that and that really is where we started. And so, if you look into that, there was, just, there was so much fun and cool stuff that we did. And this was the obviously the coolest part about being a Teachers’ at that time was this a great people, an unbelievable leash to just spend time and think about stuff and work your way through ideas and, and ultimately here you go, we got a model and here’s some risk and off you go and start running it. And so, that and that CTA, we launched that in 2005 and I think it was one of the top performing CTA’s in the universe the last 15, 17 years and it’s been literally unchanged since we launched it. So it’s been pretty powerful. And it was a-
Adam B: 00:19:29 Chris, just to press pause a little bit, how was the CTA perceived at Teacher’s? How did they treat it? What sleeve in the portfolio did it go in? Did it get respect from the senior decision makers? How did you communicate the value of the CTA front to the investment committee? A lot of those questions I think are equally interesting and are I think a little bit less frequently addressed.
Chris Schindler: 00:19:53 I don’t know what it was like over the last four or five years, but back in early the mid two thousands Teacher’s and I think a lot of shops were and maybe a lot still are very, discretionally focused. I mean if you say like what’s the main sense of like how do you make money in the world? It’s discretionary. Everyone was a George Soros Warren Buffet acolyte and everyone wanted to make money that way and thought that’s how you make money.
Chris Schindler: 00:20:11 Systematic investing to say it was the poor and ugly stepchild is not even fair. Like it was a constant struggle to be constantly defending, to be constantly explaining, to constantly educating. And the, I guess the other side of it too is our external managers. So, the guys responsible for investing in managers had just fired their last CTA in 2006 and so because they went, well, why would you ever have momentum? You don’t need it. We’re making… And so, they literally fired their last CTA 2006 2007 we were like, we love this stuff. We think this is great. And this is …
Rodrigo G.: 00:20:39 Three years after the tech crisis.
Chris Schindler: 00:20:40 Yeah. All it took was a couple of years of under-performance and we can do better. We launched it internally. The risk budget was minuscule, like very small. In the area of $10 million of risk.
Adam B.: 00:20:49 And what did it grow to at the max?
Chris Schindler: 00:20:51 The entire group is probably running $800 million at risk. So I’m going to say like call that like a, if you think of risk as the 1% tail risk you said may $250 million standardization, so it’s probably somewhere around like a two and a half to $3 billion hedge fund running at 10% volatility. So it grew enormously but really it was a very, long process of education and obviously 2008 helped. I’m going to rewind and talk about some of the things we built before 2008. But we had a, I think there’s a lot of guys did a very very good return in 2008 and 07, 06 in 2010 and 2011 in bit by bit. We started to win a little bit of respect but I would say its only last two or three years where it was really like as the group continued to do well when a lot of people were struggling, especially the discretion guys, I’ve had people talk about the systematic guys.
Chris Schindler: 00:21:36 Have had a tough four or five years of discussion and guys have as well. If not even worse and bit by bit I think it’s slowly winning respect. It was a constant uphill battle. I think it’s a fair statement. Now my boss was very supportive, the CIO because the other pieces as we presented, as I said the CTA, we also said, look, this could be an asset mix and so we created something that we called the efficient risk premium model, the ERP, and it was really a, it was a mixture of a bunch of stuff, but you could almost think it was the vol targeting and timing at the asset class level, like targeting S& P to 10% vol and then it was talking to Rod a bit earlier. It’s like now we call it hierarchical risk parity, but we built that back then as a like we clustered and as we said, we’ve got to take all our, you can’t throw 70 things into correlation matrix and do an optimization.
Chris Schindler: 00:22:18 You’re trying to do risk parity. How do you think about it? We went, well, we’ve got not only got 10 countries this week, like in our passive world, we’re just including them all. Don’t care if they’re good or bad. And there’s a statement in there and there’s a statement saying, I get the S&P is a bigger market cap than Canada, but does it have a significantly higher expected Sharpe ratio because it doesn’t have a significantly higher expected Sharpe ratio. It doesn’t deserve a significantly higher weight. The crazy math of Sharpe ratio and portfolios is if you have independent assets, four independent assets, the optimal weight is their expected Sharpe ratio. It’s a crazy statement because it’s so simple. You can invert it back and go, well if they’re uncorrelated. The only way I can justify having twice as much risk in equities as bonds is if I think it’s got twice as high a Sharpe ratio. No one can make us state with any confidence. And so, it’s a little bit of a-
Rodrigo G.: 00:22:58 You converge to the fact that they all have the same sharp ratio.
Chris Schindler: 00:23:01 If you think they all have the same Sharpe ratio, you should get equal risk to each of those pieces. And so, that’s effectively our statement because we did a huge study. And we went back to 1920 and we had equities and bonds in the US and the crazy thing was back in 2004 I think, or 2005 when we did this, the US stocks and US bonds from 1925 to that date had exactly the same Sharpe ratio, four point some. It was like to three decimal places. It was fluky. Now when we look at US, we looked at bonds, US bonds, you got to be careful because if you put all your money in 30 year bonds, your Sharpe ratio is much lower.
Chris Schindler: 00:23:31 The 10 year bond Sharpe ratio is higher and the five-year’s higher again and the two year was higher again. And we went, what’s that? And first of all, why are 30 year bonds so awful? And why are two year bonds, levered two year bonds so much better for the same amount of risk? And we thought about that for a bit. No, I think I’ve got a story for this because we’re a pension plan. I know what every pension plan does.
Adam B.: 00:23:48 L D I exactly.
Chris Schindler: 00:23:50 No it’s not just LDI, it’s like with even in your asset mix, if you want more fixed income exposure, no one levers up two year bonds. What everyone does is they roll up the curve and they buy some more 30 year bonds. You want to increase your bond duration. Well how do you do that? Well, of course you do what everyone does. Is you go up into longer duration bonds and so we started to realize that the back end of the bond curve was crowded. And if this is one thesis that I want to get out today, crowding is your enemy. Crowded trades have lower returns and higher risk just by definition. And that’s not just in the public markets in everything.
Chris Schindler: 00:24:21 The more that people buy something, the more it’s price gets bid up. It’s returns come down proportionately, but its risks go up. And so, if you’ve got an entire set of the universe sitting in the back end of the bond curve, constantly rolling it out, it’s going to have a lower Sharpe ratio. We didn’t call it leverage aversion at the time. We call it like, Oh there’s constrained investors to leverage or people don’t like using leverage. We started to have those conversations with a bunch of people around at that time and so we found-
Rodrigo G.: 00:24:42 And at that time how was leveraged perceived at Teachers’?
Chris Schindler: 00:24:45 Pretty negatively but still allowed. So we are a pension plan. We certainly use derivatives and we still, you’re not going to lever up bonds at the total fund level, but we can do it within the tactical asset allocation group. And we sort of went wow, this is interesting. Within fixed income, the less volatile, the less naturally volatile part of fixed income has a much higher Sharpe ratio than the more naturally volatile because people will crowd into the naturally volatile spot because they don’t like using leverage. And so, we went, think there’s a story there. We went looking for it elsewhere. We found it in credit and we found it in equities. And so, we launched an equity low-vol factor in 2006 as well because we went, man this is crazy. Like this seems to be like if you can use leverage, you can create a higher Sharpe ratio process and if you aren’t, if you turn into alpha because you level up the low-vol piece and you can short the beta or you can go short the long ball piece on the right side.
Chris Schindler: 00:25:29 Since then I think, and you’ve had a couple of the guys on the podcast talk about this, the effect is less that the lower vol stuff has a higher Sharpe ratio is more that the really high volatile stuff has a lower Sharpe ratio, but at the end of the day it all comes down to the same thing, which is understanding that if you have a bunch of people who are crowded into one part of the space, that space will underperform. And so all of that saying, we put all that together and said, we’re going to call this thing the ERP. We’re going to do sort of enhanced equities. We’re going to do vol targeting our equities. We’re going to balance them well and bring them together. We’re going to just treat all our equities as equal. We’re going to have 10 equity indices, we can, we’re going to put money in all of them.
Chris Schindler: 00:26:01 Now fixed income has been interesting because we have the long bonds all kind of look at each other, but the short rates are a bit different. So, we did two clusters, we did a cluster of long bonds and fall targeted the whole cluster of 10% and then we targeted each of the short rates at 10% and then targeted the cluster of short rates at 10% and then we brought those guys together and targeted the back of the 10% so that’s bonds. And the idea was that we were just trying to figure out like what is the most robust way without making any assumptions, but just investing in bonds, global bonds. Now here’s the other thing. People get bonds all wrong. They confuse a bond from the process of investing in bonds. Now an equity is a perpetuity. A bond is not a perpetuity. A bond is something that has a constantly changing set of statistics.
Chris Schindler: 00:26:41 This is a bit like an option, constantly changing statistics. If you want to know how bonds do, you have to compare a rolled process of bonds to a perpetuity like equities. And they’re very, different things. And so, if you talk about levered, fixed income rolled, versus cash bonds, they’re a very, different asset class. The cool thing about leveraged fixed income, which I think people kind of miss a little bit is there’s actually a lot more global diversification within global levered bonds. And there is even within global equities, some people talk about their desire to diversify and so we should get some EM’s and some DM’s and some Europe and some it’s like yeah, you can get a bit of diversification benefit in equities way more in global bonds. So, global bonds are a really, cool asset class that are so overlooked. And like I said, you do all that working on about the same Sharpe ratio as equities.
Chris Schindler: 00:27:26 And when you got two things that are relatively uncorrelated over time that have about the same Sharpe ratio, like obviously equal, risking them is the way to go. And so, you go, that was the start of our risk parity process. I mean, but we’ve got a problem. Stocks and bonds, they’re great because they diversify against growth shocks and like diversify against, deflation shocks and together they look really, really good. But Oh my God, 1960’s to about 1985 is a disaster. And you’ve got a big, hole in your portfolio if you just do stocks and bonds because they both get tagged in inflation.
Rodrigo G.: 00:27:54 Just at that point back then when you’re putting that together because it, my biggest pet peeve right now is people saying that risk parity is a levered bond portfolio with some equities. When in reality the concept of risk parity is making sure that you have equal risk contribution across inflation assets, growth assets. And at that point were you thinking about it as risk parity? Had you kind of-
Chris Schindler: 00:28:16 The concept of risk parity – I don’t know if it was someone’s going to, none of us are academics. None of us are all that.
Rodrigo G.: 00:28:20 Were you guys involved with Bridgerwater… at the time? And getting some concepts from them?
Chris Schindler: 00:28:23 We were certainly not getting and we did it very differently than Bridgewater. So, Bridgewater’s risk parity had a bad 2008. Ours had a Sharpe of two. It was quite different. And so, we did it quite differently in a bunch of ways. What we were trying to demonstrate was two things, (A) put stocks and bonds together, but (B), we wanted to talk about alternative risk premiums. And say that there are, there’s an equity risk premium. There’s a fixed income risk premium and most people kind of stop there and maybe you can time it a little bit, you can put them, there are so many other risk premiums that are as good as the equity as premium the fixed income premium that you can get access to. And so, we wanted to demonstrate a portfolio that was built from a risk budgeting perspective where we put stocks and bonds together and properly risk targeted and put together and then also add some other alternative risk premiums. And so, that those guys together can create a diversified balanced portfolio.
Adam B.: 00:29:10 So again, I just want to press pause because I’m really, curious how the conversations went internally at Teachers’ around these concepts. At the time were the people above you, the decision makers that were allocating risk budgets buying into this concept? How did the conversations go? How did their views evolve over time? What are current views on this concept? I mean, this is a conversation we have internally all the time. It seems dead obvious to us. In fact, it’s so obvious we don’t even know how to have a conversation with people who don’t already know this is true. How does the conversation evolve with people who are coming at it? Because keep in mind right, a lot of the top decision makers and institutions come out of investment banking, from corporate finance, at a traditional research equity research, a credit research. How do they perceive this view? It’s so anathema to the evolution of their own thinking, the evolution of their own careers. I’m dying to know how those conversations go.
Chris Schindler: 00:30:12 No, you’re exactly right. And I would say it depends who you’re speaking to. Some people saw it quickly and went, this looks amazing. And to be honest, it’s impossible to deny because the back tests look so good. So, the question is, do you believe that’s cheated or is that fake or could it persist? You can show back tests to us that go back 40, 50 ,60 years over and over again. And I would say I presented it 16 or 17 times internally to various people between 2006, 2007, 2008.
Rodrigo G.: 00:30:37 Wait, Chris, would you mind pausing here and just taking a bit of time to tell us that story about AQR and one of the first times that you met them? I think you were talking about this risk issue, right?
Chris Schindler: 00:30:48 So that can, I think it was 2006 – 2007 I’d been running internally for three or four years. Things have been going pretty well. I was feeling a bit of swagger. I’m feeling pretty confident about our thought process and how everything worked, but I wasn’t investing in managers at that point. We didn’t take over the external manager book until end of 2008 so it was probably 2006 – 2007 I can’t, get exactly right. But one of my friends at Teacher’s went, Hey, you should come join this meeting because we’ve got some guys in and you might find it interesting. So, we walked in and we sat down and we started the conversation. It was probably like, I don’t know, three o’clock they had a pitch book and I started flipping through it, and I got literally as they’re talking, I got about 20 pages in and then went, well this doesn’t make sense.
Chris Schindler: 00:31:24 They went, what? No, I don’t think you’re doing this right. They say, “What do you mean?” It’s, we talked through that for a little bit and they went, that’s interesting. We’ve been talking about ourselves. And we went a little bit further and I’m like, Oh my God, how’d you guys do risk? And they went, well we use a five year covariance matrix with a, monthly and on. What? You can’t possibly, are you kidding me? That illiterate. And we got in this very long debate and I would say and discussion, it was great. We went through the whole thing and we had like a very in depth discussion. I was enjoying myself but like I didn’t know who these guys were and at some point it was around 5:30 and I went, do you guys have a plane to catch? Oh yeah, we’ve got a flight in a bit later. And literally we kept going and it was like 6:30 and I could do this forever. I love talking like this stuff. It is some point where you guys, if you’ve got a plane you might want to go and they go, no, don’t worry. Our jet’s waiting for us.
Rodrigo G.: 00:32:05 Wait a minute. Who are you?
Chris Schindler: 00:32:08 And so I left and it was funny because like my friend Turner goes, do you have any idea who you talking about? No, he goes, well that was John Liu. That was Jeremy Getson. It was the AQR guys just gotten into like a four… You told them that they were doing stuff wrong and anyway. It was the start of a great relationship with them. Love those guys to death. But it was a very interesting and funny start, but it was like, what a cool seat to have at Teacher’s.
Rodrigo G.: 00:32:26 You probably would have modified, how you approach that. Had you known who they were from the get go? Maybe. Right.
Chris Schindler: 00:32:30 Yeah. I got to tell you, like I said, I came from the right side. I don’t think I would’ve known them anyway, but I did talk to them about their risk and portfolio construction and model building philosophy and-
Rodrigo G.: 00:32:40 And challenged a lot of their assumptions
Chris Schindler: 00:32:41 Challenged a lot of assumptions in 2008.
Rodrigo G.: 00:32:43 Hit on pain points that they were dealing with internally.
Chris Schindler: 00:32:45 It tastes like this isn’t going to work out, here’s where it’s going to go. And then 2008 and then in 2009, I think they were like, I think they changed a lot of their processes.
Speaker 3: 00:32:52 Yeah. Bridgewater as well.
Chris Schindler: 00:32:53 Yeah. So, I had those, I, didn’t have those debates with Bridgewater as much beforehand, but I would still say, like, when it comes down to where might they have missed a little bit, on the risk, on the correlation. And, to a certain extent Bridgewater’s risk and correlation, diversification assumptions I think were so, and those guys are awesome too.
Chris Schindler: 00:33:18 Like the obviously they’re amazing. But I think, I mean we were just fortunate because 2008 was just the perfect storm for us in our process because we caught it perfectly. I mean we went through a, on in, September of 08 in October, wait when like the S&P and the CAC, we’re moving like five or 10% a day and it was like, well these are like 10 single moves. I’m like, well no because we’re measuring vol as a, five vol process. And that was a 1.8 Sigma movement, was completely normal. So, we managed to get through like all of the weight without really even having a non-normal day.
Adam B.: 00:33:44 Yeah.
Chris Schindler: 00:33:46 Which was, super helpful.
Rodrigo G.: 00:33:47 Yeah. Before I met Mike and Adam, I had that similar experience in a way. My draw down was a maximum 3% draw down. No weight.
Chris Schindler: 00:33:52 Yeah, we should. If you’re trend following, it’s like trend following, you just had their best year 2014 may have been like the best year ever, but obviously it was a, sometimes it just, it works. 2011 was harder. 2011 was when people forget looking back and remembering like, 07 had some craziness. 08 had some craziness, man it was like people quickly forget the whole European crisis and the whole Greek. Are they, what’s going to happen? Like that 2011 was crazy. I mean like this is my quote at the conference in Montreal. Right. But like for the TAA guys in particular, there’s an extinction level event every five years at the very least. I mean, your lifetime as a TAA guy at a big institution is if you make it five or seven years, you’ve made it a generation. So.
Rodrigo G.: 00:34:29 I met a guy at the event that lasted a month after you said that. I’m not saying his name but.
Chris Schindler: 00:34:35 Okay. Anyway.
Rodrigo G.: 00:34:36 That was a TAA guy
Chris Schindler: 00:34:38 Yeah. All right.
Rodrigo G.: 00:34:40 Anyway
Chris Schindler: 00:34:40 That was my anecdote. I also like had debates with AQR and Bridgewater and the AQR guys a bunch in 2007 and 2008 and disagreed with a bunch of what they’re doing on risk and we were very vocal about it. And so, these conversations, but especially internally at Teachers’, I would say my boss was very supportive. The CIO, Neil Petroff at the time was very supportive. The asset mix and risk group was not, and I think it challenged them and what they felt their purpose in life was, but it also, I think a vast majority of people went, what are you talking about risk-based? Clearly you cannot invest risk based. You have to have an expected return view. And not only do they not like it, they hate it because it challenges everything they think about investing
Rodrigo G.: 00:35:19 An actuarial scientist. I mean need to have certainty.
Chris Schindler: 00:35:23 He doesn’t know what he’s talking about. He doesn’t know how can you invest without expected returns.
Rodrigo G.: 00:35:25 Without that certainty you can’t create a model
Chris Schindler: 00:35:26 I can on the other side. What I had to develop over time was a story as to why these things make money. And a story as to how you can balance the risk. And it took a lot of time to develop that story. 2008 helped a ton obviously. In 2009 our CIO said we’re going to put some of this at the total fund level. Still was a real tough challenge to sell internally. A lot of the pushback was if it’s as good as you say it is, why isn’t everyone doing it? Which is a huge pushback over and over again. By 2012 the story had turned from, no, of course we all know to do this.
Rodrigo G.: 00:35:55 But you’d been doing it since 2004
Chris Schindler: 00:35:56 It was a flashover when it’s like, well everyone knows to do this now and somewhere between 2007 which was like, because you had the GMOs on the other side and the James Montes, I don’t know pronounce his name, but like you know the guys on the other side just and everything they say or that you can go line by line and say, man, it was so hard to defend his statements because he would have nine things wrong in one paragraph and you start to feel petty. But it resonated. What he said, it resonated with a bunch of people. And so, I would say.
Adam B.: 00:36:22 Well it was great storytelling. All we know is that a good story, especially a good macro story sticks with people. It’s emotionally salient. They feel like they can reach out and touch it. It’s consistent with what they’re observing in the news or it’s feeding whatever bias they might have, bullish or bearish or an emphasis, whatever for whatever asset class silo they happen to be involved in at the moment.
Chris Schindler: 00:36:47 We had really good global macro traders that Teacher’s as well. So, to say that there weren’t guys who are also hitting it out of the park on the other side, on the expected return side, my thesis had always been Sharpe ratio is return over risk and it’s so much easier to improve the Sharpe ratio by improving the risk side and then levering it up than it is to try and create better returns. But we had guys who are coming exactly from the other side on the return side, and I actually say the two groups were pretty complimentary to be honest. But the intuition way, over on the trying to pick to expect returns better.
Rodrigo G.: 00:37:17 It’s amazing how much credence the average allocator gives to the black box inside some brilliant minds guys head that has a great narrative versus the black box that you can demonstrate through mathematics. Right. That is always the challenge and it just sells better. The global macro story sells better.
Chris Schindler: 00:37:33 Yeah. I think the quants through 15 years of out-performance now have started to win some of those battles. You know it’s been a hard run.
Adam B.: 00:37:40 Part of the problem is that the lingua franca of investing for people to emerge out of the MBA system or the CFA system or ibanking or corporate banking or what have you, is completely different than the lingua franca of investors that emerge from the computer science, math, quant space and such a big challenge is how do you even communicate concepts when you don’t speak the same language, right.
Chris Schindler: 00:38:12 100%
Adam B.: 00:38:13 How do you bridge that?
Chris Schindler: 00:38:14 I think bit by bit you start to develop a story that explains risk premiums and you have to, because you have to get to the intuition of them. And if you can’t get to the intuition of it, you’re never going to win anyone over because you cannot just show a return or back test and say, trust me on this one, because you’re up against guys telling a story and you need a story.
Adam B.: 00:38:33 I agree and I mean a hundred percent validate that. The challenge we run into is you go through the story and guys are nodding. It’s so intuitive, it’s so logical. It’s impossible not to nod along. Of course you agree. Here’s the evidence, here’s the logic, here’s how they all fit together. You get everyone nodding along at the end. They’re like, yeah, this makes total sense. I absolutely love this. Cool. So, you’re going to make a major change in this direction. Oh God, no.
Chris Schindler: 00:39:00 Yeah. So, here’s the other thing, and I think what people miss on the expected return global macro side is you have to make two or three calls and people tend to think like, okay, I think growth in India is going to be 8% or 10% and most people stop there and you hear a lot of CEOs and CIOs go, well we got to go where growth is. There’s going to be 8% or 10% growth in India. We have to be in the India market or the Chinese market. That’s where the growth is. And you go, Holy Crow. No.
Speaker 3: 00:39:22 There’s no correlation.
Chris Schindler: 00:39:24 Well not yet. Not only is there no correlation, the statement’s almost nonsense. Because if the market’s expecting 8 percent than an 8% growth is going to only ever give you back your discount rate. And this is actually kind of a hard thing where people like you only if the thing does what you expect it to do. You only ever get your discount rate back. We can talk through that in a little bit more. But to say if it’s expecting 8% and it gets 8% you don’t return 8% you get back whatever the discount rate was priced into that. And so, you get like the exact market return.
Speaker 3: 00:39:51 You got paid for surprises.
Chris Schindler: 00:39:52 Yeah. And so, the end of the paid for surprises. Exactly. And so, the way you have to do when you’re predicting the stuff is not only say what’s going to happen, but you have to say what is the market think? So you got to know I am expecting eight and the market’s expecting six and I don’t know how you figured that out because that’s actually quite hard to figure out. And then you have to have the third piece which was, and here’s why the market’s got it wrong and here’s how they are going to come to my understanding. Those are the three things that have to happen before you can make money with that call. To me that’s very, tricky.
Chris Schindler: 00:40:17 You’ve got to have a view. It’s got to be different than the market view. You have to know the market view. And you have to have a thesis as to why the market is going to come around to your point of view and while you’re right in the market’s wrong, tricky.
Adam B.: 00:40:26 The formation of that thesis is a story.
Rodrigo G.: 00:40:29 Oh, it’s a 30 page paper.
Chris Schindler: 00:40:30 And then the absolute best investors, like the absolute best ones, the ones who get the most risk and for better or for worse are the guys who tell the best story. And the investors who can tell the story after the fact you’ve got it wrong and still convince you they were right even though they lost money.
Chris Schindler: 00:40:42 The ability to tell a good story after the fact. It’s super, important for the quants to just to be honest, but like you have to be able to tell a good story like not to say like, look, there are some really good global macro guys out there. The way I’ve come around in the last little bit is some of the guys at AQR did some work on Warren Buffet and they wrote a follow-up paper that they called Superstar Investors, which was Warren buffet, George Soros Bill Gross. In the fourth, I’ll leave out because-
Adam B.: 00:41:04 John Templeton.
Chris Schindler: 00:41:04 It doesn’t help my story, but you go, the, what they did is they went look at Warren Buffett’s returns and he’s, I think unbelievable career Sharpe ratio of like around 0.8 23% returns at whatever. They were like really good at 20%.
Chris Schindler: 00:41:17 Let’s talk now about how he made his money. Because Warren Buffett’s, he’s pretty open and he tells you his thesis and he’s a deep value guy, but he’s an alpha guy. He’d be like, I pick winners, I’m a stock picker, I’m an alpha guy and I pick my businesses as well. And what this paper said is I was using the modern framework of risk factors and risk premiums. Let’s go back and see if we can explain some of these returns just with modern risk factors. And what you’ll find is that, Warren Buffett’s returns where equity, because you invest in the stock market because the stock market pays money over time. So, there’s an equity risk premium. Everyone kind of mentioned that.
Adam B.: 00:41:43 Its not just equity it’s leveraged equity.
Chris Schindler: 00:41:45 Yeah. So, it’s equity and then he has value and quality and low volatility. We’ll pause there for a second, but and then he levers it up 1.7 times and you go. So Warren buffet for 30 or 40 years.
Rodrigo G.: 00:41:58 And nobody can take the money away from him.
Chris Schindler: 00:42:00 Understood that. Yeah, the stock market pigs money over time, but there’s these other things that also pay money over time persistently. What is value and what is low volatility and what is quality and why do those things persistently pay money like equities do, is a really interesting and deep question because he intuitively understood it and the genius of Warren Buffett was to realize 30 or 40 years ago. Lever low-vol Holy Crow. I thought I found it in 2006 it’s like, no, Warren Buffett had been doing it for 30 years before me, but the question that you really have to answer is why do these things persistently make money? Because the quants struggle with this big time because the quants come at it with a, if something persistently makes money over time we’ve got to call an anomaly or an irrationality. And I went like absolutely not. You have to just understand why you should expect to get paid in this world.
Chris Schindler: 00:42:42 That’s the wrong way to think about it. That’s the egocentric. Why does the world owe me money? I took risk and I should get paid for that and you go, absolutely not. You do not get paid for taking risks. The question then is the real right answer is why should I expect people to persistently pay me over time? What is it about these things that has someone else happily handing away their money because the equity is premium. You don’t have to answer that question. No one has to. No one questions whether equities owe you money over time. You should expect a positive risk premium. How much is an interesting question, but we should agree on those positive expectations, because people would not lend to other people if they didn’t expect to get back something above what they lent out or they wouldn’t do it.
Chris Schindler: 00:43:19 On expectation I expect to get back more they lent out. But the key thing is the person I’m lending to is also expecting to pay me back more than I lent them. We agree. Both sides of this trade agree that they’re going to be giving me back more than I gave them, but they both happily entered the transaction because both players are better off for it. I’m a pension plan, I got cash in my pocket, it’s burning a hole in my pocket. I don’t know what to do with it. And I’ve got this business over here that really could use some cash to help grow their business and together we’re both better off of this trade and they’re going to pay me something in return for that. And that’s the equity risk premium in a nutshell. Fixed income risk premium is the same thing. No one questions whether you should on expectation make money over time, investing in bonds.
Chris Schindler: 00:43:51 Yes you should. How much is another question? But it should be positive. You wouldn’t lend otherwise. And so, a risk premium to me is so different than alpha. Alpha is – Rod, if you’re long forward and I’m short forward, you’re trying to take my money and I’m trying to take your money and we’re going at it and it’s very, hard to pry money out of someone’s hands who’s trying to keep it and take your money at the same time, you have to have better information. You got to be smarter, you got to be faster. You have to have insider information. You got to have some way that you can take someone else’s money, you lie, cheat, steal, whatever it is. Alpha is hard and it’s a zero sum game. And at the end of the day it’s net negative after T costs. And so, you look at alpha and you go, yeah, that’s a really, hard space to make money.
Chris Schindler: 00:44:28 But it’s so different than risk premium is where we all agree you’re going to pay me more than I’m going to give you and there’s a flow of wealth from you to me. And somehow somewhere we’re both happy for this transaction. And so, I think like the genius of Warren Buffet, and we’ll see what the other guys too, is that they went, there are areas within the stock market where there’s other risk premiums as other payers and payees that persistently pay and happily pay and consistently pay. And if you can put money into those pockets and groups, you should expect to get paid overtime for that. And so, this is like, we look at Warren Buffett and he goes, it’s value. It’s a low vol. It’s quality. When you look at Bill Gross, equity risk premium of course. You had a credit default risk premium. He also was in low-vol and then he was vol selling. He said, well, what’s vol selling and why is that a risk premium and why does that persistently pay over time like the stocks or the bonds or the, and then George Soros, was like the big global macro stuff he was doing like cross-sectional momentum and the trend following the FX carries the values and the really cool thing about this paper is between Bill Gross, Warren Buffett and George Soros.
Chris Schindler: 00:45:29 Between them they had stocks credit, which is a call as they betas maybe for a second, and then value quality momentum low-vol volatility, trend-following cross-sectional momentum value care, and you’re Holy Crow. Those three guys were doing the alt risk premiums. They’ve just been doing it for two or three decades and you go like, so the intuition is like you shouldn’t have the systematic guy shouldn’t have to defend the intuition. We’re just doing what the discretionary guys have been doing for decades and figured it out. It doesn’t really take away. Someone’s like, well, what are these things and why do they make money? But it should take away from them. Are we doing anything different? No. The answer is you’re doing the exact same stuff. You might be doing it more efficiently. You might be doing it more consistently. I think that’s an amazing takeaway.
Adam B.: 00:46:08 I like that it’s a really good way to connect the dots between systematic risk premia strategies and the people that everybody knows and loves and respects and lionizes and you sort of just say they were just following the same strategies. We’re just recognizing it now, but we can explain what those strategies are, why we should expect to get paid, why they should persist, and we’ve got a systematic way to harvest those same premia so it’s nothing new and it’s not scary and it’s not a black box. These guys have been doing the same thing for years. Just not systematically.
Chris Schindler: 00:46:43 Right, exactly. And between the three of them all quite successfully, you put them all together and there’s a nice portfolio. It looks a lot like the back test of the alts.
Rodrigo G.: 00:46:51 Going back to this premium and alpha.
Chris Schindler: 00:46:53 Yeah.
Rodrigo G.: 00:46:53 So, they would all be considered alpha players. And what you’re positing is that in fact this is all a positive exchange for all parties. So, volatility was one of them that you brought up.
Chris Schindler: 00:47:05 I kind of glazed over the well what are these building blocks and why do they make money? And for that you need a proper, you have to get back to that definition of a risk premium. Where there’s a payer, a payee, a known flow of wealth from the payer to payee. And they’re both happily doing it because they’re both better off for it and go, what is that and how do you think about that? And I think the right way to think of it is to say this is the story you’re saying back in 2005 – 2006 and we’re putting our ERP together. And you sort of say, what are some other potential alternative risk premiums between stocks and bonds? The vols are a pretty easy one, where you can say, what is the vol risk premium? Well, the vol risk premium is insurance. People tend to sit long equities because most people, long equities, people like you want to participate in the upside of equities.
Chris Schindler: 00:47:40 But if you want to hedge your downside, you’ve got to buy insurance. Now, like any form of insurance, if you buy a house insurance or insurance for your wedding ring, you know you’re doing an expected loss. You know the insurance companies expect you to make a profit on your house insurance and your weight, but you still happily enter the transaction because you’re better off for it because if your house burns down, that’s a catastrophic loss of yours. Very, risky loss. And the insurance company, has 10,000 houses and the loss of any one of them is relatively small. They can diversify that risk away. And so, the incremental premium you send them adds up. The risk diversifies and the insurance company is ahead on the trade. And you’re ahead on the trade because you’ve reduced a catastrophic risk down to something controllable and you’ve given a little bit of your wealth away. And so, both of you are are in a better state and you both happily enter the transaction even though there’s a flow of wealth from you to an insurance company.
Rodrigo G.: 00:48:22 I like that perspective. We’ve always kind of mentioned all this risk premium stuff as there is a willing loser on the other side. I like the idea of there’s two parties that enter into a contract happily and one is willing to part ways to get something back that may not be monetary. They’re giving money away in order to get to some sort of.
Chris Schindler: 00:48:42 And so this is where, I think this is the most important concept from my perspective as I say.
Rodrigo G.: 00:48:46 I like that narrative.
Chris Schindler: 00:48:47 Yes, this is the narrative. I think people get the most premium part a bit wrong because they say, I took risks and I should get paid for that. A fellow saying the risk premium and low volatility stocks is that they look different from the basket and there’s a source of risk there and she could pay for that. It’s like, well, I’m going to push back on that a bit because you do not, you should not expect to get paid for taking risk. That doesn’t make any sense at all. And I think it’s once again, very egocentric. Why would anyone pay you for taking risk? And if you think about it, if you sit short, the S&P 500 you have the exact same volatility as being long the S&P 500 but you don’t get paid for that.
Adam B.: 00:49:18 So is it this sort of nuance difference between, you don’t get paid for taking risks, but what you do get paid for taking on someone else’s unwanted risk?
Chris Schindler: 00:49:27 That’s one possibility of it. I’m going to go a little step further and say you get paid for giving someone a product or a service. It’s a marketplace. I might have cash. That might be the thing I’m selling. I’m going to get paid for. I might be offering insurance. There might be other things I’m doing, but I for sure someone’s only going to pay me if I’m making their life better in some way. And for that, I’m going to get paid. Now risk comes into it because risk I not getting paid for risk, but risk dictates how much I should get paid. So, risk is the cost of it, but it’s not the reason for it. And so if you look at it and you say no, if I look at it from the insurance side, well the actually the product I’m supplying is a risk reduction. So, from that perspective, you can say like an insurance, is a risk reduction, but there are plenty of risk premiums that aren’t risk reductions.
Adam B.: 00:50:08 So, as sidebar, you’ve mentioned quality a bunch of times. I just want to take a second and dig into that because I’ve always felt like quality. First of all, what the hell is quality? Every paper defines it completely differently and they basically combine variables in order to get whatever result they want. And second of all, I’ve never heard anybody explain to me why anyone should get paid for owning higher quality other than higher quality stocks tend to also be lower vol. And therefore there may be some leverage diversion story. So square that circle for me.
Chris Schindler: 00:50:42 Quality of all the risk premiums is a couple of these are pretty hard to describe because you may sit there and say I think lower quality should have higher risks and returns and maybe you should get paid for investing in low quality. I’m going to go with the, and I’m going to cheat on this one a little bit and say yeah, I think a little bit is low-vol. I think of a little bit is Miller paper that once again another and so this is the one of my stories from the beginning was look every single time you step away from the efficient market hypothesis what you’re really doing is relaxing one of the assumptions. Is it the ability for everyone to borrow and lend to that risk free rate? Is it homogenous expectations? There’s a variety of assumptions and I’m going to go with the quality’s a mixture of low volatility but also maybe a little bit of that.
Chris Schindler: 00:51:22 The more stable set of cash flows, the less dispersion of opinion on the outcome of the cash flows and dispersion of opinion pushes prices to the right. This is like thing like an interesting take. I know you guys have already talked about this a bit so I don’t want to harp on it too hard, but this is actually one of the really, big differences between public and private markets. I think this is super important to understand as well. In the public markets, not perfectly but close to perfectly. If 10 people have a variety of views around something and once again I’m relaxing the, everyone’s got the same view. So, let’s say you have a variety of views on something and the public markets say sort of somewhat sells for what the average person thinks it’s worth. If you say like what’s, the stock price? The market clearing price.
Chris Schindler: 00:51:57 Yeah, some people think, but the thing is it doesn’t require everyone thinking it’s worth a hundred. It requires, some people think it’s worth 80 and some worth 90 and somewhere the a hundred and some worth 110 and some worth 120 if it ever starts to slip above a hundred well then all the guys who think it’s worth one-on-one, one or two will start to sell it back down. And the guys who think it go too high will start to sell it short and bit by bit, it’ll pull back down to a hundred. So it will settle in the middle of what the dollar weighted view of what it’s worth. It doesn’t mean everyone thinks it’s worth a hundred but it will settle around a hundred. Privates are totally different. If you have 10 people and you’re saying, well, how much is that house worth? And one guy says 800 and one guy says 900, one guy says, 1,000 one guy says 1.2 it does not sell for a thousand it sells for what the most wildly optimistic person’s willing to pay.
Chris Schindler: 00:52:35 It sits way off to the right. And so, private markets by definition, absolutely go to what the craziest person is willing to pay for any asset. And so, you can see that very quickly if you have a dispersion of opinions, if people think it’s worth between nine or nine 50 a thousand 1,050 and someone, and then you have another asset where somebody was I don’t know if it’s worth 500, 1000 or 1.5 the wider dispersion of opinions, the more that the price is going to get crazy high. And so, dispersion of opinion pushes prices up. And while it’s most extreme in privates, and so the more uncertainty around the price and value of something in the private world, the more that the most wildly optimistic person’s going overpay, the more disappointed they’re going to be out of sample. As it turns out, they’ve wildly overpaid in the public market and I guess the other major different of the privates is if everyone on the planet agrees that someone overpaid except for that one person, there’s nothing everyone else can do about it because you cannot sell them short.
Rodrigo G.: 00:53:24 They’re buying the full asset or a large portion of that asset and there’s a single person doing that versus in the market you’ll have it at the margin.
Chris Schindler: 00:53:30 Yeah. I can’t short you down if you overpay for a house. All I can do is wait for you to realize you overpaid, which typically takes a recession and bankruptcy. So, privates don’t random walk the way the market works because the market is like, well someone thought this and I think this and information gets played in the face pretty quickly and privates you grind up and slam down because there was no price corrective mechanism until there’s a failure.
Adam B.: 00:53:50 Okay. So, I’m receptive to that. I like that explanation. So, it seems eminently testable because we should be able to correlate the excess returns of quality with the dispersion in, for example, analyst expectations or earnings expectations or something like that.
Chris Schindler: 00:54:08 And you’ll see that. So, that’s one of the definitions of quality is stable, reproducible cashflow or growth rates in and you will see them. And then also, and this is one of the standard tests is dispersion of analyst opinions. And so, but the thing is though, that obviously also gets you into low-vol. Because low volatility is also going to have a lower dispersion of analyst opinions on the outcome and it also gets you into value versus growth. So, it’s a little bit of like, is it a straight up answer for quality? Not exactly, but I think like, these things I’d be intertwined a little bit at the end of the day. Dispersion of opinion is going to result in under performance.
Adam B.: 00:54:39 Okay. I’m going to shift a little bit because I want to talk more about the contemporary factor environment or the contemporary systematic investing environment because I think it’s interesting. What I think I heard you say is back in 06, 07 you were talking about systematic strategies and risk premium strategies. Institutions were listening, you were getting some risk budget 2008 was very helpful. The risk budget, at least for your group went up by, call it an order of magnitude. Did we observe that elsewhere? How is the risk premia view or framework being adopted generally across institutions? Is it now well within the Overton window? Is this a large, almost a dominant perspective across institutions these days? Are they massive participants in this and if so, does that help to explain why we’ve observed a pretty well zeroing out of returns across all of the major, well-documented, most well known squarely in the Overton window risk premia that everybody know and love, like classic value, classic momentum, size, that kind of stuff.
Chris Schindler: 00:55:55 Yeah. So I would say yes, yes and yes. So, I’ll run through a little bit. Honestly, this is like what I left Teachers’ to work on because I think there’s a huge opportunity in this space right now because I would say, back in 2006 and this is my analogy, I’ve been using a fair amount of, I think the guys playing alts were like a rowboat going across a Lake leading a little bit of a riffle behind. I’m going to just make up some numbers here but I’m going to say the difference in 2006 and 2016 in the alt risk premium space, there’s probably a hundred times as much money in it if not more and you can see it in the big hedge funds. You can see it in the bank product, you can see in the internal pension plans they were all launching this stuff internally.
Chris Schindler: 00:56:28 Enormous, enormous uptake, which is great because it really did get people a better diversified portfolio and and access to more risk premiums and putting the building blocks together. And I’m going to say like the alt risk premium guys in 2016 to 2020 or like an ocean liner going across the Lake but they’re leaving a giant wake behind. The problem though like anything is when it gets crowded, the expected returns fall, the risks go up and really the correlations go up as well as more and more like multi-strategy doing the same sets of strategies and so the returns have been attacked and they’ve been driven down significantly. And this is also obviously crushed the discretion global macro guys because as we discussed, the discretion of global macro guys are just backdoor trading the alts. It’s very hard to make money persistently over a long period of time being short momentum being short quality, being short value in the global macro space, being short the carry’s, and so I think it’s suddenly gotten very, difficult for a lot of players.
Chris Schindler: 00:57:15 I’m going to call this a bit of the alchemists curse. The alchemy is like, I want to be able to turn straw into gold without realizing that the second that you figure out how to turn straw into gold, gold becomes worthless by the time every single person went – you know what? This is a thing now and every other person piled into the space like the space itself has become not as good as it was. It doesn’t mean it’s not worth doing. It’s definitely worth doing. Is it going to be a Sharpe ratio of 2 again, almost certainly not because that was probably richer than it should have been. That was part of that complexity or sophistication risk premium of not many people doing it in the world is everyone adopts something. It’s risks you get driven down to a more acceptable or probably normal marginal contribution, marginal expected return for contribution of risk. And is that a Sharpe ratio 0.4, 0.5 or 0.6 – don’t know. But the other problem is it’s almost certainly not for a single factor, it’s probably at the basket level.
Adam B.: 00:58:00 But even at the factor level, or sorry, even at the basket level, you’ve got these all premia funds with five, six, seven, eight sleeves that have at the best end of the spectrum kind of meandered along and done okay. And then at the other end of the spectrum some have been just catastrophic over the last two or three years.
Chris Schindler: 00:58:20 Yeah. And I think it’s been a tough run. I mean I think like anything, these things are called alt betas and we called them all betas and I spent some time calling them alt betas because I was trying to convince people these are risk premiums and, that they have a positive structure of returns. Turns to they’re not really alpha – alpha and I think we had to be careful that you’re not doing yourself a disservice because like anything it can be done well or poorly. Just because there’s an underlying risk premium doesn’t mean that everyone who plays the game should expect to capture or expect to capture well or equally.
Rodrigo G.: 00:58:44 And one of the questions that I have is when you look at the space, how much of that space is simply going, looking at the white papers, this supposed benchmarks of these factors in saying that is the trend factor, that is a low wealth factor. They’re zeroing in on these specific parameter sets and then running hundreds of millions of dollars on it, talking about the weight that they’re leaving behind them. These hotspots, these overcrowded spaces within the risk premia, there’s got to be opportunity to do better by doing it differently.
Chris Schindler: 00:59:17 I would think so. I mean I think different is super important. And I think there’s different and better and to say like just because there’s an alt and there’s a payer and a payee, can everyone collect the same? I think like if you go to like there’s some examples you can say like clearly and obviously not – look at market making. Market making is a risk premium. What are you doing as a market makers? You’re taking on a trade or a block and you’re working into the market over some period. You own the risk you’re in the bid asset you charge is effectively what you need to get paid to own that market risk for that time and so market making is a risk premium for sure and you’re going to find anything that persistently makes money. There’s a risk premium behind it. It could be liquidity provision, it could be insurance, it could be a bunch of other things like transfers of utilities of other sorts.
Chris Schindler: 00:59:51 But I’m going to say there’s always a risk premium behind the persistent winners, but that does not mean that every guy who goes, I’m going to become a market maker will do well. And what you’ll find in market making is, becomes pretty obvious is that like I’m only a small number of people collect all of it. Because to win in that space, you have to be really, fast. You have to be the fastest. And if you’re not like one of them, they go the fastest or the second fastest that you’re not going to get any of it.
Chris Schindler: 01:00:13 So, clearly though, there’s a risk premium it doesn’t mean every player going to collect it is going to collect it equally well. And I say it’s the same thing for all of them, is that there absolutely is active management and alpha in the collection of the alt beta’s. And so, I think that’s where the industry has gotten burned a little bit. Where a lot of players had gotten burned and it doesn’t help everyone. When the institutional investors try and get it as cheaply as possible, stick their finger in the socket, have a terrible outcome and then lose faith.
Rodrigo G.: 01:00:36 Loose faith in the whole thing.
Adam B.: 01:00:37 Oh God, yeah. I mean there was such a trend for the institutions to go to the banks and just buy whatever their risk premia strategies were through a swap and they were the most naive simple implementations imaginable and two or three years later, they’re just abandoning them and droves and they’ve had, they’ve been burned and I think a lot of big institutions have been burned even on diversified premia allocations. I’m kind of wondering whether or not we went through a honeymoon phase with alt premia maybe three, four years ago. The honeymoon phase resulted in a massive over allocation premias have been squeezed in many cases. They’ve been negative for two or three years. How has that affected the psyche of the big decision makers and institutions who probably took a long time to get behind these in the first place. By the time they get behind them, they’re already well within the Overton window. They are already well allocated to and the premiums have largely been squeezed away. So, their entire allocated experience has been negative. How is that affecting people’s psyche? How are decision makers moving forward with alt premia strategies?
Chris Schindler: 01:01:49 Yeah, so I would say, and I guess there’s two parts of this and this is I think the curse of systematic investing in a certain extent is people think, well anyone can do it or I can do it, or simply it’s an alt and when it’s done badly it does quite badly. And I’m not saying alt risk premium guys have done badly, I would say with some pride, like our group of Teachers’ has continued to do quite well over the last five or six years. So, it’s doable. But I think for sure the problem is, and I said from the beginning, there’s a bunch of people who (a) don’t get it or don’t understand it or it doesn’t really resonate with them. And there’s a bunch who hate it. If you’re coming at it going like this is the antithesis of everything I think proper investing is, and so between those two sets, they’re waiting for a fail point. And so, it’s difficult because when a value investor has a bad run, some people like, well I like him even better right now. That trade went against them. That’s an even….
Adam B.: 01:02:35 Yeah, they’ve gotten cheaper. Exactly.
Chris Schindler: 01:02:37 Yeah. And when it’s systematic investing has a bad run. It’s like, see I told you, I told you it was no good. I think you’re still constantly fighting that now. And on the other side of it, a bunch of people bought in and like legitimately a bunch of the products they bought into have not been very good. And this is the other challenge of investing in systematic investing is, and I think a lot of institutions, the people buying or investing in the systematic investing who’ve never done it themselves, it’s very, hard for them to distinguish a good story from a bad story. And this is I think is one of the biggest challenges in the space is.
Adam B.: 01:03:02 It’s the most fundamental pervasive challenge in investing in my view. Absolutely 100% like I said, the lingua franca of the decision makers is completely different than the language that the people that build these strategies speak. And I go to conferences and you can tell immediately that there are two completely different constituencies listening to a speaker. There’s the younger trained quantitatively oriented analysts. They’re not decision makers, but they completely understand what the speaker is saying. And you’ve got the old guard decision makers who hold all the power, who make all the decisions and have no clue what the speaker is saying and are completely disengaged from the conversation.
Chris Schindler: 01:03:46 Absolutely. You’re totally right. That’s the first challenge. And the second challenge is like marketers are good and they hear a good story and then repeat a good story and good ways of explaining things get caught up in the marketplace very quickly and sooner or later everyone’s pitch sounds the same regardless of what their actual process is underneath it. I mean everyone says the same four or five things and then how do you separate the noise from the signal in that world as an investor it’s super hard. And one of the seats I had a Teacher’s was, not only was I running the internal systematic group, but I got to speak to investment managers. I’ve talked to hundreds of managers.
Adam B.: 01:04:13 I was going to ask you about that. I’m glad we’re getting here.
Chris Schindler: 01:04:15 And so obviously the first thing you would tell them is, look, I run an internal group. I’m running prop shop. I don’t want to hear about your signals. I don’t want to, I’m not telling you no, but what I want to talk to you is model building philosophy, portfolio construction risk systems. My job was to sit there and just push and poke and tease and ask questions and at the end of it go my understanding of the space. You get a general sense of the thought process of the individual, but I’m just not sure how many investors could do that.
Rodrigo G.: 01:04:37 Here’s a question for you. Because you have this group of the risk premia. Every time we talk to an allocator and they look at what we’re doing, they say, well, why would I pay you anything for this if I can get it for free? That’s the biggest thing that we come up against.
Chris Schindler: 01:04:52 Yeah.
Rodrigo G.: 01:04:52 And then we show the excess alpha to the players and that still isn’t enough to want them to pay more or pay it as it is alpha rather than a beta.
Chris Schindler: 01:05:04 The thing is, you’re up against this. The banks do two things differently. A, the really, obviously the best sales people, like they’re very good salespeople. They have connections, they have relationships, they have friends that all the pension plans and so there’s like that you’re up against that to start with. Secondly, the banks only charge management not performance fee. And so, they get to cheat the back test because they actually don’t care how it does at a sample. And so, what you’ll see with some of these banks will be on literally version 250. You launch it, it does terrible. You kill it, you start it again. It does terribly. And so, you’re constantly showing back tests. And so, this is where you’re up against that. And I have reverse engineered 20 or 30 or at some point you stop sticking your finger in the socket, but like over and over again because the bank will come to you like, Hey, we’ve got this great product and you go, okay, let me a look at it.
Chris Schindler: 01:05:47 And then you have to undo every single one of their cheats. And there are many, cheats. And so, the typical bank product is full of cheats. Now, whether they’re deliberately cheating or just building badly up for others to decide. But at the end of the day, a really nice back test sells a story and I don’t really care how it does out of sample.
Adam B.: 01:06:04 And the vast majority of decision makers don’t know how to tell the difference between a hacked back test and a robust back test.
Chris Schindler: 01:06:10 Well, and the hacked back test is, it can be pretty sophisticated. I remember we had one bank come to us with a Sharpe ratio to process and the second we said, okay, like we’re going to take a look at this and build it before we think about buying it. And they went, Oh okay. And they literally went, I think it’s only 1.4 so they actually talked it down the second we said we’re going to test it. And then we went, what assets did you use for this? And they went, we use these seven currencies, like those seven currency pairs. Why didn’t you use these other seven? Liquidity. Really, you think the Canadian dollar has got more liquidity than the Yen? They went, and of course you test all 14 and they just took the losing seven out and kept the losing seven. It was like, okay. So, that took a Sharpe ratio down to 0.7 and then we kept going and layer after layer and decision after decision and we got this thing like ducked down to a zero. And so, the end of the day though it was like, and look, there are of course an infinite number of ways to cheat a back test, but it takes a certain amount extra tease I think to reverse engineer and to understand it. That doesn’t help the trust in the industry.
Rodrigo G.: 01:07:01 No.
Chris Schindler: 01:07:02 And not to say like I think alignment and fees is super important. Like I think you’re an investor, you should always pay a performance fee.
Rodrigo G.: 01:07:08 You said you managed a prop desk internally, but you also allocated to managers. First of all, what warranted an allocation to them? Given what you know and what you just explained. And then what fee do those managers that provide alpha on top of whatever premiums we already know exist? What fee is appropriate?
Chris Schindler: 01:07:29 Okay, so I would say two or three bits of this. First of all, Teacher’s was always a bit complicated on the outside looking in because every department at Teachers’ actually had an internal and external mandate. So, our public equity group would have external managers and internal group like our commodity guys, our TAA guys, our capital market guys, our privates like obviously did internal and external. And then we also had a group that was just called AI alternative investments, which was just hedge funds. So, to make that super complicated and outside looking in like no doubt. When I was using managers for was to compliment my internal model suite. I had a lot of this stuff built and I think I had like a pretty good process and a pretty good attack internally. And so, what I was looking for from managers was groups who were complimentary to what I was doing.
Rodrigo G.: 01:08:07 That complimented, do you mean minimizing the risk that your model wasn’t fully filled out and if their process aligned with your process they were in or did their process have to be broadly similar in terms of being broadly correct about the parameters set rather than specifically wrong but attacking different areas of the market?
Chris Schindler: 01:08:25 I would say orthogonal, however. Obviously looking for things that are like completely uncorrelated. If you found guys who are highly correlated, well then now you have a few problems because if you do three quarters of it internally and they’ve got this little alpha process and that’s the thing you want, but it comes with, it’s 2% risk in that and it’s 8% risk on a bunch of stuff you already have, well then your fees are really, high for that 2%. We were looking for guys who are uncorrelated in my group.
Rodrigo G.: 01:08:46 Right.
Chris Schindler: 01:08:46 And uncorrelated typically meant some crazies doing some weird little things or the big guys are doing stuff that we couldn’t attack ourselves. With a really, diversified, either you had they had some advantage in resources or headcount or data or techniques or sophistication. And so, we would go to a conference and like investing in managers was like my third job. It was like five to 10% of the time I had, we were trying to be as efficient as possible and we would go to these conferences once or twice a year and because your Teachers’, you could do this, you could talk to all the systematic guys and you’d say like you’d send out and because they like send us your daily returns. And so, we get daily returns from 200 250 managers. We put them up against our internal model suite. We just say, okay, I want to find the 30 or 40 who I can’t explain with what I’m doing internally. But what I can say with confidence is I could explain the returns of 80 or 90% of all the managers out there with what I was doing internally. Like obviously most people are sitting on some form of the alts including the discretionary guys. And the credit guys.
Chris Schindler: 01:09:35 And so you find like, you know 30 or 40 doing something differently and then some of those look like, here’s a CTA who’s only 50% correlates to my CTA. That’s potentially interesting. Let’s have a conversation. And then you get there and you’d say like some of them were like, well the reason that they’re 50% correltated is like, oh we’re only trend-following stocks and bonds. It’s like okay well that’s a bad diversification because they’re actually just doing like a more idiosyncratic thing. But then you’d run into guys who are doing like some really cool stuff and so then you look at it and say like as long as I got confident with the, model building philosophy, I could talk about risk. I can talk about portfolio construction I talked to and I would dig and dig until I got, how comfortable as it was funny because I was talking to a manager that we invested in just recently and he goes, I’ve never had a due diligence process like I went through with you because we thought for sure you weren’t going to invest in us because you poked us over and over again.
Chris Schindler: 01:10:17 And at the end of the day I was like okay we argued over some stuff and I remember what we were arguing over, which was like, like I literally remember this from 10 years later, but he was like, should we be using expanding window on a rolling window? And I like we’re having this big debate over like, because he was a machine learning guy and he and I was like, but at the end I went, okay I like the rigor of your thought process. I don’t agree with it. But that was what I was trying to get to typically. And then you say, well how do you pay fees for that? I would pay full fees. Now the way you think about fees, and this is the other pieces-
Rodrigo G.: 01:10:42 We’ll talk about this as well. Yeah.
Chris Schindler: 01:10:45 Amount of missed thought on fees blows my mind. But you get very, proudly people would say, Oh, I only pay one and 10 or I only pay one and 20 or I only pay, it’s like, but what volatility are you investing in? Because at the end of the day, the volatility of the process is what you’re buying. I mean, you can put $100 million in a 10 ball process or $50 million in a 20 vol process, and those are almost identical from an economic perspective. But everyone knows if I put a hundred million, I should pay twice as much fees.
Adam B.: 01:11:09 This is one of the great mysteries of talking to allocators. This complete lack of understanding about they’re dying to invest in four and five vol market neutral strategies at two and 20 when the entire fee’s going to consume the premium that they may be able to generate. And they don’t have any sense for capital efficiency or the fact that you need to have a certain risk budget for this strategy to have an impact of the portfolio. These all seem to be foreign concepts for a very large swath of what would otherwise be pretty sophisticated allocators.
Rodrigo G.: 01:11:49 The conversation is always, well you still getting even today, are you still getting two and 20 well I’m like, well what do you want? What’s the maximum you’ll pay? And like one and 10 is the most we’ll go, that’s great. Then I’ll give you my 10 vol product. My 20 vol product runs at two and 20. My 10 vol product one and 10. So, if fees matter to you, we’ll give you exactly what you’re asking and what you’re getting elsewhere. Right, and that blew his mind. He didn’t understand what that meant.
Chris Schindler: 01:12:12 It kills me. And you always hear the, well I don’t think of a management fee that way. I think of it as covering operational risks and, it’s like, but at the end of the day, the twenty’s fine, the 20 doesn’t matter because the 20, 25, 50 is the same no matter what vol you’re running. It”s the management fee. If someone pays two and 20 for five vol, that’s like paying four and 20 for 10 vol and no one pays four and 20 but people happily pay two and 20 for five vol. I’ve seen some crazy like one and a half and 20 for a two vol.
Rodrigo G.: 01:12:37 On the market neutrals, all that stuff.
Chris Schindler: 01:12:39 Who pays seven and a half and 20 for anything. And the answer is like those guys. They just don’t, they don’t know it. And so, that to me is like, that’s a crazy thing for me. I’m kind of agnostic as to whether a manager runs five, 10 or 20% vol. As long as the management fee adjust properly because I’m just as happy to, I can put more cash in or less cash in. Typically if you’re doing it through managed account, you’re already getting leverage anyway. Is there a slight, preference for someone running 20 vol versus 10? Maybe slight cash efficiency, but it’s not my concern. I’m much more interested in the management fee per unit of vol. Get the right vol and then, I decide how much dollar risk I want. I want $10 million of dollar risk and this manager, if there are 20 vol put $50 million in.
Rodrigo G.: 01:13:15 So what was the … just generally per unit of risk? What was acceptable to you? If it was an orthogonal bet process, what was acceptable at the institutional level if they truly delivered?
Chris Schindler: 01:13:27 Yeah, between one and a half and two and 20. I’m also picking the guys who are not doing the alts, who are just doing like the alpha who are just doing special stuff and yeah, you pay for that at a good vol. If I was doing one and a half, two and 20, some of those guys would be running 20 vol. If you’re in a one and 20 to one a quarter and 20 a 10 vol, I think like that’s right for alpha.
Adam B.: 01:13:45 So just to shift gears a little bit again, because you mentioned that the, I mean I observed many of the big old premia funds have struggled over the last three, four or five years and you said that the internal Teachers’ group has continued to do well. How has your thinking shifted or evolved or how have you adapted in order to continue to create persistent returns in this environment?
Chris Schindler: 01:14:10 So I think a lot of it has to do with carefully building the models in the first place. And like I said from that very beginning, that very, passive diversifying all sources of risk approach. When people think of what mean variance optimization or portfolio construction risk, they typically are thinking market risk and market risk is, like a stock could go up and go down. I don’t know of any, there’s a dispersion of possible outcomes. And so, you think of risk as being a dispersion of possible outcomes and you know that through diversification. If I have four or five uncorrelated things, my dispersion of possible outcomes shrinks. There’s tons of other things in investing that result in dispersion of outcomes. One of them is, well, what model did I run? And obviously I think it’s pretty clear to say different models are going to have different dispersion of outcome and that’s if I have different, if I put a portfolio of models together, maybe I can reduce my dispersion of outcome.
Chris Schindler: 01:14:51 But then even within a model you go, well what parameters did I use if I choose a 25 day lookback or 50 day look back again and have a dispersion of outcome. And it’s like, well can I diversify across that? And the answer is yes. And then you go, what other sources of risk are in the model? Because at the end of the day, diversification and portfolio construction and robust portfolio construction is about minimizing all sources of risk. You don’t want any single overarching source of risk to dominate.
Adam B.: 01:15:11 Minimizing uncompensated sources of risk, right.
Chris Schindler: 01:15:14 Uncompensated sources of risk. Exactly. And I mean that’s just investing in generally should always always do that. But you go, it’s really something that’s kind of hard to know. What are your major sources of risk? The one thing I would always push back against multi-stop managers is the guys who would proudly say, I’ve got 20 independent processes and I run them all through a mean variance optimization. I go, or kind of any optimization. I go I hate that. Because you go, I got 20 independent things, I’ve got this awesome source of independence and all these different things. And then I overlay on top of all 20, one single giant source of assumption of parameter risk, which is, I don’t know what’s the correlation between them or what’s the, and suddenly you go, all 20 are going to be contaminated by this one thing and suddenly my giant source of risk may not be 20 independent models. That maybe something to do with that optimization at the end. That’s a terrible source of extra risk. On top of that contagions everything. Is there beta neutralizing the might have a single look back to a beta and suddenly like you can get caught on all of that and some of you, so you got to go what are the sources of assumption risks that are driving this version of outcome and in any good model you try and minimize those across the board.
Adam B.: 01:16:12 Equal weighting them introduces its own set of risks though as well because you’re now naturally assuming that they’re all equally correlated. You can’t completely avoid those assumption risks by just equally allocating across the different, for example, and I think you mentioned this earlier, but a 180 day look back on trend is very highly correlated with a 200 day look back on trend and rather uncorrelated with a 20 day look back on trend on the same market. It’s the same kind of thing on carry or quality or low vol or what have you. Depending on how you specify it, it’s going to be more or less correlated.
Chris Schindler: 01:16:48 And the answer is only in very special cases. And for the parameters as you said, like one of the ways you can equal weight across your parameters is if you’ve diversified them properly. And diversifying them takes a bit of careful thought because something like a look back might require geometric growth. We said like a 5,100, 200 like those guys are going to be like the average cross correlation term between a 50, a hundred and a 200 is roughly the same. And so, I can, add those guys together. Some parameters is log, some is linear. When it comes down to model building. This is one of the, like if you’ve got a couple issues that you have to solve for, the spacing between them is super important. So, I can, you can wait. The other thing is, well what’s my start and what’s my end?
Chris Schindler: 01:17:22 And those are the two basic decisions you have to make when you paramaterize anything. If you get those right, and that’s the, I think by a combination of thought and then just looking back to see like what did I get it right from the correlation perspective. Then you can equal weight across parameters. The only way you can equal weight across models is if you can build effectively independent things. And so, that’s a big question is can I build effectively independent things and effective independence at the asset class levels super hard because correlations float all over the place. And when I said stocks and bonds are on average uncorrelatedover the last hundred years, most of the time they’re positive and they’ve been very, very negative recently. And at the end of the day, while that’s a world difference and if you get that wrong, you can be in all sorts of trouble.
Chris Schindler: 01:18:00 And that’s the other, by the way, if we’re going to splice this thing and move it around. The other major problem, the challenge that people have against risk parity is a, it’s levered bonds and I’m going think of as levered bonds or think of as de-levered equity or really people think like risk parity stock bond put together. You can also say like part of risk parity might be commodity/ bond is the package – that’s a pretty good package, commodities and bonds. Because what’s the major risk to bonds, it’s inflation and when commodities do well it’s inflation. Do you want inflation? Like the commodity bond package is almost as compelling as the stock bond package, but all three of them is actually a much, better process and, so you say-
Adam B.: 01:18:28 And you get closer and closer to a normal distribution, like a bonding them too.
Chris Schindler: 01:18:31 It looks better and better as you solve more and more of the risks. The stocks and bonds correlations float all over the place. And so, handling that at the portfolio is a little bit tricky. You’ve got to figure out a way to dynamically capture the change in correlations. And so, I think that’s something we put into our process and that really helped us in 2008 as we put it back in 2005 – 2006 it was very, effective. But at the strategy level, correlations are much, more stable than they are at the asset level. It comes down to careful construction of models, but if I had like 20 models and it turns out three of them are 0.6 or 0.7 or 0.5 correlated, I’m just going to slam us together and call those one thing and so once I’ve got effectively independent things on our portfolio, construction is super simple because as I said before, it’s either equal weight by expected risk or equal weight by expected Sharpe ratio.
Chris Schindler: 01:19:12 And I’m going to get to … and at some point too at some point, but the beauty of uncorrelated things is that portfolio construction is so easy and it’s very, robust and if you can get the risk right and you can keep the correlations independent, really optimization is no longer necessary at the model level. That’s a very variable. I can take you through this and lots different ways that’s a …
Adam B.: 01:19:30 No, that’s definitely true. It’s just that the trick is first of all, getting strategies that you have high confidence are going to be persistently uncorrelated. Like you can imagine, let’s say carry and trend. Well, you’re going to have lots of periods where carry signals are extremely aligned with trend signals and you’re going to end up with everybody on the same side of the trampoline. Now carry and trend historically have not a zero correlation to call it a 0.3 but that can lean into 0.8, 0.9 and it can lean away in a 0.0, 0.1 so it’s, I think you’ve got to have some eye on the correlations of all the different constituents in the portfolio as well as the correlations between the different strategies and it’s a dynamic process. So, I think I’m interested to hear how you address that at a strategic level rather than addressing it dynamically.
Chris Schindler: 01:20:20 First of all, everything I ever did was always addressing dynamically. So, for sure. So, the question then is how much pressure you put on the correlation piece of it versus how much you put on the risk piece and how do you capture that? And I would just say, look, I’m sure you guys did it the same, like in many different ways as well. And we did it in many different ways and we would come at it as many different ways as we could optimize, we would optimize and we were trying to reduce the pressure to any single one of them. But just to say carry and trend are transformations of assets. The assets themselves are very non constant correlations. The carry and trend have probably less non constant correlations but possibly like ish, like a fair amount. I really spent a lot of time trying to build models that are a generation past that are even more stable from the correlation perspective and so you can either attack it dynamically at the portfolio construction level or you can try and do it the signal generation level.
Chris Schindler: 01:21:05 I would say this is what I’ve been working on for the last little bit is I think I’ve got a whole bunch of models that are properly uncorrelated with each other through construction. And I think that just makes life way easier because I’d rather, treat my models as independent agents than to try and handle them all in a giant mean variance or any kind of optimization at the end personally. Can you do it with carry and momentum? It might be harder. And at which point you have to handle that risk and then the question is how you do it robustly.
Adam B.: 01:21:33 So, the next evolution thinking for you is it an objective of engineering sets of strategies that are designed to be orthogonal and or to what extent are you, have you just migrated to thinking about the portfolio from a strategy level rather than at an holding level? Scope that a little bit more for me.
Chris Schindler: 01:21:59 This is going to get a little more technical. What we call forward looking risk and backward looking risk I think is what you’re getting at here. So, like a strategy, imagine you had model one and model two and model three. You say on average those guys are uncorrelated. There’s a lot to that statement by the way. Because models-
Adam B.: 01:22:15 When you say model, is it a model, an indicator on one single security? Are they all cross sectional? Because this gets complicated fast.
Chris Schindler: 01:22:24 I would call a model an idea. You say trend following is an idea. Let’s try and find an ideas like, well you can say it in a sentence or two. You say I look at historical returns and I assume they’re going to persist in some way. So, like a very, and then how you do it. Moving average crossover, breakout, regression, zero correlation. Like there’s lots of different ways you can kind of come at that. But the idea is still I think some persistence and returns. It could be cost sectional absolute like a model is an idea. And I would say from a robust perspective, except in very, few cases where they’re clearly just very asset class specific and there’s nothing that resonates or doesn’t work outside the asset class. I wouldn’t want to have the same models and the same parameters running on every single asset that I have.
Chris Schindler: 01:23:00 So if I’ve got 60 assets, I’m trading, I’m trading the exact same thing on all 60. Now that thing is an idea and that has a return stream and you look at that return stream and it might have a sharp ratio. Let’s say it’s a Sharpe ratio of one and you look at it and go, there’s a Sharpe ratio one, there’s a process and here’s another Sharpe ratio one process, and you put them together and you should have a 1.4 if they’re uncorrelated. That’s sort of true and sort of not. And I’d say it’s very true at the right time frequency. And so, this is something we spent a lot of time researching and working on debating because underlying each of those models is in fact, I don’t know, let’s say both trading the S&P 500. And so even though the models diversify and say, how do models diversify?
Chris Schindler: 01:23:32 Like let’s say you’ve got a weekly model and a monthly model. And the weekly model, it goes in and out once a week. And the monthly model goes in and out once a month. And no models do that. But let’s just say like, the average holding periods are quite different and those guys are going to be relatively uncorrelated on average over a longer period. And so, you can say, okay, I’ve got two things that are relatively uncorrelated over a longer period. They’ve got the same Sharpe ratio and I should put equal risk and both of them and I’ll get 40% higher. Sharpe ratio, the square root of two, times higher. Here’s the main problem with that statement or the challenge in behind it is that well the models are diversifying on average instantaneously. There is no diversification, there’s only addition and subtraction and so like instantaneously imagine these models are just a single signal for a second and one of them is either long – short the S& P on any given day and the other one’s long – short the S&P on any given day…
Chris Schindler: 01:24:17 There’s only four outcomes. There are either both long, both short or they’re canceling. And you look at that signal and it goes from two to minus two to zero and it doesn’t go from 1.4 to minus 1.4. In fact, if you have 10 models, it’s possible that all 10 are long the S&P that day and that day, you’ve got 10 times as much S& P. if you treat them as independent agents. You don’t have the square root of time, like three times the S&P, which is what the diversification seems. And so, when you actually, when you diversify across models or across signals on a given asset, then statistically what you end up as with a process with excess kurtosis at the daily level that you have fat tails. Because even though you assume three times diversification, occasionally you’re going to have a much bigger than three times ….
Chris Schindler: 01:24:59 Now you’ve got two ways to handle this. The first one, if you think it’s a problem, because by the weekly and monthly level, through central limit theorem, it reduces back to normality. So, at the weekly, monthly level, it doesn’t matter. And if, you have two things that are independent and you put them together at the weekly and monthly level, you’re going to see that 40% higher Sharpe ratio for the same risk. Instantaneously you could have significantly higher or lower bets on any single thing at any single time. And if something happens in that market, the S&P has a 5% down day and you happen to be long 10 units of that day, watch out you’re in all sorts of trouble. And so, it’s a really interesting portfolio construction question and challenge is instantaneous risk versus long term risk and figuring out how to play those two guys against each other and really has a lot to do with your utility or the utility of your investors.
Chris Schindler: 01:25:42 But the most extreme form of protecting it, which you can do, so you, let’s call us up, you can buy insurance on that is if I have 20 independent models instead of running them as independent agents. And in other words, I imagine I had $100 and I put $5 in each and let them do their own thing, I can turn them into a voting machine. So, if I turn them into a voting machine that literally reduces the signal to a one or a minus one. So, if 11 models are long, the S&P your long one unit of S&P, if 15 are long the S&P you’re long one unit of S&P, if nine are long and 11 are short, you’re short a full unit of S&P. Always a one or a minus one. That process is going to have the exact same fatness as the S&P. It’s always long or short one unit, it’s got the exact same kurtosis as the S&P 500 which is great and the cost of that is a huge amount of the diversification benefit of all my models.
Chris Schindler: 01:26:30 I may have given up half my Sharpe ratio to make myself completely normal at the daily level, so that’s what I can buy that insurance if that’s important to me. But that’s a pretty expensive piece of insurance. The other way is to just treat them as completely independent agents and let them go. The only way, that’s what, and we do a lot of work on this, that is actually the right thing to do as long as they are properly independent. If your models are not fully independent but in fact are correlated, they’re all point fives. You have to squash.
Adam B.: 01:26:54 Yeah. I think that’s only true if you assume that the return streams are all IID for the individual strategies and in a single period model. But if you’ve got, and that they’re normally distributed, so if they happen to take a loss at the same time, then you’re going to expect that loss to mean revert over time. Right, and I’m not sure that that’s true if there’s fat tails in any of the individual strategies. So, in other words, there’s a violation of the conditional correlation, even though the unconditional correlation would lead you to leave to their uncorrelated. And also, I’m not sure it holds on a multi period basis because the compounding of losses, when all of those strategies take a loss at the same time, there may not be at a multi period level and expectations of mean reversion. Anyway, this is a, fabulous topic. I can see why it’s heavily debated.
Chris Schindler: 01:27:52 We will talk about that one offline. I see what you’re saying. And so, I was giving a very, simple example of ones minus ones and just showing that when you add a bunch of models together, if your position is not also one or minus one, you’re going to have a fatter tail at a daily level.
Adam B.: 01:28:04 No. I’m, more curious about the assertion that-
Chris Schindler: 01:28:07 Independent agents.
Adam B.: 01:28:08 …you can either be concerned about return dispersion or volatility, your losses in a short horizon, or you can be concerned about maximizing utility at intermediate horizons because the central limit theory that one will wash out the other.
Chris Schindler: 01:28:24 Oh, not completely washed. I think you’re absolutely right to say fat tails will also have more geometric drain than a normal process.
Adam B.: 01:28:29 But those fat tails are a function of the conditional correlations, right? It’s the-
Chris Schindler: 01:28:33 Yep. So here’s another way to think about it though, and this is the most extreme form of it, and this is really where you can get yourself with a thought experiment and figure out for yourself what you’re comfortable with. If you are only showed yourself or investors monthly returns, does the investor care that you created those monthly returns by trading 22 days out of the month at one or a 1% risk or trading just one day of the month at the square root of 22, times risk? It’s an interesting question because at the end of day, monthly returns are monthly returns and who knows. Now if the market itself is not normal, is it still no? If the market itself is fat tailed? Does it make a difference?
Adam B.: 01:29:13 Well, yeah, I mean again the question is to what extent do you have confidence that the intra-month losses are going to wash out or average out at the monthly scale and I think that is the conversation that is going to be really fun to take offline.
Chris Schindler: 01:29:25 We’ll take that offline. There’s a lot to that one. Anyway, so the trade off of course being between instantaneous short term risk and how much you care about that, versus maximum Sharpe ratio at some higher level like weekly or monthly.
Adam B.: 01:29:36 I will acknowledge that there’s probably a trade off there. That you can actively make a choice about.
Chris Schindler: 01:29:40 And only to say if you worry that your boss is going to come down after a big one day lost and shut you down, well then you have to worry about the left side a bit more and if you don’t, if you only show monthly returns to a manager, you probably move towards the right a little bit, but you always have to be aware that an over bet does expose you. If the market itself is completely normal, then it will normalize away. If you over bet for given day over a month, it probably normalizes by end of the month pretty cleanly, if not perfectly. If you’re only trading one day a month and that one day is a really, fat day, well then you got to be super careful because it will take years to normalize that away.
Chris Schindler: 01:30:06 And so, there’s just a little bit of understanding that piece of it. And then there comes down to the, of course the much more existential question which we debate about internally endlessly is the how confident could you ever be with a model that only traded one day a month. Because how much back testing do you need to do to say I’m confident that’s a Sharpe ratio of one …
Adam B.: 01:30:21 Yeah. To have a sample size that gives you something meaningful.
Chris Schindler: 01:30:24 This is where we got into the big, if you could be confident and my buddy would always go but you can’t. And so, we will come around that one over and over again. But just say, and we kind of went down the middle. If we had models that traded in non constant risk and really non constant risk, you have to pull back on them a little bit just to handle that, some of those effects. But anyway, it’s always an interesting discussion.
Adam B.: 01:30:40 Yeah, absolutely. Incredible. All right. I have a question about, because I think a lot of people on this that are listening to this are probably aspiring PM’s, and given your trajectory through Teachers’, you’re launching your own fund, your own firm. Do you have any, I mean, what advice would you give to an aspiring PM quant or non quant come into the business right now?
Chris Schindler: 01:31:06 It’s interesting because the first thing I would always say, and this is going to be the boring side of it, but I would just say I would start with this is I would say, get your CFA. When I got my CFA 17, 18 years ago, and I loved it as an exam. I came out of the actuarial exams, which I felt where I got a hoop to jump through and like, just giving a pint of blood to demonstrate you could do it. And I hit the CFA and went, Oh my God, this is the first time in a long time I studied where I feel like I’m way better off for it. It doesn’t mean I loved every piece of it. No, I don’t think anyone likes every part of the CFA, but at the end of the day, what it does is it gets you an inch deep and a mile wide and you get to look through that and go, did I find that the stock piece interesting or the bond piece of the portfolio construction or the risk or the, and I think and at that point then it’s up to you to go a mile deep in that topic and so I’d say like the CFA even like 15 years ago wasn’t a like, Hey, look at me.
Chris Schindler: 01:31:50 I’ve got it. It was really became a very quick question in the industry was like, well, if you don’t have it, why haven’t you got it? It’s one of those things where I think you really should do coming out of school. It’s for you, but I think it also really establishes that in statement of saying like, I’m interested in this. I would say the next thing is investing. I’m going to draw this analogy relative to actuarial science. Then I’m going to bother a bunch of people and I do this but I would say actuarial science is like coloring and when they give you those pictures where they paint by number and it’s like you have to paint like a number three is yellow and then coloring within the lines and the entire purpose of it was to create something that anyone else coming along could replicate exactly.
Chris Schindler: 01:32:21 And I chafed under that environment. I would say it’s fair to say and I would say investing is like the exact opposite. You have this blank canvas and it is free form art now you like there are people who make money trading global macro, central bank, alt risk premiums, discretionary credit distress. There are guys making money trading tick date on the S&P 500 it is such a wide and incredible set of things you can do, but if you are going to try and pry money of other people’s hands, it’s, to me investing is about creativity. And so, intelligence, I hate to say it is like commoditized dime a dozen think. Intelligence is, that you’ve got to have enough it to be good enough. There’s a certain amount of intelligence required to do this. I think investing is about creativity and about passion and then enough intelligence to get the stuff that you need.
Chris Schindler: 01:33:09 But it’s that intersection of creativity and passion and intelligence I think is super, important and that’s what you have to demonstrate. And not to say those are easy things to demonstrate, but if you’re trying to impress someone or if you’re trying to get into the space, you’ve got to know that. Look, I think in investing, if you want to get excess returns, you have to be doing different things. Like you can’t be doing the same thing as everyone else and expect to do better than them. And so, the question is, well, how do you do different things than everyone else, and this is an existential question for every single manager, every single hedge fund, if you’re doing the same thing. And so, a hedge fund can be different because it’s got a massive protective moat. It’s got resources, technology, infrastructure, something that allows it to defend its moat or it can be different because it’s got creative, intelligent people who come up with new ideas who are passionate and then in, behind that the most important thing for the culture of the hedge fund to support that is the right culture to support and nurture that creativity.
Chris Schindler: 01:33:57 And if you run into a culture where it’s like every single person feeding ideas up to an individual, and it’s not a culture of open discussion and open debate, like it’s not going to work if you’re in a world where you’re trying to be creative and you’re trying to be innovative, it also requires, I really think open and honest debate with people who you enjoy debating with, to really tease out ideas. Because I think like the most dangerous thing a quant can do and can ever do is sit in a corner by themselves and try and build a model on their own. I think that’s very risky. It’s very dangerous. It’s just too many assumptions. There’s too many cheats, there’s too many cheats to yourself, too many mistakes. And also I really think, I’ve always thought investing is a collaborative process and it has to be.
Chris Schindler: 01:34:33 So from my perspective, if you’re a PM coming into it and you’re trying to get into systematic investing and now there’s a toolkit, a set of toolkits you need to have, it’s math its statistics. It’s not crazy complicated math or statistics. It doesn’t have to be anyway. I know the machine learning can get a bit wild. It’s computer science and it’s some finance, so it’s a pretty broad, I think like this is why I think it’s such a cool place. Like it’s such a neat intersection of, of a lot of disciplines, but I think a lot of people who are really, really successful come at it from the side. I almost think like been classically trained has been a detriment for a lot of investors for quite a while. Everything we did at Teachers’ we kind of made up on our own. We were given an incredibly nurturing, supportive environment.
Chris Schindler: 01:35:11 We had some really cool people around and then we just went at it. We debated, we like we got kicked off floors all the time because we were shouting each other, but it was always, it was always positive. It was always supportive, but you know it was trying to get to the answer and if you don’t trust the people that you’re working with, they don’t agree with you. The last thing you want is a culture where people go, yeah, I don’t agree with what you’re saying but that’s your thing. I’m going to do my thing and we’re just going to like give each other our thing. I think that’s very, dangerous to me that in all of that, what do you tell the PM? The aspiring PM? There’s a lot of skills mixed into all of that. Communication is super important. The ability to work in a team is super important.
Chris Schindler: 01:35:41 The ability to hear a good idea and adopt it or to have a good idea and sell it. Like those are equally important skills.
Rodrigo G.: 01:35:49 Or have a good idea that doesn’t work out after discussing it and not having your ego bruised enough to move on.
Chris Schindler: 01:35:57 It has to be open, honest debate and discussion and the ability to look. When I put my team together Teachers’, like one of the first things I did is I went, we’re all sinking and swimming together. I don’t want anyone coming and going, I built this model and did well this year. I get paid more than someone else. It’s like when someone comes to the group and starts to sell an idea or pitch an idea, right? We’re going to work on it. And if you don’t like it, you have to, that’s on you. And you have to debate it until you either get comfortable or you don’t. Until we get to a point where everyone’s comfortable and then after the fact, we all own it. And that’s super, important to me. I really don’t like siloed approaches where this is mine, this is yours. And I think you got all sorts of local optimization problems and you get all this and not to, I just don’t think the product is good.
Adam B.: 01:36:33 Hundred percent and I think firms that are able to get that right have a huge advantage.
Chris Schindler: 01:36:37 That was a meandering and probably not perfect answer to your question. I could probably soundbite it better.
Adam B.: 01:36:42 No, there was a Pearl necklace in there that was …
Chris Schindler: 01:36:44 You got to be smart, you got to be good communicator, you got to be a good team player, you got to be passionate. At the end of the day though, this is like, there’s like what you’re trying to do is is I think investing to me is like you create a puzzle for yourself and then you try and solve it. Then you try and solve it again and then you try and come with a new puzzle. And like I, that’s what I feel like the space is.
Rodrigo G.: 01:36:59 Much like your career one of the things I think to note is that you don’t have to do all those things at once. These are skills you developed over your career.
Chris Schindler: 01:37:06 Okay. So, the next thing, find a good mentor. It’s like it’s the end of the day. Don’t try and reinvent the wheel yourself. It takes a long time and it’s terrible. Find a good group of people and learn from them and pick up on it. Yeah, definitely. But I mean my other point is like I think this stuff is so much fun, so I always say for sure do it. It’s an incredible career and you’re never going to find yourself bored.
Adam B.: 01:37:24 Hundred percent well, I can pretty well guarantee that no one’s going to listen to this at more than 1.25 times speed
Rodrigo G.: 01:37:30 Maybe at .5 for the first time.
Adam B.: 01:37:31 Very high level of confidence.
Rodrigo G.: 01:37:33 Unless they go back in time.
Adam B.: 01:37:35 That’s right. Chris, this has been really fun and really illustrative and I think there’s going to be a wide group of listeners who are going to get a lot out of this and I want to thank you for coming in and for your time and for sharing and being so open about your experience and your thinking and looking forward to taking a lot of these different threads offline. There’s a lot of grist for the mill.
Rodrigo G.: 01:37:56 Even that first meeting, that first lunch it was a little bit of what we have internally here. A little bit of contentious battle, different opinions. It was fantastic. Had to have you on the podcast.
Chris Schindler: 01:38:06 You’re never going to find anyone who says I don’t like debating, so absolutely love it. Yeah, this is great. I think you guys are doing some really cool stuff so I really enjoyed it.
Adam B.: 01:38:12 Do you have a name for your company yet before we go?
Chris Schindler: 01:38:14 So it’s going to be called Castlefield Asset Management, which was the road I lived on in Toronto and when I was building a lot of these models and I mean I think it’s still six months out. I think it’s going to be some neat stuff for the 32nd version of it is been working what I’m going to call like the second generation alternative risk premiums that are looking for other dislocation and crowdedness.
Rodrigo G.: 01:38:33 Right, so you’re going to try a little bit, this is a bit about providing orthogonal bets to that same space.
Chris Schindler: 01:38:39 Yeah. The key is like say I’ve got 20 to 25 independent ideas or models and each of them is uncorrelated with beta, uncorrelated l, just stocks and bonds, but also like vitally important uncorrelated to all the alts. So, uncorrelated trend-following and carry momentum and vol selling.
Rodrigo G.: 01:38:54 What is uncorrelated? What’s that …
Chris Schindler: 01:38:56 The average cross correlation term of all 20 – 25 models is like 0.05 and the correlation to carry a zero the correlation of momentum is zero. The correlation, so I think like from that perspective, I think it’s a really nice piece for an investor who already has the beta and the alt risk they should have and has a risk parity process which they should have. And he said this is something that will fit in very nicely to that portfolio.
Rodrigo G.: 01:39:15 Well looking forward to continuing our chats as you develop that and from that we’ll have you back on once you have fully fleshed out product.
Chris Schindler: 01:39:21 If it does well.
Rodrigo G.: 01:39:21 Yeah. We’ll see with that. Excellent. Thanks Chris.
Chris Schindler: 01:39:28 Thank you very much.
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