In this episode I chat with Jason Josephiac, Senior Vice President and Research Consultant at Meketa Investment Group.
Jason has largely spent his career in the institutional allocation space, first in manager research at United Technology’s pension and now on the consulting side of the table.
Given this background, I spend the first half of the conversation trying to peel back the layers of how Jason thinks allocators should attack the portfolio construction process. This includes his views on the risks of LDI, portable alpha in theory versus practice, and why he prefers to view the world in an absolute risk / absolute return framework.
In the back half of the conversation we discuss Meketa’s Risk Managed Strategies framework, with its three buckets of first responders, second responders, and diversifiers. We cover topics such as long volatility, managed futures, and what actually constitutes a diversifier.
I hope you enjoy my conversation with Jason Josephiac.
Transcript
Corey Hoffstein 00:00
Are you ready to do it?
Jason Josephiac 00:01
Always willing, hopefully always able.
Corey Hoffstein 00:04
All right 321 Let’s jam. Hello and welcome everyone. I’m Corey Hoffstein. And this is flirting with models, the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.
Narrator 00:24
Corey Hoffstein Is the co founder and chief investment officer of new found research due to industry regulations, he will not discuss any of new found researches funds on this podcast all opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of newfound research. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of newfound research may maintain positions and securities discussed in this podcast. For more information is it thinknewfound.com
Corey Hoffstein 00:55
This episode, I chat with Jason Josephiac, Senior Vice President and research consultant at Maketa Investment Group. Jason has largely spent his career in the institutional allocation space first and manager research at United Technologies pension. And now on the consulting side of the table. Given this background, I spend the first half of the conversation trying to peel back the layers of how Jason thinks allocators should attack the portfolio construction process. This includes his views on the risks of LDI portable alpha in theory versus practice, and why he prefers to view the world in a quote absolute risk absolute return framework. In the back half of the conversation we discussed makitas risk managed strategies framework with its three buckets of first responders, second responders and diversifiers. We cover topics such as long volatility, managed futures, and what actually constitutes a diversifier. I hope you enjoy my conversation with Jason Josephiac. Jason, welcome to the show, excited to have you here. I’ve listened to you on a number of podcasts, I think you and I are, I would say in the same orbit. And yet, we haven’t connected a ton in the past. So excited to actually get the chance to sit down and chat with you and pick your brain about a number of subjects. So thank you for joining me.
Jason Josephiac 02:15
Well, thanks for having me, Cory. It’s really humbling to be on flirting with models. It’s a podcast I’ve listened to for quite some time. And it’s just great to do this. Look forward to having a lot of fun.
Corey Hoffstein 02:25
Well, let’s begin at the beginning. Let’s start at the beginning of your career. Give me the background catch me up to how you got to where you are today.
Jason Josephiac 02:33
Yeah, so I’ll go a little bit further back where I grew up in Connecticut with a town that has more cows and people. That’s actually probably the largest dairy farm at least in Connecticut, maybe in the Northeast. And I found my way back here, actually, this is where I where I sit today and Ellington, Connecticut, because it’s a great place to start a family because I grew up here. And it’s less than three hours from New York City, less than 90 minutes from Boston. So you know, a nice kind of home base area. And in fact, a fairly well known hedge fund is named after my town because the founder grew up here. So there’s some hedge fund DNA in the town of Ellington, Connecticut. And from a young age, I was always always really interested in markets where I had to stay at home mom, my dad worked second shift. And my mom had CNBC on all the time now I’ll leave any comments to CNBC, you know, aside, but she was trying to learn about how to save and invest for her three children for really for college. And growing up, I had a really good friend that had an uncle that was high up at a place called Jones trading, a big block trading firm back in the day, I’m not really sure if they are today. But I spent a few summers with my best friend and his uncle on vacations with his extended family, and just got to know a little bit more about the markets. And I just got the bug. And I really fell in the stock market. Interesting because it touches so many facets of the entire economy. It’s a reflection of the entire economy and kind of why we get up every day to do work across the various different industries and sectors and whatnot. That’s why I decided I want to go to school for business finance economics, found my way out Bentley graduated in the teeth of the 2008 crisis in June of 2008. Was lucky to get the job. I guess at that point, a lot of firms really didn’t realize what was coming during the summer of 2008. Before really the bottom fell out in q4 of 2008. And somehow during the round of layoffs six months into my first stint, I survived. I like to think maybe I was in ambitious, hard working undergrad. However, I think I was probably just a low cost item right on their income statement. So I was able to survive there for five years had a few different roles in sales and client service. Always spent a lot of time in FactSet doing portfolio analytics. And then when I got more into the internal sales and business development side, I was still using data and spreadsheets to say take money market directory where you have all these plan sponsors and who they might have in their investment manager lineup. I would cross reference that with each investment and the managers that they hold. And I would identify the managers that were going through tough timeframes. And I would say to the head sales, guys, hey, you should maybe look at this prospect or that prospect because they hold this strategy that is underperforming, and we might be in a position to maybe win business there. So after that life happens, got engaged. My fiancee, my wife, now we’re thinking longer term, where do you want to family? I think the best place to raise a family is where you have family. So I found my way back to Connecticut. And during my time at the Boston company, I had lots of interactions with allocators. And I always thought, hey, that’d be pretty cool to be on the other side of the table. And it was just dumb luck, the stars align that United Technologies had a investment role on their pension team, covering equities and hedge funds. So I went there spent eight years managing the mostly the hedge fund portfolio, and a portable Alpha construct. had awesome team, great CIO, Robin dia monta. And then a little over two years ago, I want to expand my horizons, I want to help more than just one plant sponsor. So I find my way over to Makita, where I sit today as a research consultant.
Corey Hoffstein 06:05
So you’ve seen sort of both sides of the table here, you’ve gotten your hands dirty, you know, technologies, you sit on the consulting side of Makita, you’ve been able to both experience and think witness, hopefully, objectively a large number of institutional approaches to allocation and manager selection. I’d love to, from your perspective, is there any sort of commonality in best thinking that you see, when you look at different plan sponsors? Are there ways in which the sponsors could really look to learn from each other and improve is there sort of a universal gold standard that we should all be striving for?
Jason Josephiac 06:41
Hell, there’s a thing in investing, where when investment approach becomes a religion, it’s time to lose the faith. And we tend to solve problems not by really giving new information, but by rearranging what we’ve known for a long time. And at the end of the day, in this world of investing, and plan sponsors, allocators investors, we all have the same issue, we’re trying to solve the same problem, we’re trying to put up the highest absolute return with some given level of risk. Now, sometimes investors think about the level of return as a target of say, 60%. Or they may be anchoring to ever, like recent performance of what the markets are doing, or maybe looking at what other peers are doing. For example, over the past 10 years, some folks might be thinking, now only if we had private investments at a higher allocation or some allocation, we would have been able to achieve a similar level of return relative to peers that had a higher level of say, a private investment allocation. But maybe, instead of that investors should consider thinking about what they need to achieve in isolation. So what I mean by that is, what is your required minimum return for the objective you’re trying to achieve? Maybe that could be 5%, where everything would be great if we could just get 5%, year in year out. And maybe 8% is a stretch goal. But there’s a lot of wiggle room, right, and between that five and 8%. So let me just throw out a hypothetical two portfolios, Portfolio A, Portfolio B. Now let’s say A and B gives you a 100% probability of achieving a 5% annualized return over 20 years, Portfolio A, that expected return is 8%. And Portfolio B, that expected return is seven and a half percent. Now, you might say you want a over b because of the extra point 5% or 50 basis points. But now what if we looked over rolling 1357 10 year periods? What if the probability of Portfolio A exceeding a 5% return over rolling 12 month timeframes is 60%? While Portfolio B is 70%, you do the math 60% times A is 4.8. For Portfolio A 70% times 7.5. For Portfolio B is 5.25%. So in that situation, you wouldn’t want Portfolio B over Portfolio A, even though a had an 8% return, and B had a seven and a half percent return. So the answer as to what portfolio you would want really depends on if you think about the world in terms of relative risk, relative return, or absolute risk, absolute return. And that may be informed by pure risks, career risk, FOMO risk. But when you really think about investing in the asset side, sometimes folks only think about that asset aside and achieving that return. But everybody and maybe this is my bias spending a lot of time on a pension plan. Everybody has a liability. If we don’t have liabilities then we won’t have assets. If we have assets, we won’t have liabilities and break out the institutional investment space, endowments they’re trying to fund various aspects of operating a university that’s their liability. The Foundation, they’re trying to fund philanthropic missions, a pension funding retirement benefits, individuals or families funding a desire to need or spend insurance companies fund future claims, sovereign wealth funds funding some sort of spending or investments into the country. And then there’s all these other things that are out there these risks, such as mortality risk, inflation, risk event risk, that can really make a perceived smooth spending rate, a smooth liability, become a really chunky liability at some given point in time, depending on those exogenous shocks.
Corey Hoffstein 10:36
So you lead me right into my next question perfectly, because one of the common frameworks that seems to have been adopted over the last 20 years is this idea of liability driven investing, really focusing on what are the core liabilities, immunizing them, and then trying to minimize the variability in the surplus, or grow the surplus to obviously, hopefully exceed your liability. But you wrote an opinion piece, December 2020, saying liability driven investing is crap. All right. So I got to ask you what is wrong with the conventional wisdom of LDI? And how do you think pension should be allocating?
Jason Josephiac 11:15
Well, first, I need to correct one thing, I didn’t call it crap. It’s C dot r dot A dot P dot, it’s an acronym for corporate rates or puts, right and I’ll get into that in a second here. But so I don’t think there’s anything necessarily wrong with the conventional wisdom of LDI. I mean, it’s conventional wisdom for a reason, it tends to work. And it’s relatively sound if you’re matching known future liabilities with assets that are supposed to be risk free, purely risk free, such as US Treasuries. And we could go down a whole rabbit hole around whether those are really risk free or not. Let’s put that to the side. It’s more of a macro conversation, which there’s much more focus on besides a debate around that. But this assumes that what you promised in the future will never have to be adjusted for things that may not be probable, but could be possible. Now, possibilities tend to become more probable over time. And humans tend to be slow to adjust because change is hard. Right? And when I’m talking about the probable versus possible, the possible could be things like inflation, government mandated changes to pensions, demands by the retiree population for changes to benefits. Those really aren’t in the calculations of the accounting liability that pensions use to then implement LDI. And you know, what experience is what you get when you didn’t get what you wanted, when you assume the possible cannot become the probable. Now, ultimately, the plan sponsors Oh, absolute dollars to the plan beneficiaries. Now, beneficiaries can’t eat funnest as volatility. Beneficiaries can’t eat pension income or pension expense. Those are non cash items anyway. Now, the primary driver of investment decisions should be the assessment of the economic liability, with the accounting liability, perhaps being secondary. Now, however, there are usually implicit links or explicit links to the accounting liability and economic liability depending on how you think about risk. Now, in general, well funded corporate plans, or public plans, whatever pension you might be talking about, they’re guided toward immunizing the accounting liability, with investment grade corporate bonds. Now this is because the accounting liability is measured using investment grade corporate bonds. So if you want to immunize yourself based on how they counted on how the liabilities measured them by the thing that is used to measure it, but as we know, corporate bonds have an interest rate or duration component, and a credit spread component. Now, credit spreads are pretty much low volatility equities, or sometimes I like to call them diet equities. And the left tail events, credit spread, returns can behave similar to equities, and not die equities, but the full flavor version of equities. So for pensions, absolute liability, or the dollars that need to go out the door, then perhaps the investment strategy should focus on raising the probability of those absolute dollars going out the door. Now, I believe there are other ways to do that than only a combination of equities and diet equities.
Corey Hoffstein 14:26
We’re gonna get into that a bit. And I suspect given your background, the concept of portable alpha is perhaps key in that discussion. But within the piece you wrote, and I’m quoting here, equity futures combined with short duration credit strategies, not portable alpha, a portfolio of long short equity managers combined with an equity beta one overlay, not portable alpha, alternative risk premium strategies combined with beta overlays not portable alpha, filling up various hedge fund strategy buckets in pursuit of a diversified Alpha stream. No on portable alpha, and quote, you expressed a pretty strong view as to what portable alpha is not. So I’m gonna ask you what is portable Alpha?
Jason Josephiac 15:11
Usually the simplest questions are the hardest to answer, right? And alpha, it’s always fleeting. It’s just like how a maybe a fairly novel sports strategy will eventually be used by others, Bill Belichick for instance. And maybe I’m biased because I’m a Patriots fan. But Bella check, he mastered deferring on the coin toss. So then Tom Brady could better manage a clock in the back half of the second quarter, hopefully score, come out of halftime in the third quarter, and then come score again. And that’s just demoralizing, right for the other team. Now, alpha from yesterday is sometimes the ARP of today. And those old alphas or maybe call it ARP, that can persist. But perhaps the flows and the structure of the market influences how extended ARP or factor investing old alphas can get to the upside or the downside, which means that timing, perhaps could be more crucial. If you’re per se trying to pick the best factor or pick the best ARP strategy. You mean, need some sort of combination of timing, as well as probably even more so diversification rather than than the timing there. Now, in general, I think alpha should be agnostic to moves in interest rates, credit spreads, equities, volatility, you know, all the major risks that are known out there, and that you can measure. And it should be fairly agnostic to ARP strategies and factor investing and style premium. So after you strip out all those things, what you come out with is some sort of idiosyncratic risk that’s driving returns. But when you strip out all those things, that idiosyncratic risk, what you’re left over with is a fairly low spread. And if you want to put up a respectable absolute return, you need to add some leverage, right? However, introducing leverage then adds a whole other host of risks. And I’ve really become skeptical and more skeptical over time as to what is alpha and how its measured, quantitatively, and maybe what’s left over, after stripping out all those factors, like, like we mentioned, and doing all these multi regressions and factor based models. For me, really, the art of alpha, or finding alpha is just as important as a science. So in other words, I believe I’ve been well served throughout the years of understanding the people behind the computer screens, in addition to what’s coming through on the computer screen.
Corey Hoffstein 17:38
Well, let’s pull on that thread a little bit more, because you did spend a substantial part of your career evaluating and trying to identify managers. So from your perspective, how does one go about identifying a manager that would be truly a contributor within a portable Alpha framework versus a manager who wouldn’t be?
Jason Josephiac 17:57
So the thing I generally start with is looking at how a manager handles risk. I believe that focusing on risk first usually takes care of returns, at least over the longer term. But you know, Socrates wrote or said, or I know, he didn’t tweet it. But the beginning of wisdom is a definition of terms. So what is risk? Is it max drawdown? Is it standard deviation? Is it permanent impairment of capital? Is it tracking error? And then how is that risk Express? Is it express route? FX rates credit equities volatility? Is it concentrated? Is a diversified? What functional role? Does that strategy or manager play in the whole framework? Whether it’s a part Boffa framework or whether it’s something else? And are we defining alpha and portfolio outcomes at the manager specific level? And if so, over what timeframe, and then what type of environment. And so this is where really my view of portable alpha and how the built a program over the years has evolved, where I believe we need more than a suite of market neutral strategies to harvest alpha over the long term. Because when the market forces you to deliver, it can be catastrophic for a market neutral and Relative Value strategies that are highly levered. Now, we all only have one head, right? And if it’s chopped off, you’re dead. Although, maybe in the hedge fund land, sometimes people do rise back from the dead. But we don’t want to make that bet. We are trying to create a multi headed portfolio, right? Maybe like the Hydra and Greek mythology, where we want all the strategies to matter. But we don’t want one strategy to matter too much.
Corey Hoffstein 19:39
Earlier in one of your answers, you mentioned this framework, really briefly, where you talked about relative risk relative return versus absolute risk absolute return as being two different perspectives by which a institution could view what they’re trying to achieve. Can you sort of dig into that a little bit more deeply explain what you meant by those two different frameworks, how they differ and whether you have a preference for one way or another to view the world.
Jason Josephiac 20:09
relative return are things like your return versus a benchmark, your return versus your peers, where you stack up on the league tables. Relative risk is a deviation of that relative return journey, and uses measurements such as tracking error, or funding status volatility. Now, absolute return us asking yourself what is that required minimum return to achieve my goals, and absolute risk, or some might call it tail risk or max drawdown is the probability of not being able to meet your liabilities. Now, I believe a relative risk relative return approach may be rational. But it’s not always necessarily logical. And a difference between being rational and logical is that rationality can sometimes be influenced by emotions, and sentiment, and perhaps in some cases, self preservation. Now, we want to raise the probability of meeting or exceeding the absolute minimum required to return through time, as well as at any given point in time. Now, that’s not to say that you shouldn’t be aware of all these benchmarks, I believe, you still need to have a keen awareness of benchmarks and factor risks, and all these other things that are out there that are already known in advance. And those tools that absolutely should be used, and your analysis, if you are trying to distinguish what you’re doing on a relative basis, and ultimately diversifying the opportunity cost, right? And what is the opportunity costs, the opportunity cost generally, is to be a passive investor. But even a passive investor is making an active decision to be passive, based on someone else’s way to define a benchmark, whether that’s the s&p, you know, a company or a Russell or whatever that might be,
Corey Hoffstein 22:07
what are the salient differences, and perhaps, like the frictions when it comes to actually implementing portable alpha, in practice, versus thinking about the concept on paper?
Jason Josephiac 22:21
So one thing is the structure like are you getting the beta portion of portable Alpha? Because portable alpha is beta plus alpha? So are you getting the beta portion? From the managers? Or are you getting it yourself directly through the features of swamp markets? So are you really separating the Alpha portfolio from the beta portfolio where you have maybe a beta provider, their sole focus is to manage that beta based on the rules that you put in place. And then you have a suite of alpha providers that are designed to extract to some sort of alpha. Now, operationally, and structurally, it’d be fairly difficult to have multiple Alpha managers also managing the beta sleeve. And that becomes less capital efficient compared to if you could aggregate all those managers on say, a single platform, and then get your beta exposure separately through a third party or perhaps by yourself, depending on how you’re set up. Now, having those things centralized, say on a magic comp platform can provide you what I call TLC, transparency, liquidity, and control. And it perhaps helps you make more informed decisions around how to manage the beta overlay, and have a rules based approach to rebalance between all the alphas and the beta, as well as between the alpha and beta amongst the alpha and all those different iterations there. And the second thing I would say, in terms of you know, on paper versus in reality is the dispersion of managers in the Alpha pool. And the number of line items were investors that are familiar with long only active equity or long only active fixed income. That is very different than separating out your alpha sleeve and your beta sleeve. Because within the Alpha sleeve, there’s just so many different other strategies and levers that you can pull to hopefully extract alpha, where and long only active land, there’s really only one thing that you can do when you’re long only you can either buy more of a security, or buy less of a security or not own a security. So that adds complexity when you have all these other strategies that are very different than long and reactive implementations. And then the final piece here is leverage. Now, when you’re replacing your beta exposure with futures or swaps that introduces a need to determine an appropriate margin buffer, which can be a bit complex, and you really need to think about that long and hard and look at history over more than just a 10 or 15 year. Your timeframe?
Corey Hoffstein 25:01
Well, let’s talk about that last piece a bit more. You mentioned history, students of history will look back to 2008, when portable alpha, arguably was more popular, at least at the headline level. And what we found through that period, and I think this is trivially obvious in hindsight, right is that leverage introduces path dependency risk through the risk of margin calls. And I’m curious how you think about managing this risk, particularly when you are implementing portable alpha using hedge funds that might require lockups or be less liquid or could even go ahead and gate redemptions during left tail events, like we saw in 2008.
Jason Josephiac 25:43
So when you’re managing a portable Alpha program, you really need to think about the initial margin that you need for that beta overlay, and how that can change through time. So let’s just say you need 5% initial margin to get 100% notional equity exposure. Well, from there, you might only need 5%. But then you need to understand what buffer, call this the variation margin that you need over and above that initial margin. And then perhaps for equities, it’s 25 to 35%, based on the last 100 years of equity, drawdowns, maybe on the Treasury side is 15 to 25%, based on the last 50 years of treasury drawdowns. Now, the liquidity of the underlying hedge funds and the format that you hold them in whether they’re separate accounts, or commingled funds, you need to think about that in terms of liquidity, and also need to think about are those underlying strategies those Alpha strategies positively or negatively correlated with drawdowns of your associated data? And is that correlation profile embedded in the structure of the strategies? Or could it be a temporal relationship dependent on the macro environment. So for example, highly levered market neutral strategies, they’ve had a great run over the past 10 or 15 years, is that a function of the Feds sort of reaction function to what happens in the markets. Now, perhaps, it’s just something that you need to take into account where that could be more of a temporal correlation, where if central banks or fiscal authorities monetary authorities behave differently in the future, maybe that leverage risk looks different in the future. On the flip side, if you have something that is structurally negatively correlated, such as buying put options, you can be highly confident that during the storms, if you have equity volatility to the downside, that will structurally offset these draw downs. Also, you can’t rely on other parts of your strategic asset allocation for liquidity. So you can’t necessarily look to your corporate bonds, or your commodities, or gold or other areas of your asset allocation, to then be the liquidity pool to fund up or to top up your variation margin, because there’s just basis risks there. And we saw that easily in q1 of 2020, where everything goes down, it goes down at once. And unless you set that variation margin appropriately, unless you had structurally negatively correlated managers, and that alpha pool, things could go sideways very, very quickly. So I like to start at the extremes, and then work my way into the middle to determine an appropriate level of variation margin.
Corey Hoffstein 28:41
You know, we’ve been using this term portable alpha over and over and over again, and I think we, as an industry, maybe have alpha blindness, like we consider all alpha to be a good thing. And I think sometimes we don’t take a step back and ask whether we’re just pursuing a shiny object for a particular benefit or not. So I’m curious, how do you think allocators should think about measuring the potential benefits of a portable Alpha program? How can they really know whether the pursuit of that quote unquote, alpha that they’re going after is, you know, the juice is worth the squeeze?
Jason Josephiac 29:17
So unless you’re a pure passive investor, which I think in the institutional world, there’s not too many out there that are like that. So you’ve probably subscribed to some sort of long only active management sort of philosophy. So how do you measure whether beta plus alpha and affordable Alpha framework is I want to call it better, but complementary to long only active equity long only out to fixed income. So you need to kind of go through the math where if you have 100 bucks, and you need that variation margin, say of $30 in order to fund your equity, beta exposure, then you’re left with $70 to then put in alpha managers. And so that’s $70 probably needs to work harder, then compared to a long, lonely active equity strategy where you’re putting in $100, and you’re getting, you know, $100 of, say active exposure. But going back to my comments before, there’s only so many levers I can pull and active equity. But if you take say that 70 bucks, compare it to the 100, do some math take one divided by point seven? What is that? That comes out to about, I believe 1.43. What does that mean? It means that your alpha pool needs to be 1.43 or 43%, better than the excess return or perhaps alpha that you get from long only active equity. And then on top of that, you need to add some sort of implied financing costs in order to get the you know, synthetic equity overlay or its synthetic Treasury overlay. So that’s the way I think about the opportunity cost or comparing whether the juice is worth the squeeze. And going through all the complexity of building a portable Alpha program versus the alternative or the trade off of sticking with long only active implementations of whether it’s fixed income, or equities. And the last thing I would say in regards to that is, not only do you have to focus on the return trade offs, we need to focus on the risks trade offs, as well as the correlation trade offs.
Corey Hoffstein 31:27
We’ve spent a lot of time talking about alpha. So I do want to turn our eye towards risk. In particular, I want to talk a little bit about makitas Risk Mitigation Strategies program, the RMS program, given the name of the program, let me start with a unfair, quote unquote, simple question, which is, what is risk? Yeah,
Jason Josephiac 31:48
it really depends on who you ask, right. And that can be influenced by the structure of how folks are set up, and the incentives that tend to reflect that structure. But for me risk is the probability of not being able to meet a liability at any given point in time, combined with a probability of not being able to meet a liability throughout time. Now, I really have a lot of difficulty with the labels that are used in our industry, whether it’s hedge funds, or even RMS risk making strategies. It’s not because it’s a bad name. But without contexts, the meaning is really in the eye of the beholder. So some may hear risk mitigating and think back to the efficient frontier. And they may think that the only way to achieve a higher return is to take more risk. So if I take less risk, if I’m risk mitigating, then my return will be lower. But if instead we focus on the specific types of functional risks we are taking, then they can be additive to returns as well as raising the probability of meeting an investor’s liabilities over time, as well as at any given point in time. Can you explain how
Corey Hoffstein 33:01
the program is structured, and more importantly, why it’s structured the way it is?
Jason Josephiac 33:07
So I’ll stay on my soapbox for a minute in regards to labels. And the industry likes to use this term called alternative alternatives. And I’ll use my jazz hands for the alternatives. But there are no alternatives. There’s really only five asset classes and there’s nuance here, but in general, five asset classes, currency slash FX, rates, credit, equities, and commodities, you can be longer short those things, you can access those things in the public markets or the private markets. You can get linear exposure through beta one or delta one implementations, or other more convex, hopefully not concave payoffs through things like options. Now, the RMS framework is designed to be the defense of the strategic asset allocation. And we break it out into three components, first responders, second responders, and diversifiers. Think about that as a tripod. Now first responders, they’re designed to protect against sharp and deep drawdowns over days to months. Second, responders are designed to protect against drawdowns that may take months, perhaps quarters, maybe up to a year or more to play out. So kind of a longer time horizon longer duration in terms of a grind down in the markets. And lastly, diversifiers are designed to provide a balance, or I should call a ballast, a lift, and sideways and more benign periods. Where if you go through a period, say from 2012 13, up through 2019, things like first and second responders, they didn’t do that great. But if you had diversifiers, at least you can hopefully carry positive on an aggregate level once you combine these three things together, and a tripod and we really think about those three components for a second and diversifiers as balancing between convexity and coupon will be a it’s a synthetic coupon. We’re not just buying bonds and collecting some sort of income stream. So think about first responders as that convexity piece diversifiers as that synthetic coupon piece, and then second responders sit in the middle, and they have sort of characteristics of both convexity and coupon. And if you also think about the continuum of first all the way through diversifiers first responders, think about max drawdown, that’s my portfolio to protect against max drawdown, second responders more geared toward kind of like Sortino ratio diversifiers, thinking about that as more geared towards Sharpe ratio. And then maybe the last way to think about this is outcomes that are possible in first responders, and then all the way to outcomes that are probable for diversifiers. And we really think about this as an evergreen strategic allocation, where we are trying to prepare for different outcomes. We’re not trying to predict them. And good outcomes tend to happen when preparation meets those opportunities.
Corey Hoffstein 36:10
Well, let’s work our way through those three different buckets, starting with the first responders. My mental model around convexity is usually that it’s a function of path dependency, the more path dependent the outcome is, the higher the opportunity for convexity. So with that in mind, how do you think about trying to find a mix of managers that can provide a convex enough response function to the type of drawdowns you’re trying to protect against without necessarily diversifying away each other’s impact?
Jason Josephiac 36:45
So when people hear long volatility, they tend to immediately think about diba money, or some of the moneyness of s&p 500 put options. But that’s really only one part of the toolkit, getting back to there’s five asset classes and those five asset classes are across the world. So we want different muddiness whether it’s at the money, or the money, the by the money, as well as different tenors, we want short, dated, maybe days to a few months, intermediate term, maybe three months to maybe a few quarters, and then longer dated, which could be out multiple years, maybe five 710 year options. And we believe that covering different asset classes, different strikes, moneyness durations, helps to raise the probability of positive outcomes during an event. Because we don’t know when an event will happen. We don’t know what the depth will be. We don’t know what the duration will be. And we don’t know how that event might Zig or zag along the way. Now, an example could be 2022. Were managers operating any effects and rates markets did quite well. We’re equity vol depending on how you implement an equity vol. But in general, folks didn’t do too well. And they’re long ball equity programs and 2022. So people might think, Oh, well, long ball doesn’t work. Well, if you’re only focused on equity, and you’re only focused on D by the money, equity, put options. That’s just one path. And you only know the perfect hedge and hindsight and optimizing to the past, especially the recent past isn’t going to raise the probability of performing well in the future. Because again, we don’t know where that risk is coming from how will manifest itself how it will cascade throughout asset classes, and really impact your portfolio.
Corey Hoffstein 38:36
When I read the literature about the RMS program and look at that second responders bucket it seems like it leans heavily into managed futures talking about things that tend to do well during more prolonged drawdowns. And when we look at the Managed futures space, this is a space that has gone through a significant evolution over the last decade, I think managed futures used to be more synonymous with pure trend following based upon how these managers behaved in the 1970s 1980s over the 2000 10s was such a tough period that they started to include things like carry mean reversion relative value seasonality. From your perspective, when you talk about managed futures as a second responder as a risk mitigating strategy, is this really meant to be a pure trend following concept? Or do you think there’s other styles or strategies that managed futures managers can lean into
Jason Josephiac 39:32
the RMS framework is designed to be a spectrum like a continuum. And there may be things that straddle the line between components or strategies that may encompass attributes of all three really. Now short term trend may be at the leading edge of second responders, while long dated strangles may be toward the back end of first responders. But again, that depends on the nature of market event. And when these strategies fire on all cylinders, and when you know one might fire on all cylinders, and maybe another one fires on only half the cylinders. Now ideally we like to put convergent mean reverting relative value carry strategies in the diversifiers slash stabilizers bucket. So we’re not dogmatic in the bucketing. And some managers may have strategies that incorporate one, two, or all three legs of the RMS tripod. But to your point, you know, manage features CTAs trend followers over time, some have morphed into a more diversified approach. I think that can be okay. In some instances. At the same time, we like the profile of pure trend across different look back periods, whether it’s short, medium, or longer term. And then the things that are more I would call CTA ish, that may be more mean reverting kind of more carry elements, that is then entering more the world of diversifiers, where it can play a role. However, second responders are designed more so to be that pure trend following implementation.
Corey Hoffstein 41:11
So I know you have some strong opinions as to what is and is not a diversifier. Earlier you said there are no alternatives. There’s just these five asset classes. So when you think about the diversifiers bucket, what do you think should go in this bucket? And maybe more importantly, what shouldn’t?
Jason Josephiac 41:31
Yeah, in terms of what shouldn’t? We want to avoid strategies that have persistent and bedded directional beta? Right, and the term hedge fund, right, everyone uses that as if it’s homogeneous. But there’s so many different ways to slice and dice hedge fund strategies where I don’t even like to use that term. Because think hedge funds is like saying sports. What sport are you playing, you’re playing football, soccer, or baseball, hockey, figure skating, gymnastics, the dispersion is really, really high. But we want to strip out those strategies that have that embedded directional beta. So directional Long, short equity, directional Long, short credit, things that have too much explicit short volatility, such as things like maybe structured credit, opportunities to credit, in some cases, distress don’t really bode well for diversifiers. That doesn’t mean that they’re not additive to the strategic asset allocation. However, we don’t really view those as being players on the defensive team, per se. We want things that are, for the most part, market neutral, beta, neutral factor, neutral relative value. However, niche strategies that may not be directly tied to the financial flows or capital flows across markets, could be additive. Think about things like insurance link strategies, litigation, finance, or other types of event driven strategies. But make no bones about it like that should be a small part of diversifiers. And it’s really further down on the depth chart due to the negative skew and capped upside of those types of strategies. And one thing that definitely should not be in the RMS framework, again, not because you shouldn’t have any exposure, but it’s really more a part of the offensive team. It’s really those private investments, whether it’s private equity, private credit. Now, they might look good from a smooth accounting standpoint. But when you adjust for those things, those are offensively geared strategies, they’re highly correlated to GDP growth are highly correlated to draw downs in equity markets. So let’s make a clear line in the sand as to what is uncorrelated, what is negatively correlated, and what is positively correlated to equity drawdowns, and that will vary in the tails.
Corey Hoffstein 43:59
I really liked this framework when it comes to risk management. This isn’t a framework I developed. This is one that’s very publicly out there. But it’s this triangle of cost, convexity, uncertainty. And I think you and I have actually talked about this in the past where as an allocator, or a manager, you can really pick two of those, and the third is decided for you. So if you want low cost, with high convexity, it’s probably a strategy that’s going to have very low certainty, right? If you want high convexity and high certainty, it’s going to have high costs, most likely that’s the third leg of the stool get that gets decided for you. When you think about combining first responders, second responders, and diversify errs, who are all arguably going to have different trade offs of cost, convexity and certainty. How do you not end up in just sort of this muddied middle place a middle ground that maybe isn’t necessarily particularly attractive for any outcome?
Jason Josephiac 44:53
Yeah. The way we do that is by not trying to be too precise across any of those three things, whether it’s the hast convexity or certainly, we want to be generally accurate. We want enough exposure across those three things without putting all of our eggs in one basket. I guess you know, think about and I would love to rattle your brain here a bit. You know what sport is out there? Where the defensive players only play defense besides football? I say football, I mean American football?
Corey Hoffstein 45:28
That’s a good question. Nothing comes to mind.
Jason Josephiac 45:30
Also, I asked GPT this question and what it came up with was Canadian football. Okay, thank you very much. I could have thought about that on my own. But so I don’t believe there is one. And if anyone is out there and knows of one, please let me know because I think this is the best analogy to use for RMS where we’re thinking about building a defensive team on a football team. Now, each player on the defensive team has a distinct and separate role. You have the defensive front line, you have the linebackers, and you have the safeties and secondary. Now, we are not just adding more players to the offensive roster, nor are we just populating our defense with only linebackers. We have players that all play a functional role within each leg of the RMS tripod. And there’s also depth to each functional position. So you’re right, and that if you pick two across costs, convexity and certainty, then you are giving up on one. So that’s why we’re not putting 100% and any two, or 100%. In any one, we’re trying to spread our bets out and just raise the overall probability that we can have better outcomes during various different types of market environments.
Corey Hoffstein 46:54
How do you think that this framework should be used within an allocators larger portfolio? Right? It’s one thing to design a framework as a standalone. But it’s always important to consider how the puzzle pieces fit together. Is this something that pension should put 10% of their allocation to? Is it something that they need to think about using it in a portable Alpha construct? How is this actually used in practice?
Jason Josephiac 47:19
It varies investor by investor, I can’t say there’s one allocation percentage per se that would be applicable across the board. I mean, if you got me in a room and put a gun to my head, I probably give you some sort of percentage. However, let’s think about this more strategically, right? Where you want things that Zig when other things are zagging. And maybe investors need to think about simplifying their strategic asset allocations, whether that’s in like an offense, defense sort of mind frame, maybe that’s growth versus hedging, maybe it’s risk on versus risk neutral versus risk off. And actually, in that vein, one of my colleagues wrote a paper called The functional asset allocation framework that goes into more details around how to rethink strategic asset allocation. But we really want things that are complementary to one another, not to beat a dead horse here, but I love the analogy of football where offensive team, defensive team and special teams, right, we’re bonds, what are bonds? Is it the offensive team? Or is it the defensive team? I sort of think that bonds are special teams. Because special teams that field goal unit and the extra point unit are really good, dependable scores. But sometimes the kickoff and punt units can really blow you up. And recently, we have seen the special teams blow up due to inflation, and rapidly rising rates. But it shouldn’t be lost on people, right that defense can also score points, whether it’s pick six, run back after interception for a touchdown, a fumble recovery, run back, get a touchdown, a safety, a block field goal or a block punt run back for a touchdown. Now those things don’t happen all that often throughout the course of the game and throughout the course of the season. But that defense is designed to put the offense in a better position to score points on the next drive. So we can harvest the games from the defense to actually put up more points by the offense. And there’s that nice balance right between the offense and the defense. Where if we’re solely focused putting points up on the board only through the offense, then we’re losing that diversification effect, by having the defense out on the field to put your offense in a better position to score. It’s the yin and yang. It’s the two sides of the coin. It’s the rain versus the sunshine. You need to have that balance in your portfolio. And what should that balance be? I mean, think offense defense is a 5050. And then how do you split the 5050? Is it on a capital basis? Is it on On a risk basis, how do you measure risk? So it can vary drastically depending on how you measure risk and how you account for all those things. And at the end of the day, the objective of the institution where they’re coming from, what their inherent biases might be, and how they may be set up from a governance standpoint,
Corey Hoffstein 50:21
one of the things we haven’t touched on yet in this call that I’d really love your perspective on as someone who’s operated in the space for well over a decade now and saw particularly violent period of upheaval is how has the hedge fund industry changed? In our pre call, as we were preparing for this podcast, you mentioned the idea of this quote, unquote, fund of funds 3.0. I’d love to learn what you meant by that where you think the evolution is and how you think be impactful for allocators?
Jason Josephiac 50:53
Yeah, I think fund of funds have gotten a bad reputation over the years. Getting back to the labels like what is a fund of funds, though? Would you say a multi Strategy Fund the funds? Would you say a pod shop is a fund of funds, would you I’m sure folks would call a typical fund of funds, where they’re only invested in in commingled funds, that’s tends to be the traditional definition of a fund the funds. But the lines really are blurring now with all these other tools that we can use and platforms that we can use to separate the investment function from the operational functions. Now, if you do that, perhaps you’re giving up some sort of resiliency across the industry. Where if a hedge fund manager had only their commingled fund, and they only managed a commingled fund, and that’s the only way you could access it, then that tends to be a more decentralized approach. But now you have managers that may have their own commingled funds, they may do separate accounts for single investors, or they may run some sort of strategy for a multi strat or a multi pm shop. So these network effects really are starting to change the industry. And I’m still trying to wrap my head around how that works out over time, and how you map out the risks that may be out there with managers managing assets for various types of shops, and various different formats. And does this all become like just one big correlated bet that has a lot of leverage on it? I don’t know. That gets back to the reason why we want things that are also structurally negatively correlated with things that are that are market neutral that use decent amounts of leverage there. And things like long option strategies trend following manage future CTAs. Those things I think, can help to zig, while those other strategies are zagging. And things like long volatility, long options trend following those are massively capital efficient strategies, right? You don’t need $1 of capital to get $1 of exposure. Now, managers, they’re also thinking about hedging their business risk. So maybe nowadays, and I think quite for quite some time, there’s been this demand for I want a low vol return stream. And then so managers have been accommodative for that by maybe adding elements of conversion strategies where they’re divergent strategies. And then that really dampens the vol, which over time, I think dampens the convexity and the overall returns. And if you add up a bunch of low vol strategies that are uncorrelated, you’re probably going to have some fairly meager returns, right? But if you can take those uncorrelated strategies, make them more capital efficient, did I think you can raise the probability of putting up pretty respectable absolute return. And if you’re doing that, on a managed account platform, again, you can get that TLC, that transparency, that liquidity, that control, and you can perhaps consolidate fixed costs across things like audit tax legal, where instead of paying that operational costs across all these commingled funds, you can spread that cost out over various different strategies with one provider, instead of relying on a manager scale, to bring down those costs, use your own scale, to bring down those costs. Now, I don’t think everything that’s really works well on a managed account platform. But you can use a hybrid approach where you have managed accounts, perhaps for things like long volatility, trend following managed futures CTAs, some elements of diversifiers and you can still use commingled funds where needed.
Corey Hoffstein 54:50
Last question of the episode for you. Jason’s the same question I’m asking every guest this season, which was in designing the unique cover art for each guest is asks you to pick a tarot card. And I believe you picked the hermit as being the tarot card that would inspire the design for your cover. My question to you is what drove you to choose that tarot card? Was there a particular something about the symbology, or the meaning behind the card that pulled you towards it?
Jason Josephiac 55:18
I just think there’s a lot of opportunity right now, not only right now, but there has been for a long time and rethinking strategic asset allocation. And I don’t have a clear macro crystal ball. I don’t even like even having a murky, macro crystal ball. But you look back in time, even over the past, say 10 or 15 years. And things like equities, things like private investments, they’ve done really, really well. But could have there been a different path, where you could still put up a similar return, but with a much smoother profile? I think that could be achievable. And I see a lot of opportunity and blank space out in the industry to really shift the conversation around. How do you take risk? How do you manage risk? How do you build resilient, durable portfolios that are rooted in an absolute return absolute risk mindset instead of relative risk relative return? Because getting back to what I said before, you can eat Sharpe ratio, you can in some regards, if you diversify properly, but you can’t eat fun to satisfy volatility. You can’t eat tracking error. You can only eat the dollars that you generate. And ultimately, who are we serving? We’re serving individuals, families, across the US across the world that depend on those dollars to do what they want to do to live their lives. And that’s whether you’re an institution, or whether you’re a high net worth individual, or whether you’re a retail investor, build more resilient portfolios, so you can raise the probability of success at any given point in time as well as throughout time, especially during the most difficult times, which we’ll see if the next 10 years look like the last 10 years.
Corey Hoffstein 57:13
Well, Jason, thanks for joining me. This has been really fantastic. Thanks, Cory. Really appreciate it.