In this episode I speak with Martin Tarlie, a member of the Asset Allocation team at GMO and spearheading their work on Nebo, a goals-based investment platform.

Martin describes Nebo as, “bridging the gap between financial planning and portfolio management,” with a key innovation being the reformulation of risk from volatility to not having what you want/need when you want/need it. In other words, constraints on both wealth target and horizon.

This reformulation of the core problem introduces a number of complications to the portfolio optimization process. For example, under classic power utility, lower volatility is always preferred. But if you’re an investor expecting significant shortfall with respect to your wealth targets, increased volatility may be something very much worth pursuing.

We spend plenty of time in the weeds discussing topics such as: the limitations of dynamic programming via backwards indication, the term structure of return variance, ergodicity economics, and portfolio selection sensitivity to utility function choices. And while these are all important details, at the end of it all, what Martin stresses most is that it’s the reformulation of the problem being solved that ultimately leads to a more pragmatic solution for allocators.

Please enjoy my conversation with Martin Tarlie.

## Transcript

**Corey Hoffstein **00:00

321 Let’s go

**Corey Hoffstein **00:06

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 **00:19

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 new found 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 in securities discussed in this podcast for more information is it think newfound.com.

**Corey Hoffstein **00:50

In this episode, I speak with Martin Tarlie, a portfolio manager at GMO spearheading their work on Nebo. A goals based investment platform, Martin described Nebo as quote bridging the gap between financial planning and portfolio management and quote, with a key innovation being the reformulation of risk from volatility to not having what you need slash want when you need flash water. In other words, constraints on both wealth target and horizon. This reformulation of the core problem introduces a number of complications to the portfolio optimization process. For example, under classic power utility, lower volatility is always preferred. But if you’re an investor expecting significant shortfall with respect to your wealth targets, increased volatility may be something very much worth pursuing. We spend plenty of time in the weeds discussing topics such as the limitations of dynamic programming via backwards induction, the term structure of return variants ergodicity, economics and portfolio selection sensitivity to utility function choices. And while these are all important details, at the end of it all, what Martin stresses most is that it’s the reformulation of the problem being solved. That ultimately leads to a more pragmatic solution for allocators. Please enjoy my conversation with Martin Tarlie.

**Corey Hoffstein **02:14

Martin, welcome to the podcast. This is going to be probably the most differentiated episode of the season, though it’s been a pretty odd season. But I do think this is going to be an exciting one going from the traditional deep in the weeds of investments to try to cross that chasm between investments and financial planning. So excited to have you on thank you for joining me.

**Martin Tarlie **02:36

Well, thanks for having me, Corey, really happy to be here.

**Corey Hoffstein **02:39

So I know that your current work, you’ve been deep in the lab working on a project called Nebo, which I’ll have you explain in a bit. But I know that your current work is sort of not your first iteration of your career. So I was hoping we could start with a little background. And you could explain how you got to where you are today.

**Martin Tarlie **02:58

Yeah, so I’ve sort of done a bit of a random walk through life in a way started out in theoretical physics, went from the sublime to the ridiculous founded a golf company, which actually, in some ways is not that dissimilar from Niebo. But then found my way into investing. I was actually a fundamental analyst for four years at an investment fund in the Chicago area. When I started, I didn’t know what a debit and a credit were. But I went to business school and worked with an amazing stock picker who taught me a lot about fundamental investing. But I wanted to combine a lot of what I had been doing on the fundamental side with the sort of quantitative background that I had from my physics career. And that’s how I ended up actually in Boston at GMO in 2007, where I really sort of started combining concepts from fundamental investing with a lot of quantitative ideas and sort of that ultimately does tie back into Nebo. So let’s maybe take a step back, before I dive into all my questions, can you quickly high level explain what is Niebo? So Niebo is a asset management, asset allocation portfolio construction platform that solves what we perceive to be a fundamental problem in the industry. And that’s really the gap between financial planning and asset management. So let’s go into that problem. So quote, here, you describe it as the gap between financial planning and asset management. How big a problem is that gap? And if it’s such a big problem, why hasn’t it been solved yet? So we’ve been out talking to advisors for now, since really 2019. And we actually discovered this gap. We didn’t know it was a gap when we started. Just a little bit of background in terms of how nivo got started. I’ve been working on this for the better part of 10 years. So back in 2013, I was actually in the equity group GMO but I’ve been doing a lot of asset allocation work and Ben Inker, the head of asset allocation at the time came down my office and said, Hey, we have a large corporate client, who they are migrating from a defined benefit plan to defined contribution. And they want to know if we have any thoughts about glide paths, maybe you want to look into glide paths. So I started looking to glide paths. And the first question I asked was, how are the purveyors of glide paths? How are they building them, and they didn’t really explain what they were doing. So that sort of forced Ben and I to go back to the drawing board and to start thinking about the risk. Right. And so I, you know, later in this podcast, we’ll get into what that means. But basically, through this process, we started thinking about risk differently. And as we started thinking about risk differently, we started engaging with financial advisors, because we had tried to apply some of these concepts in the institutional space, but for a variety of different reasons, primarily, the retirement focused plans in the institutional space, their primary consideration is they don’t want to get sued. So they’re not really early adopters, for new ideas. The other thing that we learned is that these concepts are not really about a particular portfolio, per se. They’re much more about the umbrella idea. And in order to express that idea, you need a platform. So as we started going around and talking to advisers, they were telling us that there is a gap between what they do on the planning side and what they do on the portfolio side. Now, there are luminaries in the industry who have also been talking about this problem. Eric Clark, for example, has talked about the industry has focused so much on the investment problem, it hasn’t focused enough on the investor problem. Charlie Ellis, if you go back and look at his book on investment policy from 1985, and you read chapter four, he’ll talk about the paradox that is haunting Investment Management. And that paradox is really around. How do you balance short term considerations with long term objectives? How do you know that those long term objectives are even meaningful, and that’s essentially the problem that we’re solving. But when we go out, and we also talk to RIA consultants, so their group of professionals are, what they do is they work with financial advisors, and helping them to understand how to solve their problems better. When we talk to those people. And we ask them about this gap between financial planning and asset management. They say every financial advisor struggles with this issue. It’s a big problem. You ask the question, if it’s such a big problem, why hasn’t it been solved? So everybody knows it’s a problem. And they’ve known for decades, it’s a problem. Our view is that the reason it hasn’t been solved, in part is because academic theory is not suited to the task. academic theory starts with the notion that risk is volatility. But if you start with the notion that risk is volatility, you can’t really connect the plan to the portfolio. And we’ll sort of get into the details as to why that is. But the key to unlocking this problem to bridging the gap is to think about risk differently. Risk is that you don’t have the money that you need when you need it.

**Corey Hoffstein **08:32

You described one of Nebos core innovations as redefining risk from being volatility to this idea of not having what you need or want when you need or want it. So maybe you can answer why is it so critical to redefine risk this way? How do you think about actually explicitly quantifying that concept? And what complications does it introduce into the portfolio construction process?

**Martin Tarlie **08:59

Yeah, so that’s a lot of questions there. So we’ll go through them one by one. So why think of risk this way? So it’s not that volatility doesn’t matter. Volatility is an important characteristic of a portfolio. But when you think about the risk, for example, that somebody is facing if they’re saving and investing for retirement, or they’re saving and investing for college, so sort of real world meaningful financial problems that individuals or families face, the risk is that they don’t have the money they need when they need it. And it’s just sort of an intuitive definition of risk. And so going back to when we started this problem in 2013, the glide path purveyors were not explaining how they were building glide paths, they were essentially for the most part using the adage 110 minus your age, which is sort of an old broker adage. Now, I think we learned subsequently if you really read in the fine print. Some of them are minimizing the probability that you run out of money. But we can get into the reasons why that can be problematic. So we just went back to the drawing board and said, let’s start with a fresh sheet of paper and ask the question, what is the fundamental risk that somebody saving and investing faces, it’s, well, they don’t have the money they want or they need when they want or need it. And that seems so intuitive. We wrote a white paper on it. We published it, I think, in 2014, it was one of the most downloaded papers on advisor perspectives in 2014. So I think at that time, we sensed that we’d struck a nerve. And I think part of the reason that we struck the nerve is because the definition of risk that we’re using is so intuitive to people. So really, the reason to define risk this way is to go back to why is this such a big problem? Well, when you think about whose problem are you solving, when you ask the question, what do you need? Or when do you need it, you’re placing the asset owner at the center of the problem. And that really, to us is fundamental to the appropriate approach, you’re gonna take the right approach, solve the person’s problem, who you’re focused on, and that’s the investor, it’s not the provider of investment products that you’re trying to solve their problem. You’re trying to solve the investors problem, and that gets Eric Clark’s point. What are some of the complications? Well, there’s a lot of complications. So one of them is, well, how do you quantify falling short? So when you say, what do you need? And when do you need it? Well, what if you fall short of what you need, and when you need it. And so we had to address that problem. That actually comes up a lot when we deal with very sophisticated, quantitatively oriented investors. But we solve that problem, actually, in a very simple way. But one of the complications is, and one of the ways that you have to straddle the line here is when I’ve articulated, what do you need? And when do you need it? I’ve also said, Well, what do you want? And when do you want it, and there can be an important difference between wants and needs. And you may feel very differently about falling short of a need, that you do about falling short of a want. But when you go to actually practically implement a problem, we’ve never come across anybody who has been able to quantitatively specify their utility with respect to shortfall versus their utility with respect to gains. Now, Prospect Theory has done experiments on this, we essentially utilize as a default assumption in our platform, something that is very Prospect Theory like, and it’s very simple. It says that if you fall 20%, short of your target, you feel twice as much pain as if you fall 10%, short of your target. And you’re sort of equally happy with being above your target. Is that precisely correct? For everybody? Well, we don’t really know. But nobody’s ever come back to us and said, I have such a strong view that I want to change that. The nice thing about that is it straddles the boundary between wants and needs, and what we think is a very sort of Effective, Practical way.

**Corey Hoffstein **13:23

Can you talk a little bit about how classic finance theory has solved the single versus multi period optimization problem? And why that is classic approach breaks down when we start to introduce things like wealth targets and investment horizons and asymmetric preferences.

**Martin Tarlie **13:42

There’s sort of a couple of ways to answer that start with a single period problem, right? So although we didn’t develop the underlying concepts, starting with sort of econ 101, utility based approach, it was much more risk is you don’t have what you need when you need it. What’s intuitive, this sort of idea that if I’m 20%, below, I feel twice as much pain as on 10. We sort of started with that, but then realized over time that you could actually view everything we did through conventional sort of standard econ theory. So start with a power utility function, say okay, what I want to do is I want to introduce the target. You know, I’ve got $1 Today, maybe in five years, I want $1.25. That would be an example of introducing a target. It turns out that if you start with standard power law utility function, which is by far the most common utility function used in financial economics, nothing interesting happens if all you do is introduce a target. What you also have to do is you have to introduce a symmetric preferences you have to say, I feel differently about being below the target. And then I do about being above the target. In sort of standard risk aversion terminology, your risk aversion parameter below target has to be different from your risk aversion parameter above target. So that’s a complication that single period, sort of finance theory doesn’t really deal with, because it’s always dealing with sort of the standard power law utility function. That’s the sort of standard mean variance optimization framework. When you get into the multiperiod problems, now you have a whole different host of problems, because you have a much more complicated problem that you have to solve. And even in the sort of traditional utility functions, those problems are really hard to solve. You know, the multiperiod optimization problems are really hard to solve, especially when you have a lot of assets, and you have long investment horizons.

**Corey Hoffstein **15:54

So talking about classical power utility functions, one of the things about using power utility is that lower volatility is always preferred to a higher volatility solution. But that it’s not the case when you introduce wealth targets, I guess you could say like the intuition here is if you have a wealth target that you’re trying to hit, and you have very low assets, well, you might need very high volatility, you might need to go buy lottery tickets to actually have some sense of achieving that wealth target. Can you talk a little bit about why that doesn’t violate common sense why that is an intuitive answer.

**Martin Tarlie **16:32

I think the best way to explain that is by an example. But the basic result is that once you introduce a symmetric preferences, once you care about shortfall differently than you care about surplus, you can end up in basically situations where the optimal approach is to seek out volatility, volatility can actually be a good, not a bad. Generally, this happens when your odds of achieving your target are relatively low. So I’ll give you my favorite example, real world example of how this actually played out in the real world. So Fred Smith, is the founder of Federal Express. And in the early days of Federal Express, he found himself with a real problem. The problem was, he needed to make payroll, and he didn’t have enough money. So what did he need? He needed a lot more than he had? And when did he need it, he needed it immediately. Nobody was going to lend him money. And there was no financial asset that he could invest in, where he was going to get the return that he needed. So what did he do? He went to Vegas, and he played the blackjack tables. And he won, and he won enough money to make payroll. And that’s why FedEx is delivering our packages overnight, and not somebody else. So what does that tell us? Like standard finance theory would say, Okay, what happened was, Fred Smith went from being risk averse to risk seeking, and then back to risk averse again. And that just seems implausible. What a shortfall based approach, what do you need? And when do you need an approach, if you want to minimize the risk of falling short of your target, actually, what the shortfall optimizing approach is, in the circumstances where you need a very high return in a very short period of time, volatility seeking is your best strategy. So much more intuitive, coherent explanation for the behavior that we observe.

**Corey Hoffstein **18:39

One of the key inputs to many financial planning, software’s is the capital market assumptions. Now Niebo doesn’t impose an investment schema upon the users. But I know GMO certainly offers its own in house views. And one area you’ve written about, which is highly relevant to multi period optimization is this idea of the term structure of return variance. I was hoping you could talk a little bit about what the realized term structures of return variance for stocks and bonds have looked like over time. And what are the implications of that for multi period portfolio construction?

**Martin Tarlie **19:19

Yeah, so if you take equity returns for the US for, say, the last 100 years or so. And you take the one year returns, and you compute the standard deviation, you’ll get something around 17 or 18%. Now you take two year returns, don’t annualize. calculate the standard deviation, but don’t annualized. Take three year returns and calculate the standard deviation, et cetera, et cetera. So you do that for all of the horizons, and you plot it out. And then what you do is you plot out you take the one year standard deviation, the one year that’s 17, or 18% volatility and you multiply by the square root of that investment horizon. So the square root of that investment horizon comes, if you assumed that stock prices followed a random walk, what you find empirically is that the measured standard deviations for equities will fall below the random walk line. So as investment horizon grows, it grows more slowly, empirically than what you would observe if stock prices followed a random walk. Now do the same thing for the real return on bonds. And it’s important that it’s real return, it actually turns out that bonds are a lot harder to model and understand. And we wrote a white paper kind of explaining all the nuances. But for the real return on bonds, if you go through the same process, right, you measure the return, the one year returns calculate two standard deviations for like, you know, a 10 year treasury, it’s around seven or 8%. And you do that for two year returns, etc, etc. And then you compare that to the random walk, what you’ll find is that the volatility profile for bonds rises faster than the random walk. So what that means is that if your return generating process does not account for these effects, you will overestimate the long term volatility of stocks and underestimate the long term volatility of bonds, that can have two really important implications. If you’re running Monte Carlo simulations that use the random walk hypothesis, and to our understanding almost every sort of out of the box, random Monte Carlo simulator uses a random walk engine, you will get a distorted view of the outcomes for stock heavy portfolios, you will get variances, variations in future wealth that are wider than what you would if you use something that was more empirically consistent. Furthermore, if you have a portfolio construction process that is sensitive to long horizons, you will end up with bond heavy portfolios. So these are very real, meaningful, real world implications for using assumptions of a random walk model, versus something that is more consistent with history. Now, I would add that when we wrote the white paper, it was really important to us that we not just rely on the statistical results of actually computing the volatilities. What we really wanted to do was understand the mechanisms that were driving, because those mechanisms are actually embedded in the way that we model the return generating process. So we went to great lengths to make sure that we understood what are the mechanisms that are driving these volatility profiles, we’re not really comfortable solely relying on the statistical results. For stocks, it’s really about the idea that valuations P E ratios, in some sense, are mean reverting. So you know, if you have a P E of 30, it’s likely to come down. If you have a P E of 10, it’s likely to rise for bonds, it’s actually a different explanation for the real return on bonds, it has to do with inflation and the fact that inflation is a divisor. It’s not a numerator.

**Corey Hoffstein **23:38

So historically, for complex multiperiod optimization problems, it’s been solved using this dynamic programming and backwards induction approach. And you mentioned to me in our pre call that this approach wasn’t actually feasible for the problem that Niebo is trying to solve. Can you explain that a little bit more? Why would that approach not going to be one that could solve the problem?

**Martin Tarlie **24:05

So the standard way that multiperiod optimization is taught in finance in economics is to use a dynamic programming approach, where you start at the end of the problem, and you work your way backwards? So usually, what you have to do is you start at the end, and you say, Well, I don’t know, for example, what my state variables are going to be one period prior. And those state variables are things like, what are the expected return on assets? What are the wealth levels, etc. So because you don’t know what those variables are going to be, you have to solve for every possible combination of those state variables. So if you have a meaningful number of assets, say a handful, five or six, about a half a dozen, and if you want to solve problems that are meaningfully, long, 1020 3040 5060 years, all of a sudden that number A lot of possible combinations that you have to iterate over, becomes intractable very, very quickly. So the problem just becomes essentially infeasible to solve. It’s called a dimensionality problem, the curse, right? It’s the curse of the dimensionality in the multiperiod dynamic programming approach. So we actually take a forward looking approach when we solve the problem. And I wrote a paper that shows for a very well known problem that is, essentially in the class of problems that we’re solving, that the forward looking approach gives you essentially, well, not essentially gives you in this example, exactly the same answer for the here and now portfolio.

**Corey Hoffstein **25:09

So I read that paper where you propose this feed forward, open loop solution, I believe, is what you call it. And I’m not too proud to admit that this one stretched me a little bit. It’s been a while since I’ve done that type of work, it was difficult to grok. So I’m just going to ask the easy question I should have just asked you all along. Can you explain how this feed forward open loop solution works? And then how it helps solve for that problem, where you are introducing wealth targets and asymmetric preferences and deals with this problem, this curse of dimensionality?

**Martin Tarlie **26:21

Yeah, so I’ll try. I’ll try to explain it. So as I said, in a dynamic programming approach, you start at the end, and you work your way backwards. There’s another approach, which is to say, I know where I am, right now. Let me project forward into the future. So I know how many dollars I have today. What if I invest in a particular way where those investment weights are really my unknowns, and the rates of return on all of the assets are unknown to but I have a return generating process for those rates and returns. So what I’m going to do is I’m going to project forward, and then I’m going to calculate the expected shortfall, if you will. And so what I can do is I can eliminate the uncertainty in the return generating process by essentially computing expected shortfalls. So now I have the basic characteristics of the asset, what is the expected return, and here now it’s a term structure of expected returns, I’ve automatically built in that volatility profile that we discussed, because I’ve got a return generating process that incorporates that information. And once I’ve computed my expected shortfall, I now have an objective function, mathematical objective function that where the only unknowns are, what are the weights that I’m going to invest in over time? What are the weights of my portfolio today, let’s say a year from now, what are the weights in the portfolio, but the weights in the portfolio a year from now are the weights that I would own conditioned on the information that I have today, because I’ve set up the problem, assuming that I only know the information that I have right now. And then two years from now, I calculate the weights that I would own at that point, but conditioned on what I would know now. So now, I’ve actually turned the problem into a standard optimization problem. And you can just then use standard optimization techniques, you calculate, essentially a glide path. But the future weights are the weights that you would own. If you were sitting on a desert island, and you didn’t know what it actually happened. Those are not weights that you would really invest in, the only weight that you would invest in is the weight that is the initial weight, right? It’s the weight based on the information that you have today, a year from now, you’re going to repeat the process because now you have that information, and you just repeat that process. So hopefully, like the basic point is you can reduce the problem to just sort of a standard optimization problem, but it’s how you interpret what those optimization weights represent. And the key is to understand what information did you assume in actually coming up with those weights? And when did you know that information?

**Corey Hoffstein **29:25

I’m sure we could spend a whole podcast talking about the details of that paper. It’s a heavy one. And those who are interested can certainly look it up and spend time digging through and understanding the beauty of this solution that you came up with. But I want to turn to something you mentioned, which is one of the outputs here is sort of this idea of a glide path and glide paths are an incredibly prevalent concept in our industry, particularly given the rise of target date funds over the last 20 years. But to the point you just said the glide path is what you would hold if you’re on a desert island and never updated that information. And so my question is, does a glide path even make sense other than a visually appealing output? If our investment solution is inherently path dependent?

**Martin Tarlie **30:13

Yeah, that’s a very insightful question. So the short answer is, it makes sense in a particular use case. And that use case is to assess the viability of a game plan that you have right now. So we use glide paths heavily in the Niebo platform, but we don’t think of them as a prescription for the portfolio’s that you’re going to own over time, we say, given where you are today, what portfolios would you own if the future played out, as we expect, it is almost certainly the case that the future is not going to play out as we expect. And so let’s say a year from now, when the future is played out differently, you should repeat this entire process again. However, the glide path that we build today, based on what we expect to happen can be very useful as a test of the viability of the basic plan that you’re putting in place. And that basic plan boils down to, what do I need from my financial assets? And what constraints do I need to impose on those, and the shortfall is actually measured relative to the wealth that’s implied by compounding at those rates, conditioned on the constraints that account for risk tolerance, etc. So it’s not that we’re throwing the baby out with the bathwater with respect to risk tolerance, that’s an important consideration. But it has to be balanced against the return needs and the return considerations of what the asset owner needs in order to achieve their goals.

**Corey Hoffstein **31:51

One of the ways I really like to get an understanding of a process is by trying to think through what happens at the extremes, I think about sort of the boundary cases, it gives me limits to work within. So I was hoping you could walk me through how this multi period optimization works under sort of two extreme scenarios, the first being an investor who has far more money than they’ll ever want to spend, and the second being an investor who has far less money saved than they’ll ultimately need.

**Martin Tarlie **32:21

Yeah, so those are really interesting use cases. And the first one is really interesting, because in our extensive conversations with advisors, we run across people who have far more money than they need. And one of the things that’s really interesting about that is how much money you have relative to what you need depends a lot on how much you spend. So you don’t have to be an ultra high net worth person, in order to have much more than you need. It really is relative to your spending. But to answer the question about how do you address the problem of somebody who has far more than you need. So there’s sort of two schools of thought in the financial planning world. One is a goals based and often times that’s framed in this is how much I want to spend. And this is how much money I want in the future, just as an example, this is how much money I would like to leave for my heirs would be an example of sort of a future goal specified in wealth. There’s another school of thought, which is, well, I don’t really know how much I’m going to want or need. Tell me what’s possible. And in our platform, we’re actually agnostic between the two. Because the possibilities based approach, which is really the approach that’s relevant for the person who has far more than they need, or far more than they might want, is really to ask them, Well, here’s the range of possibilities that are available to you. And here are the trade offs that are involved in achieving those possibilities. So now, you are engaging with the asset owner questions about what is it that they want, and if this is what you want, or this is a possibility that you think you might want to achieve? Here are the trade offs that you’re going to have to make relative to another choice. So we’ve spent an enormous amount of time in really helping to frame the choices that are available to people. And ultimately, those are very subjective choices. What Niebo can do is it can answer the objective questions, given a set of preferences, what do I need? And that really, we boil that down to what rate of return Do you want or need from your financial assets and then we build portfolios that seek to minimize shortfall relative to compounding at that rate. But it’s about giving them a rating the choices of available to them. And we handle the objective to allow them to make the subjective choices about what are the appropriate trade offs. So that’s the first one, it’s really becomes a possibilities based approach. Here’s what’s possible. And here are the trade offs involved. The second one is, you’re spending way too much, or you anticipate and spending way too much, and what the analysis and Nebo would really come back with his says, the return that you would need is simply not feasible. If you need a 20% rate of return above inflation in order to achieve your goals, that’s simply not feasible. And the only way that that would be achievable, is really to go to Las Vegas, and do what Fred Smith did. But no financial advisor is going to recommend that. So because of the way that Niebo frames, really the game plan for the investor and that target compound return is expressed in terms of return units becomes very clear very quickly. When a plan is unfeasible, then the conversation reverts back to okay, this is not an investment problem. This is either a saving or a spending problem. So it reframes in very stark ways, the conversation that the advisor has to have with the asset owner

**Corey Hoffstein **36:22

One of the areas that financial planning can get very complicated is when you move from talking about an individual to talking about a family unit. So for example, I don’t really care if I have a shortfall in my monetary needs if I die early. But my wife certainly would, right and my death would introduce interesting dynamics, I have life insurance, for example, which would pay out but suddenly there might be a significant drop in family income. How do those play out in the Nebo process.

**Martin Tarlie **36:53

So Nebo is not really designed to reinvent financial planning. What Nebo is really designed to do is to integrate with existing financial planning systems. So as an example, the adviser would go through their standard financial planning process, the output of that process, which would incorporate the conversations, like you just articulated, are a series of cash flows over time in various buckets and at various times, et cetera. Nebo takes that as an input, and then allows the adviser to essentially take that to the next level, and go through the process of assessing what is the appropriate portfolio to own conditioned on having gone through that financial planning process. So this is a really a core aspect of Nebo. In the beginning, we started talking about Niebo is really bridging the gap between financial planning and asset management. We’re not trying to reinvent financial planning, we integrate with existing systems, like E Money, Money guide pro right capital, we take the outputs from those systems, those become the inputs into Niebo. We do our portfolio construction, or multiperiod shortfall, portfolio construction. And we’re not trying to reinvent, rebalancing and trading and so we take the outputs from that. And we send that into the next stage in the advisors process.

**Corey Hoffstein **38:26

In recent years ergodicity economics has seen a significant rise in popularity, largely led by the work of Ole Peters. The core argument there being that utility theory is really just a bandaid applied to a poorly formulated problem. Specifically humans exist in a world where our decisions compound over time, you know, we don’t have the luxury of averaging over multiple parallel worlds. And this seems to me to be really highly relevant to your work around Target wealth and horizon are ultimately key constraints of the problem. So my question to you would be you spent a lot of time thinking about the utility function itself. Well, how important is that utility function ultimately to the solution?

**Martin Tarlie **39:11

I mean, the short answer is if you change the utility function, you change the portfolio, given a sensible utility function, however, that is sort of consistent. And as I explained before, I don’t really think about the way we formulate the problem as the utility function per se. I think about it, the risk is that the asset owner doesn’t have what they need when they need it. And if they fall short, they feel a lot more pain than if they have access. When you read, Oles work, and I really enjoy reading his work, but he wrote a paper with Murray gell Mann in 2016. And you know, it sort of anybody who’s has experienced in the physics world is very familiar with the name Murray gell Mann, He’s a Nobel Prize winner in physics and very much a leading light. At the end of that paper, what they say is, the most important thing is to understand your objective. And that’s essentially what Nebo is all about. The objective is minimizing the risk that you don’t have what you need when you need it, this specific details of how you formulate that it has to be practical, it has to be sensible. We’ve spent years testing our portfolios on real world intuition. But that objective is very intuitive and very understandable. Ole Peters and Murray Gelman, in their paper, they sort of recommend maximizing the growth rate of wealth. Now, that is certainly meaningful objective for some people. But I don’t think it’s a meaningful objective for a lot of people, for example, you can show that that will maximize your expected wealth, but you need a really, really long horizon to achieve that, well, what if you have a five year horizon or a 10? Year or you have a 20 year horizon? Or 30? How does that change what you should do? The maximizing the growth rate of wealth doesn’t really address those real world concerns. So we completely agree with the premise that it’s the objective function that matters. It’s just that their particular objective function doesn’t necessarily apply to all people.

**Corey Hoffstein **41:34

The utility function that you’ve embedded into Nebo exhibits this asymmetric preference with respect to target wealth, do you feel in a linearly increasing amount of pain for every dollar, you undershoot your wealth target, but no added benefit? For every dollar? You overshoot? How much does the optimal portfolio solution change? If we start to tweak that assumption? If we maybe say instead of perfectly linear amount? It’s a nonlinear amount of pain or for overshooting, you actually have a minor benefit? How much does the actual portfolio result materially differ at the end of the day

**Martin Tarlie **42:13

It depends how aggressively you change the utility function. The beauty of the linear one is it does a really good job of balancing wants and needs. I recall in the early days of going out and talking about this approach, quantitative investors would really object to this sort of linear based approach. And they especially objected to the fact that it doesn’t give you any benefit for being above target. So we would ask them, okay, draw me a utility function. Now, if you read the paper that I posted in 2016, we actually, it’s very general, you can have a huge variety of shapes of utility functions. But we’ve never come across anybody that had a refined idea about what the shape should look like. But I want to also go back to this point about not caring about wealth above your target. It’s not that you don’t care about return, the return is embedded in where you set the target. And that’s what you should be focusing on you shouldn’t be focusing on where do I want to set my targets? Are these reasonable targets? And how do I feel about that target? Rather than focusing on the nuances of what the particular shape would be? There’s a wide variety of shapes that are reasonable, as long as they’re not too extreme, the shape that we’re using, like, nobody’s ever really come back to us and said, Can you really change that shape, because it doesn’t make a lot of sense to me, it’s very consistent with sort of human behavior. And there’s a very strong behavioral aspect to what we’re doing. And we sort of have James Montier is heavily involved in what we’re doing. And he’s, he’s sort of been very active in the behavioral finance world. So the pleasure and pain that you feel is captured by what we’re doing. And that’s really all we’re trying to do. We’re not trying to over engineer the problem.

**Corey Hoffstein **44:13

We spent a lot of this conversation in the theoretical weeds, I want to sort of take a step back as we get towards the end much more towards the practical. So for advisors who may be listening, what are the key considerations for setting up the clients planning problems so that the Nebo machine can ultimately solve it?

**Martin Tarlie **44:34

Yeah, so we talked about this a little before the standard workflow is they do what they’re doing on the financial planning side. So for example, if they’re using a financial planning tool, like the money money guide pro, right capital, they would continue to do that. And then basically, the output of those are the cash flows. Now, one of the things that we’ve learned over the years is that there are a fairly significant number of clients who really are unwilling to go through a full financial planning process. And so we’ve built into Niebo, what we call light financial planning. So if you have a client that doesn’t want to go through the full financial planning process, because that can take up to 10 to 15 hours of their time to fill out all that information, you can get very far by just inputting roughly how much am I saving? Roughly? How much am I going to be withdrawing from my portfolio, and really use that as a starting point. And that can go quite far. But the basic input at the starting point are the cash flows. Now, what normally happens in a conventional process is there’s all the financial planning work, then there’s a question about, okay, how risk averse are you so you give the client a risk tolerance questionnaire, and that risk tolerance questionnaire generates what’s called a risk score. So that risk score is usually scaled between zero and 100. Where the risk number represents what weight in equities could you tolerate. So if you had a risk score of 60, your max weight in stocks would be 60%. Generally, the way the process works is you go through a financial planning process, you do a risk tolerance questionnaire that says my maximum weight in stocks is 60. Then what is done are Monte Carlo simulations where you take that 6040 portfolio, and you run your Monte Carlo simulations, which are based on random walk assumptions. And then you ask a very simple question, what is the probability that I ran out of money, and if that probability is lower than a certain threshold is usually called the probability of success. If that’s higher than the threshold, let’s say 80%, then you’re basically done. The problem with that approach is that if you go back to the plan, and you change your cash flows, but you don’t change your risk tolerance, most of the time, your probability of success is not going to cross the threshold. So it’s in that sense that the standard process, the financial planning is very disconnected from the actual portfolio that you own. Because the portfolio that you own is really driven by the risk tolerance questionnaire. And it’s well known that that risk tolerance questionnaire is not that stable, right? It can depend on what you had for breakfast, it could depend on the weather, if you come back six months from now, the answer could be quite different. So the way we do it in ebos, we start with the same cash flows. But what we do is we really focus on what is your target return? What return do you need from your financial assets? What passive wealth does that imply? And then we build portfolios that minimize shortfall relative to that path of wealth. Now, what happens if you change the financial plan, if you change the cash flows that will change the target return that you need? And it will change your portfolio? How do we deal with the risk tolerance questionnaire, as I mentioned earlier, we don’t throw that away. We incorporated via the constraints that we impose when we do the optimizations. So essentially, what we’ve done is we take the client profile, and what we’re doing is elevating what we call the return profile of the client. And that return profile is really expressed via the target return that the client wants or needs.

**Corey Hoffstein **48:45

So I think you may have answered this, but I want to ask it anyway, just really try to drill it in and come back full circle to that idea of the gap that you’re trying to solve for, that we talked about at the beginning of this conversation. The inputs to Nebo that you talk about aren’t really inherently different than the inputs you have to a standard financial planning process. They are much the same. The key difference being that reframing of the problem, right, talking about things from a wealth target over time perspective, my question to you would be how does that reframing ultimately unlock a more practical solution for advisors?

**Martin Tarlie **49:25

Yeah, I mean, it goes back to where we started is that, in our opinion, the reason the gap exists is because we’ve been framing volatility as risk. And when you frame volatility as risk, your primary focus is on the risk tolerance questionnaire, and that drives the portfolio. But as I said before, that means that the portfolio is really disconnected to a large extent, from what’s being done on the planning side. So the key to unlockDuring all of this is to really think about risk as not having what you need or what you want when you need or want it. And that’s the essential conceptual idea that really unlocks this problem.

**Corey Hoffstein **50:14

So maybe to put a bow on all this, you can walk us through an example of how the same financial planning inputs put into a traditional process could lead to a dramatically different output from the portfolio you arrive at versus what you might arrive at and going through Niebo.

**Martin Tarlie **50:35

Yeah, so that’s a really, really good question. And so one of the things that you can do with Niebo is, we can take the output from sort of standard financial planning systems. And we can reverse engineer what the target returns are. And then we can ask what is the optimal shortfall portfolio today, given those target compound returns and sort of minimizing the shortfall relative to the wealth implied by those. And then we can compare those two the portfolios based on the risk score. And we actually did a case study this morning, with a client, where the client had a risk score of 60. We reverse engineered what the target returns were based on what was coming out of the financial planning system. And that was a target compound return of 2%. Net of inflation, taxes and fees. And it turned out that the optimal shortfall portfolio based on their horizons, and given those target returns, had an equity weight that was about 40%. So if the advisor is putting the client into that 6040 portfolio, and in fact, the 2% is the right target return, they’re taking more volatility risk than they need to. And the reverse situation is equally possible as well. So it’s almost happenstance if they happen to match up. But with Niebo, we take care of both we take care of what is the optimal shortfall accounting for both the target return and the risk tolerance as expressed via the constraints?

**Corey Hoffstein **52:27

Well, we’ve come to the last question of the episode. And it’s the same question I’m asking all my guests this season, I asked you to pick a tarot card that would inform the design of the cover we’re creating for this episode, and you picked the card for strength, hoping you could explain why that card resonated with you?

**Martin Tarlie **52:47

Yeah, so this is a great question, and don’t really expect it. But you know, we’re doing a new venture. And we are proposing a new idea. And that takes a lot of inner belief on our part that, you know, we are putting something out there that we actually believe is the right way to do things. But the flip side of that sort of inner resolve is doubt as to whether or not people are going to be actually receptive for the ideas. And so there’s sort of this inevitable tension between constant nagging doubt that Oh, my goodness, are people going to actually buy into this concept, and then the unwavering belief, that boy, they really should.

**Corey Hoffstein **53:38

Well, Martin, this has been a great chat. I appreciate you joining me if people want to learn more about your work in Nebo, where can they find it?

**Corey Hoffstein **53:46

So we have a website, Nebo hyphen, it’s gmo.com. And we have a lot of white papers on there. So if you just search Nebo by GMO, any standard search engine will find it and there’s a lot of material on there, white papers, videos, webinars, etc.

**Corey Hoffstein **54:04

Wonderful. Well, thank you.

**Corey Hoffstein **54:05

Thank you really appreciate it, Corey