Building a Better Strategic Asset Allocation

00:00:06 [Frederik Middelhoff]
And here you would transition away of looking at asset classes only, but rather take a lens of risk in your decision making.
00:00:16 [Vincent Weber]
Hey everyone, and welcome back to Resonanz Spotlight, where we unpack investment strategies that matter. I'm Vincent Weber, and today we are diving into something that's at the core of every institutional portfolio, whether you're a family office, a pension fund, or a foundation, strategic asset allocation. or just saa it sounds simple right decide where to put your money long term but once you get into the details models risk factors spending rules governance it gets tricky fast so how do you actually go about building an saa that sticks to help answer that i'm joined by dr frederick middlehoff He leads our work on quantitative strategies, custom portfolios, and quantitative manager research. He's also spent years in academia studying volatility, and he brings a great balance of theory and real-world practicality. Frédéric, great to have you on.
00:01:16 [Frederik Middelhoff]
Many thanks, Vincent. Great to be on the show. I'm very excited to join you today in this episode. And SAA, yeah. Pretty interesting topic. I would say probably something that never gets old because more or less every institution has to deal with it and has to face it.
00:01:33 [Vincent Weber]
So, all right. So let's ground ourselves. So we all throw the term around strategic asset allocation, but how do you actually explain it in plain language?
00:01:44 [Frederik Middelhoff]
Yeah, very good question, Vincent. If you ask me, an SAA is probably something like your long-term roadmap. It's more or less how you want to split. your capital across different asset classes, for example, such as equities, bonds, alternative, real assets, basically based on your objective, which you're trying to achieve with your investment. And it basically sets your baseline, how you should invest, and probably not over a shorter period of time, but rather a longer period of time, let's say over the next 5, 10, 20 years, maybe so, which reflects your goals and constraints and your risk appetite in a consistent way over time. time and helps you guide your investment decisions.
00:02:28 [Vincent Weber]
So is it kind of like your investment philosophy turned into numbers?
00:02:32 [Frederik Middelhoff]
Exactly. More or less, it's exactly this. And I would say the best ones are clear, intentional, and not constantly reacting to market noise. So giving a long-term perspective on your SAA.
00:02:45 [Vincent Weber]
So let's break it down. There's more than one way to set a strategic allocation. In fact, you've written about the six distinct approaches. So could you work us through them?
00:02:56 [Frederik Middelhoff]
Yeah, happy to do so. Let's probably start with the most familiar approach or maybe even simplest approach. It's mean variance optimization. So this is rather classic. You have your expectations on returns, on volatility and correlation, then put everything into a simple model and the model spits out the optimal portfolio.
00:03:18 [Vincent Weber]
Okay, so that means what's the most return I can get?
00:03:22 [Frederik Middelhoff]
for a certain level of risk is that correct yes correct it is elegant because it's it's it's simple but it's also super super sensitive so if you change one input slightly for example assume you have a different expectation about emerging markets and then out of the sudden the the model wants to go all into emerging markets and that's clearly something you probably in real life want to avoid when designing your saa
00:03:52 [Vincent Weber]
So how do you deal with this issue? Is it just about putting constraints on the models?
00:03:59 [Frederik Middelhoff]
Yeah, constraints could be one element to help guide the model to stay in line. But in the end, like every constraint in the end is like human judgment, right? You do the human override in the end. And you should probably see this only as a tool which helps you guide your investment decision, your essay, rather than something set in stone. Touching on the second approach, which is the factor-based allocation, which is also something very popular. And here you would transition away of looking at asset classes only, but rather take a lens of risk in your decision-making. That is, you look at risk drivers. This could be things such as an equity beta, the duration risk, credit risk, or inflation. And then you tailor.
your portfolio such to fulfill your objectives right so it's like asking what what really move this asset and what risk am i actually taking exactly exactly the reason is basically if you look at for example let's take a fund and you look at two funds and they might look very well diversified. But as soon as you start looking at risk, you might realize that under the surface, most of those risks are actually driven by equity risk. And so something which looked diversified in the first place turns out to be rather highly correlated from a risk perspective. And that's probably something in that approach which you want to avoid in that case. Maybe going to the third concept, which is liability-driven investing. There, it's less a question of how you invest or your risk return profile.
It's more focusing on the liabilities you have that is matching your future obligations. So if you need to pay a certain amount every year, you basically align your portfolio to exactly cover and match that.
00:06:00 [Vincent Weber]
Right. So this one is less about beating the market, but more about making sure the money is there when it's needed.
00:06:08 [Frederik Middelhoff]
Exactly. So it's especially important probably I would say for pension and endowments where they have steady spending needs. and so that could be a helpful tool to think about the liability risk in their portfolio and try to match that over a longer period of time and then maybe something which is a technique which can be blended with the other aspects which i just elaborated on it's stochastic modeling or monte carlo analysis stimulation it is where you really have the chance to do inference of your model that is you would simulate thousands of possible future path in your model and then so the question is not that you only ask what's the average return it says how likely do i get there or how often do i get there there and what happens if a crisis
hits so this one sounds technical but probably a powerful way to stress test or at least pressure test your plan Yeah, it sounds technical. It is quite technical, but I believe it's a powerful tool, as you say. It really helps committees to see the range of possible outcomes out there and then to think one step in advance and think about. probabilities for different scenarios, it really helps to get a better feeling of the potential outcomes of my investments and also the risks which are associated with it. And so that's a technique which you can basically apply. And then there is also a famous model which we see with endowments, with pensions out there quite often, which is the endowment model.
probably made famous by yale which puts more weight on private equity real estate real assets and hedge funds and has rather long
00:08:06 [Vincent Weber]
-term mindset in terms of investing and less such a liquidity focus in the portfolio construction right but is this one like really like a model on par let's say with mean variance or ldi or because this sounds to me more like philosophy to maybe a long-term view, to be very long-term or to lean heavily on alternative assets. What's your take on that?
00:08:34 [Frederik Middelhoff]
Exactly. I would say I would agree definitely. It's more a philosophy, which you, however, see out there quite often nowadays. So it's more a philosophy how to construct portfolios. It's less of a tool, but it influences a lot of SAAs from institutions at the moment, I would say.
00:08:54 [Vincent Weber]
Right.
00:08:55 [Frederik Middelhoff]
and speaking of tools probably one last tool which is quite quite famous and easy to understand is ignoring any risk return expectation but only focusing on the risk side let's say you have different asset classes and then you equally split your risk across them Say, for example, you want to invest in equities and bonds, and bonds are less risky than equities. So you would allocate more to bonds than you would to equities, such that the overall risk levels are the same in your portfolio.
00:09:31 [Vincent Weber]
Right, which often means using leverage to bring the low-risk asset up to speed. Is that correct? Exactly.
00:09:39 [Frederik Middelhoff]
And this is where execution really matters. The math makes sense, but it has to be well managed in the long run and to make sense for institutions.
00:09:50 [Vincent Weber]
Right. So maybe a more critical take on this different approach. Aren't all these approaches just like variation of the standard mean variance framework?
00:10:03 [Frederik Middelhoff]
at the end it's pretty much pretty much it right you could see say it's either of variation so just like one specific outcome if you for example take a look at the diamond model or it is a technique to enhance your your modeling in case of monte carlo analysis right and so it's also when it comes to to implementing such a thing it's never just pick one of those approaches run it and then you're good it's more or less blending everything together and what really matters there is that you need a good governance can basically make sure that different stakeholders actually understand the outcomes of those different approaches and explain the risk which is embedded with the solution.
second you probably in your team need the resources of doing all this whether it's it's more quantitative whether it's more qualitative to get good outcomes and then of course and that's probably the most major important aspect is you need to be clear about your objectives that is are you focused on long-term growth are you focused on stable income on capital preservation so setting those objectives really is the most important thing, which then leads to the outcome of your SAA. It's not so the technique helps you getting there, but being clear of your objective, what you want to achieve with your allocation. And that's something every committee member has to face.
00:11:36 [Vincent Weber]
So let's focus a bit on mean variance optimization, because it's really such a foundational tool, but it can be dangerous if misused.
00:11:47 [Frederik Middelhoff]
absolutely agree so i would say it's it's famous because it's so simple and easy understand right you're optimizing your return conditional on risk right units of risk but if your input parameters or assumptions are off the entire model will lead useless results biased results and so a better way to tackle this if you want to use mean variance optimization it's probably Make sure to be conservative and realistic with your assumptions, which you put into the model. Apply additional constraints. These could be such as minimum or maximum weight for single investments. This could be a liquidity filters and so on and so forth. And then don't stop at this. So stress test your results. If the results look off in terms of stress test, go back and do this.
00:12:45 [Vincent Weber]
repeatedly again well i think the last part your last point is very important so it's not a one and done but it's it's really an iterative process exactly so the output of this mean variance
00:12:56 [Frederik Middelhoff]
optimization isn't the answer it's just a starting point for additional conversation and then probably you also want to have a look from economic perspective does this make sense what the model is suggesting me or is it something which is completely unexpected and then probably this is something where you want to be cautious all right so in practice what does it look like to combine some of these approaches yeah so let's Start with a mean variance optimization again. So let's assume that's our baseline model. So we start with that and then we would get an optimized portfolio. And then let's say, okay, we really want to integrate our risk perspective. So you could run the factor analysis on the proposed exposure.
And maybe you realize then that your equity beta is too high and want to reiterate. this again this optimization also what you would do is you should run the monte carlo simulation for for for your portfolio that is basically allows you to see how your portfolio holds up under stress especially if you have spending rules for example and the monte carlo simulation then gives you also a way to do Sensivity analysis, not only stressing your portfolio, but also assuming, okay, what happens if I now change my expectation for emerging markets, the example which we had before, how sensitive does my model react to it? And then if you're a foundation or pension, you also carve out liability matching portfolio. and especially match that.
And then depending on your governance and risk appetite, you might tilt towards the endowment style of investing or use risk budgeting to make sure no single asset dominates the portfolio. And that's pretty much this process, which is like interacting with each element down the road until you have your agreed final SAA.
00:15:01 [Vincent Weber]
So it's kind of like building a layer cake. each framework tends to add something useful.
00:15:09 [Frederik Middelhoff]
Exactly, exactly. This is pretty much it. And key is clarity, I would say here. And you want to understand why each piece is there in your cake in the end, right? And so those tools really help you to get a better understanding why your cake looks like it does.
00:15:27 [Vincent Weber]
All right, Frederik. So before we wrap up, so give us a few key takeaways.
00:15:34 [Frederik Middelhoff]
Yeah, I would say probably no single framework is perfect. You want to blend them together based on your goals and your constraints. Something to really be cautious about is that your assumptions matter. If your assumptions are off, your results will be off, no matter which model you're taking. You want to stress test wherever possible to get a sense how your portfolio might look like if really something is going on in the world out there you probably should focus on the process so it should be the process of getting there should be transparent explainable at each state of designing the saa because basically this helps also the different stakeholders to to build trust in the saa and then finally probably something which we shouldn't lose sight of is that it's not static and not set in
stone. So you probably want to revisit your SAA from time to time to really make sure you don't miss out on any new market environment and changes and to make sure that your SAA can still fulfill the goals which you have defined in current market environments.
00:16:56 [Vincent Weber]
Frederick, thanks so much. So that was thoughtful, practical, and honestly, a great refresher for anyone on an investment committee. To our listener, if you want to dive deeper, head over to resonancecapital.com. We've got a lot of more content on asset allocation framework, implementation tools, and practical insight for institutional investors. Thanks again for tuning in. I'm Vincent Weber. This is Resonanz Spotlight. Catch you next time. Thanks a lot.