Not Another Investment Podcast
Understand investing beyond the headlines with Edward Finley, sometime Professor of Finance at the University of Virginia and veteran Wall Street investor.
Not Another Investment Podcast
Evaluating Your Investment Strategy (S1 E22)
Unlock the secrets of sophisticated investment strategies with seasoned Wall Street investor Edward Finley. Discover how to leverage probabilistic methods like Monte Carlo simulations to evaluate the potential success of your asset allocations. You'll gain a practical understanding of how to visualize potential portfolio outcomes, allowing for smarter, goal-oriented investment decisions.
Dive into the complexities of stress testing and learn how it can complement Monte Carlo simulations to give a comprehensive view of your portfolio's resilience. Edward guides you through real-world scenarios, from the dot-com crash to the post-financial crisis landscape, to illustrate the importance of understanding market dynamics on your investment journey. This discussion places a strong emphasis on personal risk tolerance and the necessity of strategic asset allocation, preparing you to withstand even the most severe economic downturns.
Edward explains how to evaluate the role of active risk in your tactical allocation and its impact on long-term investment goals. And he delves into the significance of liquidity profiles, providing invaluable insights into achieving financial objectives.
As we wrap up season one, anticipate season two's engaging interviews with market leaders. Thank you for your loyalty, and continue to spread the word about the insights and knowledge shared here!
Episode Notes: https://1drv.ms/p/s!AqjfuX3WVgp8uwEkTm1rQxc8Qod-
Thanks for listening! Please be sure to review the podcast or send your comments to me by email at info@not-another-investment-podcast.com. And tell your friends!
Hi, I'm Edward Finley, a sometime professor at the University of Virginia and a veteran Wall Street investor, and you're listening to Not Another Investment Podcast. Here we explore topics in markets and investing that every educated person should understand to be a good citizen. Welcome to the podcast. I'm Edward Finley. Welcome to the podcast. I'm Edward Finley.
Speaker 1:Well, we're going to wrap up our core episodes with this one final episode on evaluating allocations. We've done a very broad, though not terribly deep, exploration of what capital markets are, why we have them, what they're designed to do, do they do it well or not do well, and what are the ways in which different players in financial markets play a role, and to try to understand a little bit about finance theory and statistics along the way. We turned our attention then to asset classes and, one by one, we tried to take apart these asset classes to understand the kinds of risks that we own as investors when we invest in those risks. And then, over the last two episodes, we tried to put it all together, talking about how do we design a strategic asset allocation that's consistent with our goals, how do we dynamically manage that asset allocation? And today we try to answer the question how did we do. How do we evaluate the allocation that we've designed and its likelihood to achieve our goals? Well, the first way that we can think about evaluating our allocation is probabilistically. So remember from earlier discussions that with a sufficiently long data set, we can derive the mean annual return, the average standard deviation or volatility, and the average correlation for any number of asset classes, and those might tell us something about what we should expect to earn for each of those risks over time, what the expected volatility or uncertainty about those returns is and what the expected relationship of those returns is. The standard deviation is the amount when added or subtracted from the mean. Tells us where 68% of the results would occur in a normal distribution, and so over a long period of time, average returns might vary from the mean. How much so? Well, 68% of the time they'll vary, plus or minus the volatility, but that over time they will converge on the mean. Okay, that's just a little reminder of some basics of probability theory.
Speaker 1:What a Monte Carlo simulation does, which is the tool that we use in order to evaluate a portfolio probabilistically, is it allows us to use those estimates of return and volatility and correlation and simulate all the possible outcomes. Now to run a Monte Carlo simulation, we have to first generate a large number of random numbers, and that's because a Monte Carlo model makes the very strong efficient markets hypothesis assumption. It makes the assumption that in the short run, prices are completely random, and so we use the standard deviation, the range of which those outcomes can vary from the mean, as our principal measure. How do we pick, in period to period, how much they will vary? We just use a random number generator in order to simulate that. So what we do is we can then use the mean of the asset classes for the overall what's called drift or the general direction of the portfolio values. We can use the volatility times, a random number between negative one and positive one. So we just get one standard deviation to simulate shocks to those returns, that is, periods in which the returns are higher or lower than average. But always, since it's random, the average of all those random numbers will be zero. So no effect on the long-term average, and then we can evaluate those outcomes.
Speaker 1:If you've got available to you the slides that I've posted on the show notes, this would be a great time to open those up. If you don't have them handy, don't worry, you can look at them later I'll be able to, I think, describe them sufficiently. So on the first slide, what I present for you is an illustration of what the Monte Carlo simulation result might look like over time, just in terms of a portfolio of 19 stocks. So if we built an allocation of 19 stocks and we tried to imagine what would be the path of those values as I just described, this graphically shows us what that looks like. For those of you who don't have the slide handy, it is a very, very, very noisy set of threads that starts out at the same point but immediately diverges, and you can really see the thick part of those threads where most of the observations sit. But you can also see threadier, less thick parts where there are outlier observations.
Speaker 1:And so why I give you that picture mental or physical, if you're looking at it? Why I give you that picture is just to give you a sense for what the Monte Carlo simulation is doing, is it's really computing many paths using these random numbers? Because remember that when we own a portfolio, the return in any one period is going to be affected by the returns it earned in the prior periods, so we don't want to just have random returns hitting the portfolio each year. We want those random returns to be hitting prior random returns and for each path to be its own explication of the future, and so that's why lots and lots of these threads.
Speaker 1:It's not a very handy way to look at it or understand it graphically, and so what we typically see is that a Monte Carlo simulation is often represented in a way like I show you on the second slide, and on the second slide we take all of those same threads, but instead of just looking at all the threads, we can simply compute what the average value of the portfolio is in each given year, and we can also then graphically represent the outer limits. That is to say, in the slide that I'm presenting to you, there are bars for years 5, 10, 15, and 20. There's a red dot in the middle of each year that shows the average value of all of those threads in each year, and then there's a black bar, and the top of the black bar represents the value in which 95% of the observations are that much or less, and the bottom part of the bar is the value of the portfolio, in which only 5% of the observations are that value or less. So you really, in a sense, get 90% of all of the possible outcomes and you see the range of those different outcomes. Well, we can then evaluate the outcomes of those portfolios against a goal, and so on that same second slide you'll see, next to the ranges for each portfolio, in each year, I've also represented just a red dot to indicate the goal. That is, if I assume that the portfolio is just supposed to maintain its real purchasing power, so it's not supposed to grow in real terms. Like who might that be for? Well, that might be for a more conservative investor who's older, not yet retired, wants to make sure that they are maintaining the value of their wealth but not taking any excessive risks. And we can compare then in each year, in years 5, 10, 15, and 20,.
Speaker 1:How does our allocation do? In theory, we're using a simulation of the future that is probabilistic, and so we might think of the mean value of the simulation as what the portfolio would look like in fairly typical markets, consistently typical markets. What that means is just saying that, yeah, the values will vary from time to time versus the long-term average, but the variation isn't very different from year to year. It's rather stable. We can also think of the top of the bar as what the value of the portfolio might look like in consistently strong markets inconsistently strong markets so that is, a prolonged period in which markets earn higher than expected returns would produce results more like that. And we can look at the bottom of the bar as the opposite, as in consistently weak markets where, essentially, markets are earning returns for a prolonged period under.
Speaker 1:And you see, the benefit of an illustration like this is that, unlike other forms of portfolio discussion, here we're not closing our eyes to the reality that over time in the future, the portfolio is not going to look like our model, it's not going to look like the strategic allocation, and that's because it's very, very difficult to predict asset returns and getting it wrong has a very big effect on outcomes. Instead, we say, well, we can't predict, but we can see how well we do against our goals and think of it in those terms. So, starting with that idea, we can look, for example, at the illustration on slide two and we can say, well, in typical markets, our portfolio seems to very ably accomplish our goal at both short intervals five years but especially at longer intervals 20 years. Okay, how do I conjure that conclusion? Well, it's because I notice that the average value of the portfolio in the simulation is always the same or higher than the value of the portfolio. That is my goal in each of those years. So we could say it's something like that.
Speaker 1:We can also talk about the probabilities of having any one particular outcome. So, for instance, we can compute in each of those years years 5, 10, 15, and 20, we can compute the actual probability that the portfolio will fail to achieve the target value. And so in the example that I presented to you in the slide, in year 5, there's a 39% chance of failing to meet our goal. In year 10, a 35% chance. In year 15, a 29% chance and in year% or a 49% chance of something happening. We don't really mean that if we run this portfolio, many, many're really doing is we're deploying Gaussian probability theory in order to say that I'm expressing the strength of my conviction. So when I say a 50% chance of failing to meet the target, I'm saying I feel pretty good about my portfolio achieving the target value because it's about a 50% probability.
Speaker 1:For some investors that may be not good enough and so we might want to tweak the portfolio to get a better result. The other thing I think is worth pointing out is that the probability of failing to meet the goal declines over time, and that tells us something very, very useful about compounding that, despite the very difficult nature of predicting asset returns. The longer and longer the time horizon is, the less likely it becomes that we'll fail to meet our goal. But notice that we don't go from a 50% chance of meeting, of failing to meet our goal, to a 10% chance. Right, we go from a 50% chance to a 46% chance, which is just to say that goals should either be designed in such a way as to be flexible or that we should seek a goal that's, in fact, richer than what we really need, so as to hedge the risk of getting it wrong. So those are the different ways in which we can probabilistically evaluate a portfolio.
Speaker 1:What are the problems or the issues? Well, we've talked about this many times before on this podcast. We know that financial markets are non-normal. We know that they don't follow a normal distribution. They are, in general, negatively skewed and, in general, have larger outliers, and there are lots of reasons for that, which we've discussed before. And so, since they're non-normal, that tells me that a probabilistic analysis is really likely not going to get it right. It's not going to get it right because a probabilistic analysis in which 50% is my probability of success is only going to be true in the case of a normal distribution. If it's not a normal distribution, then that prediction is going to be flawed. In addition, we know that we're also allocating to assets that have active risk, that is to say, nonlinear, non-normal risks, which makes it really highly unlikely that a probabilistic analysis gives us an accurate example of what the results will be.
Speaker 1:Now, most people, most professional portfolio managers, do use Monte Carlo simulation in order to evaluate portfolios, and there's nothing wrong with doing it, but I think that if you own in your portfolio an abundance of risks that are non-linear and non-normal, I think you should, in your mind, utterly discount what a Monte Carlo simulation is telling you. If, on the other hand, you own mostly stocks and bonds, you should discount what the Monte Carlo simulation is telling you, because we know financial markets are non-normal, but you don't have to discount it significantly and what you'll find is that these kinds of probabilistic models are what you typically find in any kind of fintech solution that you might access directly. So, whether that's Robinhood, betterment or any of the other service providers, but it's also what the typical bank or trust company or investment management or brokerage firm will use in order to help you, to help illustrate for you what a portfolio is designed to do. It's a useful thing to do, but it has its weaknesses. Ok, what's another way for us to evaluate our asset allocation? To evaluate our asset allocation, well, I think the way that I prefer to do it and so many others prefer to do it is something called stress test. And what stress test really means is you're not trying to use historical results to predict the future, but instead you're using historical results to help someone imagine, picture what it might have felt like to experience different kinds of environments in markets, and the more different you can come up with them, the better it is for them to sort of understand the clearer picture of that.
Speaker 1:I gave you an illustration of an asset allocation, just a strategic asset allocation. So you'll see, it owns just US large cap and US mid cap for equity risk and it owns just municipal bonds for its interest rate risk. So that's a strategic asset allocation, not tactical allocation, not tactical. And on the right side. I just give you the summary statistics from 1997 through 2023. This portfolio's design was to own about 80% equity risk, and so you'll see that our target beta is 80% and the portfolio's actual beta is just a little less than that at 79%. Again, that's just historic. That's exactly what it would have been had you owned that mix for the 30 years or so that 27, to be precise over the 30 years or so that we have this data for. It also tells you what the average annual return would have been 8.26% what the average volatility would have been about 13.6% for an adjusted information ratio of 0.6. We see that that portfolio really looks a lot like capital markets Generally. The kurtosis is 1.8, which tells us that there are higher than normal tails outliers, and the skew is negative 0.7, which tells us that it's moderately negatively skewed.
Speaker 1:When we think about stress testing, then we want to kind of illustrate for ourselves what different environments and different periods would look like, and so the first way to do that is to sort of just pick environments, and so you'll see, on the bottom right corner of that slide, I give some illustrations of different environments. First column on the left are bad environments. So the dot-com bubble crash from March of 2000 through October of 2002. The financial crisis from October of 2008 to February of 2009. The so-called lost decade from January 2000 to December of 2009. On the right side, I give you illustrations of some really happy environments the post-bubble crash expansion so that is, the expansion in equity markets following the dot-com bubble bursting. That's typically thought of as November 2001 until December 2007. Or the post-global financial crisis expansion, june 2009 to February 2020.
Speaker 1:I suppose you could also run a stress test for the period of COVID. So say, take it from January 2020 through December of 2020. And for the good moment, you can also imagine what has post-COVID recovery looked like. So there's a lot of different ways. There's no real science here. It's just trying to pick different environments that the average person can relate to. They remember what the headlines were saying, they remember what was going on, they have a sense for what the world felt like, and then we evaluate the portfolio in those different environments.
Speaker 1:So how did this allocation do? Well, the first thing that we notice about this allocation is that during those bad moments, it was not consistently bad. So, in the aftermath of the dot-com bubble bursting, the portfolio lost 8% on an annualized basis during that period. In contrast, though, in the period of the financial crisis, the portfolio lost almost a third. So it helps us get a sense. Like the dot-com bubble crash felt pretty bad, but our portfolio would have been down only 8% a year. The financial crisis also felt bad, really bad, and the portfolio would have felt equally bad down 33% more or less and I can tell you from experience that only you know, or only the investor can know, what that feels like and whether that's something that's tolerable. And so if a person says, oh God, I just couldn't possibly manage if my portfolio was down a third, that may be once in a generation, but frankly, I would rather know that before designing a portfolio so that I can minimize it to the extent possible. Whereas if the person is not so bothered by that and instead they say, yeah, that happens, I get it, but once in a generation, kind of thing, then a portfolio allocation like this one probably feels pretty OK.
Speaker 1:In the really, really good markets, you see that the portfolio does better than its average, but not lots and lots better. So during the post dot-com bubble expansion, we had annual returns of 9 percent against the long-term average of 8.3, so a little bit more, but a lot less volatility. That is, the market was very, that the market had a great deal of momentum behind its upswing and the volatility therefore declines. You can also see the post-global financial crisis expansion, which was a little less than 12 percent, so pretty significantly higher than the long-term average, with, again, much lower volatility. So stress testing in this way, that is to say, looking at particular periods and trying to understand the context and see how it feels, is one way to do that.
Speaker 1:Next slide, I give you a little bit more, because, instead of choosing any one period, you might just sort of choose to evaluate someone's reaction to time. And so it's the same portfolio and it's the same data, but instead of showing you periods of different environments, we just look at the last calendar year, the last three years, the last five years and the last 10 years calendar year, the last three years, the last five years and the last 10 years and the goal here is again to just evaluate your own if you're doing this for yourself or if you're working with a professional to help them understand your sensitivity to failing to meet your goals at shorter time intervals. So it happened that in 2023, this portfolio earned nearly 15%. That is massively higher than what our goals are, and that's terrific. But in the last three years, the portfolio earned on average 4.5%, which is half our goal, and there's a moment to just pause and think about that.
Speaker 1:So how would I feel if, over a three-year period, I was still underperforming my goal? And let that point sink in. Three years is a long time to be underperforming, and even if it has one of those three years with a massively big return, it's still not going to feel very good, and that will tell you something about whether this allocation is too risky or not risky enough. Likewise, you can look at five years and you can look at 10 years and, not surprisingly, as you get closer and closer to the full sample of 27 years, by the time you get to 10 years, you see the portfolio looks an awful lot like the 27-year period, which is another way of pausing and saying so. Could you, as an investor, feel comfortable if your portfolio were doing things that it's not supposed to be doing for as long as 10 years, because for some people, that may feel too long? No, I really need it to be doing what it's supposed to be doing at shorter time intervals, and what that tells you is you need to own more certain risks, less equities, more bonds, and that will mean that your goals have to be balanced because you won't earn as high a return overall.
Speaker 1:Overall, we can also represent a stress test and the accomplishment of our goals graphically, and so what I do is I just create a chart in which I have that same portfolio that we just looked at a moment ago and what the value of that portfolio is year on year net of spending. And then that's the orange line and then the blue line is the goal. So it's just a 4% real growth net of spending. So the blue line is nice and smooth because it's just going up at the same interval each year and then you can see the orange line and you can also see how, at shorter time horizons, the orange line is sometimes above the goal, sometimes below the goal, sometimes right at the goal, but as time passes it stretches increasingly higher than the goal. In this case it didn't really consistently get higher than the goal for really about 14 or 15 years. So that's a long time to wait for a portfolio to be doing better than our goal.
Speaker 1:We can compute the probability in that example. This is a little like a Monte Carlo simulation, except I'm looking at actual historic data. So, over that 27-year period, how often did the portfolio fail to meet the goal? And the answer is 15% of the months in that period, 15% of the time it was below the goal. That's a good way to sort of again gauge an emotional reaction to someone, to see whether this risk mix is the right risk mix. And you can do exactly the same thing with this stress test idea, but with a tactical allocation. And so here I'm showing you in that is the same equity beta of 80%, but here, instead of the portfolio being allocated just to US large, us mid and municipals, here we have a tactical allocation where we own active risk. So we have US large and US mid, of course, but we also have an allocation to growth, equity and to venture capital. We still have an allocation to municipals, but we also have an allocation to commercial real estate and an allocation to equity long-short hedge funds. So this is a good example of, then, a tactical allocation in which we're bringing active risk to the table, active risk.
Speaker 1:Well, so first, over the very long period, what's the benefit of that change in risk? As I say, we first can observe that it has slightly higher equity market beta. Our target is 80%. This portfolio has nearly 83% compared to the strategic allocation of 79%. So a little bit more equity beta and the portfolio earned over those 27 years 8.7% return as opposed to 8.3%. Okay, well, that's better than a poke in the eye with a sharp stick.
Speaker 1:What about the efficiency of that portfolio? Well, it appears it's a little less efficient. It has an information ratio of 0.5 as opposed to the strategic information ratio of 0.6. So it's a little less efficient. It has about the same non-normality. The kurtosis, or the number of outliers, has got 1.8 and the skew is negative 0.49, which is very, very close to what the strategic allocation did.
Speaker 1:We can then look at those same periods to evaluate how this portfolio might have done in different environments. I'll call out for your attention that in some cases, like the dot-com bubble bursting, the portfolio was down less than the strategic allocation, but in other moments, like the financial crisis, the portfolio was down more than the strategic allocation. So you can look at those same periods and contrast a tactical allocation with the strategic allocation. So you can look at those same periods and contrast a tactical allocation with the strategic allocation, and you can also do the same over time. So you can see that last year the allocation earned 9.4%. That's significantly worse than the strategic allocation that owns just US large and US mid. That did almost 15%. It's also the case that over the last three years, five years and 10 years the portfolio did worse than the strategic allocation. Strategic allocation 7.4%, 6%. So the last 10 years have not been very kind for this tactical allocation. Yet over 27 years it outperforms the strategic allocation.
Speaker 1:So the question that Yuan has to sort of get a sense for is this kind of allocation takes a long time to deliver on the goods not even 10 more, like 20 years to deliver on the goods. Is that something that a client can appreciate? If not, you probably don't want to own the kind of non-normal risks that I'm describing. And then I also give you a chart that shows you again the value of the portfolio, the value of the goals over time. And here you see that what the tactical allocation does is not that it creates better annual returns. Quite the contrary. We just saw that it doesn't create better annual returns. What we see instead is that it's far less likely that the portfolio will be worth less than the goal only 2% of the time whereas, in contrast, the strategic allocation 15% of the time. So one way to evaluate a tactical allocation is by doing the same thing with the strategic, looking at different environments and then looking at time in order to evaluate whether this profile fits for a particular set of goals, and then doing the same for the tactical allocation, but contrasting it to the strategic.
Speaker 1:And one final way that we can also evaluate our portfolios is we can remember to take into account active risk. Everything that I just showed you a moment ago assumes that what we own is just systematic risk, but one of the choices we can make in running a portfolio is to own these either categories of risk where managers consistently earn better risk-adjusted returns think buyout or the better managers consistently earn better nominal returns for their risk, earn better nominal returns for their risk think venture capital or some combination of the two think hedge funds because they own non-normal and non-linear risks. And so here I share with you some of the results from active risk that I take in portfolios, just to give you a sense for how it works. And you can see that in the case of my buyout manager, they do in fact earn returns that are pretty close to what their systematic risk has returned over their time period, but they do so with a lot less volatility and therefore much higher information ratio, and that's very much what we would expect from a good buyout manager. In contrast, when we look at my venture capital and growth equity manager, they earn far in excess of what their risks would earn and they do it with just about the same amount of volatility, but that means they're wildly more efficient and they have much higher nominal returns. Again, precisely what we expect from that style of investing and why this is important, of course is that then the success of the portfolio that we looked at just a moment ago those charts that I showed you will be the minimum levels of success, and the active risk simply adds to the level of success in this one portfolio. For example, and over the period that we're looking at, the managers that I owned earned 12% return but took only about 20% equity risk and were only about 33% correlated to equity risk.
Speaker 1:And then we can contrast that performance to the peer group for all equity long-short hedge fund managers that's what we looked at earlier when we were studying hedge funds was just the peer group. This is my specific set of managers. The peer group earned 6% over the same period about half as much return but took 50% equity market beta so more than two times the equity beta and was 90% correlated to US equities as opposed to 33% correlated. We can risk adjust those. We can say, okay, well, if we risk adjust the portfolio for the amount of equity risk that it earned, what's the risk adjusted benchmark? And in this case it's a little less than 3%. So the portfolio of active risk far, far outperformed its US risk-adjusted returns. And we can also risk-adjust the peer group. The peer group took 50% equity risk. We have only 19. So if we risk-adjust the peer group for that, what the peer group should have earned would be 6.92% and the portfolio did 12%. So again, we see that the active risk has paid off historically.
Speaker 1:But what we also see on the following slide are a couple of very interesting things that we know to be true about hedge fund managers, in particular equity long short managers, and that is that there are periods of underperformance. In particular, the last three years it's been significantly underperforming. The average return of my hedge fund portfolio was basically zero per year, whereas our goal, which is something like US equity returns times the amount of equity risk I want to take should have been something more like 2% a year. So that's a pretty big underperformance. But over the last five and ten years you see that the portfolio of hedge fund managers did exceedingly well relative to those goals. The other thing to notice is we talked when we learned about hedge funds about the remember we called equity long-short dynamic market risk strategies, and that's because one of the choices that an equity long-short manager makes is how much equity risk does she want to take in any given period? And you see here in real life, this hedge fund portfolio's allocation to equity risk varies pretty substantially over time. Over the last 10 years they took about, in the aggregate, 16% equity risk, over the last five, only 12. Over the last three, 23% equity risk and in the last calendar year they had negative equity risk, which is to say they were short equity risk. So it's just a good way of seeing some of the theoretical stuff in practice. I represent also for you here a slide on slide 12 showing the manager that I use for commercial real estate exposure. Again, it's just to give you a sense for the way in which we can look at the active risk here, and so the active risk could be evaluated in terms of either higher nominal returns or higher risk-adjusted returns and some exposure to non-linear, non-normal risk, and we see all of those things present in real estate.
Speaker 1:12.3% were the returns If we use a risk-adjusted net grief. You'll remember when we talked about commercial real estate, we looked at net grief, a big index of commercial real estate. We have to adjust net grief because there's no leverage used in the net grief index, but managers typically do use leverage. So we adjust the net grief for the amount of leverage of our the net grief index. But managers typically do use leverage. So we adjust the net grief for the amount of leverage of our managers. And here, when I adjust them, the net grief average return was 11.5% and the manager that I own is 12.25%. So higher nominal returns. We also see that the volatility 11.7 was significantly lower than the index 15%, so more efficient. We also see, however, that there was much more exposure to non-normal risks. The tail risk for my manager was almost four, whereas the levered neck grief index is under one. So that means lots of outliers, good and bad we can't know from kurtosis which way it goes but a much higher and significant positive skew as contrast to the index, which basically was pretty symmetric, and so here, very non-symmetric and symmetrically tilted in the direction of positive returns.
Speaker 1:And, last but not least, we can also evaluate the portfolio from a liquidity profile. Now, why do that right, like if you've got a portfolio that has as its goal long-term appreciation and value, no spending, regular additions so we're talking about a young person saving for retirement say, why should the liquidity profile matter? Well, the answer is because we know. Should the liquidity profile matter? Well, the answer is because we know, well, we think, that owning illiquid assets earns additional return. At least finance theory tells us it should. As we've talked about many times before, it's very hard to see that in the data, but at least that's the argument. And so another way of evaluating the risks we're taking in a portfolio might be to look at the liquidity profile in order, in the example I just gave, to make sure that there's not much liquidity, that we take lots of illiquidity risk, because why wouldn't we if we think we can get paid for it? In contrast, if we're talking about someone who's saving for a child's education and that kid's going to start going to school in 10 years, we would want to know how quickly can we raise cash in that portfolio in order to get a sense for the kind of risk we're taking with the client's goals. So that concludes our core episodes. A romp, if you will, through markets and investing. And so we're going to end season one on that note.
Speaker 1:What to look forward to? Well, in season two, I have scheduled a bunch of really interesting interviews with people who do something in the field of markets and investing. I have some interviews scheduled with a chief investment officer for a really big private capital, that is, private equity fund. I have a chief investment officer for a really large university endowment. I've got an asset manager who runs an interesting behavioral strategy in equity long short strategy in equity long short. I've got an interview scheduled with somebody who sources investors for big commercial real estate projects. And many, many more academics, journalists, et cetera. I think season two is going to be a lot of fun.
Speaker 1:I want to thank you for being loyal listeners of this podcast. I really do appreciate it and I want to encourage you to pass the word. Tell your friends, invite them to pick and choose some of the core episodes, even if they think they know a lot about this topic, and come back and hear what else we can say about markets and investing in season two. Thanks, and have a great one. You've been listening to Not Another Investment Podcast hosted by me, edward Finlay, in season two. Thanks, and have a great one.
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