How to Assess Risk in Your Portfolio with Brad Kasper
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How to Assess Risk in Your Portfolio Show Notes
In my 32 years in the financial industry, I’ve learned one thing. Unless you’re working with a great financial planner, you likely don’t know exactly how much risk you’re taking on. There are countless investment products out there with different risk and return profiles, a number of ways to meaningfully assess risk, and more opportunities than ever before for retirees to be either over-conservative or overextended.
Today, I have Brad Kasper, the president and founder of LSA Portfolio Analytics returning to the podcast. If you haven’t listened to Episodes 10 and 11 of The Guided Retirement Show, where we discussed how the economy affects the marketplace and what you should know before building a portfolio, I highly recommend doing so before digging into this episode.
Today, Brad is here to dive deep into the nature of portfolio risk and what it really means. You’ll learn how to quantify risk, how to measure it, and what it really means as you build a portfolio that will help you live the retirement you’ve always wanted.
In this podcast interview, you’ll learn:
- The two dominant theories of risk analysis in financial planning, their biggest flaws, and how modern portfolio theory contributed to the Great Recession of 2008.
- How to blend investment products to create a mixture of equity and fixed income to meet your unique needs.
- Why failing to understand risk inevitably leads to making emotional decisions – and why this will almost never work out in your favor.
- Why Brad calls capitulation the “kiss of death” for any type of diversified strategy – and how it, fear, greed, and overextension can wreck your portfolio.
- The reason so few retirees can build effective portfolios on their own – and why having clear objectives is so crucial to long-term success.
- “When I think about mean-variance optimization or even modern portfolio theory, the biggest pitfall that you have is it’s all rearview mirror-looking. We’re looking at historical returns, we’re looking at standard deviations, and we’re looking at correlations over time.”
– Brad Kasper
- “If you have good positive spread, where you’re capturing more the upside versus downside over time, that’s where real wealth accumulation can happen.”
– Brad Kasper
- LSA Portfolio Analytics
- Episode 10 – How the Economy Affects the Marketplace
- Episode 11 – Things to Know Before Building Your Portfolio
If you can’t listen now, no worries! Subscribe on your favorite podcast app or by clicking the buttons below. Once you’re subscribed you can download each episode and listen at your leisure and never miss an episode.
[00:00:11] Dean Barber: Welcome to The Guided Retirement Show. I’m Dean Barber, Managing Director at Modern Wealth Management. And we’ve got a repeat guest with us today. We’re going to be talking with Brad Kasper. He is the President and a founder of LSA Portfolio Analytics. We did two episodes with Brad in Season 1. We didn’t get a chance to listen to those. I encourage you to go back. It’s Episode 10 and 11 of Season 1 of The Guided Retirement Show. Today, we’re taking a deeper dive with Brad. We’re going to really dive into portfolio construction and we’re going to talk a lot about risk, how you quantify, how you measure risk, and what it really means to you as you’re building your own portfolio. Enjoy the show.
[00:00:54] Dean Barber: All right. We’re back here on The Guided Retirement Show with Brad Kasper. Brad, we had you on here for Season 1. We had Episode 10, Episode 11. Episode 10 was all about how the economy affects the marketplace. Episode 11 was things you need to know before building your portfolio. If you haven’t listened to either one of those two podcasts, I encourage you to get back to Season 1 of The Guided Retirement Show and listen to those two podcasts. You can do it in whatever order you want to.
There are some things where we lay some basic groundwork in those particular podcasts, Brad, where we talked about the basics. Today, I want to dive deeper with you on portfolio construction, but more importantly, I want to talk to you about volatility, about risk, and more importantly about understanding the risk that you have in your portfolio. Because I’ve been in this industry now for 32 years and I think by and large unless somebody is really working with a good financial planner, I don’t think they grasp the amount of risk that their portfolio is taking.
So, there are two different theories out there. One that is widely accepted in the industry, which is modern portfolio theory and I’ll let you explain what modern portfolio theory is here in a minute and what the flaws are to it. And then there is another called the fat tail risk analysis and that sounds kind of weird. That one’s not really widely known. I think that there are some very large institutional players that follow the fat tail risk analysis. But if you look across the financial planning industry as a whole, we see that virtually every financial planning program out there relies on modern portfolio theory. So, welcome and why don’t you start us out with what is modern portfolio theory? Why is it so widely accepted? And then we’ll talk about the flaws of it.
[00:02:54] Brad Kasper: Sure. And thanks for having me on again. These are always fun to do. Let’s take a step back first, though. Before we dive into modern portfolio theory, why do we need these statistics? If I’m going to be an…
[00:03:08] Dean Barber: To understand risk.
[00:03:09] Brad Kasper: That’s right. If I’m an investor and I have a portfolio that I’m constructing, and we spent a lot of time on the last two episodes talking about identifying what your objective is, what you’re already trying to accomplish with that portfolio, well, just as in sports, if I’m building a team, I want to be able to measure the success of that team over time. And so, in the financial world, we utilize a theory that’s called modern portfolio theory, which is a subset of statistics that we can lean on to help us identify different characteristics within a portfolio.
In a number of different characteristics, you’ve highlighted probably the most important one and that’s risk. Why is risk relevant? Again, back if we circle back to why do we do full reviews of portfolios? Why do we do the deep dives? Well, if I’m a conservative investor, I need to know what’s my worst-case scenario. What’s my max drawdown? What kind of risk am I taking that could disrupt whatever the objective is that I tried to identify for that specific strategy?
And so, modern portfolio theory allows us to step into statistics like standard deviation. Standard deviation measures how much risk on average are we taking on within a portfolio over time. We can dive into data points like beta, which measures volatility against the benchmark, and that’s a whole another show and conversation. We get into stats like alpha. Each one of these have a very meaningful role that we can use as data points to understand what kind of risk a portfolio is taking on. And I think, Dean, one of the biggest concerns that a lot of investors have is. They have no idea how much risk they’re currently taking within a portfolio.
[00:04:50] Dean Barber: That’s it. And so, this whole idea of modern portfolio theory, I think what it tries to do is it tries to get rid of the averages, right? So, if you go back and look at a portfolio that says over the last 20 years this portfolio or this fund or whatever it is has averaged 8% a year, that’s the average. And you might have three different investments that all say they averaged 8% a year. Well, the question is, how did they get that 8%? And what was involved in it? What kind of risk was there in order to get to that 8% average return? And that’s where your term standard deviation comes in. In other words, how far from that average did the returns deviate in a given year?
[00:05:37] Brad Kasper: Again, just kind of build on that a little bit, so let’s just assume we were investing in something that had a 10% average annual return, okay, this is all hypothetical and that investment had a 20 standard deviation. So, what this is suggesting within modern portfolio theory is most of the time those return patterns for that 10% average annual return hypothetical investment is going to be in the range of one standard deviation of its average.
[00:06:08] Dean Barber: So, it’s plus or minus 20% from the 10%.
[00:06:11] Brad Kasper: So, let’s do the math. On the high side, you’re looking at about a 30%. return. On the low side, we’re about a negative 10% return.
[00:06:17] Dean Barber: Right. Which a lot of people going, “I could live with that.”
[00:06:20] Brad Kasper: Yeah. But unfortunately, that doesn’t explain the full experience that you might have with that investment. So, what modern portfolio was built to do is really get us to about a 95% understanding of probabilities of outcomes, which means there is a smaller subset of outcomes where that investment might operate within two standard deviations of what’s reported.
[00:06:44] Dean Barber: So, you’re negative 30 to positive 50.
[00:06:46] Brad Kasper: Negative 30 to positive 50. Well, again, if I’m building a portfolio that is very much focused on protection and down market movements, that might be an event that says I can’t stomach being down 30%. So, understanding your standard deviation, the risk within the portfolio is crucial to make sure that that portfolio is in line with what your expectation of it is. But here’s the problem. That represents 95% of the outcomes. And I don’t know about you, but if I were a student getting a 95, I’d be thrilled to death, right?
[00:07:16] Dean Barber: I mean, that’s a good score.
[00:07:17] Brad Kasper: And most investors 95% of the time, they’re going to be happy with the understanding of this investment or this type of vehicle is going to operate within these bands.
[00:07:26] Dean Barber: 95% of the time, you’ll be within a plus 50 or a minus 30. That’s your modern portfolio theory, right?
[00:07:32] Brad Kasper: In our hypothetical scenario, yes, but what does that leave? 5% is unknown, right?
[00:07:37] Dean Barber: That leaves 5, sure, but let’s extrapolate on that a little bit because this whole idea that we can operate between one to two standard deviations from that mean, there’s also a part of modern portfolio theory that talks about at any given level of risk, there’s an optimum return that can be achieved through proper asset allocation. So, the blending of maybe putting in some fixed income, some precious metals and things like that, something that might hedge a little bit, maybe that takes a little bit away from the upside over the long term, but it might give us a little bit more protection on the downside.
So, am I correct in understanding that modern portfolio theory basically is saying, “You know, if you want zero risk, you got to go cash or treasuries and if you want all the risk of the market, you’re 100% in?” And so, anywhere in between 0 and 100 is the construction of the portfolio itself to tell you where do you lie on that whole, I guess you call it an efficient frontier.
[00:08:36] Brad Kasper: That’s right. And so, when I think about this, there’s so many different investments out there that people can take advantage of and every one of those investments has a different risk-return profile as you were describing. And within those different risk-return profiles, I want to find a balance to try to achieve an objective. So, to your point, if I was ultra-conservative, I’m on the cash side, the treasury side, the short duration bonds, and then it goes all the way up to emerging markets where it’s more speculative where the return opportunity might be extremely high but you’re taking big bets and you’re paying for it in terms of the amount of risk that you’re pulling into the portfolio.
[00:09:16] Dean Barber: Same thing would be true, Brad, with small-company stocks versus large company stocks?
[00:09:20] Brad Kasper: Absolutely. I mean, this happens in so many different subsets. And this industry is so wild in terms of what’s available to investors these days versus even 10, 15, 20 years ago that we get so granular with it. But if we took a just a broad step back and said, in the simplest of terms, if I needed to be a little bit more conservative in a portfolio, I’m probably going to lean more towards that fixed income, the bond side of thing. If I’m trying to get a little bit more aggressive, I want that equity mix. But the big question is, how do I blend the two to, again, target that outcome that I want that portfolio to do for me?
[00:09:55] Dean Barber: So, this is where we come back into Episode 11, which basically for Season 1 of The Guided Retirement Show, which was the things you need to know before you try to build your portfolio, which is first understanding what does your money need to do. Then that’s when you apply modern portfolio theory that says, “Well, based on what you want your money to do and what it needs to do, here’s the optimum blend based on modern portfolio theory and the efficient frontier to get you to where you want to go.”
But then again, so that modern portfolio theory can do a lot in that respect, but it doesn’t clearly, I don’t think, fully identify how much risk you have in your portfolio. And so, when people don’t understand the amount of risk in their portfolio, they just say that this is the optimum portfolio. That doesn’t identify the risk.
[00:10:44] Brad Kasper: Right. And one of my hang-ups that I have with traditional modern portfolio theory or even mean-variance optimization, so we talked about blending different positions to get these optimal portfolios where we can either target a level of return objective or a targeted level of risk and optimize based off of the subset of investments that are underneath it. So, when I think about mean-variance optimization or even a modern portfolio theory, the biggest pitfall that you have is it’s all rearview mirror looking, right? We’re looking at historical returns, we’re looking at standard deviations, and we’re looking at correlations over time.
[00:11:23] Dean Barber: Correct. So, does this fat tail risk analysis gives a crystal ball-looking into the future? Or is it also rear-window looking?
[00:11:30] Brad Kasper: Yeah. So, fat tail risk analysis, and let me kind of build the stage for why this came about. If you went into 2008, a lot of the big financial institutions were using risk models. So, if they were lending money, if they had any type of margin calls that were out there, anything that was on the books, they want to manage that to an understanding of risk so that they can be as prudent with their business model as possible. And really, when you talked about risk management from the institutional level, what they want to know is, in the worst-case environment, do I have enough protection here? Can I survive it? Well, 2008 kind of created the perfect storm, right? That was the worst-case scenario.
[00:12:14] Dean Barber: It was awful.
[00:12:15] Brad Kasper: And here’s the problem, we talked about modern portfolio theory represents about 95% of the outcomes. 2008 was the anomaly, right? That was that 5% outlier of market performance that happened beyond what we would typically expect in a normalized type of market cycle. And so, when I look at these institutions and we can go back to Bear Stearns.
[00:12:39] Dean Barber: I was going to say Lehman Brothers.
[00:12:41] Brad Kasper: Lehman Brothers. We can go through a number of them. Their risk model, which was based on modern portfolio theory failed.
[00:12:49] Dean Barber: Yeah. And so miserably that those two companies basically evaporated overnight.
[00:12:55] Brad Kasper: And it just put a chill through the rest of the financial markets in 2008. So, what did the institutions do? Those that survived and came out the backside of this, they said, “We need to have a better understanding of what is our maximum level of risk that we have within any investment they have on their portfolio.” And one of the platforms that was introduced was a concept called heavy tail risk analysis or fat tail risk analysis. This was very similar to mean-variance optimization with the exception of they could do individual daily rundowns of worst-case scenarios and we could get to an understanding of almost 99% of the outcomes what was the worst-case scenario.
[00:13:42] Dean Barber: So, that 99% is a lot better than 95%.
[00:13:45] Brad Kasper: It is, and I know that 95 sounds great and I know that in a normalized market environment, this is why a lot of these mean-variance optimizations, these modern portfolio theory types of platforms that are out there still exist in the retail space they do find majority of the time. But let me ask you this question. So, if you were building a financial plan, and you were trying to test for the probability of that success of the financial plan, would you rather have a 95% understanding of the success of that plan or a 99%?
[00:14:17] Dean Barber: Well, obviously, I’d rather have the 99. I mean, a perfect example would be, what if let’s just go back to the example we gave early. What if you had a portfolio that could do based on 95% of the time 30% negative to 50% positive, and then all of a sudden, that portfolio through in a way loses 55%? Well, that’s way outside of what we’re talking about. And we say, well, we only count for 95% of the time. There’s that 5% that we don’t understand or we didn’t really measure for, we didn’t have a way to measure for. And I think that’s where people really lost it.
That’s where so many people got to the depths of despair in 2009. By March of 2009, people were just literally, okay, I can’t take it anymore. I don’t know how bad this is going to get. I don’t know how much worse it’s going to get. And so, there’s to your point, if they didn’t understand that risk and they didn’t know that there’s a stopping point at some point and they were comfortable with that, emotion comes into play and you make irrational decisions based on emotion as opposed to understanding it logically, which is why we’re talking about understanding the risk in your portfolio and the difference between the modern portfolio theory and the fat tail analysis.
So, what did the fat tail analysis do that is so significantly different from the modern portfolio theory? And then, is that something that anybody can do on their own or do they need somebody that actually has some programs that can put this together?
[00:15:47] Brad Kasper: Yeah. It’s still very limited in terms of access. You’re talking about extremely high computation power that you need to be able to run a heavy tail risk model within platform. So, a lot of the institutions that, as I mentioned, post-2008, 2009 have adopted these heavy tail risk models. We utilize it through a number of different platforms but it’s still somewhat limited.
[00:16:13] Dean Barber: So, when you say institutions, what do you mean?
[00:16:22] Brad Kasper: Your big banking channels, your Goldman Sachs, going down the list.
[00:16:28] Dean Barber: Are they taking that, Brad, and applying it down to the individual portfolio level? Or are they using this in their own business?
[00:16:36] Brad Kasper: They’re using it in their own business. This has, in very few cases, found its way all the way down to the retail investor.
[00:16:44] Dean Barber: So, they might run it for an endowment fund or something like that, where they’re managing billions of dollars for an individual foundation or something like that so they want to understand this fat tail risk.
[00:16:56] Brad Kasper: That’s right.
[00:16:57] Dean Barber: It’s not made it to the individual investor level in very many areas at all.
[00:17:03] Brad Kasper: Now, very few cases, and again, you have to think about with heavy tail risk models, what it really does is looks at any worst-case scenarios on a daily basis. And it runs that computation against the mix of different investments to say, collectively, in worst case, environment, here is the outcome up to a 99% understanding of that worst-case scenario, and the ability to calculate that. So, just kind of give you a concept. If we were to try to do this even 10 years ago, if you had a supercomputer, you’d be asking that computer to run thousands and thousands of computations. It’d be running for 24 to 36 hours straight just to run an entire analysis. So, very few people had the time, energy, or money to be able to do it. So, what did they lean on?
A more simplified version, which is mean-variance optimization. And I don’t want to downplay mean-variance optimization. I still think that it’s a very valid tool to help step in and understand risk in different characteristics of a portfolio. But as times advance, as the computation power continues to improve, we can now run very similar simulations. It still takes us 20, 30 minutes to run a computation like that, though. And that’s through technology.
[00:18:18] Dean Barber: That’s better than 24 hours.
[00:18:19] Brad Kasper: Oh my gosh, and then you had to pray that your computer didn’t break down in a 24-hour and restart, and then you had to start the whole thing over again. And I’m speaking of this as if we were doing that in-house or internally. That’s not the case. This are the studies that were happening with a lot of very smart people that have done a lot of very detailed work on what heavy tail risk analysis does. And I think as we continue to grow from a technology perspective, this will become kind of the core anchor of data points that most investors are going to be leaning on in the future.
[00:18:49] Dean Barber: Alright. So, this fat tail risk analysis, which is very difficult to find, first of all, if anybody that can do it, it’ll identify risk better. Does it also allow you to potentially build a better portfolio?
[00:19:09] Brad Kasper: Potentially. And we’re still in the early stages of testing some of this. And what we have found so far is the number of investments that you’re pulling into that model creation matters. Your objective matters. So, a lot of the core things that we talked about in Episodes 1 and 2 are still very much in play. We’re just saying, “Can we use better math today to get a deeper understanding of worst-case scenarios?”
Because you brought up a key point earlier, and I think it goes back to the psyche of investing, right? You go back into 2008. And what was the biggest catalyst that disrupted investors over that timeframe, it was capitulation. I have a portfolio or I have an investment. I expected it to operate within these parameters. Let’s go back to our up 50, down 30% type of hypothetical scenario here. Well, if it broke below that 30%, did I have the guts to hang on to that to get the net benefit of what I wanted that over time?
[00:20:11] Dean Barber: Well, it all of a sudden broke through that psychological barrier of what you thought your portfolio could do, right? I think a perfect example of that was these target-date retirement funds. I don’t think people understood the risk in the target-date retirement funds at all because they didn’t understand the glide path of how they worked. And so, when those things lost 30, 35 and in some cases, more, people were like, “Wait a minute. This thing says I’m going to retire in four years or five years or two years and this thing’s down.” What investment when I’m two years out from retirement would I want that’s going to go down 30% or 35%?
[00:20:45] Brad Kasper: So, what you’re describing as when they tested those models, they did a modern portfolio theory. We understood 95% of the outcome. So, here’s the scenario going back into 2008. These 2010 target-date funds to your point, I don’t want to lose. If I was two years out more than maybe 10, 15 if I had a little bit of guts behind it, maybe 20 and all the sudden, they’re capturing this downside that was what? That tail experience. It was that 5% that we just weren’t able to capture within a typical mean-variance optimization or a modern portfolio theory type of calculation.
[00:21:27] Dean Barber: So, Bear Stearns and Lehman Brothers weren’t the only two that really got it wrong.
[00:21:32] Brad Kasper: No. Oh, gosh. This was industrywide. Others just did a better job of managing their capital that they had on in-house and they were able to weather the storm.
[00:21:42] Dean Barber: Or they weren’t leveraged as much, which was a big deal. Okay. Let’s take a quick break. This is The Guided Retirement Show. I’m Dean Barber. We’ll be right back.
[00:21:52] Female: At some point in everyone’s life, you have to go to school because let’s face it, a good education is important and just because you’re nearing retirement age or you’re already there, it doesn’t mean the learning stops. One of the easiest ways to learn about retirement is at Modern Wealth Management’s Education Center. There, you’ll find things to read, to watch, and to listen to about important retirement topics. So, go to BarberFinancialGroup.com. Click on the menu dropdown. It’s in the upper right-hand corner and select Education Center.
There you can download and read our Social Security checklist, watch Dean Barber’s latest video on the current state of the markets, or listen to an audio recording about tax reduction strategies and so much more. There’s no cost. Just sign up for access at BarberFinancialGroup.com. It’s as simple as that. Besides, there’s no tests, no textbooks, and I promise, not to move your seat even if you talk too much. There’s so much to learn about retirement. Just go to BarberFinancialGroup.com, click on the menu dropdown, and select Education Center.
[00:23:06] Brad Kasper: So, when I think about mean-variance optimization or even modern portfolio theory, the biggest pitfall that you have is it’s all rearview mirror-looking, right? We’re looking at historical returns, we’re looking at standard deviations, and we’re looking at correlations over time.
[00:23:34] Dean Barber: Welcome back. I’m Dean Barber, Managing Director at Modern Wealth Management, and this is The Guided Retirement Show. A little bit off subject, but I want your opinion on this. 2008 was, as we’re talking about an anomaly, it was something that people thought would never happen. And I want to just as your opinion here, and I don’t think anybody will ever know for sure.
Do you think that the real estate debacle and the psychology of wealth erosion from people’s homes going down in value so much led to more of a panic in the stock market that caused people to say, “I’ve already lost a bunch of money in my real estate, which I never thought would lose money. I can’t take this anymore in the stock market,” which caused that capitulation that you’re talking about, which caused even more selling and fed fuel to the fire?
[00:24:32] Brad Kasper: Absolutely. I mean, we are creatures of our own habit and good, bad or indifferent. The psyche of how we think about investing plays a major role. So, when I look at a 2008 and I see the value of my home is starting to depreciate, when I coupled that with I’ve got a retirement plan or a 401(k) that is losing money. It just snowballs. Well, I may not be able to sell out of that home. But guess what, on the investment side, I can go liquidate to cash or the money market or something that’s ultra-conservative. And you see this kind of fueling itself.
You know, fear feeds on itself, the greater drawdown that you experience, which is what led to what I think is one of the most hated bull market rallies coming into 2009. You know, if I just go through a traumatic event of 2008 and I lose half of my wealth that I spent two decades growing, three decades growing, and in less than a year-and-a-half I lose 50% of my wealth, am I excited come March of 2009 to say, “I’m ringing the bell. This is a market low. I’m going to go buy back in?” And the answer is no. We’re shell shocked.
[00:25:48] Dean Barber: Which is one of the things that really kills me when you look at the financial news and everybody wants to quote the return from the bottom of the market to today, “Look how great this is.” But if you show me anybody on March 9, 2009, that was willing to say, “I’ve had all my money sitting in cash for the last year-and-a-half, I’m ready to put 100% of it in the market,” that person I don’t think exists, Brad. So, why does the financial media tried to get us to do that? Is it again building on our psyche of greed saying, “Look what you missed, you idiot?”
[00:26:25] Brad Kasper: Let’s circle this back to the miss of what I define as Wall Street. What guidance was Wall Street? I hate coupling Wall Street with the idea of a financial plan and this side of the aisle. But what guidance did we get from financial intermediaries out there for decades? It was…
[00:26:42] Dean Barber: Don’t worry, be happy.
[00:26:43] Brad Kasper: Buy and hold, right? Let’s go through a 2008. Just stomach it. Take the dividend, you’ll buy back more shares, and over time, you’ll make it back.
[00:26:53] Dean Barber: If you’re 30 years old, that works.
[00:26:55] Brad Kasper: That’s right.
[00:26:56] Dean Barber: If you’re 35 years old, that works, right? But if you get up into that time of your life where you’re going to rely on the money that you’ve accumulated to now provide your lifestyle for the next 20 or 30 years of retirement, that doesn’t work.
[00:27:08] Brad Kasper: That’s right. And again, it comes down to for the success of buy and hold to really work, you need two things. You need time, to your point, and you need gut.
[00:27:17] Dean Barber: A lot of guts.
[00:27:18] Brad Kasper: You have to have guts, right? And so often now I get the question, “S&P 500 is doing this. We need to outpace the S&P 500. We need to do this and we need to do that.” Well, in 2008, did investors want anything to do with the S&P 500?
[00:27:34] Dean Barber: No. And to be honest with you, if you said to someone, “The S&P 500 was down 55% and you were only down 35%, didn’t I do a great job?” They’re going to go, “I know you didn’t do a great job. You lost 35% of my money.”
[00:27:47] Brad Kasper: That’s right.
[00:27:49] Dean Barber: But now you get to a scenario where you get into an upmarket and the S&P is up X, say it’s up 20, well, why am I only up 10? Well, we’re not taking the risk of the S&P. That’s where I go back to the whole theme of the show is people don’t understand risk. If you want the returns of the S&P, you have to be willing to accept the risk of the S&P. Well, what is that risk? Now, you can look at the S&P by itself and say, “Kind of here’s our worst-case scenario that we’ve seen over a one-day, a one-year, or a three-year, a five-year, a 10-year, etcetera,” now, we’ve had a couple of 10-year periods where we’ve had negative returns for 10 years in the S&P.
[00:28:31] Brad Kasper: You’re talking about the lost decade between 2000 and 2010.
[00:28:34] Dean Barber: Right. But what about the decade of the 70s, right? It was another lost decade.
[00:28:40] Brad Kasper: Right. So, this all circles back to this concept of, again, that time and guts matter. I don’t disagree that buy and hold works over long market cycles, 30 years, 40 years. If you can be in that investment, stomach the downside, yes, historically, markets have a tendency to turn upwards.
[00:29:00] Dean Barber: Again, if you’re a 20-year-old or a 30-year-old and you’re putting money into your account every single month on a regular basis, and you’ve got a really long time horizon, don’t let that stuff bother you.
[00:29:10] Brad Kasper: That’s right. But let’s paint a picture. So, in 2008, 2009, to your point, the S&P 500 was down roughly around 50% over that 16-month timeframe. From the peak to the trough, the S&P 500 lost a little over 50%. It took until March of 2012 to make all those losses back up. So, let’s rephrase this so all the listeners can get this. From November of 2007, there was a high of the markets, right? To march of 2012, how much growth did you have if you were invested 100% in the S&P 500? Zero. So, when CNBC, and I remember this vividly, in March of 2012, they’re flashing all their signs saying markets are at a new all-time high. What’s going through my mind?
[00:30:00] Dean Barber: Well, as the individual investor, you’re thinking, “Man, I should have made a lot of money.”
[00:30:03] Brad Kasper: That’s right. But where am I really? I’m back to where I started in November of 2007.
[00:30:08] Dean Barber: If you didn’t spend any of it, if you weren’t retired and living off of some of that.
[00:30130] Brad Kasper: And if you didn’t capitulate.
[00:30:16] Dean Barber: Right.
[00:30:16] Brad Kasper: Capitulation’s the kiss of death for any type of diversified strategy over time. Remember, we want to identify risk within a portfolio, so that we can to the best of our ability and understand the outcomes in best-case scenarios and worst-case scenarios. And if we can match those outcomes appropriately with the investor’s objective or goals, then you have a portfolio that you have staying power, which means if we go through some turmoil like 2008, I’m not hitting the sidelines, I’m not sitting in cash because if I’m sitting in cash, March of 2009 rolls back around, what did I do?
I missed out on the debt-cap balance where a lot of the returns early on happen in the marketplace. So, again, risk matters in so many different ways and I think the biggest takeaway from all of it is, again, going back to understanding your objective and understanding to the best of your ability, the outcomes of what a portfolio was built to do for you and staying the course.
[00:31:18] Dean Barber: I agree with you. So, let’s switch this to the difference between short-term volatility and long-term volatility. I’m not just talking about investment time horizon. I’m talking about there are pockets of volatility that rear their ugly head about once a year, sometimes it’s twice a year where people think, “Oh, my God, this is it. This is the next thing.” And so, we kind of talked about how the economy affects the marketplace back in Episode 10 of Season 1 to get people to get a better understanding of that, but let’s talk a little bit about that short-term versus long-term volatility.
[00:31:58] Brad Kasper: Well, first, I think it’s important that we know when we think about investing, when we think about building portfolios, we take a very long-term approach to it. Because as you’re drawing out here, the corrections happen frequently within the marketplaces, right? Even though they’ve been fairly muted over the last decade, guess what, corrections happen. Corrections are defined as a 10% or greater move.
[00:32:24] Dean Barber: Even when those corrections happen, people freak out.
[00:32:26] Brad Kasper: They do. Absolutely. None of us like pain.
[00:32:28] Dean Barber: Because they don’t know. What they don’t know, Brad, is this the next 40% or 50% decline? And every time it starts to fall, that’s what they think. How does a person possibly understand whether this is the next big drop or whether it’s not?
[00:32:43] Brad Kasper: Well, one, turn off the news because I think the news, it goes back to the simple statement of if it bleeds, it leads, right? Negative news, negative coverage, negative press always goes a lot further in terms of ratings. So, there’s always kind of this negative condensation that’s happening or being released by a lot of the news channels that you might be watching.
I’m not saying ignore them, but take a step back and say, “Well, I didn’t invest in something because I need this to work well in the next 60 days, 90 days, one year. I did it because I looked out 10 years, 15, 20, 30 years. Here’s my objective.” And if you allow the noise of short-term market moves impact your decision making, it goes right back to that capitulation effect that I was talking about earlier. Again, for a real model to work well for you, you need a couple of things to happen. You need a spread comparison.
What I mean by that is you want to build a model that has an up capture. So, when markets are moving up, I expect to capture a certain percentage of that. In a down capture, when markets are going down, I only want to – I can’t invest in anything that participates more in this because I just can’t stomach it. So, I create this spread relationship between my upside expectation and my downside expectation.
And what that ultimately does to a portfolio is it gives me a line of sight of what my expectation collectively as an investment portfolio looks like. And as long as that spread comparison is wide enough over time, you can really that’s what leads to the greatest wealth accumulation that’s out there. So, if you spend a lot of time doing the analytics on the front end using modern portfolio theory, heavy tail, doesn’t really matter, whatever you used to build that model and it has that profile of what you expect in upside and downside, what disrupts its ability to do its job?
[00:34:37] Dean Barber: You.
[00:34:38] Brad Kasper: Me. Yeah, you the investor, the fear trades, the greed trades. It’s, “I’m not capturing enough. I captured too much on a down market movement.” It’s that capitulation. So, if you have good positive spread, where you’re capturing more the upside versus downside over time, that’s where real wealth accumulation can happen. And by the way, if you’re an ultra-aggressive investor, so we always talk about this because a lot of times I think, when we’re talking about financial planning and those types of topics, we have the concept that protection is of the utmost importance and we continue to kind of hammer on those points. But let’s say I’m a young investor, I’ve got plenty of time behind me, I want to go high octane. Well, guess what, you still need a positive spread relationship.
[00:35:24] Dean Barber: Right. And more up-capture than down-capture.
[00:35:27] Brad Kasper: Yeah. More up-capture than down-capture and you need the time and the guts to be able to hang on to that strategy.
[00:35:33] Dean Barber: So, that’s where your whole discussion of active versus passive comes in. So, active management versus passive management. Passive means I’m just going to own an index or I’m going to just participate in the market. Only active means I’m going to try to get a little bit more upside and maybe not quite as much downside. So, that’s your spread that you’re talking about.
[00:35:52] Brad Kasper: Yeah. And this argue is the age-old argument, right? Active managers versus passive managers. A lot of the passive vehicles have now translated from the mutual fund chassis to the ETF chassis. And it’s interesting because ETFs that were the original vehicle for passive management, if you look at the progression of new innovative products that have been coming out on the ETF side, what are they all built around?
[00:36:16] Dean Barber: Active.
[00:36:16] Brad Kasper: What’s called smart beta, which is just another word for active management. So, what you’re finding right now, especially when you get late in cycles, when markets have run up, when you get at higher valuation levels, the ability to step into an individual stock name, an individual position and say, “This balance sheet looks good. This balance sheet does not look good,” which name do you want to invest in? So, the ability to separate good names and bad names make a bigger difference, the higher you get or the later you get inside some of these market cycles.
[00:36:51] Dean Barber: Because the bad names where the balance sheet doesn’t look good or the earnings aren’t looking as good, that typical type of a stock or company will perform worse when the cycle turns.
[00:37:00] Brad Kasper: That’s right. There’s a number of studies that are out there that will show you the difference between positive earnings within the S&P 500 and negative earners within the S&P 500 over long periods of time. And I’m going to tell you go look it up. I mean, it’s incredible the difference in terms of performance. But what’s interesting to me with all of that is, again, depending upon where you’re at within the market cycle, if right now I decided to become a passive type of an investor where I don’t want to try to beat the benchmark, I just want to be the benchmark, okay?
What it’s going to do is it’s going to take all those good names, those positive earners, the balance sheets, the companies that are doing all the right things, and it’s going to lump it in with all the players that are negative balance sheets or negative earnings things.
[00:37:49] Dean Barber: You’re going to get the average of the whole.
[00:37:50] Brad Kasper: And they’re going to lump it all together. Well, listen, if I were building a sports team, okay, and I wanted to build the best team in the world, do I want the best players or I want to take the best players and lump them in with the worst players and just see how we do? And that’s kind of the concept of active versus passive. And again, I don’t want to create a scenario or create the message that I think that passive is wrong.
[00:38:16] Dean Barber: But there are certain times and I was going to mention this earlier, right, you’ve got certain periods of time when certain investments are going to do better than others. And you can go through a period where one investment that may be a great long term investment is just struggling at the moment or one strategy is struggling at the moment, or maybe it has a bad year or maybe it has a bad two years. It doesn’t mean that that strategy is no longer applicable and it doesn’t mean that it’s wrong or you should avoid it altogether forever. It just means that sometimes, you know, things look different.
[00:38:47] Brad Kasper: Yeah. So, two quick points. One, this is what I define as endpoint bias. This is if there’s a manager out there that just started doing really good and we said, “Oh my gosh, look at that return. I need to go get it,” that’s not a very prudent decision, right? It’s what I’m calling chasing returns. I see something, they’ve done well, I want to participate in, but usually, it’s too late. And what’s more prudent is saying, “I want to know how that investment did not only in the last 12 months. How did it do in the last three years? Five years? 10 years? How far back can I go?”
[00:39:19] Dean Barber: And what was my standard deviation doing?
[00:39:20] Brad Kasper: How much risk? Do I have a proven track record that I can tie into to have a deep understanding of that specific investment? So, again, so many of these concepts all circle back. Time matters. We can’t just start chasing returns at the tail end. Now I’ve gone on, I can’t even remember what my second point was.
[00:39:40] Dean Barber: Well, I think we can kind of come back to what was the whole idea of this was understanding your risk. And if you can go through whether all you have available is the modern portfolio theory or if you’re lucky enough to work with someone that can use the fat tail risk analysis to just understand and you need a benchmark as an investor to understand what is my worst-case scenario.
If it’s 95% of the time, that’s my worst-case scenario or if it’s 99% of the time, that’s my worst-case scenario, you need to know what that is you need to know that you can live with that. And if you can’t, then that means you’re going to have to have a tradeoff and that tradeoff is going to be I’m going to have a little bit safer portfolio. I’m going to have some more protection in there. But I’m not going to have as much of an up-capture or as good returns when everything’s soaring. So, there’s tradeoffs.
And that’s where I think if people really just understand because I think where people capitulate, they don’t capitulate on the upside, Brad. They hang on, they hang on, they hang on, “Well, it’s going down. Oh, it went down last week. Oh, it went down last month. And oh, it’s going to come back. It always does, right?” But if you understood that your maximum potential loss was far more than what you could stomach, then you need to make an adjustment and stop being greedy.
[00:41:03] Brad Kasper: I agree. I mean, you’re talking about the emotional aspects of investing. You know, fear drives us. Greed drives us, this thought that we’re missing out on returns. And I tell you, I get so exhausted of hearing what the S&P 500 is doing. You know, this brings back a conversation that anytime you’re building a strategy, it’s always important to benchmark, right? We want to know how are we doing. Are we hitting the marks of what we would expect? And we can do that through a number of different channels. It can happen through a financial plan. We can do it through a number of different market benchmarks. But if I’m an ultra-conservative investor, does the S&P 500 have any bearing on what I’m doing?
[00:41:40] Dean Barber: It shouldn’t.
[00:41:41] Brad Kasper: So, the concept of saying, “Oh, I’m not capturing enough of the upside,” my statement always it comes back to relative to what? And it’s that relative to what statement that should drive trying to identify the appropriateness of the benchmark that you’re comparing it against.
[00:42:00] Dean Barber: Is it your neighbor’s benchmark? Is it your coworker’s benchmark? Is it your uncle’s benchmark? I mean, “Oh, I did this and I didn’t do that,” and then they go get upset and they go, “What were you doing? I should do that.” Well, then they don’t understand the risk of it and that’s where I think the problems come in.
[00:42:14] Brad Kasper: And you end up chasing, right? And by the way, people are always willing to share when they have something positive that’s happening within an investment or an idea, but all of a sudden, they become tight-lipped when they get into volatility. A lot of times, it’s because they might be in an investment that they didn’t understand the risk and they capture they got caught where they captured more of the drawdown than they expected.
And I think 2008 albeit in the anomaly in the marketplace is a tremendous tool for investors to use as a gauge to say, “Here’s a pretty tough market environment. Did you have the guts to hang on to it? Did your portfolio behave the way that you expected it to? And did the investments behave the way that you expected it?” And if the question that comes to your mind is, “Well, I have no idea,” and by the way, does your current strategy look anything like it did in 2008? What kind of risk is your current strategy taking? You know, the more that we chase and tweak and edit, a lot of times it’s done for the right reasons, for positive, trying to drive greater returns or alpha within a portfolio or reducing risk.
But when those changes happen, do you have a clear understanding of the actual risks that’s associated with each one of those models? And the more clear that you can get with that understanding, I think, the better opportunity you have to avoid that capitulation effect to stay the course with a portfolio that’s more appropriate for what you’re trying to accomplish.
[00:43:37] Dean Barber: There’s no question about it. And that all goes back to the creation of a really good financial plan. We have our proprietary guided retirement system, which takes people through that entire process and really identifies not only what is the objective, but what are the resources and looking at all aspects of it and saying, “What’s the right thing?” And here’s the question for you.
Let’s just say that for an example, you’re working with somebody that can use our guided retirement system and they tell you, “All right, based on your current situation, everything you have, all of your resources, how you want to live the rest of your life, you can make it from a historical perspective using either modern portfolio theory or your fat tail risk analysis, with 20% of your portfolio in equities and 80% in fixed income, here’s your risk on that. Or you could go up to as high as 80% in equities and 20% in fixed income, here’s your risk on that.” Understanding those two things I think can help a person make a much better, more informed and intelligent decision.
[00:44:43] Brad Kasper: So, what a phenomenal concept here, right? Let’s say you ran the full financial plan, you identified that, yeah, maybe I only needed about 20% exposure to equities over time to really get high probabilities of success within that financial plan. What’s the biggest risk to that investor?
[00:45:01] Dean Barber: Well, they’re going to miss out on some potential returns and they’re going to talk to people that are making more money than they are in a given year and they will make the mistake of forgetting about risk and getting too much equity exposure into their portfolio and then they’ll capitulate.
[00:45:16] Brad Kasper: So, overextension, right? Overextension, but if I don’t know from a portfolio construction perspective that I can live here comfortably and live the lifestyle that I want to, then it’s all for naught, right? I mean, it’s overextension that disrupts that sample.
[00:45:37] Dean Barber: Taking more risk than you need it. But the problem is without a program like our Guided Retirement System, people don’t know what they need to do or what their money needs to do so then they can effectively say, “Well, how do I get to what I need with the very least amount of risk possible?” To me, that’s what provides that clarity in someone’s impending retirement or current retirement to allow them to live their one best financial life.
[00:45:59] Brad Kasper: And we can talk until we’re blue in the face about different stats of how to track risk, how to understand portfolios, how to construct portfolios, but again, until we have that base understanding of what the objective is that they need to accomplish, to hit that objective from the financial planning perspective, everything else is somewhat of a moot point. And so, always start there, understand that there is a mix and there’s tools that we can utilize to dive into the analytics as it pertains to risk and parameters to those portfolios.
[00:46:28] Dean Barber: I think the biggest takeaway from this podcast, Brad, for our listeners is this. Don’t guess. Know. Understand what your money’s doing. Understand the risk in your portfolio, but more importantly, understand how that risk in your portfolio applies to your overall financial plan. Because if you don’t, then that’s where the overextension, capitulation, the fear, and the greed, let’s call it that, will come into play and wreck your ability to either get to retirement or to retire with the lifestyle that you want.
[00:47:02] Brad Kasper: Well put. I don’t think there’s anything I could add that would improve that statement and start with what’s the objective of what I need to accomplish.
[00:47:11] Dean Barber: Well, Brad, this has been awesome. Hopefully, people have learned a little bit here. We’ll have some extra things out here in the show notes where you can pick up some of the stuff we’re talking about here. Do a little bit of research on your own but this has been great. We’ll have you back on again.
[00:47:24] Brad Kasper: Looking forward to it. Thanks for having me.
[00:47:26] Dean Barber: Thanks for being here.
[00:47:28] Dean Barber: That was Brad Kasper, President of LSA Portfolio Analytics. Check out the show notes for a lot of great collateral material there. If you’re interested in talking to one of our advisors here at Modern Wealth Management about how to construct your portfolio, getting an analysis of what you’re doing today, click on the show notes. You’ll find a way to contact us and you’ll also find some other great collateral material there.
Find the show notes, links to resources, and show transcription at GuidedRetirementShow.com/15. I always want to remind you to check out our YouTube channel where every single podcast that we do is also out on a YouTube channel. So, if you’d rather watch as opposed to just listen, you can do that. Make sure that you click subscribe to The Guided Retirement Show. Make sure you share this with all your friends and family. Our goal here is to make more informed and intelligent investors so that you can have a great retirement. That’s why we are The Guided Retirement Show. Thanks for being here.
Investment advisory service is offered through Modern Wealth Management, an SEC-registered investment advisor.
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Investment advisory services offered through Modern Wealth Management, Inc., an SEC Registered Investment Adviser.
The views expressed represent the opinion of Modern Wealth Management an SEC Registered Investment Advisor. Information provided is for illustrative purposes only and does not constitute investment, tax, or legal advice. Modern Wealth Management does not accept any liability for the use of the information discussed. Consult with a qualified financial, legal, or tax professional prior to taking any action.