How to Build Better, More Predictable Portfolios

[INTRODUCTION]

[00:00:09] Dean Barber: Hey, everybody, it’s Dean Barber, your host of The Guided Retirement Show. Have I got a treat for you today? Back for the fourth episode, I should say, Brad Kasper, President, Founder of LSA Portfolio Analytics. We’re going to be talking about smart risk today. If you remember Brad, we talked in Episode 15 about how to assess risk in your portfolio. As we sit here today, late January 2021, market valuations are extremely elevated. They’re elevated to a level that we’ve never seen before.

So, I think it’s really important that you understand how to assess risk in your portfolio. And today, Brad is actually going to be talking to us about intelligent risk or fat tail risk analysis on how to build a better and more predictable portfolio that’s geared to meet your needs. Please enjoy this conversation with Brad Kasper, President of LSA Portfolio Analytics.

[INTERVIEW]

[00:01:07] Dean Barber: Brad Kasper, welcome back to The Guided Retirement Show. For those of you that have not been regular listeners to The Guided Retirement Show, Brad’s been on three times with us. This is number four. Brad was on Episode 10 talking about the economy and how it affects the markets. He was then on in Episode 11 talking about things that you need to know before you build a portfolio.

Brad, Episode 15 was how to assess risk in your portfolio. Today, we’re going to talk about intelligent risk, how to construct that portfolio using intelligent risk and how it’s different than modern portfolio theory. So, if you have not listened to Episode 15, I’m going to ask you to stop right now, go back to Episode 15, listen to Episode 15, and then come back and rejoin this episode. Brad, we’re going to get into some technical things today. Welcome back. Good to have you here.

[00:02:05] Brad Kasper: Thanks, Dean. It’s a pleasure to be back. I have to say, thinking back to Episode 10 on the economic update, I don’t know how we missed the COVID outbreak in that review. But, wow, what a wild year 2020 has been.

[00:02:18] Dean Barber: Yeah. No question about it. Who could have known? Who could have known? And it’s interesting as you and I record this episode here in late January of 2021, I’ve been doing some reading and you know what happens at the beginning of every year is all the big brokerage firms and big banks that come out with their predictions for the year of what’s going to happen with the S&P 500, where the returns are going to be, what’s the economy going to grow at, yada, yada, yada.

And there was one writer that I thought hit us right on the head and basically, he said, “Stop right there. If you cannot explain what happened in 2020, what the hell makes you think that you can predict what’s going to happen in 2021?” And if you look at it historically, these big banks and big brokerage firms have done a horrible job trying to predict what the market’s going to do but people think that we can predict these things.

And so, what I want to start with here in this idea of building intelligent risk is that people don’t understand risk. They don’t understand the risk that’s within their portfolio and you’ve got some statistics that I’d like for you to share on risk and portfolios and kind of where people believe they live and all that.

[00:03:30] Brad Kasper: Yeah. And let’s use 2020 as kind of our baseline here, Dean. We as investors and strategists, we’re constantly trying to measure risk within portfolios. And to your point, a lot of the big institutions that are constantly pushing thoughts or opinions on what the next calendar year is going to look like from a return perspective, guess what, nobody’s crystal ball worked in 2020 because from time-to-time, markets experience what’s called a black swan event.

These are things that we just can’t project. They come out of left field. So, 2020 is fresh in our minds of this kind of market cycle that a lot of times when we look at portfolios, in the statistics that are provided, just don’t do justice to the real understanding of worst-case scenarios from a portfolio development perspective.

That is where we spend a lot of time from LSA’s perspective is drilling down and trying to use what’s the best math out there so that we have the fullest understanding of when a black swan event like COVID hits, how the portfolio can handle during that type of market environment. And unfortunately, we often find ourselves in these market cycles.

I mean, let’s take 2008, the financial crisis, what a drawdown year that was but then all of a sudden, ’09 hits and you get all the way up to 2020 before you have another market event. And guess what? Investors have short-term memory. And so, by the time you get into ’14 and ’15, you start chasing that return again, risk starts to jump up within your portfolios, so constantly taking a step back and identifying and checking the health of the portfolio from a downside perspective is always relevant when you’re constructing strategies.

[00:05:24] Dean Barber: Right. So, you have a piece here talked from the Business Insider, some of these statistics. The question that you’re asking here is, is your portfolio aligned to your risk tolerance? And this study from Business Insider basically states that 27% of people that have an advisor have had a discussion of what their downside exposure is in a significant downturn.

That, to me, is a scary statistic. If only 27% of the people that are using an advisor have had that discussion of what’s the potential drawdown, it’s no wonder that these next two statistics are so alarming. 62% of people estimate that their potential losses are less than their actual equity exposure would indicate, and 57% of people would have actual losses in excess of their panic threshold. And so, if you don’t understand the risk that’s there, Brad, that’s when the two most dangerous emotions of investing that we all have come into play, and that’s fear and greed.

[00:06:31] Brad Kasper: Yeah. I want to go back to that statistic that says 27% didn’t understand the worst-case scenario. But I’m going to argue, Dean, that 27% is probably much, much bigger because there is a problem in this industry in true understanding of downside risk, and it’s all rooted around what we talked about in Episode 15. We just barely started to get into the topic of some of the pitfalls with traditional Gaussian mathematics. And if you recall, we talked about Gaussian because it’s the root math that supports modern portfolio theory, your average return standard deviations, correlations, alphas, betas, all these great statistics that we use.

When you think about Gaussian, again, there are some real weaknesses that take place. Let me give you an example of this. I’m just going to use a hypothetical average return, a hypothetical standard deviation because in traditional Gaussian mathematics, we’re following what’s called the normalized bell curve and in a normalized bell curve, what that tells me is my average annual return, 68% of the time is going to fall within one standard deviation event of my return.

[00:07:49] Dean Barber: Okay. So, let’s stop there and let’s explain what that means, Brad. Go into detail, because one standard deviation from the return is a plus or a minus from that standard deviation, right?

[00:08:00] Brad Kasper: That’s right. So, let’s use my example, 10.04%, again hypothetical average return, with a 14.69 standard deviation. What this is suggesting is 68% of the time we’re going to find returns between negative 4.65 and a positive 27.73%. And so, if I’m a betting person, I’m looking at that and saying, “Gosh, that’s pretty good odds. 68% of the time, I’m within a range of negative 4% and 27%. That’s a pretty good outcome.”

[00:08:33] Dean Barber: Okay. Now, a lot of people, Brad, they measure their performance based on a calendar year but you’re not talking about just a calendar year here, right? You’re talking about rolling one-year periods.

[00:08:44] Brad Kasper: Rolling one-year periods. That’s correct.

[00:08:47] Dean Barber: So, what that would mean is that each and every year we would have 365 rolling one-year periods.

[00:08:53] Brad Kasper: That’s right. And each one of those rolling periods has the same odds of 68% of the time being between that negative 4 and that 27% of the time. So, let’s extrapolate on that a little bit further, because remember in that normalized bell curve scenario which supports Gaussian mathematics, 95% of the time, Dean, we experience an average return that’s within two standard deviation events. So, let’s put some numbers to it. Remember, in a one standard deviation event, we went from negative 4 to positive 27.

In a two standard deviation event, it’s telling us that there’s a probability that we could find ourselves between negative 19% and a positive 42%. I’m rounding some of these numbers just to keep our math simple. Again, if I look at that as an investor, I’m seeing negative 19%. I’m trying to decide if that type of drawdown is going to create one of the reactions that you talked about with fear and greed that could cause me to capitulate.

So, what I need to do is pair my portfolio so that I have a drawdown expectation that’s in line with what I can tolerate over time. So, that’s two standard deviation events, but you and I know that over time, often there’s these tail events that take place. And so, within a normalized bell curve, what it suggests is 99.7% of the time, I’m going to repeat that number, 99.7% of the time we experience an average return that’s within a three standard deviation event of that return. So, again, same rolling windows cycle.

Let’s use the same example. Our average return again was 10%, our standard deviation was 14.65. By the time I get out three standard deviation events, which should represent 99.73% of the outcomes at any point in time, it can range anywhere from a negative 34% to a 57%.

[00:11:00] Dean Barber: Those are big numbers now, especially that negative 34%. Everybody would love to have a positive 57 but now you’re saying 99.7% of the time we’re going to be somewhere between a negative 34 and a positive 57 when we look back at a trailing one-year period on any given day.

[00:11:16] Brad Kasper: That’s right. Now, more often we’re going to be within that 68% profile, right? It’s much more condensed but we use the standard deviation, extrapolate the standard deviation so that we know when we get into different pockets of market performance, what kind of participation could we have? Because everything that these conversations really are geared around is financial planning, right? And if I were to ask you, Dean, what’s one of the biggest disruptors to any financial plan from an investment perspective? What is it?

[00:11:48] Dean Barber: Its loss.

[00:11:49] Brad Kasper: It’s loss. It’s the unknown to the downside. Well, it’s very responsible for an investor to understand what is that worst-case scenario. And the Gaussian mathematics gives us an understanding of about 99.73% of the outcomes but that still leaves a small window, doesn’t it? And if I could tell you that there is a better way to utilize math to even close that gap further, is it important for us as advisors, as strategists, as researchers to take advantage of that math?

[00:12:28] Dean Barber: 100% it would be.

[00:12:29] Brad Kasper: 100%. And so, that math actually exists today because what you don’t capture within that traditional normalized bell curve, the Gaussian mathematics, when we go back into history, Dean, how many times have we seen an investment drop below what its target range is?

[00:12:49] Dean Barber: Almost a half a dozen, right?

[00:12:50] Brad Kasper: That’s right. And so, although it gets close to the understanding in worst-case scenarios, it doesn’t take us all the way. So, we started utilizing a mathematical system that really started to catch a lot of steam in our industry post-2009. And you have to go back to a little bit of the history of why did this happen. 2008 in the great financial crisis, we watched a lot of these banks and financial institutions on their knees, standing there on the brink of collapse, and why?

It’s because they all lean on a risk metric, how they gauge risk within all different aspects of their business. We’re all tied to traditional Gaussian mathematics. And we found ourselves in that Black Swan event, right, that took us outside of that 99.7% understanding of what Gaussian represents. Because remember, back then we saw a greater than 50% drop in the S&P 500 at that time.

And all of a sudden, these institutions are saying, “Gosh, I thought we had a great line of sight on how we were hedging risk within our business model,” and then they were darn near bankrupt. So, what do you think these institutions do when they come out of it? Well, they put their nose to the grindstone, right? They go out there and they say, “If there’s a better way to track that, we need to start digging into that mathematical approach.” And that is where heavy tail risk analysis really started to take flight within our industry.

But unfortunately, the amount of time for the calculations to run heavy tail risk math, the amount of money that it takes, it just has not found its way to the retail space. But we have been focused over the last two-and-a-half years and dedicated to trying to bring more and more of that heavy tail risk math into our process.

[00:14:48] Brad Kasper: Because if I can close the gap even by 0.02% of understanding of worst-case scenarios when these types of market environments and you can go back, right, the Black Tuesday crash. We did a whole deal on these, the Nixon movement. You had the dot-com bubble burst. You had the global financial crisis and then more recently we had the COVID movement. When these types of events come out of the woodwork, you’ve got to make sure that you have complete control of risk understanding in these draw-down types of markets.

[00:15:26] Dean Barber: Right. And if you don’t, that’s where bad things start to happen. That’s when the emotion of fear kicks in. You make a decision that’s probably the wrong decision and you’re way too late to the game to make a change because you didn’t have the right protection mechanisms built within your portfolio or you didn’t understand what that potential drawdown really was within your portfolio. And again, even in Gaussian, you went outside of that stated risk level. So, even though a person might have said, “Well, I thought my worst-case scenario was 34%, here I am down 50. What the hell is going on? How do I get out of this thing?” Then the pain is too late, right?

[00:16:03] Brad Kasper: That’s right. So, let me give you another issue that we have with Gaussian mathematics. And I keep picking on it because so much of what we do is based around our understanding of portfolios and how they align with these modern portfolio theory statistics. And I challenge the notion that there’s a dramatic amount of weakness that’s associated with it. So, let’s take again another.

I’m going to use S&P 500. This is just a broad-based market index and over time, the average standard deviation of the S&P 500 has been roughly around 14%. But, Dean, I’m going to challenge you with a question here. If I were to look at that S&P 500 index in a 36-month moving window, so three years moving window, how many times do you think the S&P 500 actually landed on about a 14% standard deviation?

[00:17:07] Dean Barber: In a 36-month window, I’m going to guess maybe five or six times.

[00:17:11] Brad Kasper: Three times. So, I should have given you a time frame here. In the last 15 years, three times but we use that average standard deviation, as matter of fact, when we’re looking at the risk within our portfolios. So, here I’m telling you that if you think about our original statement of 10% average return, again, completely hypothetical, a 14% standard deviation, here’s the second problem a Gaussian is that 14% is somewhat irrelevant. It’s a moving target over time. Do you think risk of the S&P 500 goes way up into 2008? Absolutely it does.

Do you think the risk starts to drop off a little bit when we get into a little bit more of a recovery phase? Absolutely, it does. It’s a moving target. Yet we use in a lot of mathematical calculations for portfolio construction and development and average that is identified within that normalized distribution curve.

So, one, we’re not getting the full picture within Gaussian and, two, the numbers that we are putting into it to try to generate results is somewhat rendered irrelevant because these are not static numbers. Markets are ever-changing. Data points are ever-changing. So, this is where, again, the technical name of this is leptokurtosis, right? Street name was identified as heavy tail risk analysis.

Much easier to say, although for all the brainiacs out there, it’s nice to know the actual mathematical term of it. So, when heavy tail risk analysis came out, it sat here and said, “Why am I taking averages when I’m trying to build the best understanding of risk and reward within a portfolio?” Instead, what it does is it looks at an underlying investment and how it pairs well with others on a day-to-day basis. And the difference being, I talked about the computation power, Dean, in 2009, this is one of the big struggles as to why we still don’t find it within the retail space.

[00:19:19] Brad Kasper: Back in 2009, you needed a number of supercomputers that are running 24, 34 hours at a time to run every calculation to generate a heavy tail output. It’s just not feasible. It just wasn’t feasible giving some of the technology at the time. But now we’re in 2020. That technology, that computation power is increasingly becoming more and more powerful.

And what it has allowed us to do is run the appropriate calculations that removes the hypotheticals that these average statistics based within the normalized bell curve and it goes to the actual movements so that we can see during different market cycles, not hypothetically, how did it do, actually, how did these various investments pair up together so that we can try to identify, again, best risk controls and best execution for trying to optimize for growth within strategies.

[00:20:18] Dean Barber: Okay. So, let me just make sure that our listeners are comprehending what you’re saying because you just shared a mountain of information. Essentially, what you’re doing is you’re going to say, all right, we’re going to work within not individual security selection. We’re going to work within certain market sectors like we might work in the technology sector, we might work in small-cap, large-cap growth versus value.

You’re looking at those different sectors using what’s my optimum portfolio that gives me the most consistent returns that’s put together the right way, it has the most consistent returns with the least amount of that, what you call the drawdown risk, and you’re taking it back through actual historical cycles to help create that portfolio and then you’re forever testing that.

[00:21:08] Brad Kasper: You nailed it. What it’s allowing us to do is get granular with more actual factual data and eliminate the assumptions. I don’t know about you but I remember vividly my economic class. The professor came in and he said, “You know what happens when you assume too much? You make an ass out of you and me.”

If we can remove as many of the assumptions as possible, the more powerful the output is of clear understanding of how some of these broad asset class or sectors that you had mentioned can stack up together so that we can try to put better controls on breaking mechanisms within the portfolio to try to achieve downside targets that are tolerable over time and try to balance that appropriately with what I call the octane, the alpha of the portfolio, where are we trying to generate real growth during different market cycles? When you can balance those two key factors, all of a sudden, portfolio development starts to look very different than what we have been ingrained with as an industry for decade upon decade.

[00:22:18] Dean Barber: So, let’s rephrase the risk from the Gaussian model to one to three standard deviations from the mean over any 12-month period to rephrase this in a term that I think people can actually relate to and that is what is my maximum potential loss from a peak portfolio value to a trough in that portfolio value over any given period of time?

And can I handle that maximum amount of loss at any time in my portfolio? So, in other words, if somebody’s sitting there saying, “I could never stand a loss of more than 5% from peak to trough under any circumstances,” your portfolio construction is going to look a lot different than somebody who says, “You know, I could handle a peak to trough drop of up to 20% at any given time in my portfolio if I know that my long term averages are going to be there.”

You’re going to have two totally different portfolio constructions but your Gaussian mathematics isn’t going to allow you to get to that because it doesn’t actually measure potential drawdown from peak to trough. It’s only giving you those three standard deviation numbers from an average number over time. There’s your broken problem.

[00:23:35] Brad Kasper: That’s your broken problem. And by the way, there’s not a perfect solution. What heavy tail does is it takes us closer to about a 99.9% understanding. It doesn’t mean that there’s not an event out there that we can’t foresee because it’s not represented within the numbers or the data set that’s been presented so far. So, there’s not a perfect system. If you believe that there is this, I’m telling you, you’re chasing a pipe dream in this industry.

[00:24:06] Dean Barber: I thought it was easy. You just go buy Bitcoin.

[00:24:13] Brad Kasper: So, again, having the actual data, understanding that drawdown. Once I understand the drawdown, Dean, then the question is how do I start optimizing the other side? Because heavy tails don’t just exist in a vacuum of downside, right? They also provide these upside pockets of really solid outperformance. And I don’t even know the number of times that the S&P 500 has superseded its 3% standard deviation event in the last two decades but I’m going to guess it’s more than three or four times.

And so, if we can balance our understanding of downside using heavy tail and we can define that a number of different ways, I thought you did a great job of using a max, what we call a max drawdown, peak to trough drop, there are other ways. We can say what’s our worst one-day tolerance? What’s our worst one-week tolerance? What’s our one-month tolerance?

Each one of those you cannot identify within a traditional Gaussian mathematical model, but we can identify why because we’re going day by day and saying how did these actual mix of asset classes play in the sandbox together? And once you do that, again, you can get really granular with our understanding of risk. Now, the objective is saying, where can I get that octane? And I think that’s been a big part that’s been struggling with traditional NPT over the last decade.

[00:25:41] Dean Barber: Well, because on the other side of that, when you talk about octane, that’s where the greed cycle comes in. That’s when you’re talking with a coworker, you’re talking with a relative, and the markets are doing great, and they’re going, “Look at my portfolio. You know, I made 35%,” and you’re sitting there with a portfolio that’s not designed to ever make 35%. You’re going, “Why in the hell am I only up 15?” Well, because on that portfolio, that might be up to 35, they have a lot more downside potential.

And so, I think what you’re talking about here, Brad, throws out the window this whole idea of traditional asset allocation, because I think what people think about when they think about traditional asset allocation is they’re thinking what percentage of my total portfolio should be in stocks, what percentage of my total portfolio should be in bonds, what should be an international, what should be in real estate, et cetera, on and on. That’s your traditional asset allocation model. But if you can do what you’re talking about here, which you can and you’ve done, and we’re actually utilizing that methodology within some of our strategies, now you can create an entirely new way of looking at investing.

You might say, say I’m going into retirement, I want at least five years’ worth of my retirement income to be in something where I want to experience a daily or weekly or monthly loss more than X because I want that to be rock solid. Now, I know if I do that with that particular piece of my money, I’m going to limit my upside. But now I can have another piece of money that says, “I don’t need to touch this for at least five years.”

And so, now maybe on that piece if I know that I don’t need to touch it, I can increase my amount of downside exposure, which is going to increase my potential upside. And you might have another piece, you say, “I want to put this out to where I don’t need to touch this for 15 years.” Well, now, all of a sudden, you would have three different models in this example that would all be designed to do certain things where you have a good understanding of what is that maximum risk, that maximum drawdown over any period of time so that then you have, I would call it, more confidence in your ability to do the things that you want to do.

[00:27:58] Brad Kasper: Right. It’s a great way to break it down. I mean, most investors, we want our cake and eat it, too, right? We want zero drawdown and we want as much growth as the equities are doing when they’re doing well. Well, guess what? That relationship doesn’t really exist. What you have to do, as you had just mentioned, is first identify what your tolerance is to the downside.

That’s what leads more often than not to capitulation in a portfolio, which is a whole another topic that we need to get into at some point because they can kill your ability to achieve these average returns over time of what these models or portfolio strategies are built to do. Capitulation is kind of the kiss of death. I drew down more than what I expected and we just shared the stats, right? Too many people don’t understand what that worst-case scenario looks like based on that business news review.

When they supersede that drawdown a tolerance, they’re going to capitulate, and that creates longer-term problems. So, identify that risk of what you can tolerate and then say on the other side of that, we want to grow that as much as we can based off of the root parameter that risk matters. And if you can really spend some time with the numbers, you’re really building into something that we call a very positive spread profile. And when I say spread, everybody immediately starts thinking about bonds but I want us to think about it a little bit different.

Spread for us in the way that we think about portfolio construction is how much can I participate in the markets when they’re going up? How much do I participate when the markets are going down? So, we define these as what we call up captures and down captures. And all I’m trying to do is create a baseline of expectation that when markets are moving one direction or the other, how much of that should I participate in? But here’s the beauty in portfolio development.

[00:29:55] Brad Kasper: If you can keep that spread wide, this is where great wealth accumulation can occur. And to your point, Dean, I could go being a wildly aggressive all-stock model that captures 180% of the upmarket but it also captures 150% of the down market, and that’s still a very positive spread profile that can lead to tremendous wealth accumulation. Here’s the problem, though. If you are not an investor that can stomach a 150% drawdown, so let’s put this in perspective, right? Let’s just assume the S&P 500 was our baseline. If I was capturing 150% of that drawdown and the S&P 500 is down 25%, what am I down?

[00:30:41] Dean Barber: 37.5.

[00:30:42] Brad Kasper: More than what the index is doing, right?

[00:30:44] Dean Barber: Yeah.

[00:30:45] Brad Kasper: So, if you can tolerate that, again, I continue to argue that you’ve got a great spread profile, but you’ve got to have, one, the guts to hang on and, two, the ability to allow time to play out to be the net benefactor on the other side. And most investors that we come across just don’t have a tolerance level that would allow them to find drawdowns within their portfolios greater than an all-equity benchmark.

So, we have to be very real with ourselves to understand how much can we really draw down, create that spread profile, because I can be just as powerful with a portfolio that is capturing 80% of the up and 50% of the down. It’s that spread relationship that creates the real wealth accumulation over time.

[00:31:32] Dean Barber: There you go. So, those are your numbers right there. So, let’s take it on a level here where I think everybody can maybe get a grasp on what we’re talking about. Let’s say that the S&P 500 is down 10% and you know that your downside participation because of the way that you created your portfolio is, say, 10% of the drop of the S&P. Well, you know, you’re only going to lose one, right?

If you’re only participating in 10% of the downside, you’re going to lose 1%. If the markets go down 20, you’re going to lose two, and if the markets are down 30, you’re going to lose 3. Well, you go, okay, I can participate in more than 10% of the downside so let’s move that needle a little bit. Maybe I can participate in 25% of the downside. So, if I participate in 25% of the downside, the S&P loses 10, I lose 2.5, I’m not losing any sleep. The S&P loses 20, I lose five, I’m still not losing sleep. Now, what is the maximum return I can get with that kind of downside protection? So, can I participate at a level of 50% of the upside and only capture 25% of the downside? That’s your spread.

[00:32:44] Brad Kasper: That’s your spread, and that’s where real wealth accumulation occurs. And so, let’s go back into 2000 to 2010, which seems like a lifetime ago but what did we refer to that decade as?

[00:32:58] Dean Barber: It’s a loss decade. You had a ten-year period where it had a negative return of the S&P of 11%.

[00:33:03] Brad Kasper: 0% growth out of the S&P 500 over near window but we forget about it because now we’re in a decade where we’ve seen substantial growth out of the S&P 500 from 2010 to 2020. And I use that as the baseline to say if I’m just simply saying I want to be 100% of the S&P 500 from a growth factor, boy, it didn’t work out too well for me during that 2000-2010 timeframe.

So, finding the relationship of what you can tolerate makes a lot of sense. And when you can, again, create that spread with the balance of the downside risk opportunity, now you’re talking about ways that you can potentially generate outperformance against even all equity benchmarks like the S&P 500. And that’s one of the things that you’re always striving for, is generating that risk-return profile that allows you to be relevant in market cycles, but also, again, is continuing to provide that balance to protection because we don’t want to lead that to capitulation in that fear cycle and we don’t want to be chasing in that great cycle.

[00:34:11] Dean Barber: Well, I think, Brad, what this does and what you’re talking about here, what this does more than anything else is it gives you a set of expectations and you have a higher probability of meeting those expectations more than just what the modern portfolio theory and Gaussian mathematics model was giving us. And so, if we can be 99.9% sure that those expectations are going to be met of a loss never greater than 10% if that’s what you choose, or loss never greater than 15% if that’s what you choose, we know that that’s the case.

When we say on the upside, you know, here was our worst-case scenario. Our worst-case scenario or our best-case scenario, what was our best-case scenario on the upside? Maybe it was a positive 15, maybe it was a positive 20. Can I live with those numbers? And if that’s the way that you can design your portfolios using this intelligent risk or the fat tail risk analysis that you’re talking about, that’s when you actually start to set expectations and that’s when you really remove the fear and the greed from the conversation.

[00:35:22] Brad Kasper: I think you nailed it and let’s circle this all the way back to one of the statements that we made at the very beginning. When you generate a portfolio, it needs to be in line with a financial plan because within the financial plan, it’s going to outline what types of goals you need to achieve to find success within that financial plan.

So, finding a balance of the appropriate portfolio with the financial planning efforts kind of connects the dots. And what it does is it helps us put some guardrails around expectations of those portfolios, not so that we don’t lose money or make it more than X index. It keeps us in line with the expectations of what that financial plan was geared to do. That’s where I find success, right, is if I can repeat that process over and over again and be in line with what those expectations are, that financial plan, the odds of success will go way up. And that’s the name of the game.

[00:36:22] Dean Barber: Yeah, I’m glad you brought up the financial plan because that’s the heart of how all portfolios should be constructed. But I want to make a point here, because when the financial planning software programs that are out there today, when they put in your probability of success, they’re using the Gaussian mathematical models, right? They’re using standard asset allocation models.

And so, if you can put a model or a portfolio into your plan that actually has better math, better risk controls, and more predictable outcomes, that actually improves your plan’s probability of success far beyond what the plan itself shows.

[00:37:05] Brad Kasper: That’s right. Listen, Rome was not built in a day, right? The financial industry is going to be flooded with more and more heavy tail risk analysis over the next five years. We feel blessed that we’re ahead of the curve in our introduction. And as we’re starting to use it more and more with our daily practices from a research perspective and at some point, we’re going to have to find some of these planning tools, adopt the better math as well, but it’s going to take some time and it’s going to take a lot of capital.

So, until then, understanding that the plan is well-built and we’re trying to input better math into that plan on the onset from a portfolio development perspective, we’re just trying to stay ahead of the curve of how we can go be relevant in that space and try to trailblaze for the industry.

[00:37:56] Dean Barber: Well, and Brad, let’s go back to one thing that you mentioned earlier in the podcast here and that is the biggest risk to your successful retirement or to your successful financial plan, the biggest risk is not missing out on some of the best days. The biggest risk is participating too much in the down days. It’s the loss. That’s where your biggest risk occurs.

That’s why you see the probability of success in a plan actually decline if you get too much risk in your portfolio because it’s the potential loss that will actually kill the plan. And so, having more predictable outcomes is the ideal scenario and that’s exactly what you’re trying to do here with this intelligent risk and with the fat tail risk analysis and building I would call it a better, more predictable portfolio, something where your expectations are going to be met on a far greater timeframe.

[00:38:52] Brad Kasper: Yeah. We’re just trying to close the gap of what we understand. And I thought you said it well, right? If I think about the miracle of compounding, it’s a beautiful thing in that accumulation phase. But there’s something we call the toilet bowl effect. And I heard a mentor of mine shared this with me a while back. If you are running a strategy that’s drawing down more than what you expected and let’s compound that during a distribution phase, this is like the opposite of compounding.

This is money out of accounts that are spiraling out of control. So, how do we stop that type of an impact? Well, one, we try to identify that drawdown risk to the best of our ability using top-notch math. When you can do that effectively, you can try to eliminate some of the toilet bowl effect. So, hopefully, that’s the catchphrase that everybody takes from this episode.

[00:39:49] Dean Barber: There you go. I want to leave you with one thought, and this is specifically for our listeners. Brad, you and I, before we started this podcast, talked about how we see market valuations at levels that we’ve never seen. When we look at just about any way that you want to measure the valuations of the market, they exceed where they were before the dot-com bubble right now.

So, that doesn’t immediately mean that the world’s coming to an end and the markets are going to crash or anything like that but what that should lead people to start thinking about is if we’re at valuation levels that are higher today than they were before the dot-com bubble and we know that that period from 2000 to 2010 gave us a zero rate of return for 10 years in the S&P 500, I think it’s time for you to step back and say, “All right, let’s really measure what is the risk that you’re taking in your portfolio.”.

Let’s make sure that regardless of what happens in the markets or the economy or whatever else, that you’ve given yourself the highest probability of success of achieving your long-term financial objectives because you’ve taken the time to do the right math and you understand the risk within your portfolio. And that, Brad, is where I think people really need to be looking today.

[00:41:05] Brad Kasper: I agree. I mean, you just nailed one of the headwinds that these markets are going to have to deal with. And guess what? When you start to compile headwinds, it creates what? Volatile market environments. And by the way, these are things that we can see. There are still these unknown events like the COVID-19 type of market that we just can’t project.

You can’t build into your baseline of your calendar year outlook. And so, constantly managing and understanding the risks to the portfolio is just as relevant today as it’s ever been. And identifying those risks, I think, is incredibly important for investors to constantly engage with and monitor, because sometimes, Dean, you know this well. We can get into a market cycle that are running up and we have short-term memory. So, it’s easy to forget about some of these pain points that we may have experienced in the past, but it’s those pain points that help keep us grounded around a discipline that’s appropriate, again for the purposes of success within that financial plan.

[00:42:11] Dean Barber: 100%. Well, Brad, thanks for sharing your thoughts on all kinds of things. I think the Gaussian thing, you got a lot of technical details in there. But the biggest deal, I think, is this. The computers that we have today and the ability to truly measure risk and understand that and build portfolios around a person’s real risk tolerance is better today than it’s ever been before, and it’s going to continue to get stronger. And if you’re just still using those old models, prepare to be disappointed because we know that there are going to be periods of time when things are going to fall way outside of what those standard deviations are.

[00:42:54] Brad Kasper: Yep. Well stated. Like I said, we try to stay ahead of what the technology allows us to do and push the envelope from a portfolio development perspective. And trust me, we’re just scratching the surface. I look forward to a follow-up episode where we can start to get into a little bit more empirical data that supports this movement in heavy tail risk math in the industry, because, like I said, I will be amazed if in the next 10 years it hasn’t become a much bigger part of the retail space.

[00:43:24] Dean Barber: Well, we witnessed at work in the COVID crisis and where the expectation of a maximum drawdown of, say, 10%, well, okay, we had a drawdown of 11, so we exceeded it but it wasn’t something that blew up like the old Gaussian models did. Brad, I’m not going to take any more of your time. You’ve been gracious with it. Thanks for being part of Guided Retirement Show. Remember, get back out and listen to all of the episodes that Brad’s been a part of Episode 10, Episode 11, and Episode 15. Brad, thanks so much.

[00:43:53] Brad Kasper: Thanks, Dean.

[CLOSING]

[00:43:54] Dean Barber: All right, everybody. Great stuff there. Brad did some deep, deep, deep details, I know that, and he talks over my head sometimes too but here’s the bottom line. The bottom line is that you need to truly understand what you own, why you own it, and what is the potential drawdown, and is that within your set of expectations. Is it within your comfort zone? Make sure and check out the show notes below where you can get a complimentary consultation from one of our certified financial planners.

We’re happy to analyze what you’ve got going on, let you know how that fits into your overall risk parameters, and even share with you some of how we build more intelligent portfolios. Thanks for joining me on The Guided Retirement Show. Make sure you subscribe. Make sure you share this with your friends. If you’re watching us on social media, make sure that you like it and also share it with your friends.

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Investment advisory service is offered through Modern Wealth Management, an SEC-registered investment advisor.