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Episode #636 - Why Even the Best Retirement Calculator is Wrong

Roger: Your retirement calculator gave you a 90% success rate. Today we'll talk about why that number might be the most dangerous thing in your retirement plan. Hey there. Welcome to the show dedicated to helping you not just survive retirement, but to have the confidence because you're doing the right work to lean in and rock it. My name is Roger Whitney. Welcome to the show. Today we're going to dive into retirement planning software. These things are amazing. They're getting better and better and better. We haven't even got to AI yet. We'll do an AI theme at some point when I finish my research on that. I think we're actually still early on that one. But retirement planning software is everywhere. Your advisor is using it. If you work with an advisor. If you're not and you're doing this on your own, my guess is you're using retirement planning software that's available through, through Bolden or Fidelity or all the firms that offer it. And it's really important that we understand what this software is good for, what it's not good for, and some of the dangers of how they're built. If we interpret them incorrectly, it can literally cause a lot of problems if we don't use them properly. So that's what we're going to dive into today.

Roger: Now, before we get to that, two housekeeping things. One is this Saturday, March 28, at 10am Central. I'm going to hang out with listeners live for about an hour or so. We're calling it the Noodle Live, where anybody that wants to hop on, grab your cup of coffee. We're just going to talk about maybe the recent episodes. We'll talk about retirement in general and spend an hour or so just enjoying each other's company and talking about how to create a great retirement. So you can learn more about that at livewithroger.com and then if you are signed up for our weekly email, the Noodle. So this is the Noodle Live. But if you're signed up for our weekly email, you're going to receive an exclusive interview with Paul Merriman, uh, wonderful man who has done yeoman's work on financial education. Uh, he hung out with us in the Rock Retirement Club and we recorded it so you can get the wisdom that he has to share. Paul is a great guy. So excited to hang out with you guys on Saturday. All right, with that said, let's get this party started.

PRACTICAL PLANNING SEGMENT

Roger: Today we're going to dive into retirement planning software, specifically Monte Carlo engines such as Bolden. Uh, in our firm, we use moneyguide elite, but there are other ones out there. This is the main thing that retirement planners use to help people determine whether they're able to retire or not. This topic of retirement planning software has been on my mind for a discussion with you for a while because I have some concerns.

ROGER EXPLAINS HIS PERSPECTIVE AS A LONG-TIME PRACTITIONER AND OUTLINES HIS EXPERIENCE USING MONTE CARLO-BASED RETIREMENT TOOLS.

Roger: But before we get started, I want to make sure you understand my perspective. So I've been a Retirement Planner and Financial Advisor for over 30 years. I've been using the main software types that we're going to talk about, Monte Carlo engines, for the last 25 years. I've used pretty much all of them or at least tested them, the ones that have gone away and the ones that we have present. And I use these in practical applications because I work one on one with clients and people on my team work one on one with clients walking the retirement journey. So my perspective is going to be practical from doing this with hundreds of people teaching advisors how to use these, because I've taught on that, teaching individuals in the Rock Retirement Club how to use the software, because we provide software as part of club membership. So I'm a practitioner and that's important to understand. And there's a lot of value in that. But there's also some blind spots. And my blind spots are going to be because I'm not an academic researcher when it comes to retirement planning. I don't write white papers or deep thoughts on a lot of complex topics. Looking at it from the balcony as an aggregate of this thing, retirement planning, I don't have that perspective. I'm not a statistician. I think I understand statistics more than the layman, but the statistician would put me to shame very quickly. I'm not a software developer. I understand software. I understand concepts of ui and I've been involved in certain projects, but I've never created software. I've been more of an advisor on, um, helping software developers. So I may miss some of the broader perspectives in these areas. When it comes to retirement statistics in software, I'm just a practitioner trying to help people create great lives. So that's my perspective. All right, so when we think about retirement, that's a problem we have to solve, right? You got to solve retirement. How am I going to do this? When can I retire? How much can I spend now? Will I be? Is this a safe path if I live to 90 or whatever it is? That's the problem.

COMPLICATED VS. COMPLEX PROBLEMS: WHY RETIREMENT CAN’T BE “SOLVED” LIKE A MATH EQUATION AND MUST INSTEAD BE MANAGED OVER TIME.

Roger: And the first thing we want to do is define what kind of problem we are dealing with. And I think sometimes we forget this and then we use the wrong tools to solve what our problem is. There are two major problem categories. One is complicated problems. Complicated problems are difficult. I mean, can be extremely difficult, but ultimately knowable and predictable. Putting a person on the moon is a complicated problem. A lot of stuff is going on there, but they can do it. They just, if, uh, you, they work it hard enough. Retirement is not a complicated problem, which we'll talk about. Uh, it's important to know that because we apply a lot of tools that can solve complicated problems in theory, but aren't really that great in practice in your one individual life. And this is where the 4% rule comes, the guardrails, safe withdrawal rates, et cetera. Those are math approaches that really aren't appropriate for solving a complex problem, which is what retirement is. That's the second category. So what is a complex problem? It involves interdependent adaptive parts. It's very fluid. You have all of these things interacting in not just today, but over time. And one little change in one input influences and ripples out to all the others in ways that we can't even predict. Including this is the psychological behavior of us as humans as part of this equation. So a retirement problem is a complex problem and you can't solve it. You can only manage it. And you don't want to use tools that are only focused on complicated problems. This is getting a little esoteric. And I don't mean it to be think of like raising a child. That is a complex problem, right? It evolves based on multitude of factors that we can't control or predict. And there is no single right way that works every time because of its complex nature. So as a result, the approach must be adaptive over time to what's actually happening. So that maybe that's a simple example. So we're dealing with a complex problem and that's important.

CONCERNS ABOUT OVERRELIANCE ON SOFTWARE—FROM ADVISORS SCALING BUSINESSES TO INDIVIDUALS MISINTERPRETING RESULTS.

Roger: So why has this topic been on my heart to have a chat with you about? Because I have some worries. I have some worries about how advisors rely too much on software for advice. And I have some worries about individuals and how they're using it. So let's talk about the advisors first. And I've mentored advisors, I've taught part of the CFP program. So I've interacted with a lot of advisors over a lot of years. And when you look at the advice industry, it's a business and it's about more clients. More is the first principle of an advisory business. More sales, more clients, et cetera. More and more these days because, uh, we have a lot less small batch advisory practices. And we have these huge things where we're trying to scale and grow. That’s sort of the method, you know, you're either a huge firm already or you start a firm and then you try to scale it and then sell it to a huge form and make a ton of money. That is the first principle of most advisory practices. So if you think of what an advisor does, they wear so many hats. They're salespeople, they're managers, they're investment managers, they're tax planners, they're business owners. They're a lead advisor to 200 plus clients. They got to do marketing, they got to do sales, they got to do compliance. They wear a ton of hats as they're trying to build this business to get more clients. So it's easy to look for efficiency and lose the art of retirement planning in the process. The phrase I always use is that it becomes about the business of the business of advice rather than the business of advice with clients. That is the current that pulls most advisors. And this doesn't make them all bad. Everybody has good… I'm gonna say everybody except uh, a, uh, uh, select few have great intentions, but there's just so much to do and so much going on. So it's very natural for advisors to look for efficiency and not go deeply on certain topics that are critical because they're focused on all these other things and we lose the art of retirement planning. That worries me as advice is commoditized in so many ways. It's, let's look at it for a second and then move on. And then we can rely on software to replace our thoughtful judgment. Right? That's my worry.

WHAT RETIREMENT SOFTWARE ACTUALLY MEASURES.

Roger: Now individuals, I think the same thing. Because this software is available, you know, Monte Carlo scenario software, which is basically what we're talking about, is available through Fidelity, through Schwab, probably through all the major custodians. You can use standalone retirement, uh, planning software like Bolden. And I'm sure there are some more out there that I'm not even mentioning. So this is available to everybody and it's just a tool. And using software, you know, it's just like using a productivity app. If you don't know how to use it, you're not going to extract the most value out of it. But in the, but the difference with your retirement planning software, if you're not fully understanding the complexity of the problem and you're using these tools, they can lead to some unintended consequences because we're making decisions on quicksand and these decisions are relatively high stakes. Do I retire today? How much am I spending today? Will I be okay? These are higher stake decisions that you only get one shot at. And I've observed both advisors and people spending too much time in software when they're doing retirement planning. And I don't, I spend a reasonable amount of time when I'm working with a client on retirement planning. I spend much more time in an Excel spreadsheet, uh, or in conversation than I do in software. But I find people spending way too much time on plans. And then lastly we, we, we use software. I've seen this and the software is so beautiful that we fail to build resilient plans because software isn't really good at that. So we look at the success rate number and we think we're fine. And we don't do the real work, which is building a resilient plan. So these are my worries. All right, let's talk about retirement software. They're a fantastic tool for use and used properly for managing complex problems like rocking retirement. So when we say retirement tools, we talk about spreadsheets, household balance sheets, AI generated things for our purposes. We're talking about the software that I've mentioned, which are Monte Carlo engines like Bolden or MoneyGuide Elite or E Money. There's a whole slew of them. So that's what we're talking about. So what do these software packages measure since we're using them? What uh, what's the core answer that they are trying to solve for? Well, essentially the core thing that you're trying to solve for is given your specific inputs, how often does your portfolio survive the full time horizon of your retirement plan across thousands of simulated market environments? At the core, that's what it's trying to answer. Given your inputs, how likely is your portfolio to survive in these simulated market outputs and what is success? Because we see this thing in software, success ratio or confidence ratio, what does that even mean? What it means in software terms is not running out of money before the end of the plan. The end of the plan being when the last person passes away. So a success is the person died and there was money left over. That's success. So that's important to understand because that doesn't think anything about the quality of your life and using money early in life or go go years or forgiving, etc. So it equates the result equals the percentage of simulated trials that don't hit zero. That is what you get the success rate from. So if the success rate is 85, that means 85% of the trials got to the end of the plan and we didn't hit zero. So that's the core question it's trying to answer now.

WHAT SOFTWARE DOES NOT MEASURE.

Roger: What doesn't it try to answer? Uh, what doesn't it measure? This is just as important. Whatever the software success ratio is, it's not measuring your actual life in retirement. It's only measuring the simulations based on all of the inputs it's given. Has nothing to do with your life beyond that. Doesn't measure your willingness or ability to adjust your spending, doesn't assess changes in goals and income, doesn't uh, assess actual behavior during bear markets and bull markets and spending shocks, etc. It's only measuring success of not hitting zero before end of life. That's all it's measuring and that's really important. It's just a simulation, it's not about your life.

BEST USES OF PLANNING SOFTWARE.

Roger: So what is it best used for? Because these tools are fantastic. What is the best use of using these Monte Carlo scenarios? Primarily the head. You know, let's get to the punchline first. Assessing long term feasibility of the plan of the life you want to live. Assessing the long term feasibility of the life that you have planned out. What you input into the software, that is the core thing that this is meant to be used for. Is this a safe path given all of these complex inputs and how they might interact? Doesn't mean it's going to happen, doesn't mean it's there and it's yes or no, it's just is this feasible given all of this stuff? It's really good to help us understand how these different variables interact. If I spend more early, this is what happens later in life. If inflation is at this level, this is the impact to my health care and everything else. It's really helpful to see the interaction of all these variables. And uh, primarily it's meant to support confidence in decision making, not to decide for you, but to help support your decision making by giving you proof of concept, potential long term impact of today's decisions, good or bad, et cetera. So it's meant to help inform your decision making, not make the decisions for you. And it's really helpful to bring some order to all this chaos which is retirement planning. A spreadsheet does that, but a spreadsheet is more one dimensional. This is a more multidimensional tool that helps make some order to it and by doing so it brings retirement planning away from gut and intuition and guesses towards data driven decisions. And that's a beautiful thing. Daniel Kahneman I was listening on a interview with uh, him the other day. He said protocols will always make better decisions than humans. And Daniel Kahneman, um, is a, ah, psychologist. I don't even have his bio in front of me but he is the man. He won a Nobel prize with nudge and other research that he did. And basically it's about rationality and decision making. And in the interview he said look, we can be aware of all of our biases from a behavioral finance perspective. We still aren't going to get out of them. Doesn't matter whether we're aware of them. It's a losing battle to just acknowledge them and think that we can change from our fundamental humanness. And that's why protocols and structure is important to decision making because that supplants the gut intuition and just guessing. So that's really important. It brings some discipline, some data to these decisions. The software is wonderful for if-then analysis. If I buy an RV in year two, this is what the possible outcomes could be. It also is very good in giving us common language around decision making which is a subtle thing that actually has a big impact towards us communicating, whether it's with you and your advisor or your spouse and you because we're using the same language. So these are really good things that the software does now. It's a critical part of our process in my practice and uh, probably in most advisory practices.

WHAT SOFTWARE SHOULD NOT BE USED FOR.

Roger: Now what shouldn't it be used for? Always good to flip the script here. Shouldn't be used for predicting whether your specific plan is successful or not. And that tends to be how we use it. It's not about your specific plan because it is not knowable whether your specific plan is achievable or not. It's not predicting the future, it's just giving you a sense of the future using these models. It should not be used as the sole basis for retirement decisions, not used for the sole basis of retirement decisions, when to retire, how much to spend, strategies to use, etc. It is meant to help inform that, maybe give you some proof of concept. But please don't use this to make decisions. Don't let it supplant your judgment or your advisor's judgment. It is not meant to be used for detailed cash flow planning. And I see this all the time when I'm interacting with members in the club who are using software. Uh, of we see these spreadsheets or these forecasts that the software generates and it does it in such beautiful ways that we want to try to get the Cash flows as precise as we can. Even if that cash flow is happening 15 years from now on some random goal that might never happen. It's not good for planning a paycheck. It's not good for mapping out your paycheck strategy. It's not good for building a multi year Roth conversion strategy. It's just not it. All the utility uh, starts to degrade very, very quickly. It should not be used for determining your retirement allocation. It can inform these decisions but it shouldn't determine it. And it should not be used to determine detailed strategies like a Roth conversion. It doesn't have the ability to do that. But we use this stuff and advisors use this stuff all day long to, to do that. And that worries me.

KEY DANGERS OF USING RETIREMENT SOFTWARE.

Roger: So what are some dangers or things that we need to look out for in using retirement planning software? I think one big one is garbage in, garbage out. You know, if you think of what a retirement planning software tool is doing is it's taking a lot of assumptions. I'm going to go through a lot of assumptions just to give you a sense of how these things interact. You're never going to get perfect assumptions. But the more poorer your assumptions are, the more or uh, the less meaningful any of the results are. And we're going to go through that in a minute. One thing they're dangerous for is that success or confidence ratio trap this. These softwares look like they are very precise. They present all of the data in beautiful charts and graphs with green color for safe and red color for not safe, etc. They this gives a false sense of certainty that the software is giving us something that's valid. It gives us, you know, well, but before that it gives us a false sense of certainty. It's an unbiased results based on a mountain of biased assumptions and we'll go through those assumptions in a minute here. It's easy to feel like it's predicting the future of, of how we're going to be. And it's not. It's just showing the simulations of possible outcomes, not yours and treating as a false sense of precision. A uh, 92% ratio of success actually is not that different than an 84% ratio of success or confidence ratio. But we think it's really precise. I've had these conversations, these questions have come in, Roger, what target of success ratio should I shoot for? I'm at 88, but I would like to be at 92. We spend a lot of mental energy working and thinking about these things when it's really meaningless. It's Just noise. Because we're modeling decades of life with all of these complex inputs. We don't want to use it for that stuff. And we can know this and still do it. And that's the risk of just the psychological aspect of it. Uh, another danger in using is overconfidence of trusting that the software is giving us a precise answer. And it's beautiful and it's clean and it can articulate it well. So we just are overconfident that the software says, I'm okay, so I'm okay, so I don't have to do any more work. And then the flip side, the software can paralyze us. I've seen this happen as well, of over testing. Well, what if this happens? What if I buy a car this time? What if I buy that file cabinet three years from now? You know, all these different tests of scenarios of things that might happen and we, we forget to go live our retirement. The danger r is a substitute for our judgment. We let it determine the decisions rather than informing us. And honestly, it substitutes us doing the work. It takes work. Software isn't enough. If we want to build a retirement plan we can have confidence in. It's not just the success ratio number. We're going to have to do the work to make the plan resilient, to create a paycheck.

FEASIBILITY VS. RESILIENCE: WHY A PLAN THAT “WORKS” ON PAPER MAY STILL BE FRAGILE IN REAL LIFE.

Roger: So like I said before, and maybe before, we move on to all of the assumptions that go into these things. That's why I always preach that software is great for determining feasibility of a plan. This is the life that I want. Here are the resources I have. I'm facing 30 years with all these crazy variables happening. Software is great at helping us see, is this a safe direction? Is this a feasible direction? Software is wonderful for that. But something that is feasible is not necessarily resilient. Right. It's possible that Roger could sail across the ocean in a boat because he had all the supplies and he had some basic knowledge that's feasible. But that doesn't make it a resilient choice to go do it. What happens if a water pump breaks? What happens if Roger gets sick? What happens if a storm comes in? What happens if the nav goes out? You have to have redundancies, you have to have margin for error, et cetera. In this case, we're talking about retirement paycheck in order to make it resilient. So if that storm comes in, Roger can be resilient enough to continue on his journey. That's the difference we're talking here. And software isn't going to do that for you, in my opinion or in my experience.

THE REAL RISK: OVERSPENDING EARLY AND JEOPARDIZING LATER YEARS & UNDERSPENDING AND MISSING OUT ON LIFE

Roger: Okay, before we get to the assumption, one last thing, and this is why this is so important, and I'm worried about how easily advisors and individuals just use this stuff and don't think as deeply as I think they should about this stuff. Is the severity of getting it wrong. What is the severity of getting it wrong by not doing the work or shortchanging the work because the software just gives this false sense of security. The severity is pretty big in my mind. First is the severity is if you retire, the software says you can retire early and says you can spend a lot of money early in retirement. And if it's not feasible and it's a brittle plan and then you do so well, what's the severity of that? It may present you with a lot more constrained resources later in life. You might run out of money because you got the bad roll of the die. Uh, if a perfect storm comes early in retirement, inflation markets are down, major life expectancies, it could impair the rest of your retirement plan, and you've already retired. That's a pretty severe outcome on really big decisions. Now let's flip the coin on that. Another severe outcome is underspending because remember, software is just trying to get you to, you know, to the end of the plan with a dollar at least. A, uh, severity of getting it wrong in retirement planning is underspending either working too long to make the software result get from say, an 85% confidence to a 92% confidence, working too long, or not spending as much early in retirement because you want to keep the percentage at a certain percentage. And then the cost is literally your life of missing your life early when you're as healthy as you'll likely ever be, et cetera. And then having too much money towards the end, that's an equally severe, uh, outcome. So this is a pretty high stakes decision that you're trying to manage. So I think we should probably be really thoughtful in how we do it. Not just not to run out of money, but to create a great life.

THE MASSIVE NUMBER OF ASSUMPTIONS BEHIND EVERY PLAN—AND HOW SMALL CHANGES CAN DRAMATICALLY ALTER OUTCOMES OVER TIME.

Roger: All right, to give you an idea of how inaccurate all of this software, and I want to talk about all of the inputs that go into these software engines. There are a lot more than you might realize. And how they interact is a pretty big deal. Because when you run the scenarios, they all feel objective because the computer ran them. But they are only as unbiased as the assumptions baked into them, many of which you never see and never choose because there are a lot of Simplifying assumptions that have to go into them. And each assumption that we enter into the software, whether you see it or not, doesn't sit in isolation. If you choose an inflation rate or the software chooses it for you, that interacts with everything else in lots of different ways that are hard to understand and they multiply over time. Here's an example. If your returns are slightly optimistic and your return assumption is paired with a pessimistic inflation assumption with a shorter life expectancy, you may produce an overconfident result. Now, you could inverse that and exactly one little tweak in some of these numbers that feel small in the moment. They're like a little butterfly wing. Oh, I'm going to change my performance assumption from 7% to 6.9. Seems inconsequential. Tenth of a percent. But if you do that over 30 years and how it interacts with all the other things in the software, suddenly you've changed the plan over a large area. So let's talk about some of the assumptions that pile on top of each other and top of each other and interact in different ways. Well, you're going to have to input some assumptions, right? You're going to have to put your retirement age. If you're not already retired, you're going to have to put your planning horizon, which is essentially when you're going to die. And if you have a spouse, when the first person dies and when the second person dies, that is going to show the life of the plan that these simulations are going to run. So let's say you're 60 years old and you say you're going to die at 90. Now, we've set the time horizon. That's an assumption, but we don't know what that number actually is. You could live a lot longer. You could live a shorter life expectancy. We have to input the spending over the life of the plan, right? You got to say what you know, plan wants to know. What do you got to spend? Sometimes that can just be one number. That could be multiple different types of goals of go go travel and gifting and healthcare. Healthcare before Medicare, healthcare after Medicare, doing that trip every three years and what that number is. These things pile and pile on top of each other. You have to input these things and basically you have to input your spending for what you want for the rest of your life in order to do the simulations. I don't know about you, but my spending time horizon, I have my base great life that's probably more consistent. But all the discretionary stuff I can see out maybe two years, but you got to hardwire it in there for the rest of your life and that is going to inform the results. So here's like a simple example of something that we don't think about. Let's assume your base. Great. Life is $120,000 a year. And so you enter it as you enter it at I'm 60 years old, I need to spend $120,000 a year between now and 90 when I die. Because you've done the math and you looked at the research. Well, behind that, another assumption that we'll talk about in a minute is it's going to assume that that $120,000 number that you input is going to increase by inflation every single year. Makes sense, right? Because we want to maintain our standard of living. But what if 20,000 of that 120,000 is actually a mortgage that has 20 years left on it? Well, the principal and interest on that mortgage, that represents that $20,000 a year. It's not going to inflate, right? You have a fixed rate mortgage, let's say you have 20 years left. But if you input it as just one number of $120,000 now you're overstating significantly because we're compounding interest or ah, inflation on top of that $20,000 a year, it's going to influence the results. That may cost you in results because it's going to overstate the spending. It may come out with a result that says you have to work another year or you have to spend less now. Whereas if you had modeled it more accurately, maybe you would have been able to retire early. These things have consequences, what we input, that's just a simple example. We also have to input your income sources, your Social Security estimate. That's probably something that we can relatively estimate or get somewhat precise with because of Social Security statements, rental income or part time work, other major inflows of inheritances or future sale of some equity you have. We have to put the actual dollar amount and the software is just going to assume that's exactly what's going to happen. Major, um, expenses, the new home, the future rv. We have to input those things. Specifically, we have to input what asset allocation we're going to use. Talk about that a little bit more later assets, uh, that we're going to sell in the future. So these are inputs that we get to put in. And each one of these doesn't just sit in isolation. They interact with all the other inputs over a 30 year time frame in lots of different ways. So we can see how complex this can get very quickly. Now let's talk about some of the inputs that the software has to make. Many of this is hardwired into the software by the developer and each tool is different, so it has to. Let's talk about the portfolio. Right. We have to know what performance data are we using for our assumptions when it comes to whatever kind of portfolio you have. So those are called capital market assumptions in the industry. Right. So it's generally we're doing asset allocation, so we'll have some allocation models that you choose from and the return assumption and all the other assumptions are going to be based on these capital market assumptions. And there's three basic ones. There's what type of return on average does each individual asset class or portfolio have? What's the average return of that target portfolio? That's the first thing. The second thing we need to know is the correlation of the investments in that allocation. How do they interact with each other? When one goes up, does the other one go up and, or does it go down or does it move in tandem and everywhere in between? So we need to know that to determine that average return. And then the third one is something called standard deviation, which is essentially how much does this bounce around? How variable are the returns around the average? You know, real simple. The best way to think about that is. Well, a good way to think about it is in Texas the average temperature is in the low 70s. So over a, uh, 12 month period, average temperature low 70s. But we get really high into the hundreds, sometimes for a month, constant, and then we can get down into the teens and twenties. So there's a lot of variability around that 70 degree average. And again, I'm just simplifying it for this example. Well, 70 degrees is roughly right around. 70 degrees is roughly the average temperature in San Diego. Right. But that doesn't move around a lot. Right. They don't get into the hundreds as often as Texas or as low as we get. So that's what standard deviation is. These are important because this is going to be hardwired into it to help determine the results. We also need to know the source of this data. And that's a big rabbit hole you want to go down. The two major sources are, uh, one is we just use historical data for returns and standard deviation, et cetera. We just look to history to inform that. And if we use historical data, how far do we use the data? Do we just go back 30 years? Do we go back to all the data we have? There's questions about how good the data is because The S&P 500 wasn't always the S&P 500. It was the S&P 90 for a while, et cetera. International only goes back to 1970, 1972. So do we use historical data and if so, what period of time? What, uh, or do we use projected returns and projected standard deviations? And this is adding another layer of people guessing about the future. All of these things will have a material impact on the results and each software is going to address it differently. And then not to get too geeky here, but also when they run the simulations, how they manage the distributions of those simulations around the average? Is it just a standard bell curve or longitudinal that's going to impact results as well? Okay, let's not get too geeky. Inflation, we have to have an inflation number that can be static or dynamic. And where is that going to come from? Like in my practice we use just inflation. We use the average. Since CPI started to be followed. We have tax rates that are assumed in the plan. Some software is very complex in how they think about standard deductions, how they keep up with changes in the tax law. But we also have to have the tax rates not just now, but for the future because we're planning over 30 years. These are assumptions that are embedded in these things and how those taxes are actually applied each year within the simulation. So here's a practical example. If you have an after tax account, like an investment account, joint investment account, let's say, let's assume it has a million dollars and it has mutual funds or stocks in there, well, the software is going to have to assume one, that there are dividends and interest coming off of those investments. So it's going to have to make an assumption of how much dividends off this million dollar investment portfolio do I need to have taxed each year so I can create a tax estimate. But also it has to assume how much capital gains are going to occur every year because unless you hold individual stocks and you never ever, ever, ever sell them, you're going to incur capital gains each year. And so it has assumptions in there of what that capital gain will be and that can have a huge impact on the results. And how this stuff gets tax has to have an assumption in here for it. And then how IRMAA is applied, how Social Security taxation is applied, how all these things handled. Medicare, how is that? How, you know, what, what are you paying for out of pocket, you know, during Medicare and pre Medicare, what are you going to pay for part B, part D. All these assumptions get in there, and they're all piling on top of each other. They're creating this mountain, and they're all interacting in ways, not just once, but all through all the simulations. Oh, my goodness. These things are so inaccurate and so messy. I don't expect you to go down the rabbit hole. I don't expect an advisor to go down the rabbit hole. Maybe. Actually, I sort of do. Let me retract that statement. I do expect an advisor to go down the rabbit hole to appreciate and understand, or understand and appreciate the complexity of what this software is doing in order to create those beautifully, uh, graft, precise responses. And the more we multitask and the more we supplant our judgment in thinking for the illusion of precision, the less confidence we should have in our decisions. And the severity of getting it wrong is pretty big.

HOW TO INTERPRET RESULTS PROPERLY.

Roger: All right, how do we interpret results? Because we got to use these things because they're wonderful in giving us some context to these complex problems. How do we interpret the results? Well, number one is. Let's talk about how to read a success rate, because that's the one number. Everything comes down to one number. Your success ratio is. Your confidence number is. How do we read that number? Let's assume it's 85%. How do we read that number? What is that number first actually telling us? Well, that 85% number is telling us that that is the percentage of the trials that had at least $1 at the end of plan, which is how software defines success. I didn't run out of money until the last person passed with all these assumptions. So that 85% success ratio means 85% of the simulated trials were successful. That's actually a really good percentage. Now, some people would ask, well, if 85 is great, is 95 better? Better? What do you mean by better? Is 99 better? We have a lot of people that want to try to get to 99. Is 99 better than 85? Well, it depends on how you define better. Because if you're at 99% success ratio, you could argue that, no, that's not better, because that may mean your plan is too conservative. And you could actually retire earlier or spend more earlier or take less risk right now or give more money away, uh, because you have too much money towards the end of retirement. We'll get to that in a minute. So it's not an exact science of what is the right number. You know, an 85% success ratio is different for Sally than It is for Bob, but that's essentially what it means. It also means the inverse of 15% of the trials ran out of money before the end of the plan. So they failed. And of course we care about those because that's a severe outcome, but that's what that number actually means. It's just how many of the trials were successful has nothing to do with your life. So, but we want to look at that. And that I use as a, like a compass reading, because as life unfolds and you update all the, your inputs into the models, you can gauge your success ratio. And if it starts going down, that could help you to prompt you to do some work in order to navigate and correct course a little bit, so we can catch problems early. So that's the way that I like to use it. But we can't just stop at success ratio.

LOOKING BEYOND THE NUMBER: EVALUATING THE DISTRIBUTION OF OUTCOMES AND PLAN SENSITIVITY.

Roger: The second thing we want to look at are the distribution of all the trials. So let's assume there's a thousand trials with all the assumptions that we talked about previously, and the success rate tells us how many trials survived. This distribution tells the story of how they, they lived or died each of these individual trials. So what you'll see is usually you'll see a graph with each individual trial and showing like age 60. And there'll be a squiggly line going out to age 90. And some will go way up, some will go way down. A lot of them will be in the middle. And then usually there's a table below it that shows the distribution of ending values and at what age, etc. How that distribution looks is very informative. And the two things you want to look for are one, how clustered are they? So like, two plans that have an 85% confidence can look very different when you look at the distributions. So if we think of all of these distributions, these individual trials, if they're all bunched together, but you have some, a few outliers that are really negative, and that represents the 15% failure rate. That plan is less sensitive to market environments if they're all clustered, because each one of those trials, the only difference between one and the other are, uh, the return sequences. And so if the majority of them clustered together, it's probably a more resilient plan, naturally. And then the other extreme of that is if there's a wide spread of distribution of those thousand trials. So if the spread is not clustered, but really everywhere, you have where you're dying with $100 million and you have where you run out of money at age, you know 70, if it's just all over the place, that means your plan is probably less resilient and very sensitive to market environments. And even if they Both say an 85% confidence rating, because at 85%, if we go back to that compass analogy, let's say that's our heading. So a plan that has a very tight distribution of outcomes within the trials is more of a stable heading, meaning if markets move around or even if spending's off a little bit, it'll stay closer to its heading than one that's wildly all over the place. That one a little tweak in markets could drastically change. It's not a stable heading, even though initially in the plan reading they're both 85% confidence. And if you don't know this and your or your advisor doesn't knows this and they say, oh, you're 85% confidence, you can retire and then you do so. But you had this wide distribution where it was a really sensitive plan, you could get catch yourself in a pickle really quickly. This happened to Rosie, who was a retirement plan live case study a few years ago where their advisor don't know, um, him, never met him, never talked to him. They did, didn't do a good job. They didn't understand this stuff. She retired with a confidence ratio in the software that said they were fine, but her plan wasn't resilient. It was very sensitive. Them, um, guessing. If I had looked at the distribution of all these trials, it was all over the place. And she went through a bear Market and 3/4 of the year into retirement, her plan wasn't feasible because the bad thing happened. And all of a sudden that 85% confidence, whatever it was, went down significantly. That's what we want to look at here. Like in our practice, we like to look at both to help inform what's going on here. And we like to usually look at the distribution between the 75th and the 25th percentile to get an idea. All right, those are two things you want to try to interpret and there's some color to how to do that.

UNDERSTANDING TIMING AND SEVERITY FAILURES

Roger: How do you look at failures? Let's talk about, is there anything that we can learn from the failures within this distribution? Two things. One is we want to observe what is the timing that one of the iterations breaks down in this feasible plan of 85%. So the one, the 15% that failed, what is the timing of those failures? Early failures, let's say 10%, you know, the majority of that 15% failure rate, um, most of those Happened early, like in the 70s or even in the early 80s. Early failures tell you that you, you probably have a broken plan, regardless of what the 85% is, because it, the, the, the disaster's outcomes happen so quick. Late failures happening, you know, say you planning on live to 90. Late failures at 88, at 87, at 86, even those are less worrisome because they're way out, and you have a lot more time to manage those. So that's important. So we want to look at when those failures happen, number one. And the next thing we want to look at, because, you know, that tells you when it breaks down. The next thing you want to look at is, well, when it breaks down, how broke is it? And we want to look at the depth of the failures. So let's think of two plans. Plan A fails at age 82, and you would have needed, like $50,000 more to survive. That's, uh, a near miss and highly correctable with small adjustments. But if plan B is exactly the same, but it ran out of money at $82,000 with $400,000 more needed to survive, that's more of a structural problem that needs to be addressed in order to make the plan resilient. So those are the types of things that you want to look at. So here are some questions for you to consider as you're evaluating this stuff. Number one is look at how flexible is my spending. This is important because when you're using retirement planning software like the one we use, Money moneyguide, uh, Elite, they have needs, needs and wants, which are more discretionary spending, and then really discretionary spending, which are wishes. Usually when you're looking at the result, it's including all of those categories. When in reality, if things really go wrong, what we care about is that needs, that base, great life. The wants and the wishes can go away very quickly as long as we can have that base, great life. So we got to be careful when we look at what spending we're actually modeling, especially if we have some more of these aspirational things in there, because they could cause us to misinterpret the result of, like, okay, it's really sensitive, but, oh, wait a second, there's that $100,000 gift I'm going to do in 10 years. Well, take that out of there, because if it all goes sideways, I don't want that in there. We got to make sure we know that when we're just looking at the one pretty number. So review that. How flexible is your spending and what spending are you actually modeling. And then another thing that you can look at is how much of your base great life or your needs are covered by guaranteed income. How much of it is just naturally covered by your Social Security, pension, annuity income. The more of your base great life you have covered by guaranteed income, the more you can handle a lower probability of success or success ratio because really all we're dealing with is discretionary spending. So those are some good things to review within the context of interpreting these results.

BEST PRACTICES

Roger: All right, let's finish this up with some best practices. Number one, hold your success ratio or your confidence ratio. Hold it very lightly, like a little birdie in your hand. Don't take it as gospel or as proof that you're okay. It's just a reading at a point in time to help inform decision making. Number two, when you're using software, compartmentalize the tool to help assess the feasibility of your plan or of a decision that you're making. Just assessing the long term feasibility of what you're trying to do. So if you have a plan, think of it that way. And then when you come to a change, I've decided I want to buy a lake house. Use the software to help determine the feasibility of it and uh, to conceptualize how the long term impacts might be if you do the household. So it's great in informing that kind of decision making. Don't use it for resilience, assuming it's resilient. Use it as a snapshot in time, like a compass, not a forecast that I'm fine for the next 30 years. When you hear or read the 85% or 87% confidence, don't forget the messiness of the assumptions. Hold it really lightly. I don't care how pretty the graphs are, how convicted your advisor is if he doesn't understand all this or she doesn't understand all this. A best practice is to keep your goals tight. Don't overcomplicate all the cash flows going on. So what do I mean by that? Well, a lot of times when we're figuring out base great life and wants, some clients will do five or six wants. I want to travel, I want to buy a uh, Jaguar or a, or a boat, I have this hobby, et cetera. But some clients, individuals will proliferate their goals and have like 35 different goals with different time frames on each goal and different amounts and different categories. Don't do that. Don't over complicate the amount. Better to have I need a base great life of 150,000 and I want discretionary go go spending of 40,000? Better to have that than to have all of these goals that are cluttering the dashboard because they're all going to interact in crazy ways. And it's the pursuit of a false sense of precision because you're predicting these cash flows going out 30 years. So don't do that. Keep it nice and tight. Next, a best practice. And this is something that needs to be ongoing and that is quality. Check your inputs. Review the goals that you're entering and the sequence of those goals when you think they're going to happen. Review the sequence of any cash flows that you're going to receive, that inheritance and the sale of, uh, this and the purchase of that. Review the specific things on a regular basis. You know, a good example is a pension. If you're entering a pension, well, you have lots of different things you can enter, right? It can be life only. It can be joint life. It could be 100% joint life, it could be 50% joint life. It could have an inflation rider, it could have an inflation rider that doesn't survive the primary, uh, beneficiary. All of those things I can't tell you even in my practice with my team. And they're amazing how often things are modeled incorrectly. So we constantly quality check inputs. And you can do that two ways. One is go through screen by screen what you're looking at and say them out loud. Don't just look at them. There's something about saying them out loud that will help you cognitively connect some dots. And then the second way is that when you have your plan of record built, every software I've used, you can open up a table that shows you all the cash flows, spending and income and tax assumptions year by year for the life of the plan. Look at the timing of those cash flows because that will help you see it more visually. Of, uh, wait a second, my Social Security doesn't start in that year, it starts in this year. So those are the two best ways to quality check. We've done some talks on that. Next is don't over plan, don't over plan. Don't change your feasible plan of record all the time. Don't get in there and tweak assumptions because you read an article about how bad the markets are going to be or about inflation, et cetera. Don't tweak the assumptions. Be very careful about changing assumptions too often. Likely once a year is all you need to do. If you're in calm waters, updating your feasible plan of record using Software once a year is probably fine, or if we have a huge bear market, then maybe do it. There's anything more than that is going to create noise and not actually help you with decision making. And don't tweak as things move around. Think of it this way. It's like if you're on a GPS and you're traveling, you don't change your GPS because you decided to go to this restaurant that's two miles off the road, right? You just go and then you get back on course. You're going to meander a little bit. You don't have to capture all that and tweaking the software. The less you touch the software, as long as you're updating consistently, the better it'll get you out of over planning. Use software as part of a much more robust planning process. A more planning process, which means having regular little conversations, updating it at least once a year when you're thinking of changing something, using it to help give you context, to make judgments about that. It's all about those little conversations more than it is about having the most precise software. And then lastly, you need to define what success means to you. Does it mean, as the software is defining it, that it's just not running out of money before the end of plan? Because that's how the software is going to view a, uh, failure, right? A failure of a dollar at age 90 is treated exactly like the failure of $400,000 at age 90. It doesn't notice a difference. It's binary. And so that can cause you to miss life if you just use the definition in the software. How important are the go go years with you? Be honest with what you want to prioritize for. Is it retiring sooner? Is it saving less and living more now? Is it maximizing go go years? Is it lowering risk? Is it leaving assets at the end? The more you have some clarity of which one of these are the most important, the better you're going to be able to use the software. My hope for you is that this gave you some context to think more deeply about interacting with very elegant and beautiful software and to be cautious not to let that elegance overshadow your judgment and the art of retirement planning, because that is where the real work is done. And if you're working with an advisor, I would be cautious and have these conversations and ask a few follow up questions to see if they understand in depth the nuance and the fragility of software and Monte Carlo projections. All right, with that said, that was an hour there hopefully that was helpful to you. Let's go. Take a smart sprint.

SMART SPRINT

Roger: And we're off to take a little baby step you can take in the next seven days to not just rock retirement, but rock life. All right, in the next seven days, simply schedule a time to review all of the assumptions in the planning software of your choice. Don't you have to do it this week? Just schedule a time. It could be six months from now, but just put it on the calendar for you to review these one by one to understand them better so you can master this tool. And it's going to be clunky at first, but the more you master the tools you use, the more of a craftsman you can be for your retirement.

CLOSING THOUGHTS

Roger: I've been really struggling with what to do at the end of the show, so I'm going to take a lead from Cal Newport, who I listen to one of the only podcasts I listen to, and I'm just going to talk about things that I've been noodling on and working on that are related to retirement planning. Tanya Nichols, Uh, she and I merged last year. Her firm, Aligned Financial, my firm, Agile Retirement Management, have merged. We're working on this quarter launching the new firm, which is going to be called Retire Agile. Can't wait to share that with you. I think we've shared a bottle. I got to get the swag first for that stuff. But as part of that process, we've been taking her process because she's a retirement planner and a very thoughtful one, and my process. And, uh, we've been going through the very painful but necessary project of merging to one process. And the overall theme that we have been going for as we merge our processes together is elegant simplicity. Because even we can tend to do too much to get in the way of creating a plan that someone can have confidence in and comfort in so they can go live their life. Because that's the whole point of retirement planning, is to go live your life. And a lot of times this planning stuff can get in the way. And that can easily happen when you have two geeks like Tanya and I engineering how the process works. It's been a wonderful little stressful process of shedding our past selves and creating a new process, a more elegant process with all the. So that's one reason why I've been diving into, you know, the software and making sure we understand how we use this. So it can be. We know what we're reading and we don't get too simplistic. Which can have severe consequences. So that's what we've been geeking out on. I hope you're geeking out on something really fun. Hope you're having a great spring. Next month in April is going to be all about questions and answers. So we got a lot of questions that you've shared with me, and then we're going to try to hammer those out so we can help you take a baby step towards rocking retirement.

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