I don’t like bad models, or models used for the wrong reason, or models used by people who don’t understand them, especially when people who don’t understand them try to explain them to other people who don’t understand them.
It also concerns me that many people attribute mystical powers to computers. A computer model is no better than the human that programs it. It’s just a whole lot faster and doesn’t get complacent or bored.
So, I certainly don’t like all models.
If you’re going to use computer models to help plan your retirement, there are many things to consider.
First, economic models can be very useful to study your retirement prospects and figure out the best bets but they in no way predict your future. As the saying goes, all models are wrong but some are useful.
A Monte Carlo model, for example, can test thousands of possible future scenarios for your household but your retirement is a one-time event. There is no way to know which one of a multitude of simulated scenarios might be similar to the future you will experience or if any of them will. The tendency is to guess that your retirement will be like the median model outcome but that means you will be overly optimistic half the time.
The output that models create is only as realistic as the assumptions we feed them. Unfortunately, we can’t estimate with any precision what future market returns will be, how long we will live or even how much we will need to spend over the coming decades. These are some of the key assumptions that drive models and we are, for the most part, guessing at what they might be.
In computer science there is an old saying, garbage in, garbage out. What we mean is that the output of a program is only as good as the input. Make a wild guess at the input and the output will be a wild guess. Unfortunately, many of our guesses, or “assumptions”, are by necessity fairly wild.
A reader recently commented that “retirement planning is an unsolvable problem with unlimited variables.” You could say that about chess, too, but some players clearly solve it better than others.
Retirement finance is unsolvable if your definition of “solvable” is finding a single, optimal solution in advance for your individual household. But, there are lots of “games” in economics that are probabilistic — retirement planning can be considered a “stochastic game against nature” in game theory parlance — for which we can determine the best strategies even though we can’t be guaranteed to win.
We should never expect a model to provide a single, optimal solution to the retirement planning “game”, nor should we expect that from a human advisor. The optimal solution can only be identified with certainty after retirement is over and that isn’t very helpful for planning purposes.
Our goal, like that of the chess player, should be to find and implement strategies that produce the outcomes we want and avoid the ones we fear more often than alternative strategies do.
Many so-called retirement models concentrate almost solely on investment results. Those are investment models, not retirement models. A comprehensive retirement plan will consider many factors including Social Security maximization, annuitization, life insurance, estate planning, taxes, and others.
It is possible to use multiple models (perhaps an investment model, a Social Security model, and a tax program) to solve these problems individually but a comprehensive plan needs to also consider the interplay of these factors. (See a sample list of free limited-purpose models below.) Change any of these factors and other factors will be impacted. We would pay a big price in the planning process if we didn’t consider that. A comprehensive retirement planning model is a much better tool.
A major benefit of Monte Carlo models is that we can test changes to many factors and see how they interact in one model run.
Assume, for example, that we are planning retirement with a spreadsheet model, such as the Bogleheads spreadsheet below, that considers many retirement-funding options. Let’s say that I want to run the spreadsheet to consider all possible combinations of market returns ranging from 4% to 10% in 1% increments, asset allocations from 0% to 100% equities in 10% increments, and annuitization from 0% to 50% in 10% increments. Considering just those three factors, I would need 462 runs to capture the combined effects with a spreadsheet model or I could capture them and many more with one run of a Monte Carlo model. There are actually several other factors I should include and note that the spreadsheet is not modeling sequence risk.
It is unlikely that a retirement toolkit provided by an investment firm will give annuities, life insurance or reverse mortgages consideration equal to equities and vice versa. Better to find a model with no agenda.
It’s also important to know who built the model and their qualifications. I have a lot of confidence in the Bogleheads and more in Laurence Kotlikoff, who created MaxiFi. Wealthfront identifies the developer of their retirement model. But, unless you know the qualifications of the model builder, I’d steer clear. Anyone can build a model and post it on the Internet.
Building a retirement model requires an understanding of finance, modeling skills, expertise in the computer language used (even if it is only Excel), and a sound understanding of statistics and probabilities. If you don’t have all four, then building your own model is a very bad idea.
Retirement planning software can answer a lot of questions but you have to know what to ask.
Models can answer a lot of questions but you have to know what to ask. A model is unlikely to suggest that Roth conversions might be profitable, for example, or that you should consider a combination of annuities, whole life insurance, and equities, as Wade Pfau and Michael Finke have suggested. A good human retirement planner knows what to ask.
I am wary of models that use probability of ruin as their metric of success. Probability of ruin measures the probability of portfolio failures but does not measure the magnitude of losses. For example, it will count a strategy that funds 29 out of 30 years an unequivocal failure. It will count a retirement strategy that successfully funds 30 years as a success but no more successful than one that funds 50 years.
As Zvi Bodie points out, probability of ruin doesn’t consider utility. Presumably, retirees will be less satisfied coming up $100 short of paying the bills than they will be satisfied with a $100 surplus. Paying the bills is a necessity; having a little extra is a nicety.
Probability of ruin is a particularly bad metric for Monte Carlo models because, among other reasons, results can change significantly by changing nothing but the random number draw. If you use such a model, try running it several times with the same input and see if you get nearly identical results each time. If the results change a lot for each run and never converge then the model is problematic. If the results are precisely the same for each run with the same input it may be because the model always uses the same set of random numbers for every run to speed up computation, so we still can’t say for sure that the model is properly constructed.
If you are going to plan with a “retirement toolkit”, I recommend the following:
- Be aware that today’s retirement models are not a replacement for a human advisor. If you plan your own retirement with these models then you and not the model will be replacing the advisor.
- Understand that no model can predict your future, certainly not for 30 years. You will need to recalculate periodically.
- Know the credentials of the model builder.
- Use models to explore the possible outcomes and better understand the economic forces at play. When you see bad outcomes, try to come up with a way to mitigate them.
- Be aware that a model is only as good as the input we provide and the assumptions we make and that we can’t make very precise assumptions. That means we won’t get very precise results.
- Understand that a model is not a retirement plan. It is one tool to help build a plan.
- Find a model from a provider that won’t profit from the sale of retirement-funding products.
- Find a comprehensive retirement model that tests several key factors — spending rules, taxes, Social Security claiming, pensions, annuities, life insurance, asset allocation, etc. — and their interactions instead of trying to combine the results of single-purpose models.
I think the best and most comprehensive retirement planning software with a reasonable price tag for consumers at present is Laurence Kotlikoff’s MaxiFi.com. It’s not free but it is affordable. It can be tricky to use and, again, the more you know about retirement finance, the better the results you can expect. MaxiFi provides all the capabilities that I mentioned above and more and it completely avoids probability-of-ruin issues by maximizing lifetime consumption, instead.
What should you do with this information?
Use all “toolkits” with caution. I have a toolkit in my garage that contains all the tools needed to perform most household plumbing chores but for some reason, my wife still insists that I call a professional plumber.
Given that finding a great retirement planner can be challenging and expensive and that many of this blog’s readers tend to do their own planning, it’s easy to see the allure of finding a great software package and doing it yourself. Today’s software, however, is much closer to a toolkit than to a “robo-advisor.” Those who choose this path should avoid being overconfident in the results and should build plenty of safety margin into their plan.
Sample List of Free Limited-Purpose Retirement Planning Tools
- Estimate a budgetary amount to spend from savings for the current year: Ken Steiner’s How Much Can I spend in Retirement Spreadsheet. Note: Ken Steiner mentioned to me that my original wording here, “safe amount to spend” should instead say “budgetary amount to spend”, as we agree there is no way to predict a “safe” spending amount. Ken’s goal is to provide a budgetary spending estimate based on sound actuarial principles.
Improving Retirement Outcomes with Investments, Life Insurance, and Income Annuities, Wade Pfau and Michael Finke.
 Toward Determining the Optimal Investment Strategy for Retirement, Javier Estrada.