Many retirement planning analyses rely on Monte Carlo simulations with static assumptions for withdrawals.
Incorporating dynamic spending rules can more closely align the simulations with how investors would likely behave during times when the plan looked like it was on a path to failure.
Even a modest reduction in withdrawals (e.g. 10%) can have a meaningful impact on reducing failure rates, nearly cutting it in half in a sample simulation.
Combining dynamic spending rules with other marginal improvements, such as supplemental income and active risk management, can lead to more robust retirement plans and give investors a better understanding of the variables that are within their realm of control.
Monte Carlo simulations are a prevalent tool in financial planning, especially pertaining to retirement success calculations.
Under a typical framework of normally distributed portfolio returns and constant inflation-adjusted withdrawals, calculating the success of a given retirement portfolio is straightforward. But as with most tools in finance, the art lies both in the assumptions that go into the calculation and in the proper interpretation of the result.
If a client is told they have a 10% chance of running out of money over their projected retirement horizon, what does that mean for them?
They cannot make 9 copies of themselves to live out separate lives, with one copy (hopefully not the original) unfortunately burning through the account prematurely.
They also cannot create 9 parallel universes and ensure they do not choose whichever one does not work out.
We wrote previously how investors follow a single path (You Are Not a Monte-Carlo Simulation). If that path hits zero, the other hypothetical simulation paths don’t mean a thing.
A simulation path is only as valuable as the assumptions that go into creating it, and fortunately, we can make our simulations align more closely with investor behavior.
The best way to interpret the 10% failure rate is to think of it as a 10% chance of having to make an adjustment before it hits zero. Rarely would an investor stand by while their account went to zero. There are circumstances that are entirely out of investor control, but to the extent that there was something they could do to prevent that event, they would most likely do it.
Derek Tharp, on Michael Kitces’ blog, wrote a post a few years ago weighing the relative benefit of implementing small but permanent adjustments vs. large but temporary adjustments to retirement withdrawals and found that making small adjustments and leaving them in place led to greater likelihoods of success over retirement horizons (Dynamic Retirement Spending Adjustments: Small-But-Permanent Vs Large-But-Temporary).
In this week’s commentary, we want to dig a little deeper into some simple path dependent modifications that we can make to retirement Monte-Carlo simulations with the hope of creating a more robust toolset for financial planning.
The Initial Plan
Suppose an investor is 65 and holds a moderate portfolio of 60% U.S. stocks and 40% U.S. Treasuries. From 1871 until mid-2019, this portfolio would have returned an inflation-adjusted 5.1% per year with 10.6% volatility according to Global Financial Data.
Sticking with the rule-of-thumb 4% annual withdrawal of the initial portfolio balance and assuming a 30-year retirement horizon, this yields a predicted failure rate of 8% (plus or minus about 50 bps).
The financial plan is complete.
If you start with $1,000,000, simply withdraw $3,333/month and you should be fine 92% of the time.
But what if the portfolio drops 5% in the first month? (It almost did that in October 2018).
The projected failure rate over the next 29 years and 11 months has gone up to 11%. That violates a 10% threshold that may have been a target in the planning process.
Or what if it drops 30% in the first 6 months, like it would have in the second half of 1931?
Now the project failure rate is a staggering 46%. Retirement success has been reduced to a coin flip.
Admittedly, these are trying scenarios, but these numbers are a key driver for financial planning. If we can better understand the risks and spell out a course of action beforehand, then the risk of making a rash emotion-driven decision can be mitigated.
Aligning the Plan with Reality
When the market environment is challenging, investors can benefit by being flexible. The initial financial plan does not have to be jettisoned; agreed upon actions within it are implemented.
One of the simplest – and most impactful – modifications to make is an adjustment to spending. For instance, an investor might decide at the outset to scale back spending by a set amount when the probably of failure crosses a threshold.Source: Global Financial Data. Calculations by Newfound.
This reduction in spending would increase the probability of success going forward through the remainder of the retirement horizon.
And if we knew that this spending cut would likely happen if it was necessary, then we can quantify it as a rule in the initial Monte Carlo simulation used for financial planning.
Graphically, we can visualize this process by looking at the probabilities of failure for varying asset levels over time. For example, at 10 years after retirement, the orange line indicates that a portfolio value ~80% of the initial value would have about a 5% failure rate.
Source: Global Financial Data. Calculations by Newfound.
As long as the portfolio value remains above a given line, no adjustment would be needed based on a standard Monte Carlo analysis. Once a line is crossed, the probability of success is below that threshold.
This chart presents a good illustration of sequence risk: the lines are flatter initially after retirement and the slope progressively steepens as the time progresses. A large drawdown initially puts the portfolio below the threshold for making and adjustment.
For instance, at 5 years, the portfolio has more than a 10% failure rate if the value is below 86%. Assuming zero real returns, withdrawals alone would have reduced the value to 80%. Positive returns over this short time period would be necessary to feel secure in the plan.
Looking under the hood along the individual paths used for the Monte Carlo simulation, at 5 years, a quarter of them would be in a state requiring an adjustment to spending at this 10% failure level.
Source: Global Financial Data. Calculations by Newfound.
This belies the fact that some of the paths that would have crossed this 10% failure threshold prior to the 5-year mark improved before the 5-year mark was hit. 75% of the paths were below this 10% failure rate at some point prior to the 5-year mark. Without more appropriate expectations of a what these simulations mean, under this model, most investors would have felt like their plan’s failure rate was uncomfortable at some point in the first 5 years after retirement!
Dynamic Spending Rules
If the goal is ultimately not to run out of funds in retirement, the first spending adjustment case can substantially improve those chances (aside from a large negative return in the final periods prior to the last withdrawals).
Each month, we will compare the portfolio value to the 90% success value. If the portfolio is below that cutoff, we will size the withdrawal to hit improve the odds of success back to that level, if possible.
The benefit of this approach is greatly improved success along the different paths. The cost is forgone income.
But this can mean forgoing a lot of income over the life of the portfolio in a particularly bad state of the world. The worst case in terms of this total forgone income is shown below.
Source: Global Financial Data. Calculations by Newfound.
The portfolio gives up withdrawals totaling 74%, nearly 19 years’ worth. Most of this is given up in consecutive periods during the prolonged drawdown that occurs shortly after retirement.
This is an extreme case that illustrates how large of income adjustments could be required to ensure success under a Monte Carlo framework.
The median case foregoes 9 months of total income over the portfolio horizon, and the worst 5% of cases all give up 30% (7.5 years) of income based off the initial portfolio value.
That is still a bit extreme in terms of potential cutbacks.
As a more realistic scenario that is easier on the pocketbook, we will limit the total annual cutback to 30% of the withdrawal in the following manner:
If the current chance of failure is greater than 20%, cut spending by 30%. This equates to reducing the annual withdrawal by $12,000 assuming a $1,000,000 initial balance.
If the current chance of failure is between 15% and 20%, cut spending by 20%. This equates to reducing the annual withdrawal by $8,000 assuming a $1,000,000 initial balance.
If the current chance of failure is between 10% and 15%, cut spending by 10%. This equates to reducing the annual withdrawal by $4,000 assuming a $1,000,000 initial balance.
These rules still increase the success rate to 99% but substantially reduce the amount of reductions in income.
Looking again at the worst-case scenario, we see that this case still “fails” (even though it lasts another 4.5 years) but that its reduction in come is now less than half of what it was in the extreme cutback case. This pattern is in line with the “lower for longer” reductions that Derek had looked at in the blog post.
Source: Global Financial Data. Calculations by Newfound.
On the 66% of sample paths where there was a cut in spending at some point, the average total cut amounted to 5% of the portfolio (a little over a year of withdrawals spread over the life of the portfolio).
Even moving to an even less extreme reduction regime where only 10% cuts are ever made if the probability of failure increases above 10%, the average reduction in the 66% of cases that required cuts was about 9 months of withdrawals over the 30-year period.
In these scenarios, the failure rate is reduced to 5% (from 8% with no dynamic spending rules).
Source: Global Financial Data. Calculations by Newfound.
Conclusion
Retirement simulations can be a powerful planning tool, but they are only as good as their inputs and assumptions. Making them align as closes with reality as possible can be a way to quantify the impact of dynamic spending rules in retirement.
While the magnitude of spending reductions necessary to guarantee success of a retirement plan in all potential states of the world is prohibitive. However, small modifications to spending can have a large impact on success.
For example, reducing withdrawal by 10% when the forecasted failure rate increases above 10% nearly cut the failure rate of the entire plan in half.
But dynamic spending rules do not exist in a vacuum; they can be paired with other marginal improvements to boost the likelihood of success:
Seek out higher returns – small increases in portfolio returns can have a significant impact over the 30 -ear planning horizon.
Supplement income – having supplements to income, even small ones, can offset spending during any market environment, improving the success rate of the financial plan.
Actively manage risk – managing risk, especially early in retirement is a key factor to now having to reduce withdrawals in retirement.
Plan for more flexibility – having the ability to reduce spending when necessary reduces the need to rely on the portfolio balance when the previous factors are not working.
While failure is certainly possible for investors, a “too big to fail” mentality is much more in line with the reality of retirement.
Even if absolute failure is unlikely, adjustments will likely be a requirement. These can be built into the retirement planning process and can shed light on stress testing scenarios and sensitivity.
From a retirement planning perspective, flexibility is simply another form of risk management.
The Perfect Withdrawal Rate (PWR) is the rate of regular portfolio withdrawals that leads to a zero balance over a given time frame.
4% is the commonly accepted lower bound for safe withdrawal rates, but this is only based on one realization of history and the actual risk investors take on by using this number may be uncertain.
Using simulation techniques, we aim to explore how different assumptions match the historical experience of retirement portfolios.
We find that simple assumptions commonly used in financial planning Monte Carlo simulations do not seem to reflect as much variation as we have seen in the historical PWR.
Including more stress testing and utilizing richer simulation methods may be necessary to successfully gauge that risk in a proposed PWR, especially as it pertains to the risk of failure in the financial plan.
Financial planning for retirement is a combination of art and science. The problem is highly multidimensional, requiring estimates of cash flows, investment returns and risk, taxation, life events, and behavioral effects. Reduction along the dimensions can simplify the analysis, but introduces consequences in the applicability and interpretation of the results. This is especially true for investors who are close to the line between success and failure.
One of the primary simplifying assumptions is the 4% rule. This heuristic was derived using worst-case historical data for portfolio withdrawals under a set of assumptions, such as constant inflation adjusted withdrawals, a fixed mix of stock and bonds, and a set time horizon.
Below we construct a monthly-rebalanced, fixed-mix 60/40 portfolio using the S&P 500 index for U.S. equities and the Dow Jones Corporate Bond index for U.S. bonds. Using historical data from 12/31/1940 through 12/31/2018, we can evaluate the margin for error the 4% rule has historically provided and how much opportunity for higher withdrawal rates was sacrificed in “better” market environments.
Source: Global Financial Data and Shiller Data Library. Calculations by Newfound Research. Returns are backtested and hypothetical. Past performance is not a guarantee of future results. Returns are gross of all fees. Returns assume the reinvestment of all distributions. None of the strategies shown reflect any portfolio managed by Newfound Research and were constructed solely for demonstration purposes within this commentary. You cannot invest in an index.
But the history is only a single realization of the world. Risk is hard to gauge.
Perfect Withdrawal Rates
The formula (in plain English) for the perfect withdrawal rate (“PWR”) in a portfolio, assuming an ending value of zero, is relatively simple since it is just a function of portfolio returns:
The portfolio value in the numerator is the final value of the portfolio over the entire period, assuming no withdrawals. The sequence risk in the denominator is a term that accounts for both the order and magnitude of the returns.
Larger negative returns earlier on in the period increase the sequence risk term and therefore reduce the PWR.
From a calculation perspective, the final portfolio value in the equation is typically described (e.g. when using Monte Carlo techniques) as a log-normal random variable, i.e. the log-returns of the portfolio are assumed to be normally distributed. This type of random variable lends itself well to analytic solutions that do not require numerical simulations.
The sequence risk term, however, is not so friendly to closed-form methods. The path-dependent, additive structure of returns within the sequence risk term means that we must rely on numerical simulations.
To get a feel for some features of this equation, we can look at the PWR in the context of the historical portfolio return and volatility.
Source: Global Financial Data and Shiller Data Library. Calculations by Newfound Research. Returns are backtested and hypothetical. Past performance is not a guarantee of future results. Returns are gross of all fees. Returns assume the reinvestment of all distributions. None of the strategies shown reflect any portfolio managed by Newfound Research and were constructed solely for demonstration purposes within this commentary. You cannot invest in an index.
The relationship is difficult to pin down.
As we saw in the equation shown before, the –annualized return of the portfolio– does appear to impact the –PWR– (correlation of 0.51), but there are periods (e.g. those starting in the 1940s) that had higher PWRs with lower returns than in the 1960s. Therefore, investors beginning withdrawals in the 1960s must have had higher sequence risk.
Correlation between –annualized volatility– and –PWR– was slightly negative (-0.35).
The Risk in Withdrawal Rates
Since our goal is to assess the risk in the historical PWR with a focus on the sequence risk, we will use the technique of Brownian Bridges to match the return of all simulation paths to the historical return of the 60/40 portfolio over rolling 30-year periods. We will use the historical full-period volatility of the portfolio over the period for the simulation.
This is essentially a conditional PWR risk based on assuming we know the full-period return of the path beforehand.
To more explicitly describe the process, consider a given 30-year period. We begin by computing the full-period annualized return and volatility of the 60/40 portfolio over that period. We will then generate 10,000 simulations over this 30-year period but using the Brownian Bridge technique to ensure that all of the simulations have the exact same full-period annualized return and intrinsic volatility. In essence, this approach allows us to vary the path of portfolio returns without altering the final return. As PWR is a path-dependent metric, we should gain insight into the distribution of PWRs.
The percentile bands for the simulations using this method are shown below with the actual PWR in each period overlaid.
Source: Global Financial Data and Shiller Data Library. Calculations by Newfound Research. Returns are backtested and hypothetical. Past performance is not a guarantee of future results. Returns are gross of all fees. Returns assume the reinvestment of all distributions. None of the strategies shown reflect any portfolio managed by Newfound Research and were constructed solely for demonstration purposes within this commentary. You cannot invest in an index.
From this chart, we see two items of note: The percentile bands in the distribution roughly track the historical return over each of the periods, and the actual PWR fluctuates into the left and right tails of the distribution rather frequently. Below we plot where the actual PWR actually falls within the simulated PWR distribution.
Source: Global Financial Data and Shiller Data Library. Calculations by Newfound Research. Returns are backtested and hypothetical. Past performance is not a guarantee of future results. Returns are gross of all fees. Returns assume the reinvestment of all distributions. None of the strategies shown reflect any portfolio managed by Newfound Research and were constructed solely for demonstration purposes within this commentary. You cannot invest in an index.
The actual PWR is below the 5th percentile 12% of the time, below the 1st percentile 4% of the time, above the 95th percentile 11% of the time, and above the 99th percentile 7% of the time. Had our model been more well calibrated, we would expect the percentiles to align; e.g. the PWR should be below the 5th percentile 5% of the time and above the 99th percentile 1% of the time.
This seems odd until we realize that our model for the portfolio returns was likely too simplistic. We are assuming Geometric Brownian Motion for the returns. And while we are fixing the return over the entire simulation path to match that of the actual portfolio, the path to get there is assumed to have constant volatility and independent returns from one month to the next.
In reality, returns do not always follow these rules. For example, the skew of the monthly returns over the entire history is -0.36 and the excess kurtosis is 1.30. This tendency toward larger magnitude returns and returns that are skewed to the left can obscure some of the risk that is inherent in the PWRs.
Additionally, returns are not totally independent. While this is good for trend following strategies, it can lead to an understatement of risk as we explored in our previous commentary on Accounting for Autocorrelation in Assessing Drawdown Risk.
Over the full period, monthly returns of lags 1, 4, and 5 exhibit autocorrelation that is significant at the 95% confidence level.
Source: Global Financial Data and Shiller Data Library. Calculations by Newfound Research. Returns are backtested and hypothetical. Past performance is not a guarantee of future results. Returns are gross of all fees. Returns assume the reinvestment of all distributions. None of the strategies shown reflect any portfolio managed by Newfound Research and were constructed solely for demonstration purposes within this commentary. You cannot invest in an index.
To incorporate some of these effects in our simulations, we must move beyond the simplistic assumption of normally distributed returns.
First, we will fit a skewed normal distribution to the rolling historical data and use that to draw our random variables for each period. This is essentially what was done in the previous section for the normally distributed returns.
Then, to account for some autocorrelation, we will use the same adjustment to volatility as we used in the previously reference commentary on autocorrelation risk. For positive autocorrelations (which we saw in the previous graphs), this results in a higher volatility for the simulations (typically around 10% – 25% higher).
The two graphs below show the same analysis as before under this modified framework.
Source: Global Financial Data and Shiller Data Library. Calculations by Newfound Research. Returns are backtested and hypothetical. Past performance is not a guarantee of future results. Returns are gross of all fees. Returns assume the reinvestment of all distributions. None of the strategies shown reflect any portfolio managed by Newfound Research and were constructed solely for demonstration purposes within this commentary. You cannot invest in an index.
The historical PWR now fall more within the bounds of our simulated results.
Additionally, the 5th percentile band now shows that there were periods where a 4% withdrawal rule may not have made the cut.
Source: Global Financial Data and Shiller Data Library. Calculations by Newfound Research. Returns are backtested and hypothetical. Past performance is not a guarantee of future results. Returns are gross of all fees. Returns assume the reinvestment of all distributions. None of the strategies shown reflect any portfolio managed by Newfound Research and were constructed solely for demonstration purposes within this commentary. You cannot invest in an index.
Conclusion
Heuristics can be a great way to distill complex data into actionable insights, and the perfect withdrawal rate in retirement portfolios is no exception.
The 4% rule is a classic example where we may not be aware of the risk in using it. It is the commonly accepted lower bound for safe withdrawal rates, but this is only based on one realization of history.
The actual risk investors take on by using this number may be uncertain.
Using simulation techniques, we explored how different assumptions match the historical experience of retirement portfolios.
The simple assumptions (expected return and volatility) commonly used in financial planning Monte Carlo simulations do not seem to reflect as much variation as we have seen in the historical PWR. Therefore, relying on these assumptions can be risky for investors who are close to the “go-no-go” point; they do not have much room for failure and will be more likely to have to make cash flow adjustments in retirement.
Utilizing richer simulation methods (e.g. accounting for negative skew and autocorrelation like we did here or using a downside shocking method like we explored in A Shock to the Covariance System) may be necessary to successfully gauge that risk in a proposed PWR, especially as it pertains to the risk of failure in the financial plan.
Having a number to base planning calculations on makes life easier in the moment, but knowing the risk in using that number makes life easier going forward.
Cash balance plans are retirement plans that allow participants to save higher amounts than in traditional 401(k)s and IRAs and are quickly becoming more prevalent as an attractive alternative to defined benefit retirement plans.
The unique goals of these plans (specified contributions and growth credits) often dictate modest returns with a very low volatility, which often results in conservative allocations.
However, at closely held companies, there is a balance between the tax-deferred amount that can be contributed by partners and the returns that the plan earns. If returns are too low, the company must make up the shortfall, but if the returns are too high the partners cannot maximize their tax-deferred contributions.
By allocating to risk-managed strategies like trend equity, a cash balance plan can balance the frequency and size of shortfalls based on how the trend following strategy is incorporated within the portfolio.
Trend following strategies have historically reduced the exposure to large shortfalls in exchange for more conservative performance during periods where the plan is comfortably hitting its return target.
Retirement assets have grown each year since the Financial Crisis, exhibiting the largest gains in the years that were good for the market such as 2009, 2013, and 2017.
Source: Investment Company Institute (ICI).
With low interest rates, an aging workforce, and continuing pressure to reduce expected rates of return going forward, many employers have shifted from the defined benefit (DB) plans used historically to defined contribution (DC) models, such as 401(k)s and 403(b)s. While assets within DB plans have still grown over the past decade, the share of retirement assets in IRAs and DC plans has grown from around 50% to 60%.
But even with this shift toward more employee directed savings and investment, there is a segment of the private DB plan space that has seen strong growth since the early 2000s: cash balance plans.
It’s sort of a hybrid retirement plan type. Employers contribute to it on behalf of their employees or themselves, and each participant is entitled to those assets plus a rate of return according to a prespecified rule (more on that in a bit).
Like a defined contribution plan, participants have an account value rather than a set monthly payment.
Like a defined benefit plan, the assets are managed professionally, and the actual asset values do not affect the value of the participant benefits. Thus, as with any liability-driven outcome, the plan can be over- or under-funded at a given time.
What’s the appeal?
According to Kravitz, (2018)1 over 90% of cash balance plans are in place at companies with fewer than 100 participants. These companies tend to be white-collar professionals, where a significant proportion of the employees are highly compensated (e.g. groups of doctors, dentists, lawyers, etc.).
Many of these professionals likely had to spend a significant amount of time in professional school and building up practices. Despite higher potential salaries, they may have high debt loads to pay down. Similarly, entrepreneurs may have deferred compensating themselves for the sake of building a successful business.
Thus, by the time these professionals begin earning higher salaries, the amount of time that savings can compound for retirement has been reduced.
Source: Kravitz. 2018 National Cash Balance Research Report.
One option for these types of investors is to simply save more income in a traditional brokerage account, but this foregoes any benefit of deferring taxes until retirement.
Furthermore, even if these investors begin saving for retirement at the limit for 401(k) contributions, it is possible that they could end up with a lower account balance than a counterpart saving half as much per year but starting 10 years earlier. Time lost is hard to make up.
Cash balance plans can help solve this dilemma through significantly higher contribution limits.
Source: Kravitz.
An extra $6,000 in catch-up contributions starting for a 401(k) at age 50 seems miniscule compared to what a cash balance plan allows.
Now that we understand why cash balance plans are becoming more prevalent in the workplace, let’s turn to the investment side of the picture to see how a plan can make good on its return guarantees.
The Return Guarantee
Aside from the contribution schedule for each plan participant, the only other piece of information needed to determine the size of the cash balance plan liability in a given year is the annual rate at which the participant accounts grow.2 There are a few common ways to set this rate:
A fixed rate of return per year, between 2% and 6%.
The 30-year U.S. Treasury rate.
The 30-year U.S. Treasury rate with a floor of between 3% and 5%.
The actual rate of return of the invested assets, often with a ceiling between 3% and 6%.
The table below shows that of the plans surveyed by Kravitz (2018), the fixed rate of return was by far the most common and the actual rate of return credit was the least common.
The Actual Rate of Return option is actually becoming more popular, especially with large cash balance plans, now that federal regulations allow plan sponsors to offer multiple investments in a single plan to better serve the participants who may have different retirement goals. This return option removes much of the investment burden from the plan sponsor since what the portfolio earns is what the participants get, up to the ceiling. Anything earned above the ceiling increases the plan’s asset value above its liabilities. Actual rate of return guarantees make it so that there is less risk of a liability shortfall when large stakeholders in the cash balance plan leave the company unexpectedly.
In this commentary, we will focus on the cases where the plan may become underfunded if it does not hit the target rate of return.
We often say, “No Pain, No Premium.” Well, in the case of cash balance plans, plan sponsors typically only want to bear the minimal amount of pain that is necessary to hit the premium.
With large firms that can rely more heavily on actuarial assumptions for participant turnover, much of this risk can be borne over multiyear periods. A shortfall in one year can be replenished by a combination of extra contributions from the company according to IRS regulations and (hopefully) more favorable portfolio gains in subsequent years. Any excess returns can be used to offset how much the company must contribute annually for participants.
In the case of closely held firms, things change slightly.
At first glance, it should be a good thing for a plan sponsor to earn a higher rate of return than the committed rate. But when we consider that many cash balance plans are in place at firms where the participants desire to contribute as much as the IRS allows to defer taxation, then earning more than the guaranteed rate of return actually represents a risk. At closely held firms, “the company” and “the participants” are essentially one in the same. The more the plan earns, the less you can contribute.
And with higher return potential comes a higher risk of earning below the guaranteed rate. When a company is small, making up shortfalls out of company coffers or stretching for higher returns in subsequent years may not be in the company’s best interest.
Investing a Cash Balance Plan
Because of the aversion to both high returns and high risk, many cash balance plans are generally invested relatively conservatively, typically in the range of a 20% stock / 80% bond portfolio (20/80) to a 40/60.
To put some numbers down on paper, we will examine the return profile of three different portfolios: a 20/80, 30/70, and 40/60 fixed mix of the S&P 500 and a constant maturity 10-year U.S. Treasury index.
We will also calculate the rate of return guarantees described above each year from 1871 to 2018.
Starting each January, if the return of one of the portfolio profiles meets hits the target return for the year, then we will assume it is cashed out. Otherwise, the portfolio is held the entire year.
As the 30-year U.S. Treasury bond came into inception in 1977 and had a period in the 2000s where it was not issued, we will use the 10-year Treasury rate as a proxy for those periods.
The failure rate for the portfolios are shown below.3
Source: Robert Shiller Data Library, St. Louis Fed. Calculations by Newfound Research. Past performance is not a guarantee of future results. All returns are hypothetical and backtested. Returns are gross of all fees. This does not reflect any investment strategy offered or managed by Newfound Research and was constructed exclusively for the purposes of this commentary. It is not possible to invest in an index.
We can see that as the rate of return guarantee increases, either through the fixed rate or the floor on the 30-year rate, the rate of shortfall increases for all allocations, most notably for the conservative 20/80 portfolio.
In these failure scenarios, the average shortfall and the average shortfall in the 90% of the worst cases (similar to a CVaR) are relatively consistent.
Source: Robert Shiller Data Library, St. Louis Fed. Calculations by Newfound Research. Past performance is not a guarantee of future results. All returns are hypothetical and backtested. Returns are gross of all fees. This does not reflect any investment strategy offered or managed by Newfound Research and was constructed exclusively for the purposes of this commentary. It is not possible to invest in an index.
Source: Robert Shiller Data Library, St. Louis Fed. Calculations by Newfound Research. Past performance is not a guarantee of future results. All returns are hypothetical and backtested. Returns are gross of all fees. This does not reflect any investment strategy offered or managed by Newfound Research and was constructed exclusively for the purposes of this commentary. It is not possible to invest in an index.
These shortfall numbers may not be a big deal for new plans when the contributions represent a significant percentage of the asset base. For example, for a $1M plan with $500k in contributions per year, a 15% shortfall is only $150k, which can be amortized over a number of years. Higher returns in the subsequent years can offset this, or partners could agree to reduce their personal contributions so that the company can have free cash to make up for the shortfall.
The problem is more pressing for plans where the asset base is significantly larger than the yearly contributions. For a $20M plan with $500k in yearly contributions, a 15% shortfall is $3M. Making up this shortfall from company assets may be more difficult, even with amortization.
Waiting for returns from the market can also be difficult in this case when there have been historical drawdowns in the market lasting 2-3 years from peak to trough (e.g. 1929-32, 2000-02, and 1940-42).
Risk-managed strategies can be a natural way to mitigate these shortfalls, both in their magnitude and frequency.
Using Trend Following in a Cash Balance Plan
Along the lines of our Three Uses of Trend Equity, we will look at adding a 20% allocation to a simple trend-following equity (“trend equity”) strategy in a cash balance plan. By taking the allocation either from all equities, all bonds, or an equal share of each.
For ease of illustration, we will only look at the 20/80 and 40/60 portfolios. The following charts show the benefit (i.e. reduction in shortfall) or detriment (i.e. increase in shortfall) of adding the 20% trend equity sleeve to the cash balance plan based on the metrics from the previous section.
Source: Robert Shiller Data Library, St. Louis Fed. Calculations by Newfound Research. Past performance is not a guarantee of future results. All returns are hypothetical and backtested. Returns are gross of all fees. This does not reflect any investment strategy offered or managed by Newfound Research and was constructed exclusively for the purposes of this commentary. It is not possible to invest in an index.
For most of these return guarantees, substituting a greater proportion of bonds for trend equity reduced the frequency of shortfalls. This makes sense over a period where equities generally did well and a trend equity strategy increased participation during the up-markets.
Substituting in trend equity solely from the equity allocation was detrimental for a few of the return guarantees, especially the higher ones.
But the frequency of shortfalls is only one part of the picture.
Source: Robert Shiller Data Library, St. Louis Fed. Calculations by Newfound Research. Past performance is not a guarantee of future results. All returns are hypothetical and backtested. Returns are gross of all fees. This does not reflect any investment strategy offered or managed by Newfound Research and was constructed exclusively for the purposes of this commentary. It is not possible to invest in an index.
Many of the cases that showed a benefit from a frequency of shortfall perspective sacrifice the average shortfall or average shortfall in the most extreme scenarios. Conversely, case that sacrifice on the frequency of shortfalls generally saw a meaningful reduction in the average shortfalls.
This is in line with our philosophy that risks are not destroyed, only transformed.
Source: Robert Shiller Data Library, St. Louis Fed. Calculations by Newfound Research. Past performance is not a guarantee of future results. All returns are hypothetical and backtested. Returns are gross of all fees. This does not reflect any investment strategy offered or managed by Newfound Research and was constructed exclusively for the purposes of this commentary. It is not possible to invest in an index.
So which risks should a cash balance plan bear?
This can be answered by determining the balance of the plan to be exposure to failing fast and failing slow.
If a cash balance plan is large, even a moderate shortfall can be very large in dollar terms. These plans are at risk of failing fast. Mitigating the size of the shortfalls is definitely a primary concern.
If a cash balance plan is new or relatively small, it is somewhat like an investor early in their working career. Larger losses from a percentage perspective are smaller in dollar terms compared to a larger plan. These plans can stand to have larger shortfalls. If the shortfalls occur less frequently, there is the ability to generate higher returns in years after a loss to recoup some of the losses.
However, these small plans should still be concerned mostly about fast failure. The yearly reckoning of the liability to the participants skews the risks more heavily in the direction of fast failure. This is especially true when we factor in the demographic of the workforce. When employees leave, they are entitled to their account value based on the guaranteed return, not the underlying asset value. If a participant cashes out at a time when the assets are down, then the remaining participant are less funded based on the assets that are left.
Therefore, allocating to the trend strategy out of the equity sleeve or an equal split between equities and bonds is likely more in line with the goals of a cash balance plan.
Conclusion
Cash balance plans are quickly becoming more prevalent as an attractive alternative to defined benefit retirement plans. They are desirable both from an employer and employee perspective and can be a way to accelerate retirement savings, especially for highly compensated workers at small companies.
The unique goals of these plans (e.g. guaranteed returns, maximizing tax-deferred contributions, etc.) often dictate modest returns with a very low volatility. Since some risk must be borne in order to generate returns, these portfolios are typically allocated very conservatively.
Even so, there is a risk they will not hit their return targets.
By allocating to risk-managed strategies like trend equity, a cash balance plan can balance the frequency and size of shortfalls based on how the trend following strategy is incorporated within the portfolio.
Allocating to a trend equity strategy solely from bonds can reduce the frequency of shortfalls in exchange for larger average shortfalls. Allocating to a trend following equity strategy solely from equities can increase the frequency of shortfalls but reduce the average size of shortfalls and the largest shortfalls.
The balance for a specific plan depends on its size, the demographic of the participants, the company’s willingness and ability to cover shortfalls, and the guaranteed rate of return.
As with most portfolio allocation problems the solution exists on a sliding scale based on what risks the portfolio is more equipped to bear. For cash balance plans, managing the size of shortfalls is likely a key issue, and trend following strategies can be a way to adjust the exposure to large shortfalls in exchange for more conservative performance during periods where the plan is comfortably hitting its return target.
This post is available for download as a PDF here.
Summary
The current return expectations for core U.S. equities and bonds paint a grim picture for the success of the 4% rule in retirement portfolios.
While varying the allocation to equities throughout the retirement horizon can provide better results, employing tactical strategies to systematically allocate to equities can more effectively reduce the risk that the sequence of market returns is unfavorable to a portfolio.
When a tactical strategy is combined with other incremental planning and portfolio improvements, such as prudent diversification, more accurate spending assessments, tax efficient asset location, and fee-conscious investing, a modest allocation can greatly boost likely retirement success and comfort.
Over the past few weeks, we have written a number of posts on retirement withdrawal planning.
In the first post, we presented a visualization like the following:
Historical Wealth Paths for a 4% Withdrawal Rate and 60/40 Stock/Bond Allocation Source: Shiller Data Library. Calculations by Newfound Research. Analysis uses real returns and assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
The horizontal (x-axis) represents the year when retirement starts. The vertical (y-axis) represents the years post-retirement. The coloring of each cell represents the savings balance at a given point in time. The meaning of each color as follows:
Green: Current account value greater than or equal to initial account value (e.g. an investor starting retirement with $1,000,000 has a current account balance that is at least $1,000,000).
Yellow: Current account value is between 75% and 100% of initial account value
Orange: Current account value is between 50% and 75% of the initial account value.
Red: Current account value is between 25% and 50% of the initial account value.
Dark Red: Current account value is between 0% and 25% of initial account value.
Black: Current account value is zero; the investor has run out of money.
We then recreated the visualization, but with one key modification: we adjusted the historical stock and bond returns downward so that the long-term averages are in line with realistic future return expectations[1] given current valuation levels. We did this by subtracting the difference between the actual average log return and the forward-looking long return from each year’s return. With this technique, we capture the effect of subdued average returns while retaining realistic behavior for shorter-term returns.
Historical Wealth Paths for a 4% Withdrawal Rate and 60/40 Stock/Bond Allocation with Current Return Expectations
Source: Shiller Data Library. Calculations by Newfound Research. Analysis uses real returns and assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
One downside of the above visualizations is that they only consider one withdrawal rate / portfolio composition combination. If we want the see results for withdrawal rates ranging from 1% to 10% in 1% increments and portfolio combinations ranging from 0/100 stocks/bonds to 100/0 stocks/bonds in 20% increments, we would need sixty graphs!
To distill things a bit more, we looked at the historical “success” of various investment and withdrawal strategies. We evaluated success on three metrics:
Absolute Success Rate (“ASR”): The historical probability that an individual or couple will not run out of money before their retirement horizon ends.
Comfortable Success Rate (“CSR”): The historical probability that an individual or couple will have at least the same amount of money, in real terms, at the end of their retirement horizon compared to what they started with.
Ulcer Index (“UI”): The average pain of the wealth path over the retirement horizon where pain is measured as the severity and duration of wealth drawdowns relative to starting wealth. [2]
As a quick refresher, below we present the ASR for various withdrawal rate / risk profile combinations over a 30-year retirement horizon first using historical returns and then using historical returns adjusted to reflect current valuation levels. The CSR and Ulcer Index table illustrated similar effects.
Absolute Success Rate for Various Combinations of Withdrawal Rate and Portfolio Composition – 30 Yr. Horizon
Absolute Success Rate for Various Combinations of Withdrawal Rate and Portfolio Composition with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon
Source: Shiller Data Library. Calculations by Newfound Research. Analysis uses real returns and assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Overall, our analysis suggested that retirement withdrawal rates that were once safe may now deliver success rates that are no better – or even worse – than a coin flip.
The combined conclusion of these two posts is that the near future looks pretty grim for retirees and that an assumption that is slightly off can make the outcome even worse.
Now, we are going to explore a topic that can both mitigate low growth expectations and adapt a retirement portfolio to reduce the risk of a bad planning assumption. But first, some history.
How the 4% Rule Started
In 1994, Larry Bierwirth proposed the 4% rule, and William Bengen expanded on the research in the same year.[3], [4]
In the original research, the 4% rule was derived assuming that the investor held a 50/50 stock/bond portfolio, rebalanced annually, withdrew a certain percentage of the initial balance, and increased withdrawals in line with inflation. 4% is the highest percentage that could be withdrawn without ever running out of money over an historical 30-year retirement horizon.
Graphically, the 4% rule is the minimum value shown below.
Maximum Inflation Indexed Withdrawal to Deplete a 60/40 Portfolio Over a 30 Yr. Horizon
Source: Shiller Data Library. Calculations by Newfound Research. Analysis uses real returns and assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Since its publication, the rule has become common knowledge to nearly all people in the field of finance and many people outside it. While it is a good rule-of-thumb and starting point for retirement analysis, we have two major issues with its broad application:
It assumes that not running out of money is the only goal in retirement without considering implications of ending surpluses, return paths that differ from historical values, or evolving spending needs.
It provides a false sense of security: just because 4% withdrawals never ran out of money in the past, that is not a 100% guarantee that they won’t in the future.
For example, if we adjust the stock and bond historical returns using the estimates from Research Affiliates (discussed previously) and replicate the analysis Bengen-style, the safe withdrawal rate is a paltry 2.6%.
Maximum Inflation Indexed Withdrawal to Deplete a 60/40 Portfolio Over a 30 Yr. Horizon using Current Return Estimates
Source: Shiller Data Library and Research Affiliates. Calculations by Newfound Research. Analysis uses real returns and assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
While this paints a grim picture for retirement planning, it’s not likely how one would plan their financial future. If you were to base your retirement planning solely on this figure, you would have to save 54% more for retirement to generate the same amount of annual income as with the 4% rule, holding everything else constant.
In reality, even with the low estimates of forward returns, many of the scenarios had safe withdrawal rates closer to 4%. By putting a multi-faceted plan in place to reduce the risk of the “bad” scenarios, investors can hope for the best while still planning for the worst.
One aspect of a retirement plan can be a time-varying asset allocation scheme.
Temporal Risk in Retirement
Conventional wisdom says that equity risk should be reduced as one progresses through retirement. This is what is employed in many “through”-type target date funds that adjust equity exposure beyond the retirement age.
If we heed the “own your age in bonds” rule, then a retiree would decrease their equity exposure from 35% at age 65 to 5% at the end of a 30-year plan horizon.
Unfortunately, this thinking is flawed.
When a newly-minted retiree begins retirement, their success is highly dependent on their first few years of returns because that is when their account values are the largest. As they make withdrawals and are reducing their account values, the impact of a large drawdown in dollar terms is not nearly as large. This is known as sequence risk.
As a simple example, consider three portfolio paths:
Portfolio A: -30% return in Year 1 and 6% returns for every year from Year 2 – Year 30.
Portfolio B: 6% returns for every year except for Year 15, in which there is a -30% return.
Portfolio C: 6% returns for every year from Year 1 – Year 29 and a -30% return in Year 30.
These returns work about to the expected returns on a 60/40 portfolio using Research Affiliates’ Yield & Growth expectations, and the drawdown is approximately in line with the drawdown on a 60/40 portfolio over the past decade. We will assume 4% annual withdrawals and 2% annual inflation with the withdrawals indexed to inflation.
3 Portfolios with Identical Annualized Returns that Occur in Different Orders
Portfolio C fares the best, ending the 30-year period with 12% more wealth than it began with. Portfolio B makes it through, not as comfortably as Portfolio C but still with 61% of its starting wealth. Portfolio A, however, starts off stressful for the retiree and runs out of money in year 27.
Sequence risk is a big issue that retirement portfolios face, so how does one combat it with dynamic allocations?
The Rising Glide Path in Retirement
Kitces and Pfau (2012) proposed the rising glide path in retirement as a method to reduce sequence risk.[5] They argued that since retirement portfolios are most exposed to market risk at the beginning of the retirement period, they should start with the lowest equity risk and ramp up as retirement progresses.
Based on Monte Carlo simulations using both capital market assumptions in line with historical values and reduced return assumptions for the current environment, the paper showed that investors can maximize their success rate and minimize their shortfall in bad (5th percentile) scenarios by starting with equity allocations of between 20% and 40% and increasing to 60% to 80% equity allocations through retirement.
We can replicate their analysis using the reduced historical return data, using the same metrics from before (ASR, CSR, and the Ulcer Index) to measure success, comfort, and stress, respectively.
Absolute Success Rate for Various Equity Glide Paths with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon with a 4% Initial Withdrawal Rate
Comfortable Success Rate for Various Equity Glide Paths with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon with a 4% Initial Withdrawal Rate
Ulcer Index for Various Equity Glide Paths with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon with a 4% Initial Withdrawal Rate
Source: Shiller Data Library and Research Affiliates. Calculations by Newfound Research. Analysis uses real returns and assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Note that the main diagonal in the chart represents static allocations, above the main diagonal represents the decreasing glide paths, and below the main diagonal represents increasing glide paths.
Since these returns are derived from the historical returns for stocks and bonds (again, accounting for a depressed forward outlook), they capture both the sequence of returns and shifting correlations between stocks and bonds better than Monte Carlo simulation. On the other hand, the sample size is limited, i.e. we only have about 4 non-overlapping 30 year periods.
Nevertheless, these data show that there was not a huge benefit or detriment to using either an increasing or decreasing equity glide path in retirement based on these metrics. If we instead look at minimizing expected shortfall in the bottom 10% of scenarios, similar to Kitces and Pfau, we find that a glide path starting at 40% rising to around 80% performs the best.
However, it will still be tough to rest easy with a plan that has an ASR of around 60 and a CSR of around 30 and an expected shortfall of 10 years of income.
With these unconvincing results, what can investors do to improve their retirement outcomes through prudent asset allocation?
Beyond a Static Glide Path
There is no reason to constrain portfolios to static glide paths. We have said before that the risk of a static allocation varies considerably over time. Simply dictating an equity allocation based on your age does not always make sense regardless of whether that allocation is increasing or decreasing.
If the market has a large drawdown, an investor should want to avoid this regardless of where they are in the retirement journey. Missing drawdowns is always beneficial as long as enough upside is subsequently realized.
In recent papers, Clare et al. (2017 and 2017) showed that trend following can boost safe withdrawal rates in retirement portfolios by managing sequence risk. [6],[7]
The million-dollar question is, “how tactical should we be?”
The following charts show the ASR, CSR, and Ulcer index values for static allocations to stocks, bonds, and a simple tactical strategy that invests in stocks when they are above their 10-month simple moving average (SMA) and in bonds otherwise.
The charts are organized by the minimum and maximum equity exposures along the rows and columns. The charts are symmetric across the main diagonal so that they can be compared to both increasing and decreasing equity glide paths.
The equity allocation is the minimum of the row and column headings, the tactical strategy allocation is the absolute difference between the headings, and the bond allocation is what’s needed to bring the total allocation to 100%.
For example, the 20% and 50% column is a portfolio of 20% equities, 30% tactical strategy, and 50% bonds. It has an ASR of 75, a CSR of 40, and an Ulcer index of 22.
Absolute Success Rate for Various Tactical Allocation Bounds Paths with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon with a 4% Initial Withdrawal Rate
Comfortable Success Rate for Various Tactical Allocation Bounds with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon with a 4% Initial Withdrawal Rate
Ulcer Index for Various Tactical Allocation Bounds with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon with a 4% Initial Withdrawal Rate
Source: Shiller Data Library and Research Affiliates. Calculations by Newfound Research. Analysis uses real returns and assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
These charts show that being tactical is extremely beneficial under these muted return expectations and that being highly tactical is even better than being moderately tactical.
So, what’s stopping us from going whole hog with the 100% tactical portfolio?
Well, this is a case where a tactical strategy can reduce the risk of not making it through the 30-year retirement at the risk of greatly increasing the ending wealth. It may sound counterintuitive to say that ending with too much extra money is a risk, but when our goal is to make it through retirement comfortably, taking undue risks come at a cost.
For instance, we know that while the tactical strategy may perform well over a 30-year time horizon, it can go through periods of significant underperformance in the short-term, which can lead to stress and questioning of the investment plan. For example, in 1939 and 1940, the tactical strategy underperformed a 50/50 portfolio by 16% and 11%, respectively.
These times can be trying for investors, especially those who check their portfolios frequently.[8] Even the best-laid plan is not worth much if it cannot be adhered to.
Being tactical enough to manage the risk of having to make a major adjustment in retirement while keeping whipsaw, tracking error, and the cost of surpluses in check is key.
Sizing a Tactical Sleeve
If the goal is having the smallest tactical sleeve to boost the ASR and CSR and reduce the Ulcer index to acceptable levels in a low expected return environment, we can turn back to the expected shortfall in the bad (10th percentile) scenarios to determine how large of a tactical sleeve to should include in the portfolio. The analysis in the previous section showed that being tactical could yield ASRs and CSRs in the 80s and 90s (dark green). This, however, requires a tactical sleeve between 50% and 70%, depending on the static equity allocation.
Thankfully, we do not have to put the entire burden on being tactical: we can diversify our approaches. In the previous commentaries mentioned earlier, we covered a number of topics that can improve retirement results in a low expected return environment.
Thoroughly examine and define planning factors such as taxes and the evolution of spending throughout retirement.
Be strategic, not static: Have a thoughtful, forward-looking outlook when developing a strategic asset allocation. This means having a willingness to diversify U.S. stocks and bonds with the ever-expanding palette of complementary asset classes and strategies.
Utilize a hybrid active/passive approach for core exposures given the increasing availability of evidence-based, factor-driven investment strategies.
Be fee-conscious, not fee-centric. For many exposures (e.g. passive and long-only core stock and bond exposure), minimizing cost is certainly appropriate. However, do not let cost considerations preclude the consideration of strategies or asset classes that can bring unique return generating or risk mitigating characteristics to the portfolio.
Look beyond fixed income for risk management given low interest rates.
Recognize that the whole can be more than the sum of its parts by embracing not only asset class diversification, but also strategy/process diversification.
While each modification might only result in a small, incremental improvement in retirement outcomes, the compounding effect can be very beneficial.
The chart below shows the required tactical sleeve size needed to minimize shortfalls/surpluses for a given improvement in the annual returns (0bp through 150bps).
Tactical Allocation Strategy Size Needed to Minimize 10% Expected Shortfall/Surplus with Average Stock and Bond Returns Equal to Current Expectations for a Range of Annualized Return Improvements – 30 Yr. Horizon with a 4% Initial Withdrawal Rate
Source: Shiller Data Library and Research Affiliates. Calculations by Newfound Research. Analysis uses real returns and assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
For a return improvement of 125bps per year over the current forecasts for static U.S. equity and bond portfolios, with a static equity allocation of 50%, including a tactical sleeve of 20% would minimize the shortfall/surplus.
This portfolio essentially pivots around a static 60/40 portfolio, and we can compare the two, giving the same 125bps bonus to the returns for the static 60/40 portfolio.
Comparison of a Tactical Allocation Enhanced Portfolio with a Static 60/40 Portfolio with Average Stock and Bond Returns Equal to Current Expectations + 125bps per year – 30 Yr. Horizon with a 4% Initial Withdrawal Rate
Source: Shiller Data Library and Research Affiliates. Calculations by Newfound Research. Analysis uses real returns and assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
In addition to the much more favorable statistics, the tactically enhanced portfolio only has a downside tracking error of 1.1% to the static 60/40 portfolio.
Conclusion: Being Dynamic in Retirement
From this historical analysis, high valuations of core assets in the U.S. suggest a grim outlook for the 4% rule. Predetermined dynamic allocation paths through retirement can help somewhat, but merely specifying an equity allocation based on one’s age loses sight of the changing risk a given market environment.
The sequence of market returns can have a large impact on retirement portfolios. If a drawdown happens early in retirement, subsequent returns may not be enough to provide the tailwind that they have in the past.
Investors who are able to be fee/expense/tax-conscious and adhere to prudent diversification may be able to incrementally improve their retirement outlook to the point where a modest allocation to a sleeve of tactical investment strategies can get their portfolio back to a comfortable success rate.
Striking a balance between shortfall/surplus risk and the expected experience during the retirement period along with a thorough assessment of risk tolerance in terms of maximum and minimum equity exposure can help dictate how flexible a portfolio should be.
In our QuBe Model Portfolios, we pair allocations to tactically managed solutions with systematic, factor based strategies to implement these ideas.
While long-term capital market assumptions are a valuable input in an investment process, adapting to shorter-term market movements to reduce sequence risk may be a crucial way to combat market environments where the low return expectations come to fruition.
[1] Specifically, we use the “Yield & Growth” capital market assumptions from Research Affiliates. These capital market assumptions assume that there is no valuation mean reversion (i.e. valuations stay the same going forward). The adjusted average nominal returns for U.S. equities and 10-year U.S. Treasuries are 5.3% and 3.1%, respectively, compared to the historical values of 9.0% and 5.3%.
[2] Normally, the Ulcer Index would be measured using true drawdown from peak, however, we believe that using starting wealth as the reference point may lead to a more accurate gauge of pain.
[3] Bierwirth, Larry. 1994. Investing for Retirement: Using the Past to Model the Future. Journal of Financial Planning, Vol. 7, no. 1 (January): 14-24.
[4] Bengen, William P. 1994. “Determining Withdrawal Rates Using Historical Data.” Journal of Financial Planning, vol. 7, no. 4 (October): 171-180.
[5] Pfau, Wade D. and Kitces, Michael E., Reducing Retirement Risk with a Rising Equity Glide-Path (September 12, 2013). Available at SSRN: https://ssrn.com/abstract=2324930
[6] Clare, A. and Seaton, J. and Smith, P. N. and Thomas, S. (2017). Can Sustainable Withdrawal Rates Be Enhanced by Trend Following? Available at SSRN: https://ssrn.com/abstract=3019089
[7] Clare, A. and Seaton, J. and Smith, P. N. and Thomas, S. (2017) Reducing Sequence Risk Using Trend Following and the CAPE Ratio. Financial Analysts Journal, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2764933
This article is available for download as a PDF here.
Summary
The low current market outlook for stocks and bonds paints a gloomy picture for retirees under common retirement forecasting assumptions.
However, assumptions such as net investment returns and retirement spending can have a large impact on forecasted retirement success, even for small changes in parameters.
By boosting returns through a combination of broader asset class and strategy diversification, considering lower fee options for passive exposures, and nailing down how retirement spending will evolve over time, we can arrive at retirement success projections that are both more reflective of a retiree’s actual situation and more in line with historical experience.
A few weeks back, we wrote about the potential impact that high core asset valuations – and the associated muted forward return expectations – may have on retirement[1].
In the post, we presented the following visualization:
Historical Wealth Paths for a 4% Withdrawal Rate and 60/40 Stock/Bond Allocation
Source: Shiller Data Library. Calculations by Newfound Research. Credit to Reddit user zaladin for the graph format. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
The horizontal (x-axis) represents the year when retirement starts. The vertical (y-axis) represents a given year in history. The coloring of each cell represents the savings balance at a given point in time. The meaning of each color as follows:
Green: Current account value greater than or equal to initial account value (e.g. an investor starting retirement with $1,000,000 has a current account balance that is at least $1,000,000).
Yellow: Current account value is between 75% and 100% of initial account value
Orange: Current account value is between 50% and 75% of the initial account value.
Red: Current account value is between 25% and 50% of the initial account value.
Dark Red: Current account value is between 0% and 25% of initial account value.
Black: Current account value is zero; the investor has run out of money.
We then recreated the visualization, but with one key modification: we adjusted the historical stock and bond returns downward so that the long-term averages are in line with realistic future return expectations[2] given current valuation levels. We did this by subtracting the difference between the actual average log return and the forward-looking long return from each year’s return. With this technique, we capture the effect of subdued average returns while retaining realistic behavior for shorter-term returns.
Historical Wealth Paths for a 4% Withdrawal Rate and 60/40 Stock/Bond Allocation with Current Return Expectations
Source: Shiller Data Library. Calculations by Newfound Research. Credit to Reddit user zaladin for the graph format. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
One downside of the above visualizations is that they only consider one withdrawal rate / portfolio composition combination. If we want the see results for withdrawal rates ranging from 1% to 10% in 1% increments and portfolio combinations ranging from 0/100 stocks/bonds to 100/0 stocks/bonds in 20% increments, we would need sixty graphs!
To distill things a bit more, we looked at the historical “success” of various investment and withdrawal strategies. We evaluated success on three metrics:
Absolute Success Rate (“ASR”): The historical probability that an individual or couple will not run out of money before their retirement horizon ends.
Comfortable Success Rate (“CSR”): The historical probability that an individual or couple will have at least the same amount of money, in real terms, at the end of their retirement horizon compared to what they started with.
Ulcer Index (“UI”): The average pain of the wealth path over the retirement horizon where pain is measured as the severity and duration of wealth drawdowns relative to starting wealth[3].
As a quick refresher, below we present the ASR for various withdrawal rate / risk profile combinations over a 30-year retirement horizon first using historical returns and then using historical returns adjusted to reflect current valuation levels.
Absolute Success Rate for Various Combinations of Withdrawal Rate and Portfolio Composition – 30 Yr. Horizon
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Absolute Success Rate for Various Combinations of Withdrawal Rate and Portfolio Composition with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Overall, our analysis suggested that retirement withdrawal rates that were once safe may now deliver success rates that are no better – or even worse – than a coin flip.
Over the coming weeks, we want to delve a bit deeper into this topic. Specifically, we are going to explore some key properties of distribution portfolios – portfolios from which investors take regular withdrawals to finance retirement spending – as well as some strategies that investors may consider in order to improve retirement outcomes.
This week we are going to focus on the high degree of sensitivity that retirement planning outcomes can have to initial assumptions. In upcoming weeks, we will explore other retirement investment topics, including:
The sequence of returns and risk management.
The impact of behavioral finance and investor emotions.
Finding the right portfolio risk profile through retirement.
The Butterfly Effect in Retirement Portfolios
Quoting from a great piece on distribution portfolio theory by James Sandidge[4]:
“The butterfly effect refers to the ability of small changes early in a process that lead to significant impact later. It gets its name from the idea that a butterfly flapping its wings in Brazil could trigger a chain of events that would culminate in the formation of a tornado in Texas[5]. The butterfly effect applies to distribution portfolios where even small changes early in retirement can have significant impact long-term.”
One example of the butterfly effect in the context of retirement planning is the impact of small changes in long-term average returns. These differences could arise from investment outperformance or underperformance, fees, expenses, or taxes.
In the example below, we consider 60/40 stock/bond investor with a 30-year investment horizon and a 4% target withdrawal rate, adjusted each year for inflation. We consider three scenarios:
Pessimistic Scenario: Average annual portfolio returns are 100bps below our long-term assumption (e.g. we picked bad managers, allocated assets poorly, paid high fees, etc.).
Base Case Scenario: Average annual portfolio returns are equal to our long-term assumption.
Optimistic Scenario: Average annual portfolio returns are 100bs above our long-term assumption (e.g. we picked good managers, nailed our asset allocation, paid lower than expected fees, etc.).
We see that varying our return assumption by just +/-100bps can swing our probability of fully funding retirement – without decreasing withdrawals below plan – from 48% to 74%. Similarly, the probability of ending retirement with our original nest egg fully intact ranges from 11% in the pessimistic scenario to 47% in the optimistic scenario.
In the optimistic scenario, the median ending wealth after 30 years is $800k for an initial investment of $1mm. Not outstanding but certainly nothing to complain about. In the pessimistic scenario, however, our median ending wealth is zero, meaning the most likely outcome is running out of money!
The Butterfly Effect and Changes to Average Long-Term Return Assumption: 30-Yr. Horizon, 60/40 Allocation, 4% Withdrawals
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Below, we present one example that is particularly telling: an investor that retired in 1973[6]. We see that a 100bps difference in returns in either direction can literally be the difference between running out of funds (gray), sweating every dollar and cent (orange), or a relatively comfortable retirement (blue).
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Camouflaged Butterflies: Assumptions in Spending Rate Changes
An example of a secondary input that sometimes may be glossed over, but nonetheless can have a large impact on outcomes is the assumption regarding how quickly withdrawals will increase relative to inflation. Again, we consider three scenarios:
Withdrawals increase at a rate that is 1% slower than inflation (i.e. spending will rise by 2% year-over-year when inflation is 3% – spending falls in real terms).
Withdrawals increase at the same rate of inflation (spending stays constant in real terms).
Withdrawals increase at a rate that is 1% faster than inflation (i.e. spending will rise by 4% year-over-year when inflation is 3% – spending rises in real terms). This is probably an unrealistic scenario, for reasons that we will discuss later, but it still helps illustrate the sensitivity of planning analysis to its inputs.
The Butterfly Effect and Changes to the Spending Growth Assumption: 30-Yr. Horizon, 60/40 Allocation, 4% Withdrawals
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Overall, the results are very similar in magnitude to what we saw when we adjusted the return assumption.
Implications of the Butterfly Effect
The examples above provide clear evidence that retirement success is significantly impacted by both primary and secondary assumptions. But what does this mean for investors? We think there are two main implications.
Getting the details right is crucial.
First, it’s important to get the details right when planning for retirement. To highlight this, let’s return to the topic of spending. Many financial calculators assume that spending increases one-for-one with inflation through retirement. Put differently, this assumes that spending is constant after adjusting for inflation.
Data from the Employee Benefit Research Institute (“EBRI”) suggests that this is generally an erroneous assumption. Instead, spending tends to decline as retirees age. Specifically, EBRI found that on average spending declines 20% from age 50-64 to 65-79, 22% from age 65-79 to 80-89, and 12% from age 80-89 to 90+.
(Note: This is obviously a gross oversimplification of actual spending behavior. At the end of this commentary, we discuss a few interesting research pieces on this topic. They make clear the importance of customizing spending assumptions to each client’s situation and preferences.)
Source: “Adaptive Distribution Theory” by James B. Sandidge
Implementing more realistic spending assumptions makes a material difference in our Absolute Success Rate (“ASR”), Comfortable Success Rate (“CSR”), and Ulcer Index stats.
Below, we recreate our ASR, CSR, and Ulcer Index tables assuming that real spending declines by 1% per year. We also compare these measures across three scenarios for a 4% withdrawal rate:
Historical return assumptions and constant real spending
Current return assumptions and constant real spending
Current return assumptions and 1% per year decline in real spending
We see that our adjusted spending assumption helps to close the gap between the historical and forward-looking return scenarios. This is especially true when we look at the ASR.
For example, a 60/40 portfolio and 4% constant real withdrawal rate produced an ASR of 99% across all historical market scenarios. The success rate dropped all the way to 58% when we adjusted the historical stock and bond returns downward for our future expectations. Changing to the declining spending path increases the success rate from 58% to 75%.
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Absolute Success Rate for Various Combinations of Withdrawal Rate and Portfolio Composition with Average Stock and Bond Returns Equal to Current Expectations and Real Spending Declining by 1% Per Year – 30 Yr. Horizon
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Comfortable Success Rate for Various Combinations of Withdrawal Rate and Portfolio Composition with Average Stock and Bond Returns Equal to Current Expectations and Real Spending Declining by 1% Per Year – 30 Yr. Horizon
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Ulcer Index for Various Combinations of Withdrawal Rate and Portfolio Composition with Average Stock and Bond Returns Equal to Current Expectations and Real Spending Declining by 1% Per Year – 30 Yr. Horizon
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Incremental increases (decreases) in portfolio returns (spending) matter, a lot.
Reducing spending is a very personal topic, so we will focus on some potential ways to grind out some incremental portfolio gains. (Note: another important topic when constructing withdrawal portfolios is to manage sequence of return risk. We will address this topic in a future post).
First, it’s important to be strategic, not static. To us, this means having a thoughtful, forward-looking outlook when setting a strategic asset allocation. A big part of this is fighting the temptation of home-country bias.
This tendency to prefer home-country assets not only leaves quite a bit of diversification on the table, but also puts U.S. investors on the wrong side current equity market valuations.
Based upon a blended set of capital market assumptions sourced from J.P. Morgan, Blackrock, and BNY Mellon, we see that it’s possible to increase long-term expected returns by between 30bps and 50bps, depending on desired risk profile, by moving beyond U.S. stocks and bonds[7]. Last week we discussed the “weird portfolios” that may be best positioned for the future.
Source: J.P. Morgan, Blackrock, BNY Mellon, Newfound Research. Return forecasts are forward-looking statements based upon the reasonable beliefs of Newfound Research and are not a guarantee of future performance. Forecasts are not representative of any Newfound Research strategy or fund. Forward-looking statements speak only as of the date they are made and Newfound assumes no duty to and does not undertake to update forward-looking statements. Forward looking statements are subject to numerous assumptions, risks, and uncertainties, which change over time. Actual results may differ materially from those anticipated in forward-looking statements. Returns are presented gross of taxes and fees.
Second, we recommend using a hybrid active/passive approach for core exposures given the increasing availability of evidence-based, favor-driven investment strategies. Now this sounds great in theory, but with over 300 factors now identified across the global equity markets and the proliferation of “smart beta” ETFs, it is reasonable to wonder how in the world one can have a view of which factors will actually work going forward. To dig into this a bit deeper, let’s look at one of our favorite examples of factor-based investing.
This portfolio, suggested by Vanguard, buys companies whose tickers start with the letters S, M, A, R, or T. This is not a real portfolio that anyone should invest in; yet it has identified an anomalous outperformance pattern. On a backtested basis, the S.M.A.R.T. Beta portfolio nearly doubled the annualized return of the S&P 500.
In order to determine the validity of this so-called factor, we need to understand:
What is the theory that explains why the factor works (provides excess return)? Without a theory for why something works, we cannot possibly form an intelligent view as to whether or not it will world in the future.
How has the factor performed on an out-of-sample basis? This is math speak for the following types of questions: How as the factor performed after its discovery? How does the factor work with slightly alternative implementations? Does the factor perform well in other assets classes and geographies?
In the case of the S.M.A.R.T. Beta factor, these questions allow us to quickly dismiss it. There is obviously no good reason – at least no good reason we can think of off the top of our heads – for why the first letter in a stock’s ticker should drive returns[8]. While we have not tested S.M.A.R.T. Beta across asset classes and geographies, we know that this was simply a tongue-in-cheek example presented by Vanguard trying to get the point across that it’s easy to find something that works in the past, but much harder to find something that works in the future. We suspect that if we did test the strategy in other countries, as an example, that it would probably outperform in some cases and underperform in others. This lack of robustness would be a clear sign that our level of confidence in this factor going forward should be very low.
So, what factors do meet these criteria (in our view)? Only four that are applicable to stocks:
Value: Buy cheap stocks and sell expensive ones
Momentum: Buy outperforming securities and sell underperforming ones
Defensive: But lower risk/higher quality securities and sell higher risk/lower quality ones
Size/Liquidity: Buy smaller/less liquid companies and sell larger/more liquid ones[9]
Data Source: AQR, Calculations by Newfound Research. Value is the HML Devil factor. Momentum is the UMD factor. Defensive is a blend of the BAB and QMJ factors. Size is the SMB factor. Equal Weight is an equally weighted blend of all four factors, rebalanced monthly. Returns include the reinvestment of dividends and are gross of all fees and expenses. Past performance does not guarantee future results.
Going back to 1957, an equally-weighted blend of the four factors mentioned above would have generated in excess of 500bps of excess annualized return before fees and expenses. Even if we discount future performance by 50% for reduced strategy efficacy and fees, the equal weight factor portfolio could add nearly 160bps for a 60/40 investor[10].
Third, we recommend looking beyond fixed income for risk management. Broadly speaking, we divide asset classes and strategies into two categories: return generators and risk mitigators.
Over the last 30+ years, investors have been very fortunate that their primary risk mitigator – fixed income – happened to experience an historic bull market.
Unfortunately, our situation today is much different than the early 1980s. Current yields are very low by historical standards, implying that fixed income is likely to be a drag to portfolio performance especially after accounting for inflation. However, that does not mean that bonds should not still play a key role in all but the most aggressive portfolios. It simply means that the premium for using bonds as a form of portfolio insurance is high relative to historical standards. As a result, we advocate looking for complementary risk management tools.
One option here would be to employ a multi-strategy, unconstrained sleeve like we constructed in a recent commentary[11]. When constructed with the right objectives in mind, these types of portfolios can act as an effective buffer to equity market volatility without the cost of large fixed income positions in a low interest rate environment. Let’s take the Absolute Return strategy that we discussed in that piece. It was constructed by optimizing for an equal risk contribution across the following seven asset classes and strategies:
U.S. Treasuries: 25%
Low volatility equities: 8%
Trend-based tactical asset allocation: 9%
Value-based tactical asset allocation: 12%
Unconstrained fixed income: 25%
Risk Parity: 9%
Managed Futures: 12%
Now let’s consider our typical 60/40 investor. Historically, a 25% allocation to this unconstrained sleeve with 18.8% (3/4 of the 25%) taken from fixed income and 6.3% (1/4 of the 25%) taken from equities would have left the investor in the same place as the original 60/40 from a risk perspective. This holds true whether we measure risk as volatility or maximum drawdown.
When we regress the absolute return strategy on world equities and U.S. Treasuries, we get the following results (data for this analysis covers the period from January 1993 to June 2016):
A loading to global equities of 0.25
A loading to U.S. Treasuries of 0.49
Annualized alpha of approximately 2%
Annualized residual volatility of 2.2%.
An R-squared of around 0.77
From the relatively high R-squared, we can conclude that a decent way to think of the absolute return portfolio is as a combination of three positions: 1) a 25% allocation to world stocks, 2) a 49% allocation to U.S. Treasuries, and 3) a 100% allocation to an unconstrained long/short portfolio with historical performance characterized by a 2% excess return and 2.2% volatility.
Using this construct, we can get at least a very rough idea of what to expect going forward by plugging in our capital market assumptions for world equities and U.S. Treasuries and making a reasonable assumption for what the long/short portfolio can deliver going forward on a net-of-fee basis. Let’s assume as we did for the factor discussion that the long/short portfolio only captures around 50% of its historical performance after fees. This would still imply an expected forward-looking return of 4.1% compared to an average expected return of 2.5% for U.S. core bonds[15]. For the 60/40 investor, this could mean close to 25bps of incremental return.
Finally, we should seek to reduce fees, all else being equal. Four things that we think are worth mentioning here.
We need to consider fees holistically. This means looking beyond expense ratios and considering factors like execution costs (e.g. bid/ask spread), commissions, and ticket charges.
The “all else being equal” part is really important. We want to be fee-conscious, not fee centric. Just like you probably don’t always buy the cheapest home, clothes, and electronics, we don’t believe in defaulting to the lowest cost investment option in all cases. We want to find value in the investments we choose. If market-cap weighted equity exposure costs 5bps and we can get multi-factor exposure for 25bps, we will not eliminate the factor product from consideration just due higher fees if we believe it can offer more than 20bps in incremental value. Fortunately, the proliferation of passive investment vehicles effectively being offered for free has helped put downward pressure on products throughout the industry.
We have to remember that while there are many, many merits to a passive, market-cap weighted approach, the rise of this type of investing has largely coincided with upward trends in equity and bond valuations. In other words, the return pie has been very big and therefore the name of the game has been capturing as much of the pie as possible, usually by minimizing fees and staying disciplined (after all, a passive approach to investing, like any other approach, only works long-term if we can stick with it, and behavioral science and experience suggests there are real difficulties doing so especially when markets get volatile). Today, we are in a fundamentally different situation. The pie is nearly as small as it’s ever been. For many investors, even capturing 100% of the pie may not be enough. Instead, many must search out ways to expand the pie in order to meet their goals.
From a behavioral perspective, there is nothing wrong with channeling our inner Harry Markowitz and going with a hybrid active[13]/passive approach within the same portfolio. Markowitz, who helped revolutionize portfolio construction theory with his landmark paper “Portfolio Selection,” famously explained that when building his own portfolio he knew he should have “…computed the historical covariances of the asset classes and drawn an efficient frontier.” Instead, he said, “I visualized my grief if the stock market went way up and I wasn’t in it – or if it went way down and I was completely in it. So, I split my contributions 50/50 between stocks and bonds.” We are strong advocates for passive, just not for 100% concentration in passive.
Let’s say as an example that by using these techniques, we are able to improve returns by 150bps annually. What would the impact be on ASR, CSR, and Ulcer Index using our same framework? For this analysis, we retain our assumption from earlier that real spending declines by 1% per year.
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Absolute Success Rate for Various Combinations of Withdrawal Rate and Portfolio Composition with Average Stock and Bond Returns Equal to Current Expectations Plus 150bps and Real Spending Declining by 1% Per Year – 30 Yr. Horizon
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Comfortable Success Rate for Various Combinations of Withdrawal Rate and Portfolio Composition with Average Stock and Bond Returns Equal to Current Expectations Plus 150bps and Real Spending Declining by 1% Per Year – 30 Yr. Horizon
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Ulcer Index for Various Combinations of Withdrawal Rate and Portfolio Composition with Average Stock and Bond Returns Equal to Current Expectations Plus 150bps and Real Spending Declining by 1% Per Year – 30 Yr. Horizon
Source: Shiller Data Library. Calculations by Newfound Research. Analysis assumes the reinvestment of dividends. Returns are hypothetical index returns and are gross of all fees and expenses. Results may differ slightly from similar studies due to the data sources and calculation methodologies used for stock and bond returns.
Conclusion: The Sum of All Assumptions in Retirement
Retirement projections are based on many different assumptions including asset class returns, time horizon, allocation strategies, inflation, and how withdrawals evolve over time. Small changes in many of these assumptions can have a large impact on retirement success rates (the Retirement Butterfly Effect).
High valuations of core assets in the U.S. suggest that retirement withdrawal rates that were once safe may now deliver success rates that are no better – or even worse – than a coin flip. However, by focusing our efforts on refining the assumptions that go into retirement planning, we can arrive at results that do not spell doom and gloom for retirees.
While getting all the details right is ideal, there are specific areas that matter the most.
For returns, increasing net returns is what matters, which means there are many knobs to adjust. Incorporating factor based strategies and broader diversification are good initial starting points. Expanding the usage of international equity and unconstrained strategy exposure can be simple modifications to traditional U.S. equity and bond heavy portfolios that may give a boost to forward-looking returns.
Fees, expenses, and taxes can be other areas to examine as long as we keep in mind that it is best to be fee/expense/tax-conscious, not fee/expense/tax-centric. Slight fee or tax inefficiencies can cause a “guaranteed” loss of return, but these effects must be weighed against the potential upside.
For many exposures (e.g. passive and long-only core stock and bond exposure), minimizing cost is certainly appropriate. However, do not let cost considerations preclude the consideration of strategies or asset classes that can bring unique return generating or risk mitigating characteristics to the portfolio.
With spending, the assumption that retirees will track inflation with their withdrawals throughout a 30 year retirement is not applicable across the board. Nailing down spending is tough, but improved assumptions can have a big impact on retirement forecasts. A thorough conversation on housing, health care, travel, insurance, and general consumption is critical.
As with any model that produces a forecast, there will always be errors in retirement projections. When asset class returns are strong, as they have been in previous decades, we can comfortably brush many assumptions under the rug. However, with muted future returns, achieving financial goals requires a better understanding of model sensitivities and more diligent research into how to equip portfolios to thrive in such an environment.
David Blanchett, Head of Retirement Research for Morningstar Investment Management, argues that the common assumptions of a generic replacement rate[15], constant real spending, and a fixed retirement horizon do not accurately capture the highly personalized nature of a retiree’s spending behavior.
Key takeaways include:
From a category perspective, the main changes through retirement are a decline in relative spending on insurance and pensions and an increase in health care spending.
Source: Blanchett’s Estimating the True Cost of Retirement
Forecasts on spending by category can be used to determine a customized spending inflation rate for a given household. For example, Blanchett plots general inflation vs. medical inflation. Using this relationship, we can predict that 2% general inflation would lead to medical cost inflation of approximately 4%. One theme of many research papers on the topic of retirement spending is that health care planning should be accounted for in a separate line item. Not only does the future of the health care system have the potential to look much different from the past, but the actual financial impact of health care costs can differ greatly depending on each individual’s insurance situation. Blanchett also finds that health care spending does not differ materially across income levels.
Source: Blanchett’s Estimating the True Cost of Retirement
Blanchett finds that spending does decline through retirement and on average follows a “U” pattern whereby spending declines accelerate before age 75 and decelerate afterwards.
Source: Blanchett’s Estimating the True Cost of Retirement
Blanchett decomposed the population of his dataset into four groups based on spending and net worth. $30,000 was the threshold for separating spenders into high and low groups. $400,000 was the threshold for dividing the population by net worth. He found that households with “matched” spending and net worth (i.e. low spending and low net worth or high spending and high net worth) exhibited the “U” pattern that we saw with the full dataset. However, households with mismatched spending/net worth behaved differently. High net worth and low spending households saw spending increase through retirement, although the rate of this increase was faster earlier in retirement. Conversely, households with high spending and low net worth reduced their spending more aggressively than the other groups.
Source: Blanchett’s Estimating the True Cost of Retirement
The EBRI studied linked above also documents spending reductions through retirement. It presents very interesting data on the distribution of health care spending by age group. We see that the distribution widens out significantly over time with the largest increases occurring in the right tail (90th and 95th percentile of spending).
In this piece, J.P. Morgan analyzed retirement spending using a unique dataset of 613,000 households that utilize the Chase platform (debit cards, credit cards, mortgage payments, etc.) for the majority of their spending. The authors found the same general trend of declining spending as in the EBRI and Morningstar pieces.
Spending declines were largest in the transportation, apparel & services, and mortgage categories. The overall and category-specific patterns were generally consistent across wealth levels. The researchers were able to classify households into five categories: foodies, homebodies, globetrotters, health care spenders, and snowflakes. This categorization is relevant because each group can expect to see their spending needs evolve differently over time. Some key takeaways for each group are:
Foodies
Most common group
Generally frugal
Low housing expenses due to mortgages being paid off and low property tax bills
Tend to spend less as they get older and so an assumption of faster declines in real spending may be appropriate
Homebodies
High share of spending on mortgages, real estate taxes, and ongoing maintenance
May be prudent to assume that expenses track inflation
For planning purposes, it’s important to consider future plans related to housing
Globetrotters
Highest overall spending
More common among households with higher net worth
May be prudent to assume that expenses track inflation
Health care spenders
Medicare-related expenses were the largest share of spending for these households
[2] Specifically, we use the “Yield & Growth” capital market assumptions from Research Affiliates. These capital market assumptions assume that there is no valuation mean reversion (i.e. valuations stay the same going forward). The adjusted average nominal returns for U.S. equities and 10-year U.S. Treasuries are 5.3% and 3.1%, respectively, compared to the historical values of 9.0% and 5.3%.
[3] Normally, the Ulcer Index would be measured using true drawdown from peak, however, we believe that using starting wealth as the reference point may lead to a more accurate gauge of pain.
[4] References to ideas similar to the butterfly effect date back as far as the 1800s. In academia, the idea is prevalent in the field of chaos theory.
[6] We continue to adjust returns to account for current valuations. Therefore, this example takes the actual returns for U.S. stocks and bonds from 1973 to 2003 and then adjusts them downward based on the Research Affiliates’ long-term return assumptions.
[7] Potential increases in expected return, based upon the capital market assumptions of the three institutions listed, are actually larger than what we present here. This results from two aspects of the QuBe investment process. First, we utilize a simulation-based approach that incorporates downside shocks to the correlation matrix and that accounts for parameter estimate uncertainty. Second, we consider two behaviorally-based optimizations, one that attempts to smooth the absolute path of returns and another that attempts to smooth the path of returns relative to a common benchmark, which is tilted toward U.S. equities. Both of these techniques reduce the expected returns generated when we combine the resulting weights with the stated capital market assumptions.
[8] There actually has been research published suggesting evidence that stock tickers can be useful in picking stocks. For example, “Would a stock by any other ticker smell as sweet?” by Alex Head, Gary Smith, and Julia Wilson find evidence that stocks with “clever” tickers (e.g. Southwest’s choice of LUV to reflect its brand) outperform the broader market. Their results were robust to the Fama-French 3-factor model. As a rationale for these results, the authors posited that clever tickers might signal manager ability or that the memorable tickers feed into the behavioral biases of investors.
[9] The size premium is probably the most hotly debated of the four today. Recent research suggests that that size prospers once we control for quality (i.e. we want to buy small, high quality companies not just small companies).
[10] As we’ve written about in the past, factor portfolios do not have to generate excess returns to justify an allocation in equity portfolios. Even with zero to slightly negative premiums, moderate allocations to these strategies would have historically led to increased risk-adjusted returns due to the diversification that they provide to market-cap weighted portfolios.
[12] Again using data from J.P. Morgan, Blackrock, and BNY Mellon.
[13] When we say active, we usually (but not always) mean systematic strategies that are factor-based and implemented using a quantitative and rules-based investment process.
[14] Blanchett, David. 2013. Estimating the True Cost of Retirement. Working paper, Morningstar Investment Management. https://corporate.morningstar.com/ib/documents/MethodologyDocuments/ResearchPapers/Blanchett_True-Cost-of-Retirement.pdf
[15] Quoting from Blanchett, “The replacement rate is the percentage of household earnings need to maintain a similar standard of living during retirement.
[16] Banerjee, Sudipto. 2014. How Does Household Expenditure Change with Age for Older Americans? Employee Benefits Research Institute. Notes 35, no. 9 (September). https://www.ebri.org/pdf/notespdf/Notes.Sept14.EldExp-Only.pdf
[17] Roy, Katherine and Sharon Carson. 2015. Spending in Retirement. J.P. Morgan. https://am.jpmorgan.com/gi/getdoc/1383244966137.
[18] Carson, Sharon and Laurance McGrath. 2016. Health care costs in retirement. J.P. Morgan. https://am.jpmorgan.com/blob-gim/1383331734803/83456/RI_Healthcare%20costs_2016_r4.pdf?segment=AMERICAS_US_ADV&locale=en_US
[19] Roy, Katherine, Sharon Carson, and Lena Rizkallah. 2016. Guide to Retirement. J.P. Morgan. https://am.jpmorgan.com/blob-gim/1383280097558/83456/JP-GTR.pdf
Source: Global Financial Data and Shiller Data Library. Calculations by Newfound Research. Returns are backtested and hypothetical. Past performance is not a guarantee of future results. Returns are gross of all fees. Returns assume the reinvestment of all distributions. None of the strategies shown reflect any portfolio managed by Newfound Research and were constructed solely for demonstration purposes within this commentary. You cannot invest in an index.
Dynamic Spending in Retirement Monte Carlo
By Nathan Faber
On July 15, 2019
In Risk Management, Sequence Risk, Weekly Commentary
This post is available as a PDF download here.
Summary
Monte Carlo simulations are a prevalent tool in financial planning, especially pertaining to retirement success calculations.
Under a typical framework of normally distributed portfolio returns and constant inflation-adjusted withdrawals, calculating the success of a given retirement portfolio is straightforward. But as with most tools in finance, the art lies both in the assumptions that go into the calculation and in the proper interpretation of the result.
If a client is told they have a 10% chance of running out of money over their projected retirement horizon, what does that mean for them?
They cannot make 9 copies of themselves to live out separate lives, with one copy (hopefully not the original) unfortunately burning through the account prematurely.
They also cannot create 9 parallel universes and ensure they do not choose whichever one does not work out.
We wrote previously how investors follow a single path (You Are Not a Monte-Carlo Simulation). If that path hits zero, the other hypothetical simulation paths don’t mean a thing.
A simulation path is only as valuable as the assumptions that go into creating it, and fortunately, we can make our simulations align more closely with investor behavior.
The best way to interpret the 10% failure rate is to think of it as a 10% chance of having to make an adjustment before it hits zero. Rarely would an investor stand by while their account went to zero. There are circumstances that are entirely out of investor control, but to the extent that there was something they could do to prevent that event, they would most likely do it.
Derek Tharp, on Michael Kitces’ blog, wrote a post a few years ago weighing the relative benefit of implementing small but permanent adjustments vs. large but temporary adjustments to retirement withdrawals and found that making small adjustments and leaving them in place led to greater likelihoods of success over retirement horizons (Dynamic Retirement Spending Adjustments: Small-But-Permanent Vs Large-But-Temporary).
In this week’s commentary, we want to dig a little deeper into some simple path dependent modifications that we can make to retirement Monte-Carlo simulations with the hope of creating a more robust toolset for financial planning.
The Initial Plan
Suppose an investor is 65 and holds a moderate portfolio of 60% U.S. stocks and 40% U.S. Treasuries. From 1871 until mid-2019, this portfolio would have returned an inflation-adjusted 5.1% per year with 10.6% volatility according to Global Financial Data.
Sticking with the rule-of-thumb 4% annual withdrawal of the initial portfolio balance and assuming a 30-year retirement horizon, this yields a predicted failure rate of 8% (plus or minus about 50 bps).
The financial plan is complete.
If you start with $1,000,000, simply withdraw $3,333/month and you should be fine 92% of the time.
But what if the portfolio drops 5% in the first month? (It almost did that in October 2018).
The projected failure rate over the next 29 years and 11 months has gone up to 11%. That violates a 10% threshold that may have been a target in the planning process.
Or what if it drops 30% in the first 6 months, like it would have in the second half of 1931?
Now the project failure rate is a staggering 46%. Retirement success has been reduced to a coin flip.
Admittedly, these are trying scenarios, but these numbers are a key driver for financial planning. If we can better understand the risks and spell out a course of action beforehand, then the risk of making a rash emotion-driven decision can be mitigated.
Aligning the Plan with Reality
When the market environment is challenging, investors can benefit by being flexible. The initial financial plan does not have to be jettisoned; agreed upon actions within it are implemented.
One of the simplest – and most impactful – modifications to make is an adjustment to spending. For instance, an investor might decide at the outset to scale back spending by a set amount when the probably of failure crosses a threshold.
Source: Global Financial Data. Calculations by Newfound.
This reduction in spending would increase the probability of success going forward through the remainder of the retirement horizon.
And if we knew that this spending cut would likely happen if it was necessary, then we can quantify it as a rule in the initial Monte Carlo simulation used for financial planning.
Graphically, we can visualize this process by looking at the probabilities of failure for varying asset levels over time. For example, at 10 years after retirement, the orange line indicates that a portfolio value ~80% of the initial value would have about a 5% failure rate.
Source: Global Financial Data. Calculations by Newfound.
As long as the portfolio value remains above a given line, no adjustment would be needed based on a standard Monte Carlo analysis. Once a line is crossed, the probability of success is below that threshold.
This chart presents a good illustration of sequence risk: the lines are flatter initially after retirement and the slope progressively steepens as the time progresses. A large drawdown initially puts the portfolio below the threshold for making and adjustment.
For instance, at 5 years, the portfolio has more than a 10% failure rate if the value is below 86%. Assuming zero real returns, withdrawals alone would have reduced the value to 80%. Positive returns over this short time period would be necessary to feel secure in the plan.
Looking under the hood along the individual paths used for the Monte Carlo simulation, at 5 years, a quarter of them would be in a state requiring an adjustment to spending at this 10% failure level.
Source: Global Financial Data. Calculations by Newfound.
This belies the fact that some of the paths that would have crossed this 10% failure threshold prior to the 5-year mark improved before the 5-year mark was hit. 75% of the paths were below this 10% failure rate at some point prior to the 5-year mark. Without more appropriate expectations of a what these simulations mean, under this model, most investors would have felt like their plan’s failure rate was uncomfortable at some point in the first 5 years after retirement!
Dynamic Spending Rules
If the goal is ultimately not to run out of funds in retirement, the first spending adjustment case can substantially improve those chances (aside from a large negative return in the final periods prior to the last withdrawals).
Each month, we will compare the portfolio value to the 90% success value. If the portfolio is below that cutoff, we will size the withdrawal to hit improve the odds of success back to that level, if possible.
The benefit of this approach is greatly improved success along the different paths. The cost is forgone income.
But this can mean forgoing a lot of income over the life of the portfolio in a particularly bad state of the world. The worst case in terms of this total forgone income is shown below.
Source: Global Financial Data. Calculations by Newfound.
The portfolio gives up withdrawals totaling 74%, nearly 19 years’ worth. Most of this is given up in consecutive periods during the prolonged drawdown that occurs shortly after retirement.
This is an extreme case that illustrates how large of income adjustments could be required to ensure success under a Monte Carlo framework.
The median case foregoes 9 months of total income over the portfolio horizon, and the worst 5% of cases all give up 30% (7.5 years) of income based off the initial portfolio value.
That is still a bit extreme in terms of potential cutbacks.
As a more realistic scenario that is easier on the pocketbook, we will limit the total annual cutback to 30% of the withdrawal in the following manner:
These rules still increase the success rate to 99% but substantially reduce the amount of reductions in income.
Looking again at the worst-case scenario, we see that this case still “fails” (even though it lasts another 4.5 years) but that its reduction in come is now less than half of what it was in the extreme cutback case. This pattern is in line with the “lower for longer” reductions that Derek had looked at in the blog post.
Source: Global Financial Data. Calculations by Newfound.
On the 66% of sample paths where there was a cut in spending at some point, the average total cut amounted to 5% of the portfolio (a little over a year of withdrawals spread over the life of the portfolio).
Even moving to an even less extreme reduction regime where only 10% cuts are ever made if the probability of failure increases above 10%, the average reduction in the 66% of cases that required cuts was about 9 months of withdrawals over the 30-year period.
In these scenarios, the failure rate is reduced to 5% (from 8% with no dynamic spending rules).
Source: Global Financial Data. Calculations by Newfound.
Conclusion
Retirement simulations can be a powerful planning tool, but they are only as good as their inputs and assumptions. Making them align as closes with reality as possible can be a way to quantify the impact of dynamic spending rules in retirement.
While the magnitude of spending reductions necessary to guarantee success of a retirement plan in all potential states of the world is prohibitive. However, small modifications to spending can have a large impact on success.
For example, reducing withdrawal by 10% when the forecasted failure rate increases above 10% nearly cut the failure rate of the entire plan in half.
But dynamic spending rules do not exist in a vacuum; they can be paired with other marginal improvements to boost the likelihood of success:
While failure is certainly possible for investors, a “too big to fail” mentality is much more in line with the reality of retirement.
Even if absolute failure is unlikely, adjustments will likely be a requirement. These can be built into the retirement planning process and can shed light on stress testing scenarios and sensitivity.
From a retirement planning perspective, flexibility is simply another form of risk management.