Month: September 2017

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.

The first was about the potential impact that high core asset valuations – and the associated muted forward return expectations – may have on retirement.

The second was about the surprisingly large impact that small changes in assumptions can have on retirement success, akin to the Butterfly Effect in chaos theory. Retirement portfolios can be very sensitive to assumed long-term average returns and assumptions about how a retiree’s spending will evolve over time.

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

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

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 (5^th 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

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

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 (10^thpercentile) 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

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

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

[8] https://blog.thinknewfound.com/2017/03/visualizing-anxiety-active-strategies/

Tax-Managed Models & Asset Location

By Corey Hoffstein

On September 11, 2017

In Portfolio Construction, Weekly Commentary

This post is available for download as a PDF here.

Summary

In a world of anemic asset returns, tax management may help significantly contribute to improving portfolio returns.
Ideally, asset location decisions would be made with full investor information, including goals, risk tolerances, tax rates, and distribution of wealth among account types.
Without perfect information, we believe it is helpful to have both tax-deferred and tax-managed model portfolios available.
We explore how tax-adjusted expected returns can be created, and how adjusting for taxes affects an optimized portfolio given today’s market outlook.

Before we begin, please note that we are not Certified Public Accountants, Tax Attorneys, nor do we specialize in tax management. Tax law is complicated and this commentary will employ sweeping generalizations and assumptions that will certainly not apply to every individual’s specific situation. This commentary is not meant as advice, simply research. Before making any tax-related changes to your investment process, please consult an expert.

Tax-Managed Thinking

We’ve been writing a lot, recently, about the difficulties investors face going forward.[1][2][3] It is our perspective that the combination of higher-than-average valuations in U.S. stocks and low interest rates in core U.S. bonds indicates a muted return environment for traditionally allocated investors going forward.

There is no silver bullet to this problem. Our perspective is that investors will likely have to work hard to make many marginal, but compounding, improvements. Improvements may include reducing fees, thinking outside of traditional asset classes, saving more, and, for investors in retirement, enacting a dynamic withdrawal plan.

Another potential opportunity is in tax management.

I once heard Dan Egan, Director of Behavioral Finance at Betterment, explain tax management as an orthogonal improvement: i.e. one which could seek to add value regardless of how the underlying portfolio performed. I like this description for two reasons.

First, it fits nicely into our framework of compounding marginal improvements that do not necessarily require just “investing better.” Second, Dan is the only person, besides me, to use the word “orthogonal” outside of a math class.

Two popular tax management techniques are tax-loss harvesting and asset location. While we expect that tax-loss harvesting is well known to most (selling investments at a loss to offset gains taken), asset location may be less familiar. Simply put, asset location is how investments are divided among different accounts (taxable, tax-deferred, and tax-exempt) in an effort to maximize post-tax returns.

Asset Location in a Perfect World

Taxes are a highly personal subject. In a perfect world, asset location optimization would be applied to each investor individually, taking into account:

State tax rates
Federal tax rates
Percentage of total assets invested in each account type

Such information would allow us to run a very simple portfolio optimization that could take into account asset location.

Simply, for each asset, we would have three sets of expected returns: an after-tax expected return, a tax-deferred expected return, and a tax-exempt expected return. For all intents and purposes, the optimizer would treat these three sets of returns as completely different asset classes.

So, as a simple example, let’s assume we only want to build a portfolio of U.S. stocks and bonds. For each, we would create three “versions”: Taxable, Tax-Deferred, and Tax-Exempt. We would calculate expected returns for U.S. Stocks – Taxable, U.S. Stocks – Tax-Deferred, and U.S. Stocks – Tax-Exempt. We would do the same for bonds.

We would then run a portfolio optimization. To the optimizer, it would look like six asset classes instead of two (since there are three versions of stocks and bonds). We would add the constraint that the sum of the weights to Taxable, Tax-Deferred, and Tax-Exempt groups could not exceed the percentage of our wealth in each respective account type. For example, if we only have 10% of our wealth in Tax-Exempt accounts, then U.S. Stocks – Tax Exempt + U.S. Bonds – Tax Exempt must be equal to 10%.

Such an approach allows for the explicit consideration of an individual’s tax rates (which are taken into account in the adjustment of expected returns) as well as the distribution of their wealth among different account types.

Case closed.[4]

Asset Location in a Less Than Perfect World

Unfortunately, the technology – and expertise – required to enable such an optimization is not readily available for many investors.

As an industry, the division of labor can significantly limit the availability of important information. While financial advisors may have access to an investor’s goals, risk tolerances, specific tax situation, and asset location break-down, asset managers do not. Therefore, asset managers are often left to make sweeping assumptions, like infinite investment horizons, defined and constant risk tolerances, and tax indifference.

Indeed, we currently make these very assumptions within our QuBe model portfolios. Yet, we think we can do better.

For example, consider investors at either end of the spectrum of asset location. On the one end, we have investors with the vast majority of their assets in tax-deferred accounts. On the other, investors with the vast majority of their wealth in taxable accounts. Even if two investors at opposite ends of the spectrum have an identical risk tolerance, their optimal portfolios are likely different. Painting with broad strokes, the tax-deferred investor can afford to have a larger percentage of their assets in tax-inefficient asset classes, like fixed income and futures-based alternative strategies. The taxable investor will likely have to rely more heavily on tax-efficient investments, like indexed equities (or active equities, if they are in an ETF wrapper).

Things get much messier in the middle of the spectrum. We believe investors have two primary options:

Create an optimal tax-deferred portfolio and try to shift tax-inefficient assets into the tax-deferred accounts and tax-efficient assets into taxable accounts. Investor liquidity needs need to be carefully considered here, as this often means that taxable accounts will be more heavily tilted towards more volatile equities while bonds will fall into tax-deferred accounts.
Create an optimal tax-deferred portfolio and an optimal taxable portfolio, and invest in each account accordingly. This is, decidedly, sub-optimal to asset location in a perfect world, and should even under most scenarios be sub-optimal to Option #1, but it should be preferable to simply ignoring taxes. Furthermore, it may be easier from an implementation perspective, depending on the rebalancing technology available to you.

With all this in mind, we have begun to develop tax-managed versions of our QuBe model portfolios, and expect them to be available at the beginning of Q4.

Adjusting Expected Returns for Taxes

To keep this commentary to a reasonable length (as if that has ever stopped us before…), we’re going to use a fairly simple model of tax impact.

At the highest level, we need to break down our annual expected return into three categories: unrealized, externally realized, and internally realized.

Unrealized: The percentage of the total return that remains un-taxed. For example, the expected return of a stock that is bought and never sold would be 100% unrealized (ignoring, for a moment, dividends and end-of-period liquidation).
Externally Realized: The percentage of total return that is taxed due to asset allocation turnover. For example, if we re-optimize our portfolio annually and incur 20% turnover, causing us to sell positions, we would say that 20% of expected return is externally realized.
Internally Realized: The percentage of total return that comes from internal turnover, or income generated, within our investment. For example, the expected return from a bond may be 100% internally realized. Similarly, a very active hedge fund strategy may have a significant amount of internal turnover that realizes gains.

Using this information, we can fill out a table, breaking down for each asset class where we expect returns to come from as well as within that category, what type of tax-rate we can expect. For example:

For example, in the table above we are saying we expect 70% of our annual U.S. equity returns to be unrealized while 30% of them will be realized at a long-term capital gains rate. Note that we also explicitly estimate what we will be receiving in qualified dividends.

On the other hand, we only expect that 35% of our hedge fund returns to be unrealized, while 15% will be realized from turnover (all at a long-term capital gains rate) and the remaining 50% will be internally realized by trading within the fund, split 40% short-term capital gains and 60% long-term capital gains.For example, in the table above we are saying we expect 70% of our annual U.S. equity returns to be unrealized while 30% of them will be realized at a long-term capital gains rate. Note that we also explicitly estimate what we will be receiving in qualified dividends.

Obviously, there is a bit of art in these assumptions. How much the portfolio turns over within a year must be estimated. What types of investments you are making will also have an impact. For example, if you are investing in ETFs, even very active equity strategies can be highly tax efficient. Mutual funds on the other hand, potentially less so. Whether a holding like Gold gets taxed at a Collectible rate or a split between short- and long-term capital gains will depend on the fund structure.

Using this table, we can then adjust the expected return for each asset class using the following equations:

Where,

In English,

Take the pre-tax return and subtract out the amount we expect to come from qualified dividend yield.
Take the remainder and multiply it by the total blended tax rate we expect from externally and internally realized gains.
Add back in the qualified dividend yield, after adjusting for returns.

As a simple example, let’s assume U.S. equities have a 6% expected return. We’ll assume a 15% qualified dividend rate and a 15% long-term capital gains rate. We’ll ignore state taxes for simplicity.

Our post-tax expected return is, therefore 6% – (6%-2%)*(30%*15%) – 2%*15% = 5.52%.

We can follow the same broad steps for all asset classes, making some assumptions about tax rates and expected sources of realized returns.

(For those looking to take a deeper dive, we recommend Betterment’s Tax-Coordinated Portfolio whitepaper[5], Ashraf Al Zaman’s Tax Adjusted Portfolio Optimization and Asset Location presentation[6], and Geddes, Goldberg, and Bianchi’s What Would Yale Do If It Were Taxable? paper[7].)

How Big of a Difference Does Tax Management Make?

So how much of a difference does taking taxes into account really make in the final recommended portfolio?

We explore this question by – as we have so many times in the past – relying on J.P. Morgan’s capital market assumptions. The first portfolio is constructed using the same method we have used in the past: a simulation-based mean-variance optimization that targets the same risk level as a 60% stock / 40% bond portfolio mix.

For the second portfolio, we run the same optimization, but adjust the expected return[8] for each asset class.

We make the following assumptions about the source of realized returns and tax rates for each asset class (note that we have compressed the above table by combining rates together after multiplying for the amount realized by that category; e.g. realized short below represents externally and internally realized short-term capital gains).

Again, the construction of the below table is as much art as it is science, with many assumptions embedded about the type of turnover the portfolio will have and the strategies that will be used to implement it.

	Collectible	Ordinary Income	Realized Short	Realized Long	Unrealized	Dividend
Alternative – Commodities	0%	0%	10%	20%	70%	0%
Alternative – Event Driven	0%	0%	26%	53%	21%	0%
Alternative – Gold	30%	0%	0%	0%	70%	0%
Alternative – Long Bias	0%	0%	26%	53%	21%	1%
Alternative – Macro	0%	0%	26%	53%	21%	0%
Alternative – Relative Value	0%	0%	26%	53%	21%	0%
Alternative – TIPS	0%	100%	0%	0%	0%	0%
Bond – Cash	0%	100%	0%	0%	0%	0%
Bond – Govt (Hedged) ex US	0%	100%	0%	0%	0%	0%
Bond – Govt (Not Hedged) ex US	0%	100%	0%	0%	0%	0%
Bond – INT Treasuries	0%	100%	0%	0%	0%	0%
Bond – Investment Grade	0%	100%	0%	0%	0%	0%
Bond – LT Treasuries	0%	100%	0%	0%	0%	0%
Bond – US Aggregate	0%	100%	0%	0%	0%	0%
Credit – EM Debt	0%	100%	0%	0%	0%	0%
Credit – EM Debt (Local)	0%	100%	0%	0%	0%	0%
Credit – High Yield	0%	100%	0%	0%	0%	0%
Credit – Levered Loans	0%	100%	0%	0%	0%	0%
Credit – REITs	0%	100%	0%	0%	0%	0%
Equity – EAFE	0%	0%	10%	20%	70%	2%
Equity – EM	0%	0%	10%	20%	70%	2%
Equity – US Large	0%	0%	10%	20%	70%	2%
Equity – US Small	0%	0%	10%	20%	70%	2%

We also make the following tax rate assumptions:

Ordinary Income: 28%
Short-Term Capital Gains: 28%
Long-Term Capital Gains: 28%
Qualified Dividend: 15%
Collectibles: 28%
Ignore state-level taxes.

The results of both optimizations can be seen in the table below.

	Tax-Deferred	Tax-Managed
Equity – US Large	3.9%	5.3%
Equity – US Small	5.9%	7.0%
Equity – EAFE	3.3%	4.8%
Equity – Emerging Markets	11.1%	12.0%
Sum	24.2%	29.1%

Bond – US Aggregate	0.1%	0.1%
Bond – Int US Treasuries	0.6%	0.4%
Bond – LT US Treasuries	12.4%	12.2%
Bond – Investment Grade	0.0%	0.0%
Bond – Govt (Hedged) ex US	0.3%	0.1%
Bond – Govt (Not Hedged) ex US	0.3%	0.2%
Sum	13.8%	13.1%

Credit – High Yield	6.2%	3.9%
Credit – Levered Loans	11.8%	8.9%
Credit – EM Debt	4.2%	2.7%
Credit – EM Debt (Local)	5.2%	3.5%
Credit – REITs	8.6%	8.1%
Sum	36.0%	27.1%

Alternative – Commodities	4.0%	3.9%
Alternative – Gold	11.3%	13.9%
Alternative – Macro	6.8%	8.6%
Alternative – Long Bias	0.1%	0.1%
Alternative – Event Driven	1.6%	2.2%
Alternative – Relative Value	0.5%	1.3%
Alternative – TIPS	1.6%	0.8%
Sum	26.0%	30.8%

Broadly speaking, we see a shift away from credit-based asset classes (though, they still command a significant 27% of the portfolio) and towards equity and alternatives.

We would expect that if the outlook for equities improved, or we reduced the expected turnover within the portfolio, this shift would be even more material.

It is important to note that at least some of this difference can be attributed to the simulation-based optimization engine. Percentages can be misleading in their precision: the basis point differences between assets within the bond category, for example, are not statistically significant changes.

And how much difference does all this work make? Using our tax-adjusted expected returns, we estimate a 0.20% increase in expected return between tax-managed and tax-deferred versions right now. As we said: no silver bullets, just marginal improvements.

What About Municipal Bonds?

You may have noticed municipal bonds are missing from the above example. What gives?

Part of the answer is theoretical. Consider the following situation. You have two portfolios that are identical in every which way (e.g. duration, credit risk, liquidity risk, et cetera), except one is comprised of municipal bonds and one of corporate bonds. Which one do you choose?

The one with the higher post-tax yield, right?

This hypothetical highlights two important considerations. First, the idea that municipal bonds are for taxable accounts and corporate bonds are for tax-deferred accounts overlooks the fact that investors should be looking to maximize post-tax return regardless of asset location. If municipal bonds offer a better return, then put them in both accounts! Similarly, if corporate bonds offer a more attractive return after taxes, then they should be held in taxable accounts.

For example, right now the iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD) has a 30-day SEC yield of 3.16%. The VanEck Vectors ATM-Free Intermediate Municipal Index ETF (ITM) has a 30-day SEC yield of just 1.9%. However, this is the taxable equivalent to an investor earning a 3.15% yield at a 39.6% tax rate.

In other words, LQD and ITM offer a nearly identical return within in a taxable account for an investor in the highest tax bracket. Lower tax brackets imply lower taxable equivalent return, meaning that LQD may be a superior investment for these investors. (Of course, we should note that municipal bonds are not corporate bonds. They often are often less liquid, but of higher credit quality.)

Which brings up our second point: taxes are highly personal. For a wealthy investor, an ordinary income tax of 35% could make municipal bonds far more attractive than they are for an investor only paying a 15% ordinary income tax rate.

Simply put: solving the when and where of municipal bonds is not always straight forward. We believe the best approach is account for them as a standalone asset class within the optimization, letting the optimizer figure out how to maximize post-tax returns.

Conclusion

We believe that a low-return world means that many investors will have a tough road ahead when it comes to achieving their financial goals. We see no silver bullet to this problem. We do see, however, many small steps that can be taken that can compound upon each other to have a significant impact. We believe that asset location provides one such opportunity and is therefore a topic that deserves far more attention in a low-return environment.

[1] See The Impact of High Equity Valuations on Safe Withdrawal Rates – https://blog.thinknewfound.com/2017/08/impact-high-equity-valuations-safe-retirement-withdrawal-rates/

[2] See Portfolios in Wonderland & The Weird Portfolio – https://blog.thinknewfound.com/2017/08/portfolios-wonderland-weird-portfolio/

[3] See The Butterfly Effect in Retirement Planning – https://blog.thinknewfound.com/2017/09/butterfly-effect-retirement-planning/

[4] Clearly this glosses over some very important details. For example, an investor that has significant withdrawal needs in the near future, but has the majority of their assets tied up in tax-deferred accounts, would significantly complicate this optimization. The optimizer will likely put tax-efficient assets (e.g. equity ETFs) in taxable accounts, while less tax-efficient assets (e.g. corporate bonds) would end up in tax-deferred accounts. Unfortunately, this would put the investor’s liquidity needs at significant risk. This could be potentially addressed by adding expected drawdown constraints on the taxable account.

[5] https://www.betterment.com/resources/research/tax-coordinated-portfolio-white-paper/

[6] http://www.northinfo.com/documents/337.pdf

[7] https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2447403

[8] We adjust volatility as well.

The Butterfly Effect in Retirement Planning

By Justin Sibears

On September 5, 2017

In Sequence Risk, Weekly Commentary

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

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].

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

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

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).

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

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%.

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

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

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

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.

Source: https://personal.vanguard.com/pdf/icrrhb.pdf

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.

Source: https://personal.vanguard.com/pdf/icrrhb.pdf

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.

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

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

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

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.

These are all ideas that help form the foundation for our QuBe Model Portfolios.

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.

Appendix: Retiree Spending Behavior

Estimating the True Cost of Retirement[14]

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

How Does Household Expenditure Change with Age for Older Americans? [16]

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 (90^th and 95^th percentile of spending).

Source: EBRI

Spending in Retirement [17]

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
1. Most common group
2. Generally frugal
3. Low housing expenses due to mortgages being paid off and low property tax bills
4. Tend to spend less as they get older and so an assumption of faster declines in real spending may be appropriate
Homebodies
1. High share of spending on mortgages, real estate taxes, and ongoing maintenance
2. May be prudent to assume that expenses track inflation
3. For planning purposes, it’s important to consider future plans related to housing
Globetrotters
1. Highest overall spending
2. More common among households with higher net worth
3. May be prudent to assume that expenses track inflation
Health care spenders
1. Medicare-related expenses were the largest share of spending for these households
2. These expenses may grow faster than inflation.
3. For further reading, see:
  1. Health care costs in retirement [18]
  2. Guide to Retirement [19]
Snowflakes
1. These households are more unique and do not fit into one of the other four categories.

[1] https://blog.thinknewfound.com/2017/08/impact-high-equity-valuations-safe-retirement-withdrawal-rates/

[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.

[5] https://www.imca.org/sites/default/files/current-issues/JIC/JIC172_AdaptiveDistributionTheory.pdf

[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.

[11] https://blog.thinknewfound.com/2017/07/building-unconstrained-sleeve/

[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

The Research Library of Newfound Research

Month: September 2017

Addressing Low Return Forecasts in Retirement with Tactical Allocation

Summary

How the 4% Rule Started

Temporal Risk in Retirement

The Rising Glide Path in Retirement

Beyond a Static Glide Path

Sizing a Tactical Sleeve

Conclusion: Being Dynamic in Retirement

Tax-Managed Models & Asset Location

Summary

Tax-Managed Thinking

Asset Location in a Perfect World

Asset Location in a Less Than Perfect World

Adjusting Expected Returns for Taxes

How Big of a Difference Does Tax Management Make?

What About Municipal Bonds?

Conclusion

The Butterfly Effect in Retirement Planning

Summary

The Butterfly Effect in Retirement Portfolios

Camouflaged Butterflies: Assumptions in Spending Rate Changes

Implications of the Butterfly Effect

Conclusion: The Sum of All Assumptions in Retirement

Appendix: Retiree Spending Behavior

Month: September 2017

Addressing Low Return Forecasts in Retirement with Tactical Allocation

Summary­­

How the 4% Rule Started

Temporal Risk in Retirement

The Rising Glide Path in Retirement

Beyond a Static Glide Path

Sizing a Tactical Sleeve

Conclusion: Being Dynamic in Retirement

Tax-Managed Models & Asset Location

Summary­­

Tax-Managed Thinking

Asset Location in a Perfect World

Asset Location in a Less Than Perfect World

Adjusting Expected Returns for Taxes

How Big of a Difference Does Tax Management Make?

What About Municipal Bonds?

Conclusion

The Butterfly Effect in Retirement Planning

Summary

The Butterfly Effect in Retirement Portfolios

Camouflaged Butterflies: Assumptions in Spending Rate Changes

Implications of the Butterfly Effect

Conclusion: The Sum of All Assumptions in Retirement

Appendix: Retiree Spending Behavior

Summary

Summary