Author: Justin Sibears Page 2 of 3

From 2012-2019, Justin Sibears served as Managing Director and Portfolio Manager at Newfound Research. At Newfound, Justin was responsible for portfolio management, investment research, strategy development, and communication of the firm's views to clients.

Justin holds a Master of Science in Computational Finance and a Master of Business Administration from Carnegie Mellon University as a well as a BBA in Mathematics and Finance from the University of Notre Dame.

Impact of High Equity Valuations on Safe Retirement Withdrawal Rates

By Justin Sibears

On August 14, 2017

In Sequence Risk, Weekly Commentary

This post is available as a PDF here.

Summary

While valuation-based market timing is notoriously difficult, present and future retirees should prepare for muted U.S. stock and bond returns relative to historical experience.
High valuations 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.
This outlook is by no means a call for despair, but rather highlights the increasing need for taking control of one’s destiny by controlling both investment and non-investment factors that can improve the odds of successfully meeting one’s retirement goals.

We are always on the lookout for interesting data visualizations related to the financial markets. Recently, two such charts have come across our computer screens.

The Drumbeat of High Equity Valuations Grows Louder

The first chart is from a recent article from Goldman Sachs Asset Management (“GSAM”)[1]. It reinforces the importance of developing realistic forward-looking expectations for asset class returns. This is a topic that we have droned on and on about over the last couple of years and one that we feel is especially important today, when the valuation backdrop for many core asset classes are stretched by historical standards.

The clear takeaway, at least in GSAM’s eyes, is found in the blue text in the upper right: “In 99% of the time at current valuation levels, equity returns have been single digit or negative.”

Now, there are a few complicating factors with the chart and this conclusion:

There is some hindsight bias embedded in the chart. In December 1999, when the S&P 500 reached an all-time high Shiller CAPE of 44.2, there was no way of knowing with certainty that valuations weren’t going even higher. After all, for an example of higher than tech bubble valuations, we need look no further than Japan.
The median rolling 10-year return for the S&P 500 over this period was 8.5%, so be careful in drawing the following conclusion: Equity returns have been “bad” 99% of the time when we’ve been at or near current valuation levels. A better conclusion to draw would be something like: Equity returns have tended to be average to below average when we’ve been at or near current valuation levels. When S&P 500 valuations were between the 75^th and 100^th percentile, subsequent 10-year returns were below the median of 8.5% approximately 80% of the time. The odds of a negative 10-year return, even at these valuation levels, is a pretty modest one in eight.
Mean reversion in valuations can take a very, very long time. For those looking to sell high and buy low (or vice-versa), the path to success can be terribly frustrating, requiring Buffett-like discipline to capture the eventual rewards. For example, Shiller’s CAPE rose above the 75^th percentile in January 1992. From this already high point, equities rallied another 300%+ before valuations peaked in late 1999. CAPE would not fall below the January 1992 value of 19.8 until October 2008.
There is a strong argument that valuations are driven by behavioral factors. For example, Jeremy Grantham discussed such a behavioral model in GMO’s most recent quarterly letter. He argues that the two factors most important in explaining high valuations are high profit margins and low inflation volatility. Viewed in this way, mean reversion would require one or both of these conditions to reverse course.

Visualizing Retirement Success and Failure

The second visualization comes from a recent post on Reddit; a news aggregation, web content rating, and discussion website; by a user going by the name zaladin. The graph shows the retirement wealth paths for various combinations of withdrawal rates and stock/bond splits.

However, before we start we want to point out that this is a highly simplified example. We only consider U.S. stocks and bonds, we don’t consider taxes or fees, etc.

In reality, the following factors can play a significant role in developing a retirement strategy: Alpha (investment performance vs. broadly diversified market portfolios), fees, taxes, desire to leave an inheritance to heirs, longevity/time horizon, diversification/risk management, spending flexibility, risk tolerance, valuation environment, etc.

Returning to our simplistic world, we’ve recreated the graph for a 4% inflation-adjusted withdrawal rate and a 60/40 stock/bond split below. In order to present data going back more than a century, we stick to U.S. equities for our stock exposure and 10-Year U.S. Treasuries for our bond exposure.

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.

The diagonal gray lines represent 20, 30, 40, and 50 years, respectively, after retirement.

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 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 visualization is that it only considers 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 will look at the historical “success” of various investment and withdrawal strategies. We will evaluate 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. [Note: 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.]

We will evaluate these three metrics over a 30-year retirement horizon. Please feel free to reach out if you’d like to see the analysis for different horizon length.

Absolute Success Rate for Various Combinations of Withdrawal Rate and Portfolio Composition – 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.

We see that withdrawal rates of 3% or less succeeded 95%+ of the time based on “ASR” regardless of asset allocation. A 4% withdrawal likewise succeeded with 90%+ historical probability as long as some equity exposure was incorporated into the portfolio. No stock/bond mix was able to support a withdrawal rate of 5% or more while succeeding at least nine times out of ten.

Comfortable Success Rate for Various Combinations of Withdrawal Rate and Portfolio Composition – 30 Yr. Horizon

The results with “CSR” as our success measure largely mirror the “ASR” results. The only main differences are:

A 100% bond portfolio with a 3% withdrawal rate only leaves the investor with 100% of more of their initial wealth at the end of retirement in about two-thirds of scenarios. For an investor to achieve 90%+ CSR success with a 3% withdrawal rate, some equity is required.
Succeeding 90%+ of the time with a 4% withdrawal rate requires holding more stocks than bonds.

Ulcer Index for Various Combinations of Withdrawal Rate and Portfolio Composition – 30 Yr. Horizon

The Ulcer Index is a measure that summarizes the severity and duration of wealth drawdowns. We like this metric as it provides us some idea of how emotionally stressful a given market path is for investors. In our view, high investing stress not only is unenjoyable, but also raises the likelihood of making poor, emotionally-charged decisions.

Interpreting an individual Ulcer index alone can be difficult, but the relative values provide context. For example, for a 4% withdrawal rate, even though the portfolios with some equity had 90%+ ASRs, the 60/40 portfolio had the least stress, on average – even less than the slightly more successful (from a CSR standpoint) 80/20 portfolio.

So, what do these equity valuation and retirement visualizations have to do with one another?

For many investors, market returns are only the means to an end. Ultimately, investors are looking to achieve their financial goals. We certainly know that muted long-term returns in core stocks and bonds are not a good thing. But it can be hard to immediately understand what the true impact of such an outcome would be.

To see the effect of muted returns more clearly, we are going to recreate the retirement visualizations from earlier, but with one key modification: we adjust historical stock and bond returns downward so that the long-term averages are in line with realistic future return expectations given current valuation levels. We do this by subtracting the difference between the actual average log return and the forward-looking log return from each year’s return. By doing this, we reflect subdued average returns while retaining the peaks and valleys that we would expect in actual rolling 30-year periods.

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%.

Historical Wealth Paths for a 4% Withdrawal Rate and 60/40 Stock/Bond Allocation with Current Return Expectations

With updated return assumptions, we see a dramatically different picture with a lot less green and a lot more of the dreaded black (i.e. fully exhausting one’s savings). The results are similar across withdrawal rates and asset allocations.

We see that only withdrawal rates of 2% or less would have achieved 90%+ success over thirty years regardless of asset allocation. High success rates can still be attained with a 3% withdrawal rate assuming investors are willing to bear the risk of moderate to aggressive equity allocations. Unfortunately, a 4% withdrawal rate no longer offers the safety that actual experience has suggested.

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

In our example, passing on starting wealth to heirs at the end of retirement looks difficult except at withdrawal rates of less than 3%. The same can be said for investors looking for a stress-free journey as Ulcer Index values are much higher in this scenario for 3%+ withdrawal rates than what we saw using historical returns.

Comfortable Success Rate for Various Combinations of Withdrawal Rate and Portfolio Composition with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon

Ulcer Index for Various Combinations of Withdrawal Rate and Portfolio Composition with Average Stock and Bond Returns Equal to Current Expectations – 30 Yr. Horizon

Conclusion: Taking Control of Retirement

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. Unfortunately, we cannot control the returns of U.S. stocks or bonds (or any asset class returns for that matter).

But we can take control of the factors that we can influence.

For a current or future retiree, this means controlling to the extent possible factors like taxes, saving, and spending. From an investment perspective, it means:

Being 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.
Directly address the role of behavioral finance by recognizing that an investor must have the willingness to stick with a plan in order to succeed (e.g. the journey is just as important as the destination).
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.

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

Retirement success and muted future returns are not mutually exclusive. However, achieving financial goals in such an environment requires careful planning for factors that may have been safely ignored given the generous market tailwinds of prior decades.

[1] Goldman Sachs Asset Management, “The Synchronized Expansion.” https://www.gsam.com/content/gsam/us/en/advisors/market-insights/market-strategy/market-know-how/2017/Q32017.html#section-background_ebd2_background_moduletitle_874b

Navigating Municipal Bonds With Factors

By Justin Sibears

On May 15, 2017

In Carry, Defensive, Momentum, Popular, Risk & Style Premia, Value, Weekly Commentary

This post is available as a PDF download here.

Summary

In this case study, we explore building a simple, low cost, systematic municipal bond portfolio.
The portfolio is built using the low volatility, momentum, value, and carry factors across a set of six municipal bond sectors. It favors sectors with lower volatility, better recent performance, cheaper valuations, and higher yields. As with other factor studies, a multi-factor approach is able to harvest major benefits from active strategy diversification since the factors have low correlations to one another.
The factor tilts lead to over- and underweights to both credit and duration through time. Currently, the portfolio is significantly underweight duration and modestly overweight credit.
A portfolio formed with the low volatility, value, and carry factors has sufficiently low turnover that these factors may have value in setting strategic allocations across municipal bond sectors.

Recently, we’ve been working on building a simple, ETF-based municipal bond strategy. Probably to the surprise of nobody who regularly reads our research, we are coming at the problem from a systematic, multi-factor perspective.

For this exercise, our universe consists of six municipal bond indices:

Bloomberg Barclays AMT-Free Short Continuous Municipal Index
Bloomberg Barclays AMT-Free Intermediate Continuous Municipal Index
Bloomberg Barclays AMT-Free Long Continuous Municipal Index
Bloomberg Barclays Municipal Pre-Refunded-Treasury-Escrowed Index
Bloomberg Barclays Municipal Custom High Yield Composite Index
Bloomberg Barclays Municipal High Yield Short Duration Index

These indices, all of which are tracked by VanEck Vectors ETFs, offer access to municipal bonds across a range of durations and credit qualities.

Source: VanEck

Before we get started, why are we writing another multi-factor piece after addressing factors in the context of a multi-asset universe just two weeks ago?

The simple answer is that we find the topic to be that pressing for today’s investors. In a world of depressed expected returns and elevated correlations, we believe that factor-based strategies have a role as both return generators and risk mitigators.

Our confidence in what we view as the premier factors (value, momentum, low volatility, carry, and trend) stems largely from their robustness in out-of-sample tests across asset classes, geographies, and timeframes. The results in this case study not only suggest that a factor-based approach is feasible in muni investing, but also in our opinion strengthens the case for factor investing in other contexts (e.g. equities, taxable fixed income, commodities, currencies, etc.).

Constructing Long/Short Factor Portfolios

For the municipal bond portfolio, we consider four factors:

Value: Buy undervalued sectors, sell overvalued sectors
Momentum: Buy strong recent performers, sell weak recent performers
Low Volatility: Buy low risk sectors, sell high risk sectors
Carry: Buy higher yielding sectors, sell lower yielding sectors

As a first step, we construct long/short single factor portfolios. The weight on index i at time t in long/short factor portfolio f is equal to:

In this formula, c is a scaling coefficient, S is index i’s time t score on factor f, and N is the number of indices in the universe at time t.

We measure each factor with the following metrics:

Value: Normalized deviation of real yield from the 5-year trailing average yield[1]
Momentum: Trailing twelve month return
Low Volatility: Historical standard deviation of monthly returns[2]
Carry: Yield-to-worst

For the value, momentum, and carry factors, the scaling coefficient is set so that the portfolio is dollar neutral (i.e. we are long and short the same dollar amount of securities). For the low volatility factor, the scaling coefficient is set so that the volatilities of the long and short portfolios are approximately equal. This is necessary since a dollar neutral construction would be perpetually short “beta” to the overall municipal bond market.

All four factors are profitable over the period from June 1998 to April 2017. The value factor is the top performer both from an absolute return and risk-adjusted return perspective.

Data Source: Blooomberg. Calculations by Newfound Research. All returns are hypothetical and backtested. Returns reflect the reinvestment of all distributions and are gross of all fees (including any management fees and transaction costs). The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results. The benchmark is an equal-weight portfolio of all indices in the universe adjusted for the indices that are calibrated and included in each long/short factor index based on data availability.

There is significant variation in performance over time. All four factors have years where they are the best performing factor and years where they are the worst performing factor. The average annual spread between the best performing factor and the worst performing factor is 11.3%.

The individual long/short factor portfolios are diversified to both each other (average pairwise correlation of -0.11) and to the broad municipal bond market.

Moving From Single Factor to Multi-Factor Portfolios

The diversified nature of the long/short return streams makes a multi-factor approach hard to beat in terms of risk-adjusted returns. This is another example of the type of strategy diversification that we have long lobbied for.

As evidence of these benefits, we have built two versions of a portfolio combining the low volatility, value, carry, and momentum factors. The first version targets an equal dollar allocation to each factor. The second version uses a naïve risk parity approach to target an approximately equal risk contribution from each factor.

Both approaches outperform all four individual factors on a risk-adjusted basis, delivering Sharpe Ratios of 1.19 and 1.23, respectively, compared to 0.96 for the top single factor (value).

Data Source: Bloomberg. Calculations by Newfound Research. All returns are hypothetical and backtested. Returns reflect the reinvestment of all distributions and are gross of all fees (including any management fees and transaction costs). The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results. The benchmark is an equal-weight portfolio of all indices in the universe adjusted for the indices that are calibrated and included in each long/short factor index based on data availability. The factor risk parity construction uses a simple inverse volatility methodology. Volatility estimates are shrunk in the early periods when less data is available.

To stress this point, diversification is so plentiful across the factors that even the simplest portfolio construction methodologies outperforms an investor who was able to identify the best performing factor with perfect foresight. For additional context, we constructed a “Look Ahead Mean-Variance Optimization (“MVO”) Portfolio” by calculating the Sharpe optimal weights using actual realized returns, volatilities, and correlations. The Look Ahead MVO Portfolio has a Sharpe Ratio of 1.43, not too far ahead of our two multi-factor portfolios. The approximate weights in the Look Ahead MVO Portfolio are 49% to Low Volatility, 25% to Value, 15% to Carry, and 10% to Momentum. While the higher Sharpe Ratio factors (Low Volatility and Value) do get larger allocations, Momentum and Carry are still well represented due to their diversification benefits.

From a risk perspective, both multi-factor portfolios have lower volatility than any of the individual factors and a maximum drawdown that is within 1% of the individual factor with the least amount of historical downside risk. It’s also worth pointing out that the risk parity construction leads to a return stream that is very close to normally distributed (skew of 0.1 and kurtosis of 3.0).

In the graph on the next page, we present another lens through which we can view the tremendous amount of diversification that can be harvested between factors. Here we plot how the allocation to a specific factor, using MVO, will change as we vary that factor’s Sharpe Ratio. We perform this analysis for each factor individually, holding all other parameters fixed at their historical levels.

As an example, to estimate the allocation to the Low Volatility factor at a Sharpe Ratio of 0.1, we:

Assume the covariance matrix is equal to the historical covariance over the full sample period.
Assume the excess returns for the other three factors (Carry, Momentum, and Value) are equal to their historical averages.
Assume the annualized excess return for the Low Volatility factor is 0.16% so that the Sharpe Ratio is equal to our target of 0.1 (Low Volatility’s annualized volatility is 1.6%).
Calculate the MVO optimal weights using these excess return and risk assumptions.

As expected, Sharpe Ratios and allocation sizes are positively correlated. Higher Sharpe Ratios lead to higher allocations.

That being said, three of the factors (Low Volatility, Carry, and Momentum) would receive allocations even if their Sharpe Ratios were slightly negative.

The allocations to carry and momentum are particularly insensitive to Sharpe Ratio level. Momentum would receive an allocation of 4% with a 0.00 Sharpe, 9% with a 0.25 Sharpe, 13% with a 0.50 Sharpe, 17% with a 0.75 Sharpe, and 20% with a 1.00 Sharpe. For the same Sharpe Ratios, the allocations to Carry would be 10%, 15%, 19%, 22%, and 24%, respectively.

Holding these factors provides a strong ballast within the multi-factor portfolio.

Moving From Long/Short to Long Only

Most investors have neither the space in their portfolio for a long/short muni strategy nor sufficient access to enough affordable leverage to get the strategy to an attractive level of volatility (and hence return). A more realistic approach would be to layer our factor bets on top of a long only strategic allocation to muni bonds.

In a perfect world, we could slap one of our multi-factor long/short portfolios right on top of a strategic municipal bond portfolio. The results of this approach (labeled “Benchmark + Equal Weight Factor Long/Short” in the graphics below) are impressive (Sharpe Ratio of 1.17 vs. 0.93 for the strategic benchmark and return to maximum drawdown of 0.72 vs. 0.46 for the strategic benchmark). Unfortunately, this approach still requires just a bit of shorting. The size of the total short ranges from 0% to 19% with an average of 5%.

We can create a true long only portfolio (“Long Only Factor”) by removing all shorts and normalizing so that our weights sum to one. Doing so modestly reduces risk, return, and risk-adjusted return, but still leads to outperformance vs. the benchmark.

Below we plot both the historical and current allocations for the long only factor portfolio. Currently, the portfolio would have approximately 25% in each short-term investment grade, pre-refunded, and short-term high yield with the remaining 25% split roughly 80/20 between high yield and intermediate-term investment grade. There is currently no allocation to long-term investment grade.

Data Source: Blooomberg. Calculations by Newfound Research. All allocations are backtested and hypothetical. The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results.

A few interesting observations relating to the long only portfolio and muni factor investing in general:

The factor tilts lead to clear duration and credit bets over time. Below we plot the duration and a composite credit score for the factor portfolio vs. the benchmark over time.
Data source: Calculations by Newfound Research. All allocations are backtested and hypothetical. The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results. Weighted average durations are estimated using current constituent durations.
Data source: Calculations by Newfound Research. All allocations are backtested and hypothetical. The hypothetical indices start on June 30, 1998. The start date was chosen based on data availability of the underlying indices and the time necessary to calibrate the factor models. Data is through April 30, 2017. The portfolios are reconstituted monthly. Past performance does not guarantee future results. Weighted average credit scores are estimated using current constituent credit scores. Credit scores use S&P’s methodology to aggregate scores based on the distribution of credit scores of individual bonds.
Currently, the portfolio is near an all-time low in terms of duration and is slightly titled towards lower credit quality sectors relative to the benchmark. Historically, the factor portfolio was most often overweight both duration and credit, having this positioning in 53.7% of the months in the sample. The second and third most common tilts were underweight duration / underweight credit (22.0% of sample months) and underweight duration / overweight credit (21.6% of sample months). The portfolio was overweight duration / underweight credit in only 2.6% of sample months.
Even for more passive investors, a factor-based perspective can be valuable in setting strategic allocations. The long only portfolio discussed above has annualized turnover of 77%. If we remove the momentum factor, which is by far the biggest driver of turnover, and restrict ourselves to a quarterly rebalance, we can reduce turnover to just 18%. This does come at a cost, as the Sharpe Ratio drops from 1.12 to 1.04, but historical performance would still be strong relative to our benchmark. This suggests that carry, value, and low volatility may be valuable in setting strategic allocations across municipal bond ETFs with only periodic updates at a normal strategic rebalance frequency.
We ran regressions with our long/short factors on all funds in the Morningstar Municipal National Intermediate category with a track record that extended over our full sample period from June 1998 to April 2017. Below, we plot the betas of each fund to each of our four long/short factors. Blue bars indicate that the factor beta was significant at a 5% level. Gray bars indicate that the factor beta was not significant at a 5% level. We find little evidence of the active managers following a factor approach similar to what we outline in this post. Part of this is certainly the result of the constrained nature of the category with respect to duration and credit quality. In addition, these results do not speak to whether any of the managers use a factor-based approach to pick individual bonds within their defined duration and credit quality mandates.
Data source: Calculations by Newfound Research. Analysis over the period from June 1998 to April 2017.
The average beta to the low volatility factor, ignoring non-statistically significant values, is -0.23. This is most likely a function of category since the category consists of funds with both investment grade credit quality and durations ranging between 4.5 and 7.0 years. In contrast, our low volatility factor on average has short exposure to the intermediate and long-term investment grade sectors.
Data source: Calculations by Newfound Research. Analysis over the period from June 1998 to April 2017.
Only 14 of the 33 funds in the universe have statistically significant exposure to the value factor with an average beta of -0.03.
Data source: Calculations by Newfound Research. Analysis over the period from June 1998 to April 2017.
The average beta to the carry factor, ignoring non-statistically significant values, is -0.23. As described above with respect to low volatility, this is most likely function of category as our carry factor favors the long-term investment grade and high yield sectors.
Data source: Calculations by Newfound Research. Analysis over the period from June 1998 to April 2017.
Only 9 of the 33 funds in the universe have statistically significant exposure to the momentum factor with an average beta of 0.02.

Conclusion

Multi-factor investing has generated significant press in the equity space due to the (poorly named) “smart beta” movement. The popular factors in the equity space have historically performed well both within other asset classes (rates, commodities, currencies, etc.) and across asset classes. The municipal bond market is no different. A simple, systematic multi-factor process has the potential to improve risk-adjusted performance relative to static benchmarks. The portfolio can be implemented with liquid, low cost ETFs.

Moving beyond active strategies, factors can also be valuable tools when setting strategic sector allocations within a municipal bond sleeve and when evaluating and blending municipal bond managers.

Perhaps more importantly, the out-of-sample evidence for the premier factors (momentum, value, low volatility, carry, and trend) across asset classes, geographies, and timeframes continues to mount. In our view, this evidence can be crucial in getting investors comfortable to introducing systematic active premia into their portfolios as both return generators and risk mitigators.

[1] Computed using yield-to-worst. Inflation estimates are based on 1-year and 10-year survey-based expected inflation. We average the value score over the last 2.5 years, allowing the portfolio to realize a greater degree of valuation mean reversion before closing out a position.

[2] We use a rolling 5-year (60-month) window to calculate standard deviation. We require at least 3 years of data for an index to be included in the low volatility portfolio. The standard deviation is multiplied by -1 so that higher values are better across all four factor scores.

Anatomy of a Bull Market: Follow-Up

By Justin Sibears

On February 13, 2017

In Equity, Risk & Style Premia, Weekly Commentary

Based on our post from earlier today (“Anatomy of a Bull Market“), we received a request to decompose U.S. equity returns over rolling 10-year periods.

The graph presenting this data is below. To perform these calculations, we calculate the annualized return generated by each source (inflation, dividends, earnings growth, and valuation changes), take the absolute value, and then normalize so that the total sums to one.

Data Source: Robert Shiller’s data library. Calculations by Newfound Research. Monthly data is used to make these calculations. Past performance does not guarantee future results.

On average over all rolling 10-year periods, each source contributed the following percentage to total return (ordered from biggest to smallest contributor):

Dividends: 31%
Valuation Changes: 28%
Inflation: 25%
Earnings Growth: 16%

We also performed the analysis for shorter (3-year) and longer (30-year) rolling periods.

Data Source: Robert Shiller’s data library. Calculations by Newfound Research. Monthly data is used to make these calculations. Past performance does not guarantee future results.

Over shorter time horizons, valuation changes start to dominate returns:

Valuation Changes (40%)
Dividends (25%)
Inflation (21%)
Earnings Growth (15%)

Data Source: Robert Shiller’s data library. Calculations by Newfound Research. Monthly data is used to make these calculations. Past performance does not guarantee future results.

But over longer periods, the impact of valuations starts to approach zero as shorter-term fluctuations offset each other. Inflation and dividend yield together drive 70%+ of 30-year returns on average:

Dividends (44%)
Inflation (28%)
Earnings Growth (15%)
Valuation Changes (13%)

[Note: An earlier version of this post used total return data. We amended the analysis to use annualized returns.]

Anatomy of a Bull Market

By Justin Sibears

On February 13, 2017

In Equity, Risk & Style Premia, Weekly Commentary

This blog post is available for download as a PDF here.

Summary

Long-term average stock returns smooth over the bull and bear markets that investors experience, and no two market cycles ever unfold the exact same way. Bull and bear markets can vary significantly in both duration and magnitude.
But there are other characteristics of bull markets that can also differ in meaningful ways, such as velocity, sources of return, and investor experience.
When it comes to analyzing bull markets, inflation, interest rates, equity valuations, earnings, and dividends all play a part.
Assessing the current economic environment in the context of historical U.S. and international bull markets can help set better expectations and reduce the risk of surprises that can lead to emotional decisions.

A few days back, we found this “History of U.S. Bear & Bull Markets Since 1926” one-pager from First Trust. In our opinion, the graph is a nice visualization of market expansions and contractions over the last 90 years.

We’ve recreated the graph below. There are some slight differences in what we show vs. the First Trust data since we use a different data source[1] and stick to monthly data. We also go back to the beginning of the first bull market of the 20^th century.

Over the period from 1903 to 2016, there were 12 bull markets in the S&P 500. The average bull market lasted 8.1 years with a total return of 387%. The average bear market lasted 1.5 years with a total loss of 35%.

The current bull market, which began in March 2009, is the 7^th longest and the 6^th strongest. For it to be the longest ever, it would have to continue through the fourth quarter of 2023. For it to be the largest ever, the S&P would have to return another 665%.

While this analysis is informative, it’s still an incomplete picture of the anatomy of bull (and bear) markets. Below, we will examine this same data from four other perspectives:

Velocity: How fast do bull and bear markets unfold?
Sources of return: How much of bull market returns are composed of inflation? Dividend yield? Earnings growth? Valuation changes?
Experience: What was the experience of an investor using a balanced 50/50 asset allocation during these bull and bear markets?
Context: How does the experience of bull and bear markets in the U.S. compare to other markets around the world?

Velocity: How fast do bull and bear markets unfold?

More often than not, market cycle analysis focuses on duration and magnitude. We can change the focus to velocity by graphing the annualized return experienced in each bull and bear market.

Data Source: Robert Shiller’s data library. Calculations by Newfound Research. Bull markets are defined from the lowest close reached after the market has fallen 20% or more to the next market high. Bear markets are defined from the last market high prior to the market closing down at least 20% to the lowest close after it’s down 20% or more. Monthly data is used to make these calculations. Returns are not annualized for market cycles that lasted less than one year. Past performance does not guarantee future results.

This snapshot highlights three important characteristics of the historical behavior of U.S. equity markets.

First, we don’t experience the average. Over the 113+ year period we considered, the U.S. equity market returned an annualized 9.8%. Yet, the path of returns has been defined by thrilling bull markets and crushing bear markets.

Consider this: since 1903, there has not been a market cycle with a single digit annualized return.

Ten of the twelve bull markets had annualized gains greater than 15%. Similarly, annualized losses exceeded 15% in ten of the eleven bear markets.

Second, bear markets typically unfold more rapidly than bull markets. The average annualized returns for bull and bear markets are 19% and -25%, respectively.

Third, the current bull market is slow by historical standards. It ranks 17^th in velocity out of the 23 market cycles that we studied. This same phenomenon occurred in the bull market that followed the Great Depression, the only bear market that was more severe than the Financial Crisis.

Sources of Return: How much of a given bull market can be attributed to inflation? Dividend yield? Earnings growth? Valuation changes?

Equity returns can be decomposed into four components:

Inflation
Dividend Yield
Earnings Growth
Valuation Changes

Using this framework, it quickly becomes clear that not all bull markets are created equal.

For example, the bull market of the 70s and 80s was driven largely by inflation. On a nominal or pre-inflation basis, this was the second largest bull market of all time. On a real basis or post-inflation basis, however, it drops to just the fifth largest.

Both of the two most recent bull markets are unique in their own right.

The pre-global financial crisis bull market – lasting from February 2003 to October 2007 – had the largest share of return driven by earnings growth at just north of 30%. The current bull market is only the second instance of a large (greater than 100%) bull market where more than half the gains have come from expanding valuation multiples.

The contribution from valuation expansion is larger than even the buildup of the tech bubble.

Going beyond headline shock and awe, however, we recognize that classifying all valuation changes into a single bucket is probably painting with too broad of a brush. Valuations returning to normal after a market crash is not the same as valuations expanding from historical averages to all-time highs. We can address this by modifying the previous graphic. Specifically, we break the “Valuation Changes” category into two parts[2]:

“Valuation Normalization”: Valuations increasing from historically low levels to the long-term median.
“Valuation Expansion”: Valuations increasing from the long-term median to higher levels.

When all valuation changes are lumped together, the five most valuation-centric bull markets of the nine in the graphic are:

August 1921 to September 1929 (79%)
March 2009 to December 2016 (59%)
December 1987 to August 2000 (53%)
June 1932 to May 1946 (48%)
February 2003 to October 2007 (37%)

When we focus, however, on only “Valuation Expansion,” the top five changes to:

December 1987 to August 2000 (43%)
February 2003 to October 2007 (37%)
June 1962 to December 1968 (32%)
August 1921 to September 1929 (32%)
March 2009 to December 2016 (27%)

When we ignore “Valuation Normalization,” the current bull market drops from the 2^nd most valuation-centric to the 5^th most valuation-centric. The majority of the valuation gains in this cycle were the result of the recovery from the bottom of the financial crisis.

Experience: How did balanced investors fare during historical equity bull markets?

Many investors do not hold 100% stock allocations. As a result, their experience during equity bull markets will also depend on bond returns. The chart below shows the upside capture for a 50/50 stock/bond investor during the twelve equity bull markets since 1903.

Data Source: Robert Shiller’s data library. Calculations by Newfound Research. Bull markets are defined from the lowest close reached after the market has fallen 20% or more to the next market high. Bear markets are defined from the last market high prior to the market closing down at least 20% to the lowest close after it’s down 20% or more. Monthly data is used to make these calculations. Returns are not annualized for market cycles that lasted less than one year. The 50% bond allocation is a hypothetical index created using the interest rate data from Shiller’s data library. Past performance does not guarantee future results. Past performance does not guarantee future results. The balanced portfolio is rebalanced annually.

Despite the continued secular decline in interest rates, the last two bull markets (February 2003 to October 2007 and March 2009 to December 2016) have actually been below average for balanced investors.

Why? Because the relative performance of a balanced investors vs. a stock investor will not only depend on the path of interest rates (i.e. do rates increase or decrease), but also on the average interest rate over the period.

For ideal bull market up capture, balanced investors should hope for high and declining interest rates. Recently, we’ve had the latter, but not the former.

Data Source: Robert Shiller’s data library. Calculations by Newfound Research. Bull markets are defined from the lowest close reached after the market has fallen 20% or more to the next market high. Bear markets are defined from the last market high prior to the market closing down at least 20% to the lowest close after it’s down 20% or more. Monthly data is used to make these calculations. Returns are not annualized for market cycles that lasted less than one year. The 50% bond allocation is a hypothetical index created using the interest rate data from Shiller’s data library. Past performance does not guarantee future results. Past performance does not guarantee future results. The balanced portfolio is rebalanced annually.

Going forward, we may move toward the bottom right-hand corner, which has historically had the lowest up-capture.

Context: How does the experience of bull and bear markets in the U.S. compare to other markets around the world?

In the following pages, we recreate the First Trust graph for Japan, the United Kingdom, Europe ex-UK, and Asia ex-Japan.

Looking beyond the United States can be a useful reminder that the future behavior of the S&P 500 is not constrained by past experiences.

It’s possible to have larger bull markets than what we have seen in the U.S., as evidenced by the 1970s and 1980s in Japan and the UK.

It’s also possible for bear markets to drag on for years. The longest bear market in the U.S. since 1903 lasted slightly less than three years. Japan, on the other hand, saw a 20+ year bear market that lasted the entirety of the 1990s and 2000s.

Data Source: MSCI. Calculations by Newfound Research. Bull markets are defined from the lowest close reached after the market has fallen 20% or more to the next market high. Bear markets are defined from the last market high prior to the market closing down at least 20% to the lowest close after it’s down 20% or more. Monthly data is used to make these calculations. Past performance does not guarantee future results.

Conclusion

While long-term average stock returns have been high, they smooth over the bull and bear markets that investors experience along the way.

These large directional swings have many characteristics that make them unique, including their durations and magnitudes. Velocity, sources of return, and investor experience have also shown significant variation across market cycles.

This current bull market has been slow by historical standards and has largely been driven by normalization of equity valuations following the financial crisis. Balanced investors have benefitted from declining interest rates, but saw muted up-capture since interest rates started declining from a relatively low level.

Putting the current market environment into context by considering other geographies can lead to a more thorough understanding of how to position our portfolios and develop a plan that can be adhered to regardless of how a given market cycle unfolds.

[1] We use data from Robert Shiller’s website. This data was used in Shiller’s book, Irrational Exuberance. Shiller presents monthly data. Prior to January 2000, price data is the average of the S&P 500’s (or a predecessor’s) daily closes for that monthly.

[2] To avoid hindsight bias when calculating the historical median, we used rolling 50 year periods.

Indexed Annuity: Masking Risk, Not Destroying It

By Justin Sibears

On May 4, 2016

In Risk Management

What is an Indexed Annuity?

In recent conversations with current and potential clients, we have seen an uptick in the use of indexed annuities as a tool for risk management.

For the uninitiated, Fidelity succinctly described an indexed annuity in a recent blog post:

“An indexed annuity is a contract issued and guaranteed by an insurance company. You invest an amount of money (premium) in return for protection against down markets; the potential for some investment growth, linked to an index (e.g., the S&P 500^® Index); and, in some cases, a guaranteed level of lifetime income through optional riders.”

The rules that govern the performance credited to an indexed annuity account tend to be relatively simple and intuitive. A hypothetical example would be something like this:

If the S&P 500 loses value over the policy year, the account is credited 0%.
If the S&P 500 gains between 0% and 5% over the policy year, the policy is credited with the S&P 500’s gain.
If the S&P 500 gains more than 5% over the policy year, the policy is credited with 5%.

In this example, the 5% figure is referred to as the “cap.”

While these rules may be simple and intuitive, the trade-offs inherent in such a contract are less clear.

Recently, I’ve been stealing the following phrase from my co-PM, Corey, quite frequently: “Risk cannot be destroyed, it can only be transformed.” I think this concept is especially applicable to indexed annuities.

Fortunately, indexed annuity-like payoff structures can be created with stocks, bonds, and options. By evaluating these replicating portfolios, we can start to develop a more complete cost/benefit analysis and perhaps better understand how these types of products may or may not fit into certain client portfolios.

For those not interested in the details, the takeaways are quite simple:

Indexed annuities depend on interest income to finance investments in the equity markets.
When interest rates are low, there is little capital available to make these equity investments.
Limited capital means either (i) low equity participation rates or (ii) low caps that restrict potential upside.
Low participation rates and/or low caps on index participation are a recipe for muted returns, which may make it difficult to stay ahead of inflation.

In short, indexed annuities suffer from many of the same problems that plague traditional asset classes in low interest rate and high valuation environments.

Example #1: Stocks and Bonds

Say we have $1,000,000 to invest. We want to invest it for ten years. We’d like some equity upside, but want to guarantee that we will get back our $1,000,000 at maturity. How might we go about doing this?

It’s not all that complicated. We just need to make two investments.

Buy a Treasury STRIP that matures 10 years from today with face value of $1,000,000. Today, this would cost approximately $834,000.
Invest the remaining $166,000 in the S&P 500 (or any other equity strategy).

10 years from now, the Treasury STRIP will be worth $1,000,000. As a result, we will breakeven even if we lose our entire equity investment. If equities end the period flat, we will have $1,166,000 – an annualized return of 1.55%. If equities appreciate over the next decade, our return will exceed 1.55%. The chart below plots the annualized portfolio return for various S&P 500 scenarios.

So where is the risk?

The portfolio consists of a 83.4% allocation to a zero-coupon Treasury bond and a 16.6% allocation to equities. For those familiar with indexed annuity lingo, this 16.6% is the participation rate. This is essentially a very conservative asset allocation model. It may be low risk, but it is certainly not risk-free despite the fact that the portfolio will be worth at least the minimum $1,000,000 in 10 years.

First, the value of the account can dip below $1,000,000 prior to maturity. Suppose that over the next year interest rates are unchanged and equities crash 50%. The account value will be $932,277, a 6.8% loss. On a side note, I actually think this may be one of the key benefits of an indexed annuity product: helping investors maintain a more optimal investment horizon by masking over short-term fluctuations.

Second, the go-forward appeal of this strategy will be highly dependent on interest rates. Higher interest rates will make the strategy relatively more attractive. Why?

Higher interest rates –> Lower STRIP prices –> More money to invest in equities

If 10-year STRIP rates were 5.00% instead of 1.83%, the STRIP would only cost approximately $614,000, leaving a $386,000 to invest in equities. Now instead of a 16.6%/83.4% stock/bond split, we get a 38.6%/61.4% split while still taking no risk of a 10-year loss.

Below, we plot what our hypothetical indexed annuity replicating portfolio would have looked like historically over different interest rate regimes.

Unsurprisingly, the performance of the hypothetical indexed annuity tends to lag in strong equity markets and shine when equity markets crash. That being said, the simulated performance is quite compelling on a risk-adjusted-basis.

The picture changes, however, when we re-run the historical simulations using today’s interest rates. The average annual return drag increases from just 1.05% with historical rates to a whopping 6.40% with current rates. 6.40% of drag vs. equities is especially problematic once we factor in low expected equity returns and inflation. While the risk of capital loss may be effectively mitigated, we have just substituted it for the risk that we fail to meet our growth objectives.

Once again, risk cannot be destroyed, it can just be transformed.

Indexed annuities are not immune from the low interest rate malaise currently gripping the markets.

I think it’s also important to consider the appropriate benchmark for this type of investment. In my view, ending the 10-year period with $1,000,000 is not “breaking even.” In our initial example, we could have avoided equities entirely and used all of our capital to buy a Treasury STRIP. Today, our $1,000,000 could purchase approximately $1,199,000 notional of these bonds. In other words, if we stick to our 10-year investment horizon, then we can guarantee that our account is worth $1,199,000 10 years down the road. This equates to a 1.83% annualized return. This is our benchmark.

When we plot the simulated performance (assuming today’s interest rates) vs. this breakeven benchmark, we see that performance did in fact slip below 1.83% for investor’s that initiated their investment between October 1998 and January 2001. These investors would have struggled because they experienced both the popping of the tech bubble and the global financial crisis. Lo and behold, risk exists. In essence, the replicated indexed annuity is investing the future interest to be earned on the STRIP investment in equities. If this investment isn’t profitable, the investor would have been better off sticking the Treasuries.

Example #2: Options and Bonds

One way we can deal with the low equity participation rates caused by low interest rates from our first example is to introduce leverage. Specifically, we can do so by using equity index options.

Again assume that we have $1,000,000 to invest for ten years. We still want to impose a $1,000,000 floor on our account value at the end of the period, but now we want 100% participation with equity gains (at least up to some cap).

How would we go about doing this?

We start by buying $1,000,000 of 10-year Treasury notes at par. Today, the interest rate on this investment would be 1.88%. Treasury bonds pay interest semi-annually and so the investment will generate $9,400 in interest payments every six months.

To get our equity participation, we will use this cash flow to buy at-the-money call options on SPY that expire in six months. Let’s say each of these options costs $10, so we can buy 940 options. This is problematic. We want 100% participation in equity gains. To get this at SPY’s current price of around $206, we need to buy 4,854 options ($1,000,000 divided by $206).

940 options gives us a participation rate of less than 20%, not too much different than our portfolio in Example #1 above.

Fortunately, we can solve our issue with a bit of financial engineering. Say that call options with the same expiry and a strike of $209 (about 1.5% out-of-the-money) are trading at $8. If we sell one of these options for each $206 strike call we buy, we have created a bullish call spread. These call spreads only cost $2 each, allowing us to buy the 4,854 units we need.

We now get 100% participation in equity gains. However, we have paid a price for this. Namely, we only get 100% participation for gains up to 1.5%. We have sold the rights to any gains in excess of 1.5% in order to finance our call purchases. We have “capped” our six equity return at 1.5%.

At the end of six months, we will re-invest any option payoffs into Treasury notes/bills. As a result, we may have slightly more than $9,400 to buy options for the next six-month period.

We continue this process for ten years (or 20 six-month periods). Even if the worst case scenario plays out and the market goes down each and every period, we will still receive our $1,000,000 principal back from the 10-year Treasury note investment.

Below, we again simulate how such an approach would have hypothetically performed relative to the S&P 500.

When we use historical interest rates, the results are once again pretty compelling. On average, the simulated indexed annuity trails the S&P 500 by less 1% per year, while providing nice downside protection.

Unfortunately, when we repeat the simulation using today’s interest rates, we see that this simulation has the same shortcomings as our first one.

When interest rates are low, our Treasury bond position throws off very little cash. With low cash flow, we aren’t able to buy very many at-the-money call options. As a result, we need to sell calls with strikes that are quite close to today’s equity prices in order to finance our at-the-money call purchases. This effectively sets our cap very low and puts strict limits on how much equity upside can be realized. The annualized drag to the equity markets is now nearly 5% per year.

Once again, risk has not been eliminated. Our “reward” for buying the Treasury bond is the interest payments. We use these interest payments to get leveraged market exposure through options. If the market declines, the options will expire worthless and we have lost our interest payments.

The commonsense benchmark for this portfolio is just a 10-year Treasury bond. The 3/31/16 rate that was used in the simulation was 1.78%. This 1.78% is our benchmark. We see below that the simulated indexed annuity barely beats out this benchmark in most cases.

A Word About Dividends

Research Affiliates estimates that U.S. large-cap equities have a 10-year expected return of 1.3% after inflation. On a nominal basis – or adding back in inflation of 2.0% – this number becomes 3.3%. Of this 3.3%, they believe that inflation will contribute +2.0%, dividends will contribute 2.2%, and growth will contribute 1.3%. But, this adds up to 5.5%. What gives? Research Affiliates believes that equity valuations will gradually revert back to historical norms. They estimate that this will be a 2.2% annualized drag on performance.

As you can see, dividends are a crucial part of equity returns. If we remove the 2.2% dividend yield, the above expected return number drops from an already meager 3.3% to only 1.1%.

This is problematic for indexed annuity investors, since credits are often based on price, not total, return of the equity index.

To test the impact of this, we can perform some Monte Carlo simulations using the Research Affiliates capital market assumptions. We compare a 20/80 S&P 500/Barclays Aggregate portfolio to the following indexed annuity (note: we took this structure from a popular product in today’s market):

4% bonus on initial investment
100% participation rate on S&P 500 with a cap of 2.5%
S&P 500 return is measured using the annual, point-to-point methodology (i.e. we compute the return using just the beginning of year and end of year S&P 500 values)

We performed 10,000 simulations of 10-year periods. The following histogram plots the annualized out/underperformance of the 20/80 portfolio vs. the indexed annuity over 10-year periods. Positive numbers mean the 20/80 portfolio outperformed. Negative numbers mean the indexed annuity outperformed. All returns are annualized.

On average, the 20/80 returned 3.45% per year over a 10-year period. The indexed annuity returned 1.89% per year. The 20/80 portfolio beat the indexed annuity in 88.8% of the simulations.

Indexed annuity proponents may point to the risk management benefits of the product in trying to reconcile these statistics. There are a few problems with this argument.

First, the 20/80 portfolio isn’t all that risky to begin with. It only lost money over 10-years in 1.1% of the simulations. And this is using today’s capital market assumptions where both U.S. stocks and bonds are overvalued and therefore offer low future expected returns.

Second, and much more importantly, we have to consider inflation. To see why, consider the simplest form of risk management, holding cash. This will guarantee that you protect your capital, until you wake up a decade later only to realize that inflation has eroded your purchasing power.

If we deduct 2.0% of inflation per year, the “risk management” scoreboard changes dramatically. The 20/80 loses money on an inflation-adjusted basis in 15.9% of the simulations, while the annuity fails to keep up with inflation 59.9% of the time! In our experiment, you are more likely to lose money than make money with the annuity over a decade. Hardly risk-free!

Conclusion

Risk cannot be destroyed, it can only be transformed. Warren Buffett famously said, “If you’ve been playing poker for half an hour and still don’t know who the patsy is, you’re the patsy.” The same idea holds true with any financial product. There is always risk somewhere. If someone selling a product says otherwise, then be very, very suspicious.

For indexed annuities, the main risk is that potential returns are severely limited when interest rates are as low as they are now. High interest rates are the fuel that may allow these products to deliver equity-like returns with less downside risk. Without high interest rates, however, you are going nowhere fast. Going nowhere fast is a problem when inflation is always nipping at your heels. Downside risk management is great, until it restricts your growth so much that your purchasing power erodes over time.

Data Sources and Disclosures

Data comes from the Federal Reserve, Research Affiliates, CBOE, and Morningstar. Calculations were performed by Newfound Research.

Index annuity guarantees are subject to the credit of the issuing insurance company.

All returns are hypothetical and backtested and reflect unmanaged index returns. Returns do not reflect fees. Past performance does not guarantee future results. Results are not indicative of any Newfound index or strategy. Hypothetical performance results have many inherent limitations and are not indicative of results that any investor actually attained. An investor cannot invest directly in an index. Index returns are unmanaged and do not reflect fees and expenses.

For the options analysis, we use historical VIX levels with a 20% premium applied to reflect the higher implied volatility typically associated with longer-term options.