Category: Portfolio Construction Page 8 of 10

The New Glide Path

On July 2, 2018

In Portfolio Construction, Risk Management, Sequence Risk, Weekly Commentary

This post is available as a PDF download here.

Summary

In practice, investors and institutions alike have spending patterns that makes the sequence of market returns a relevant risk factor.
All else held equal, investors would prefer to make contributions before large returns and withdrawals before large declines.
For retirees making constant withdrawals, sustained declines in portfolio value represent a significant risk. Trend-following has demonstrated historical success in helping reduce the risk these types of losses.
Traditionally, stock/bond glide paths have been used to control sequence risk. However, trend-following may be able to serve as a valuable hybrid between equities and bonds and provide a means to diversify our diversifiers.
Using backward induction and a number of simplifying assumptions, we generate a glide path based upon investor age and level of wealth.
We find that trend-following receives a significant allocation – largely in lieu of equity exposure – for investors early in retirement and whose initial consumption rate closely reflects the 4% level.

In past commentaries, we have written at length about investor sequence risk. Summarized simply, sequence risk is the sensitivity of investor goals to the sequence of market returns. In finance, we traditionally assume the sequence of returns does not matter. However, for investors and institutions that are constantly making contributions and withdrawals, the sequence can be incredibly important.

Consider for example, an investor who retires with $1,000,000 and uses the traditional 4% spending rule to allocate a $40,000 annual withdrawal to themselves. Suddenly, in the first year, their portfolio craters to $500,000. That $40,000 no longer represents just 4%, but now it represents 8%.

Significant drawdowns and fixed withdrawals mix like oil and water.

Sequence risk is the exact reason why traditional glide paths have investors de-risk their portfolios over time from growth-focused, higher volatility assets like equities to traditionally less volatile assets, like short-duration investment grade fixed income.

Bonds, however, are not the only way investors can manage risk. There are a variety of other methods, and frequent readers will know that we are strong advocates for the incorporation of trend-following techniques.

But how much trend-following should investors use? And when?

That is exactly what this commentary aims to explore.

Building a New Glidepath

In many ways, this is a very open-ended question. As a starting point, we will create some constraints that simplify our approach:

The assets we will be limited to are broad U.S. equities, a trend-following strategy applied to U.S. equities, a 10-year U.S. Treasury index, and a U.S. Treasury Bill index.
In any simulations we perform, we will use resampled historical returns.
We assume an annual spend rate of $40,000 growing at 3.5% per year (the historical rate of annualized inflation over the period).
We assume our investor retires at 60.
We assume a male investor and use the Social Security Administration’s 2014 Actuarial Life Table to estimate the probability of death.

Source: St. Louis Federal Reserve and Kenneth French Database. Past performance is hypothetical and backtested. Trend Strategy is a simple 200-day moving average cross-over strategy that invests in U.S. equities when the price of U.S. equities is above its 200-day moving average and in U.S. T-Bills otherwise. Returns are gross of all fees and assume the reinvestment of all dividends. None of the equity curves presented here represent a strategy managed by Newfound Research.

To generate our glide path, we will use a process of backwards induction similar to that proposed by Gordon Irlam in his article Portfolio Size Matters (Journal of Personal Finance, Vol 13 Issue 2). The process works thusly:

Starting at age 100, assume a success rate of 100% for all wealth levels except for $0, which has a 0% success rate.
Move back in time 1 year and generate 10,000 1-year return simulations.
For each possible wealth level and each possible portfolio configuration of the four assets, use the 10,000 simulations to generate 10,000 possible future wealth levels, subtracting the inflation-adjusted annual spend.
For a given simulation, use standard mortality tables to determine if the investor died during the year. If he did, set the success rate to 100% for that simulation. Otherwise, set the success rate to the success rate of the wealth bucket the simulation falls into at T+1.
For the given portfolio configuration, set the success rate as the average success rate across all simulations.
For the given wealth level, select the portfolio configuration that maximizes success rate.
Return to step 2.

As a technical side-note, we should mention that exploring all possible portfolio configurations is a computationally taxing exercise, as would be an optimization-based approach. To circumvent this, we employ a quasi-random low-discrepancy sequence generator known as a Sobol sequence. This process allows us to generate 100 samples that efficiently span the space of a 4-dimensional unit hypercube. We can then normalize these samples and use them as our sample allocations.

If that all sounded like gibberish, the main thrust is this: we’re not really checking every single portfolio configuration, but trying to use a large enough sample to capture most of them.

By working backwards, we can tackle what would be an otherwise computationally intractable problem. In effect, we are saying, “if we know the optimal decision at time T+1, we can use that knowledge to guide our decision at time T.”

This methodology also allows us to recognize that the relative wealth level to spending level is important. For example, having $2,000,000 at age 70 with a $40,000 real spending rate is very different than having $500,000, and we would expect that the optimal allocation would different.

Consider the two extremes. The first extreme is we have an excess of wealth. In this case, since we are optimizing to maximize the probability of success, the result will be to take no risk and hold a significant amount of T-Bills. If, however, we had optimized to acknowledge a desire to bequeath wealth to the next generation, you would likely see the opposite extreme: with little risk of failure, you can load up on stocks and to try to maximize growth.

The second extreme is having a significant dearth of wealth. In this case, we would expect to see the optimizer recommend a significant amount of stocks, since the safer assets will likely guarantee failure while the risky assets provide a lottery’s chance of success.

The Results

To plot the results both over time as well as over the different wealth levels, we have to plot each asset individually, which we do below. As an example of how to read these graphs, below we can see that in the table for U.S. equities, at age 74 and a $1,600,000 wealth level, the glide path would recommend an 11% allocation to U.S. equities.

A few features we can identify:

When there is little chance of success, the glide path tilts towards equities as a potential lottery ticket.
When there is a near guarantee of success, the glide path completely de-risks.
While we would expect a smooth transition in these glide paths, there are a few artifacts in the table (e.g. U.S. equities with $200,000 wealth at age 78). This may be due to a particular set of return samples that cascade through the tables. Or, because the trend following strategy can exhibit nearly identical returns to U.S. equities over a number of periods, we can see periods where the trend strategy received weight instead of equities (e.g. $400,000 wealth level at age 96 or $200,000 at 70).

Ignoring the data artifacts, we can broadly see that trend following seems to receive a fairly healthy weight in the earlier years of retirement and at wealth levels where capital preservation is critical, but growth cannot be entirely sacrificed. For example, we can see that an investor with $1,000,000 at age 60 would allocate approximately 30% of their portfolio to a trend following strategy.

Note that the initially assumed $40,000 consumption level aligns with the generally recommended 4% withdrawal assumption. In other words, the levels here are less important than their size relative to desired spending.

It is also worth pointing out again that this analysis uses historical returns. Hence, we see a large allocation to T-Bills which, once upon a time, offered a reasonable rate of return. This may not be the case going forward.

Conclusion

Financial theory generally assumes that the order of returns is not important to investors. Any investor contributing or withdrawing from their investment portfolio, however, is dramatically affected by the order of returns. It is much better to save before a large gain or spend before a large loss.

For investors in retirement who are making frequent and consistent withdrawals from their portfolios, sequence manifests itself in the presence of large and prolonged drawdowns. Strategies that can help avoid these losses are, therefore, potentially very valuable.

This is the basis of the traditional glidepath. By de-risking the portfolio over time, investors become less sensitive to sequence risk. However, as bond yields remain low and investor life expectancy increases, investors may need to rely more heavily on higher volatility growth assets to avoid running out of money.

To explore these concepts, we have built our own glide path using four assets: broad U.S. equities, 10-year U.S. Treasuries, U.S. T-Bills, and a trend following strategy. Not surprisingly, we find that trend following commands a significant allocation, particularly in the years and wealth levels where sequence risk is highest, and often is allocated to in lieu of equities themselves.

Beyond recognizing the potential value-add of trend following, however, an important second takeaway may be that there is room for significant value-add in going beyond traditional target-date-based glide paths for investors.

Factor Investing & The Bets You Didn’t Mean to Make

By Corey Hoffstein

On January 16, 2018

In Craftsmanship, Portfolio Construction, Risk & Style Premia, Risk Management, Weekly Commentary

This post is available as a PDF download here.

Summary

Factor investing seeks to balance specificity with generality: specific enough to have meaning, but general enough to be applied broadly.
Diversification is a key tool to managing risk in factor portfolios. Imprecision in the factor definitions means that unintended bets are necessarily introduced.
This is especially true as we apply factors across securities that share fewer and fewer common characteristics. Left unmonitored, these unintended bets have the potential to entirely swamp the factor itself.
By way of example, we explore a simple value-based country model.
While somewhat counter-intuitive, constraints have the potential to lead to more efficient factor exposures.

In quantitative investing, we seek a balance between generality and specificity. When a model is too specific – designed to have meaning on too few securities or in too few scenarios – we lose our ability to diversify. When a model is too generic, it loses meaning and forecasting power.

The big quant factors – value, momentum, defensive, carry, and trend – all appear to find this balance: generic enough to be applied broadly, but specific enough to maintain a meaningful signal.

As we argued in our past commentary A Case Against Overweighting International Equity, the imprecision of the factors is a feature, not a bug. A characteristic like price-to-earnings may never fully capture the specific nuances of each firm, but it can provide a directionally accurate roadmap to relative firm valuations. We can then leverage diversification to average out the noise.

Without diversification, we are highly subject to the imperfections of the model. This is why, in the same piece, we argued that making a large regional tilt – e.g. away from U.S. towards foreign developed – may not be prudent: it is a single bet that can take decades to resolve. If we are to sacrifice diversification in our portfolio, we’ll require a much more accurate model to justify the decision.

Diversification, however, is not just measured by the quantity of bets we take. If diversification is too naively interpreted, the same imprecision that allows factors to be broadly applied can leave our portfolios subject to the returns of unintended bets.

Value Investing with Countries

If taking a single, large regional tilt is not prudent, perhaps value investing at a country level may better diversify our risks.

One popular way of measuring value is with the Shiller CAPE: a cyclically-smoothed price-to-earnings measure. In the table below, we list the current CAPE and historical average CAPE for major developed countries.

	CAPE	Mean CAPE	Effective Weight
Australia	18.5	17.2	2.42%
Belgium	25.0	15.4	0.85%
Canada	22.0	21.4	3.76%
Denmark	36.5	24.5	0.73%
France	20.9	21.9	4.85%
Germany	20.6	20.6	4.36%
Hong Kong	18.2	18.3	5.21%
Italy	16.8	22.1	1.33%
Japan	28.9	43.2	11.15%
Netherlands	23.5	14.8	1.45%
Singapore	13.9	22.1	1.09%
Spain	13.4	18.3	1.58%
Sweden	21.5	23.0	1.21%
Switzerland	25.9	21.9	3.15%
United Kingdom	16.5	15.3	6.55%
United States	30.5	20.3	50.30%

Source: StarCapital.de. Effective weight is market-capitalization weight of each country, normalized to sum to 100%. Mean CAPE figures use data post-1979 to leverage a common dataset.

While evidence[1] suggests that valuation levels themselves are enough to determine relative valuation among countries, we will first normalize the CAPE ratio by its long-term average to try to account for structural differences in CAPE ratios (e.g. a high growth country may have a higher P/E, a high-risk country may have a lower P/E, et cetera). Specifically, we will look at the log-difference between the mean CAPE and the current CAPE scores.

Note that we recognize there is plenty to criticize and improve upon here. Using a normalized valuation metric will mean a country like Japan, which experienced a significant asset bubble, will necessarily look under-valued. Please do not interpret our use of this model as our advocacy for it: we’re simply using it as an example.

Using this value score, we can compare how over and undervalued each country is relative to each other. This allows us to focus on the relative cheapness of each investment. We can then use these relative scores to tilt our market capitalization weights to arrive at a final portfolio.

	Value Score	Relative Z-Score	Scaled Z-Score	Scaled Weights
Australia	-0.07	-0.13	0.88	2.31%
Belgium	-0.48	-1.50	0.40	0.37%
Canada	-0.03	0.02	1.02	4.15%
Denmark	-0.40	-1.22	0.45	0.36%
France	0.05	0.27	1.27	6.65%
Germany	0.00	0.11	1.11	5.24%
Hong Kong	0.01	0.13	1.13	6.37%
Italy	0.27	1.02	2.02	2.92%
Japan	0.40	1.45	2.45	29.59%
Netherlands	-0.46	-1.43	0.41	0.65%
Singapore	0.46	1.65	2.65	3.14%
Spain	0.31	1.15	2.15	3.68%
Sweden	0.07	0.33	1.33	1.75%
Switzerland	-0.17	-0.45	0.69	2.36%
United Kingdom	-0.08	-0.14	0.88	6.22%
United States	-0.41	-1.25	0.45	24.26%

Source: StarCapital.de. Calculations by Newfound Research. “Value Score” is the log-difference between the country’s Mean CAPE and its Current CAPE. Relative Z-Score is the normalized value score of each country relative to peers. Scaled Z-Score applies the following function to the Relative Z-Score: (1+x) if x > 0 and 1 / (1+x) if x < 0. Scaled weights multiply the Scaled Z-Score against the Effective Weights of each country and normalize such that the total weights sum to 100%.

While the Scaled Weights represent a long-only portfolio, what they really capture is the Market Portfolio plus a dollar-neutral long/short factor tilt.

	Market Weight	+ Long / Short	= Scaled Weights
Australia	2.42%	-0.11%	2.31%
Belgium	0.85%	-0.48%	0.37%
Canada	3.76%	0.39%	4.15%
Denmark	0.73%	-0.37%	0.36%
France	4.85%	1.80%	6.65%
Germany	4.36%	0.88%	5.24%
Hong Kong	5.21%	1.16%	6.37%
Italy	1.33%	1.59%	2.92%
Japan	11.15%	18.44%	29.59%
Netherlands	1.45%	-0.80%	0.65%
Singapore	1.09%	2.05%	3.14%
Spain	1.58%	2.10%	3.68%
Sweden	1.21%	0.54%	1.75%
Switzerland	3.15%	-0.79%	2.36%
United Kingdom	6.55%	-0.33%	6.22%
United States	50.30%	-26.04%	24.26%

To understand the characteristics of the tilt we are taking – i.e. the differences we have created from the market portfolio – we need only look at the long/short portfolio.

Unfortunately, this is where our model loses a bit of interpretability. Since each country is being compared against its own long-term average, looking at the increase or decrease to the aggregate CAPE score is meaningless. Indeed, it is possible to imagine a scenario whereby this process actually increases the top-level CAPE score of the portfolio, despite taking value tilts (if value, for example, is found in countries that have higher structural CAPE values). We can, on the other hand, look at the weighted average change to value score: but knowing that we increased our value score by 0.21 has little interpretation.

One way of looking at this data, however, is by trying to translate value scores into return expectations. For example, Research Affiliates expects CAPE levels to mean-revert to the average level over a 20-year period.[2] We can use this model to translate our value scores into an annualized return term due to revaluation. For example, with a current CAPE of 30.5 and a long-term average of 20.3, we would expect a -2.01% annualized drag from revaluation.

By multiplying these return expectations against our long/short portfolio weights, we find that our long/short tilt is expected to create an annualized revaluation premium of +1.05%.

The Unintended Bet

Unfortunately, re-valuation is not the only bet the long/short portfolio is taking. The CAPE re-valuation is, after all, in local currency terms. If we look at our long/short portfolio, we can see a very large weight towards Japan. Not only will we be subject to the local currency returns of Japanese equities, but we will also be subject to fluctuations in the Yen / US Dollar exchange rate.

Therefore, to achieve the re-valuation premium of our long/short portfolio, we will either need to bear the currency risk or hedge it away.

In either case, we can use uncovered interest rate parity to develop an expected return for currency. The notion behind uncovered interest rate parity is that investors should be indifferent to sovereign interest rates. In theory, for example, we should expect the same return from investing in a 1-year U.S. Treasury bond that we expect from converting $1 to 1 euro, investing in the 1-year German Bund, and converting back after a year’s time.

Under uncovered interest rate parity, our expectation is that currency change should offset the differential in interest rates. If a foreign country has a higher interest rate, we should expect that the U.S. dollar should appreciate against the foreign currency.

As a side note, please be aware that this is a highly, highly simplistic model for currency returns. The historical efficacy of the carry trade clearly demonstrates the weakness of this model. More complex models will take into account other factors such as relative purchasing power reversion and productivity differentials.

Using this simple model, we can forecast currency returns for each country we are investing in.

	FX Rate	1-Year Rate	Expected FX Rate	Currency Return
Australia	1.2269	-0.47%	1.2546	-2.21%
Belgium	1.2269	-0.47%	1.2546	-2.21%
Canada	0.8056	1.17%	0.8105	-0.60%
Denmark	0.1647	-0.55%	0.1685	-2.29%
France	1.2269	-0.47%	1.2546	-2.21%
Germany	1.2269	-0.47%	1.2546	-2.21%
Hong Kong	0.1278	1.02%	0.1288	-0.75%
Italy	1.2269	-0.47%	1.2546	-2.21%
Japan	0.0090	-0.13%	0.0092	-1.88%
Netherlands	1.2269	-0.47%	1.2546	-2.21%
Singapore	0.7565	1.35%	0.7597	-0.42%
Spain	1.2269	-0.47%	1.2546	-2.21%
Sweden	0.1241	0.96%	0.1251	-0.81%
Switzerland	1.0338	-0.72%	1.0598	-2.46%
United Kingdom	1.3795	0.43%	1.3981	-1.33%
United States	1.0000	1.78%	1.0000	0.00%

Source: Investing.com, XE.com. Euro area yield curve employed for Eurozone countries on the Euro.

Multiplying our long/short weights against the expected currency returns, we find that we have created an expected annualized currency return of -0.45%.

In other words, we should expect that almost 50% of the value premium we intended to generate will be eroded by a currency bet we never intended to make.

One way of dealing with this problem is through portfolio optimization. Instead of blindly value tilting, we could seek to maximize our value characteristics subject to currency exposure constraints. With such constraints, what we would likely find is that more tilts would be made within the Eurozone since they share a currency. Increasing weight to one Eurozone country while simultaneously reducing weight to another can capture their relative value spread while remaining currency neutral.

Of course, currency is not the only unintended bet we might be making. Blindly tilting with value can lead to time varying betas, sector bets, growth bets, yield bets, and a variety of other factor exposures that we may not actually intend. The assumption we make by looking at value alone is that these other factors will be independent from value, and that by diversifying both across assets and over time, we can average out their impact.

Left entirely unchecked, however, these unintended bets can lead to unexpected portfolio volatility, and perhaps even ruin.

Conclusion

In past commentaries, we’ve argued that investors should focus on achieving capital efficiency by employing active managers that provide more pure exposure to active views. It would seem constraints, as we discussed at the end of the last section, might contradict this notion.

Why not simply blend a completely unconstrained, deep value manager with market beta exposure such that the overall deviations are constrained by position limits?

One answer why this might be less efficient is that not all bets are necessarily compensated. Active risk for the sake of active risk is not the goal: we want to maximize compensated active risk. As we showed above, a completely unconstrained value manager may introduce a significant amount of unintended tracking error. While we are forced to bear this risk, we do not expect the manager’s process to actually create benefit from it.

Thus, a more constrained approach may actually provide more efficient exposure.

That is all not to say that unconstrained approaches do not have efficacy: there is plenty of evidence that the blind application of value at the country index level has historically worked. Rather, the application of value at a global scale might be further enhanced with the management of unintended bets.

[1] For example, Predicting Stock Market Returns Using the Shiller CAPE (StarCapital Research, January 2016) and Value and Momentum Everywhere (Asness, Moskowitz, and Pedersen, June 2013)

[2] See Research Affiliate’s Equity Methodology for their Asset Allocation tool.

Levered ETFs for the Long Run?

By Corey Hoffstein

On January 8, 2018

In Portfolio Construction, Weekly Commentary

This blog post is available as a PDF download here.

Summary

We believe that capital efficiency should remain a paramount objective for investors.
The prudent use of leverage can help investors employ more risk efficient portfolios without necessarily sacrificing potential returns.
Many investors, however, do not have access to leverage (be it via borrowing or derivatives). They may, however, have access to leverage via Levered ETFs.
Levered ETFs are often dismissed as trading vehicles, not suited for buy-and-hold investors due to the so-called “volatility drag.” We show that the volatility drag is a component of all compounding returns, whether they are levered or not.
We explore the impact that the reset period can have on Levered ETFs and demonstrate how these ETFs may be used in the context of a portfolio to introduce diversifying, alternative exposures.

Early last month, we published a piece titled Portable Beta: Making the Most of the Returns You’re Already Getting, in which we outlined an argument whereby investors should focus on capital efficiency. We laid out four ways in which we believe that investors can achieve greater efficiency:

Reduce fees to take home more of what you earn.
Express active views more purely so that we are not caught paying active management prices for closet beta.
Focus on risk management by “diversifying your diversifiers” with strategies like trend following that can help increase exposure to higher return asset classes without necessarily increasing the overall portfolio risk profile.
Utilize modest leverage so that investors can create more risk-efficient portfolios without necessarily sacrificing potential return.

Unfortunately, for many investors, access to true leverage – either through borrowing or the use of derivatives – may be beyond their means. Fortunately, there are a number of ETFs available today that allow investors to access leverage in a packaged manner.

Wait, Aren’t Levered ETFs Dangerous?

Levered ETFs have quite a reputation, and not a good one at that. A quick search will result in numerous articles that tell you why they are a dangerous, bad idea. They are pejoratively dismissed as “trading vehicles,” unsuitable for “buy and hold.”

Most often, the negative publicity hinges on the concept of volatility decay (or, sometimes “volatility drag”). To illuminate this concept, let’s assume there is a stock that can only go up either +X% or down –X%. Thus, in any two-day period, we have the following growth in our wealth:

	Up	Down
Up	(1 + X%)(1 + X%)	(1 – X%)(1 + X%)
Down	(1 + X%)(1 – X%)	(1 – X%)(1 – X%)

If we expand out the returns, we are left with:

	Up	Down
Up	1 + 2X% + X%²	1 – X%²
Down	1 – X%²	1 – 2X% + X%²

Note that in the case where the stock went up +X% and then down -X% (or down –X% and then up +X%), we did not end up back at our starting wealth. Rather, we ended up with a loss of -X%².

On the other hand, we can see that when the stock goes the same direction, we actually outperform twice the daily return by +X%².

What’s going on here?

It is nothing more than the math of compound returns. The returns of the second day compound the returns of the first.

The effect earns the moniker volatility decay because in return environments that are mean-reversionary (e.g. positive returns follow negative returns, and vice versa), our capital decays due to the -X%² term.

Note, however, that we haven’t even introduced leverage into the scenario yet. This drag is not unique to levered ETFs: it is just the math of compounding returns. Why it gets brought up so frequently with respect to levered ETFs is because the leverage can accentuate it. Consider what happens if we introduce a daily leverage factor of L:

	Up	Down
Up	1 + 2LX% + L²X%²	1 – L²X%²
Down	1 – L²X%²	1 – 2LX% + L²X%²

When L=1, we have a standard long-only investment. When L=2, we have our 2X daily levered ETFs. What we see is that when L=1, our drag is simply –X%². When L=2, however, our drag is 4X%². When L=3, the drag skyrockets to 9X%². Of course, the so-called drag turns into a benefit in trending markets (whether positive or negative).

So why do we not see this same effect when we use traditional leverage? After all, are these ETFs not using leverage under the hood to achieve their returns?

The answer lies in the daily reset. Note that these ETFs aim to give you a multiple of returns every day. The same is not true if we simply lever our notional exposure and never reset it. By “reset,” we mean pay back what we owe and re-borrow capital in order to maintain our leverage ratio.

To achieve 2X daily returns, the levered ETFs basically borrow their NAV, invest in the asset class, and then pay back what they borrowed. Hence, every day they reset how much they borrow.

If we never reset, however, the proportion of our capital that is levered varies over time. Consider, for example, investing $10,000 of our own capital in the SPDR S&P 500 ETF and borrowing another $10,000 to invest alongside (for convenience, we’re going to assume zero borrowing cost). As the market has gone up over time, the initial $10,000 borrowed becomes a smaller and smaller proportion of our capital.

Source: CSI. Calculations by Newfound Research. Assumes portfolio applies 100% notional leverage applied to SPDR S&P 500 ETF (“SPY”) at inception of ETF. Assumes zero cost of leverage.

This happens because while we owe the initial $10,000 back, the returns made on that $10,000 are ours to keep. In the beginning, our portfolio will behave very much like a 2X daily levered ETF. As the market trends upward over time, however, we not only compound our own capital, but compound our gains on the levered capital. This causes our actual leverage to decline over time. As a result, our daily returns will gradually converge towards that of the market.

In practice, of course, there would be a cost associated with borrowing the $10,000. However, the same fact pattern applies so long as the growth of the portfolio exceeds the cost of leverage.

Resetting, therefore, is a necessary component of maintaining leverage. On the one hand, we have daily resets, which keeps our leverage proportion constant. On the other hand, we have “never reset,” which will decay the leverage proportion over time (assuming the portfolio grows faster than the cost of leverage). There are, of course, shades of gray as well. Consider a 1-year reset:

Source: CSI. Calculations by Newfound Research. Assumes portfolio applies 100% notional leverage applied to SPDR S&P 500 ETF (“SPY”) at inception of ETF and reset every 252-days thereafter. Assumes zero cost of leverage.

Note that in 2008, the debt proportion of our balance sheet spiked up to nearly 90% of our capital. What happened? This is reset timing risk. On 4/2008, the portfolio reset, borrowing $92,574 against our equity of $92,574. Over the next year, the market fell approximately 39%. Our total assets tumbled from $185,148 to $114,753 and we still owed the initial $92,574 we borrowed. Thus, our actual equity over this period fell an astounding -76.7%.

(It is worth pointing out that if we had considered a “never reset” portfolio that started on 4/2008, we’d have the same result.)

Frequent readers of our commentary may be wondering, “can this reset timing risk be controlled with overlapping portfolios just like other timing risks?” Yes … ish. On the one hand, there is not a whole lot we can do about the drawdown itself: 100% notional leverage plus a 37% drawdown means you’re going to have a bad time. Where overlapping portfolios can help is in ensuring that resets do not necessarily occur at the worst possible point (e.g. the bottom of the drawdown) and lock in losses.

As a general rule, we probably don’t want to apply N-times exposure over a time frame an asset class can experience a return of -1/N%. For example, if we want 2x equity exposure, we want to make sure we reset our leverage exposure well before equities have a chance to lose 50% (1/2). Similarly, if we want 3x exposure, we need to reset well before we can lose 33.3% (1/3).

So, Are They Evil or What?

We would argue that volatility decay takes the blame when it is not actually the culprit. Volatility decay is nothing more than the math of compounding returns: it happens whether you are levered or not.

The danger of most levered ETFs is more easily explained. If I told you I was going to take your investment, use it as collateral to gain 100% notional exposure to equities, and then invest that collateral in equities as well – an asset class than can easily lose 50% –what would you say? When put that way, it sounds a little nuts. It really isn’t much more complicated than that.

The reset effect really just introduces a few more nuanced wrinkles. The more frequently we reset, the less risk we run of going bust, as we take risk off the table as our debt-to-equity ratio climbs. That’s how we can avoid complete ruin with 100% leverage in an asset class that falls more than 50%.

On the other hand, the more frequently we reset, the closer we keep the portfolio to the target volatility level, increasing the drag from short-term mean reversion.

We’ve said it before and we’ll say it again: risk cannot be destroyed, only transformed.

But, these things might have their use yet…

Levered ETFs in a Portfolio

Held as 100% of our wealth, a 2X daily reset equity ETF may not be too prudent. In the context of a portfolio, however, things change.

Consider, for example, using 50% of our capital to invest in a 2x equity exposure and the remaining 50% to invest in bonds. In effect, we have created 150% exposure to a 67/33 stock/bond mixture. For example, we could hold 50% of our capital in the ProShares Ultra S&P 500 ETF (“SSO”) and 50% in the iShares Core U.S. Bond ETF (“AGG”).

To understand the portfolio exposure, we have to look under the hood. What we really have, in aggregate, is: 100% equity exposure and 50% bond exposure. To get to 150% total notional exposure, we have to borrow an amount equal to 50% of our starting capital. Indeed, at the portfolio level, we cannot differentiate whether we are using that 50% borrowing to lever up stocks, bonds, or the entire mixture!

In this context, levered ETFs become a lot more interesting.

The risk, of course, is in the resets. To really do this, we’d have to rebalance our portfolio back to a 50/50 mix of the 2x levered equity exposure and bonds on a daily basis. If we could achieve that, we’d have built a daily reset 1.5x 66/33 portfolio.

More realistically, investors may be able to rebalance their portfolio quarterly. How far does that deviate from the daily rebalance? We plot the two below.

Source: CSI. Calculations by Newfound Research. Returns for the Daily Rebalance and Quarterly Rebalance portfolios are backtested and hypothetical. Returns are gross of all fees except underlying ETF expense ratios. Returns assume the reinvestment of all distributions. Cost of leverage is assumed to be equal to the return of a 1-3 Year U.S. Treasury ETF (“SHY”). Past performance is not indicative of future results. The Daily Rebalance portfolio assumes 50% exposure to a hypothetical index providing 2x daily exposure of the SPDR S&P 500 ETF (“SPY”) and 50% exposure to the iShares US Core Bond ETF (“AGG”) and is rebalanced daily. The Quarterly Rebalance portfolio assumes the same exposure, but rebalances quarterly.

Indeed, for aggressive investors, a levered equity ETF mixed with bond exposure may not be such a bad idea after all. However – and to steal a line from our friends at Toroso Asset Management – levered ETFs are likely “buy-and-adjust” vehicles, not buy-and-hold. The frequency of adjusting, and the cost of doing so, will play an important role in results.

A Particular Application with Alternatives

Where levered ETFs may be particularly interesting is in the context of liquid alternatives.

In the past, we have said that many liquid alternatives, especially those offered as ETFs, have a volatility problem. Namely, they just don’t have enough volatility to be interesting.

Traditionally, allocating to a liquid alternative requires us removing capital from one investment to “make room” in our portfolio, which creates an implicit hurdle rate. If, for example, we sell a 5% allocation of our equity portfolio to make room for a merger arbitrage strategy, not only do we have to expect that the strategy can create alpha beyond its fees, but it also has to be able to deliver a long-term return that is at least in the same neighborhood of the equity risk premium. Otherwise, we should be prepared to sacrifice return for the benefit of diversification.

One solution to this problem with lower volatility alternatives is to fund their allocation by selling bonds instead of stocks. Bonds, however, are often our stable ballast in the portfolio. Regardless of how poorly we expect core fixed income to perform over the next decade, we have a high degree of certainty in their return. Asking us to sell bonds to buy alternatives is often asking us to throw certainty out the window.

By way of example, consider the Reality Shares DIVS ETF (“DIVY”). We wrote about this ETF back in August 2016 and think it is a particularly compelling story. The ETF buys the floating leg of dividend swaps, which in theory captures a premium from investors who want to insure their dividend growth exposure in the S&P 500.

For example, if the swap is priced such that the expected growth rate of S&P 500 dividends is 5% over the next year, but the realized growth is 6%, then the floating leg keeps the extra 1%. The “insurance” aspect comes in during years where realized growth is below the expected rate, and the floating leg has to cover the difference. To provide this insurance, the floating leg demands a premium.

A dividend swap of infinite length should, in theory, converge to the equity risk premium. Short-term dividend swaps (e.g. 1-year), however, seem to exhibit a potentially unique risk premium, making them an interesting diversifier within a portfolio.

While DIVY has performed well since inception, finding a place for it in a portfolio can be difficult. With low volatility, we have two problems. First, for the fund to make a meaningful difference, we need to make sure that our allocation is large enough. Second, we likely have to slot DIVY in for a low volatility asset – like core fixed income – so that we make sure that we are not creating an unreasonable hurdle rate for the fund.

Levered ETFs may allow us to have our cake and eat it too.

For example, ProShares offers an Ultra 7-10 Year Treasury ETF (“UST”), which provides investors with 2x daily return exposure to a 7-10 year U.S. Treasury portfolio. For investors who hold a large portfolio of intermediate-term U.S. Treasuries, they could potentially sell some exposure and replace it with 50% UST and 50% DIVY.

As before, the question of “when to reset” arises: but even with a quarterly [rebalance, we think it is a compelling concept.

Source: CSI. Calculations by Newfound Research. Returns for the S&P 500 Dividend Swaps Index and 50% 2x Daily 7-10 Year US Treasuries / 50% Dividend Swap Index portfolios are hypothetical and backtested. Returns are gross of all fees except underlying ETF expense ratios. Returns assume the reinvestment of all distributions. Cost of leverage is assumed to be equal to the return of a 1-3 Year U.S. Treasury ETF (“SHY”). Past performance is not indicative of future results. The 50% 2x Daily 7-10 Year US Treasuries / 50% Dividend Swap Index assumes a quarterly rebalance.

Conclusion

Leverage is a tool. When used prudently, it can help investors potentially achieve much more risk-efficient returns. When used without care, it can lead to complete ruin.

For many investors who do not have access to traditional means of leverage, levered ETFs represent one potential opportunity. While branded as a “trading vehicle” instead of a buy-and-hold exposure, we believe that if prudently monitored, levered ETFs can be used to help free up capital within a portfolio to introduce diversifying exposures.

Beyond the leverage itself, the daily reset process can introduce risk. While it helps maintain the leverage ratio – reducing risk after losses – it also re-ups our risk after gains and generally will increase long-term volatility drag from mean reversion.

This daily reset means that when used in a portfolio context, we should, ideally, be resetting our entire portfolio daily. In practice, this is impossible (and likely imprudent, once costs are introduced) for many investors. Thus, we introduce some tracking error within the portfolio.

We should note that there are monthly-reset leverage products that may partially alleviate this problem. For example, PowerShares and ETRACS offer monthly reset products and iPath offers “no reset” leverage ETNs that simply apply a leverage level at inception and never reset until the ETN matures.

Perhaps the most glaring absence in this commentary has been a discussion of fees. Levered ETP fees vary wildly, ranging from as low as 0.35% to as high as 0.95%. When considering using a levered ETP in a portfolio context, this fee must be added to our hurdle rate. For example, if our choice is between just holding the iShares 7-10 Year U.S. Treasury ETF (“IEF”) at 0.15%, or 50% in the ProShares Ultra 7-10 Year Treasury ETF (“UST”) and 50% in the Reality Shares DIVS ETF (“DIVY”) for a combined cost of 0.93%, the extra 0.78% fee needs to be added to our hurdle rate calculation.

Nevertheless, as fee compression marches on, we would expect fees in levered ETFs to come down over time as well, potentially making these products interesting for more than just expressing short-term trading views.

Portable Beta: Making the Most of the Returns You’re Already Getting

By Corey Hoffstein

On December 4, 2017

In Podcast, Portfolio Construction, Risk Management, Weekly Commentary

This post is available as a PDF download here.

Summary

Traditionally, investors have used a balance between stocks and bonds to govern their asset allocation. Expanding this palette to include other asset classes can allow them to potentially both enhance return and reduce risk, benefiting from diversification.
Modern portfolio theory tells us, however, that the truly optimal choice is to apply leverage to the most risk-efficient portfolio.
In a low expected return environment, we believe that capital efficiency is of the utmost importance, allowing investors to better capture the returns they are already earning.
We believe that the select application of leverage can allow investors to both benefit from enhanced diversification and capital efficiency, in a concept we are calling portable beta.

Diversification has been the cornerstone of investing for thousands of years as evidenced by timeless proverbs like “don’t put all your eggs in one basket.” The magic behind diversification – and one of the reasons it is considered the only “free lunch” available in investing – is that a portfolio of assets will always have a risk level less-than-or-equal-to the riskiest asset within the portfolio.

Yet it was not until Dr. Harry Markowitz published his seminal article “Portfolio Selection” in 1952 that investors had a mathematical formulation for the concept. His work, which ultimately coalesced into Modern Portfolio Theory (MPT), not only provided practitioners a means to measure risk and diversification, but it also allowed them to quantify the marginal benefit of adding new exposures to a portfolio and to derive optimal investment portfolios. For his work, Dr. Markowitz was awarded a Nobel prize in 1990.

What became apparent through this work is that the risk and expected reward trade-off is not necessarily linear. For example, in shifting a portfolio’s allocations from 100% bonds to 100% stocks, risk may actually initially decrease and expected return may increase due to diversification benefits. For example, in the hypothetical image below, we can see that the 60/40 stock-bond blend offers a nearly identical risk level to the 100% bond portfolio with significantly higher expected return.

Of course, these benefits are not limited solely to stock/bond mixes. Indeed, many investors focus on how they can expand their investment palette beyond traditional asset classes to include exposures that can expand the efficient frontier: the set of portfolios that represents the maximum expected return for each given risk level.

In the example graph below we can see this expectation labeled as the diversification benefit.

The true spirit of MPT suggests something different, however. MPT argues that in an efficient market, all investors would hold an identically allocated portfolio, which turns out to be the market portfolio. Holding any other portfolio would be sub-optimal. The argument goes that rational investors would all seek to maximize their expected risk-adjusted return and then simply introduce cash or leverage to meet their desired risk preference. This notion is laid out below.

In practice, however, while many investors are willing to expand their investment palette beyond just stocks and bonds, few ultimately take this last step of adding leverage. Conservative investors rarely barbell a riskier portfolio with cash, instead opting to be fully invested in fixed income centric portfolios. Aggressive investors rarely apply leverage, instead increasing their allocation to risky assets. Some argue that this leverage aversion actually gives rise to the low volatility / betting-against-beta anomaly.

This is unfortunate, as the prudent use of leverage can potentially enhance returns without necessarily increasing risk. For example, below we plot the hypothetical growth of a dollar invested in the S&P 500, a 60/40 portfolio, and a 60/40 portfolio levered to target the volatility level of the S&P 500.

	Ann. Return	Ann. Volatility	Max Drawdown
S&P 500	9.2%	14.1%	55.2%
60/40 Portfolio	8.0%	7.7%	29.8%
Levered 60/40 Portfolio	12.5%	14.9%	54.4%

Source: CSI. Calculations by Newfound Research. Results are hypothetical and backtested. Past performance is not an indicator of future results. Returns assume the reinvestment of all dividends and income and are gross of all fees except for underlying ETF expense ratios. The S&P 500 represented by SPDR S&P 500 ETF (”SPY”). 60/40 Portfolio is a 60% SPDR S&P 500 ETF (“SPY”) and 40% iShares 7-10 Year U.S. Treasury ETF (“IEF”) mix, rebalanced annually. Levered 60/40 applies 182% leverage to the 60/40 Portfolio by shorting an 82% position in the iShares 1-3 Year U.S. Treasury ETF (“SHY”). The leverage amount was selected so that the Levered 60/40 Portfolio would match the annualized volatility level of the S&P 500.

We can see that the Levered 60/40 portfolio trounces the S&P 500, despite sharing nearly identical risk levels. The answer as to why is two-fold.

First is the diversification benefits we gain from introducing a negatively correlated asset to a 100% equity portfolio. We can see this by comparing the annualized return and volatility of the S&P 500 versus the standard 60/40 portfolio. While the S&P 500 outperformed by 120 bps per year, it required bearing 640 bps of excess volatility (14.9% vs. 7.7%) and a realized drawdown that was of 2540 bps deeper (55.2% vs. 29.8%). Introducing the diversifying asset made the portfolio more risk-efficient. Unfortunately, in doing so, we were forced to allocate to an asset with a lower expected return (from equities to bonds), causing us to realize a lower return.

This lower return but higher risk-adjusted return is the thinking behind the common saying that “investors can’t eat risk-adjusted returns.”

That is where the benefits from leverage come into play. Leverage creates capital efficiency. In this example, we were able to treat each $1 invested as if it were $1.82. This allowed us to match the risk level of equities and benefit from the enhanced risk-efficiency of the diversified portfolio.

Efficiency Over Alpha

In a recent Barron’s roundtable[1], we were asked our thoughts on the future of ETFs. We receive this question fairly often when speaking on panels. The easy, obvious answers are, “more niche products,” or “an ETF for every asset class,” or even “smarter beta” (as if somehow beta has gone from high school to college and is now matriculating to graduate school).

In truth, none of these answers seem particularly innovative or even satisfactory when we consider that they will likely do little to help investors actually achieve their financial goals. This is especially true in a low expected return environment, where finding the balance between growth and safety is akin to sailing between Scylla and Charybdis[2]: too much exposure to risky assets can increase sequence risk and too little can increase longevity risk. Edging too close to either can spell certain financial doom.

With this in mind, our answer as of late has deviated from tradition and instead has focused on greater efficiency. Instead of trying to pursue excess returns, our answer is to maximize the returns investors are, largely, getting already. Here are a few examples of how this can be achieved:

Lower Costs. As expected excess returns go down, the proportion taken by fees goes up. The market may bear a 1% fee when expected excess returns are abundant, but that same fee may be the difference between retirement success and failure in a low return environment. Therefore, the most obvious way to increase efficiency for investors is to lower costs: both explicit (fees) and implicit (trading costs and taxes). Vanguard has led the charge in this arena for decades, and active managers are now scrambling to keep up. While simply lowering fees is the most obvious solution, more creative fee arrangements (e.g. flexible fees) may also be part of the solution.
Increased Exposure to Active Views. In a recent commentary, It’s Long/Short Portfolios All the Way Down[3], we explored the idea that an active investment strategy is simply a benchmark plus a dollar-neutral long/short portfolio layered on top. This framework implies that if the cost of accessing beta goes down, the implied cost for active necessarily goes up, creating a higher hurdle rate for active managers to clear. In our perspective, the way to clear this hurdle is for active managers to offer portfolios with greater exposure to their active views, with the most obvious example being be a high active share / active risk, concentrated equity portfolio. Such an approach increases the efficiency of exposure to active strategies.
Risk Management. Traditional risk management focuses exclusively on the use of capital diversification. Traditionally allocated portfolios, however, are often significantly dominated by equity volatility and can therefore carry around a disproportionate amount of fixed income exposure to hedge against rare tail events. We believe that diversifying your diversifiers – e.g. the incorporation of trend-following approaches – can potentially allow investors to increase their allocation to asset classes with higher expected returns without significantly increasing their risk profile.
Leverage. As we saw in our example above, leverage may allow us to invest in more risk-efficient, diversified portfolios without necessarily sacrificing return. In fact, in certain circumstances, it can even increase return. So long as we can manage the risk, increasing notional exposure to $1.50 for every $1 invested in a low return environment is effectively like increasing our returns by 1.5x (less the cost of leverage). For active strategies, a subtler example may be the return to a 130/30 style investment strategy (130% long / 30% short), which can allow investors enhanced access to a manager’s active views without necessarily taking on more beta risk. We expect that institutional investors may begin to re-acquaint themselves with ideas like portable alpha, where traditional portfolio exposures may be used as collateral for market-neutral, alpha-seeking exposures.[4]

Portable Beta Theory

We see lower costs as inevitable: Vanguard has made sure of that. We see increased exposure to active views as the only way for traditional active management (i.e. long-only stock pickers) to survive. A number of alternative diversifiers have already made their way to market, including defensive factor tilts, long/flat trend-following, options strategies, and managed futures. Leverage is where we really think new innovation can happen, because it allows investors to re-use capital to invest where they might not otherwise do so because it would have reduced their risk profile.

For example, for young investors the advice today is largely to invest predominately in equities and manage risk through their extended investment horizon. This has worked historically in the United States, but there are plenty of examples where such a plan would have failed in other markets around the globe. In truth, in almost no circumstance is 100% equities a prudent plan when leverage is available.[5]

As a simple example, let us constrain ourselves to only investing in stocks and bonds. Using J.P. Morgan’s 2018 capital market assumption outlook[6], we can create a stock-bond efficient frontier. In these assumptions, U.S. large-cap equities have an expected excess return of 4.4% with a volatility of 14.0%, while U.S. aggregate bonds have an expected excess return of 1.3% with a volatility of 3.8%. The correlation between the two asset classes is zero.

Plotting the efficient frontier, we can also solve for the portfolio that maximizes the risk-adjusted expected excess return (“Sharpe optimal”). We find that this mixture is almost exactly a 20% stock / 80% bond portfolio: a highly conservative mixture. However, this mix has an expected excess return of just 1.92%.

Source: J.P. Morgan. Calculations by Newfound Research.

However, if we are willing to apply 3.4-times leverage to this portfolio, so as to match the volatility profile of equities, the story changes. A levered maximum Sharpe ratio portfolio – 278% bonds and 66% stocks – would now offer an expected excess return of 6.6%: a full 2.2% higher than a 100% stock portfolio (again ignoring the spread charged above the risk-free rate in real world for accessing leverage).

What if an investor already has a 100% equity portfolio with significant capital gains? One answer would be to overlay the existing position with the exposure required to move the portfolio from its currently sub-optimal position to the optimal allocation. In this case, we could sell-short a 34% notional position in the S&P 500, use the proceeds to buy 34% in a core U.S. bond position, and then borrow to buy the remaining 244%. We would consider the -34% equity and +278% position in bonds our “portable beta.”

	Original Portfolio	Target Portfolio	Portable Beta
U.S. Equities	100%	66%	-34%
U.S. Aggregate Bonds	0%	278%	+278%

Portable Beta in Practice: Risk Cannot Be Destroyed, Only Transformed

In theory, the optimal decision is to lever a 20/80 stock/bond mix by 340%. In practice, however, volatility is not an all-encompassing risk metric. We know that moving from a portfolio dominated by equities to one dominated by bonds introduces significant sensitivity to interest rates. Furthermore, the introduction of leverage introduces borrowing costs and operational risks that are not insignificant.

Risk parity proponents would argue that this is actually a beneficial shift, creating a more diversified profile to different risk factors. In our example above, however, we can compare the results of a 100% stock portfolio to a 66% bond / 278% stock portfolio during the 1970s, when not only did interest rates climb precipitously, but the yield curve inverted (and remained inverted) on several occasions.

Source: Federal Reserve of St. Louis and Robert Shiller. Calculations by Newfound Research. Results are hypothetical and backtested. Past performance is not an indicator of future results. Returns assume the reinvestment of all dividends and income and are gross of all fees. The Levered 20/80 portfolio is comprised of a 66% position in U.S. equities and a 278% position in a 10-year constant maturity U.S. Treasury index and a -244% position in a constant maturity 1-year U.S. Treasury index. The period of 12/31/1969 to 12/31/1981 was used to capture an example period where interest rates rose precipitously.

While $1 invested on 12/31/1969 U.S. equities was worth $2.29 on 12/31/1981, the same dollar was worth only $0.87 in the levered portfolio. Of course, the outlook for stocks and bonds (including expected excess return, volatility, and correlation) was likely sufficiently different in 1969 that the Sharpe optimal portfolio may not have been a 20/80. Regardless, this highlights the significant gap between theory and practice. In modern portfolio theory, capital market assumptions are assumed to be known ex-ante and asset returns are assumed to be normally distributed, allowing correlation to fully capture the relationship between asset classes. In practice, capital market assumptions are a guess at best and empirical asset class returns exhibit fat-tails and non-linear relationships.

In this case in particular, an inverted yield curve can lead to negative expected excess returns for U.S. fixed income, correlation changes can lead to dramatic jumps in portfolio volatility, and the introduction of duration can lead to losses in a rising rate environment. Thus, a large, concentrated, and static portable beta position may not be prudent.

Traditional portfolio theory tells us that an asset should only be added to a portfolio (though, the quantity not specified) if its Sharpe ratio exceeds the Sharpe ratio of the existing portfolio times the correlation of that asset and the portfolio. We can use this rule to try to introduce a simple timing system to help manage risk.

When the trigger says to include bonds, we will invest in the Levered 20/80 portfolio; when the trigger says that bonds will be reductive, we will simply hold U.S. equities (labeled “Dynamic Levered 20/80” below). We can see the results below:

Source: Federal Reserve of St. Louis and Robert Shiller. Calculations by Newfound Research. Results are hypothetical and backtested. Past performance is not an indicator of future results. Returns assume the reinvestment of all dividends and income and are gross of all fees. The Levered 20/80 portfolio is comprised of a 66% position in U.S. equities, a 278% position in a 10-year constant maturity U.S. Treasury index and a -244% position in a constant maturity 1-year U.S. Treasury index. The period of 12/31/1969 to 12/31/1981 was used to capture an example period where interest rates rose precipitously.

Tactical timing, of course, introduces its own risks (including estimation risk, model risk, whipsaw risk, trading cost risk, reduced diversification risk, et cetera). Regardless, empirical evidence suggests that styles like value, momentum, and carry may have power in forecasting the level and slope of the yield curve.[7] That said, expanding the portable beta palette to include more asset classes (through explicit borrowing or derivatives contracts) may reduce the need for timing in preference of structural diversification. Again, risk parity argues for exactly this.

In practice, few investors may be comfortable with notional leverage exceeding hundreds of percentage points. Nevertheless, even introducing a modest amount of portable beta may have significant benefits, particularly for investors lacking in diversification.

For example, equity heavy investors may add little risk by introducing modest amounts of exposure to U.S. Treasuries. Doing so may allow them to harvest the term premium over time and potentially even benefit from flight-to-safety characteristics that may offset equity losses in a crisis. On a forward-looking basis (again, using J.P. Morgan’s 2018 capital market assumptions), we can see that using leverage to exposure to intermediate-term U.S. Treasuries is expected to both enhance return and reduce risk relative to a 100% equity portfolio.

Source: J.P. Morgan. Calculations by Newfound Research.

How would this more moderate approach have fared historically? Below we plot the returns of U.S. equities, a constant 100/50 portfolio (a 100% equity / 50% bond portfolio achieved using leverage), a dynamic 100/50 portfolio (100% equity portfolio that selectively adds a levered 50% bond position using the same timing rules discussed above).

	Ann. Return	Ann. Volatility	Max Drawdown
U.S. Equities	10.0%	15.7%	54.7%
100/50 Portfolio	10.7%	16.3%	51.1%
Dynamic 100/50 Portfolio	11.1%	15.8%	50.8%

Source: Federal Reserve of St. Louis and Robert Shiller. Calculations by Newfound Research. Results are hypothetical and backtested. Past performance is not an indicator of future results. Returns assume the reinvestment of all dividends and income and are gross of all fees. The Constant 100/50 portfolio is comprised of a 100% position in U.S. equities and a 50% position in a 10-year constant maturity U.S. Treasury index funded by a -50% position in a constant maturity 1-year U.S. Treasury index. The Dynamic 100/50 portfolio invests in either the U.S. Equity portfolio or the Constant 150/50 portfolio depending on the dynamic trade signal (see above). The period of 2/1962 to 10/2017 represents the full set of available data.

We can see that the Dynamic 100/50 strategy is able to add 110 bps in annualized return with only an added 10 bps in increased volatility, while reducing the maximum realized drawdown by 390 bps. Even naïve constant exposure to the Treasury position proved additive over the period. Indeed, by limiting exposure, the Constant 100/50 portfolio achieved a positive 95.7% total return during the 1969-1981 period versus the -13% return we saw earlier. While this still underperformed the 129.7% and 136.6% total returns achieved by U.S. equities and the Dynamic 100/50 portfolio respectively, it was able to add value compared to U.S. equities alone in 67% of years between 1981 and 2017. For comparison, the Dynamic 100/50 strategy only achieved a 60% hit rate.

Conclusion

We will be the first to admit that these ideas are neither novel nor unique. Indeed, the idea of portable beta is simply to take the theoretically inefficient exposure most investors hold and move it in the direction of a more theoretically optimal allocation through the prudent use of leverage. Of course, the gap between theory and practice is quite large, and defining exactly what the optimal target portfolio actually is can be quite complicated.

While the explicit concept of portable beta may be more palatable for institutions, we believe the concepts can, and should, find their way into packaged format. We believe investors can benefit from building blocks that enable the use of leverage and therefore allow for the construction of more risk- and capital-efficient portfolios. Indeed, some of these ideas already exist in the market today. For example:

Risk parity portfolios.
An alpha-generating fixed-income portfolio overlaid with equity futures.
The S&P 500 overlaid with a position in gold futures.
A strategic 60/40 allocation overlaid with a managed futures strategy.

We should consider, at the very least, how packed leverage applied to our traditional asset class exposures may allow us to free up capital to invest in other diversifying or alpha-seeking opportunities. The 100/50 portfolio discussed before is, effectively, a 66/34 portfolio levered 1.5 times. Thus, putting 2/3^rds of our capital in the 100/50 portfolio gives us nearly the same notional exposure as a 60/40, effectively freeing up 1/3^rd of our capital for other opportunities. (Indeed, with some mental accounting gymnastics, we can actually consider it to be the same as holding a 66/34 portfolio with 100% of our capital and using leverage to invest elsewhere.)

While “no derivatives, leverage, or shorting” may have been the post-2008 mantra for many firms, we believe the re-introduction of these concepts may allow investors to achieve much more risk-efficient investment portfolios.

[1] https://www.barrons.com/articles/whats-next-for-etfs-1510976833

[2] Scylla and Charybdis were monsters in Greek mythology. In The Odyssey, Odysseus was forced to sail through the Strait of Messina, where the two monsters presided on either side, posing an inescapable threat. To cross, one had to be confronted. The equivalent English seafaring phrase is, “Between a rock and a hard place.”

[3] https://blog.thinknewfound.com/2017/11/longshort-portfolios-all-the-way-down/

[4] https://en.wikipedia.org/wiki/Portable_alpha

[5] https://www.aqr.com/library/journal-articles/why-not–equities

[6] https://am.jpmorgan.com/us/institutional/our-thinking/2018-long-term-capital-market-assumptions

[7] See Duration Timing with Style Premia (Newfound 2017) and Yield Curve Premia (Brooks and Moskowitz 2017)

It’s Long/Short Portfolios All The Way Down

By Corey Hoffstein

On November 6, 2017

In Portfolio Construction, Weekly Commentary

There is a PDF version of this post available for download here.

Summary

Long/short portfolios are helpful tools for quantifying the value-add of portfolio changes, especially for active strategies.
In the context of fees, we can isolate the implicit fee of the manager’s active decisions (active share) relative to a benchmark and ask ourselves whether we think that hurdle is attainable.
Bar-belling low fee beta with high active share, higher fee managers may actually be cheaper to incorporate than those managers found in the middle of the road.
However, as long as investors still review their portfolios on an itemized basis, this approach runs the risk of introducing greater behavioral foibles than a more moderated – yet ultimately more expensive – approach.

After a lecture on cosmology and the structure of the solar system, William James was accosted by a little old lady.
“Your theory that the sun is the centre of the solar system, and the earth is a ball which rotates around it has a very convincing ring to it, Mr. James, but it’s wrong. I’ve got a better theory,” said the little old lady.
“And what is that, madam?” Inquired James politely.
“That we live on a crust of earth which is on the back of a giant turtle,”
Not wishing to demolish this absurd little theory by bringing to bear the masses of scientific evidence he had at his command, James decided to gently dissuade his opponent by making her see some of the inadequacies of her position.
“If your theory is correct, madam,” he asked, “what does this turtle stand on?”
“You’re a very clever man, Mr. James, and that’s a very good question,” replied the little old lady, “but I have an answer to it. And it is this: The first turtle stands on the back of a second, far larger, turtle, who stands directly under him.”
“But what does this second turtle stand on?” persisted James patiently.
To this the little old lady crowed triumphantly. “It’s no use, Mr. James – it’s turtles all the way down.”
— J. R. Ross, Constraints on Variables in Syntax 1967

The Importance of Long/Short Portfolios

Anybody who has read our commentaries for some time has likely found that we have a strong preference for simple models. Justin, for example, has a knack for turning just about everything into a conversation about coin flips and their associated probabilities. I, on the other hand, tend to lean towards more hand-waving, philosophical arguments (e.g. The Frustrating Law of Active Management[1] or that every strategy is comprised of a systematic and an idiosyncratic component[2]).

While not necessarily 100% accurate, the power of simplifying mental models is that it allows us to explore concepts to their – sometimes absurd – logical conclusion.

One such model that we use frequently is that the difference between any two portfolios can be expressed as a dollar-neutral long/short portfolio. For us, it’s long/short portfolios all the way down.

This may sound like philosophical gibberish, but let’s consider a simple example.

You currently hold Portfolio A, which is 100% invested in the S&P 500 Index. You are thinking about taking that money and investing it entirely into Portfolio B, which is 100% invested in the Barclay’s U.S. Aggregate Bond Index. How can you think through the implications of such a change?

One way of thinking through such changes is that recognizing that there is some transformation that takes us from Portfolio A to portfolio B, i.e. Portfolio A + X = Portfolio B.

We can simply solve for X by taking the difference between Portfolio B and Portfolio A. In this case, that difference would be a portfolio that is 100% long the Barclay’s U.S. Aggregate Bond Index and 100% short the S&P 500 Index.

Thus, instead of saying, “we’re going to hold Portfolio B,” we can simply say, “we’re going to continue to hold Portfolio A, but now overlay this dollar-neutral long/short portfolio.”

This may seem like an unnecessary complication at first, until we realize that any differences between Portfolio A and B are entirely captured by X. Focusing exclusively on the properties of X allows us to isolate and explore the impact of these changes on our portfolio and allows us to generalize to cases where we hold allocation to X that are different than 100%.

Re-Thinking Fees with Long/Short Portfolios

Perhaps most relevant, today, is the use of this framework in the context of fees.

To explore, let’s consider the topic in the form of an example. The iShares S&P 500 Value ETF (IVE) costs 0.18%, while the iShares S&P 500 ETF (IVV) is offered at 0.04%. Is it worth paying that extra 0.14%?

Or, put another way, does IVE stand a chance to make up the fee gap?

Using the long/short framework, one way of thinking about IVE is that IVE = IVV + X, where X is the long/short portfolio of active bets.

But are those active bets worth an extra 0.14%?

First, we have to ask, “how much of the 0.18% fee is actually going towards IVV and how much is going towards X?” We can answer this by using a concept called active share, which explicitly measures how much of IVE is made up of IVV and how much it is made up of X.

Active share can be easily explained with an example.[3] Consider having a portfolio that is 50% stocks and 50% bonds, and you want to transition it to a portfolio that is 60% stocks and 40% bonds.

In essence, your second portfolio is equal to your first plus a portfolio that is 10% long stocks and 10% short bonds. Or, equivalently, we can think of the second portfolio as equal to the first plus a 10% position in a portfolio that is 100% long stocks and 100% short bonds.

Through this second lens, that 10% number is our active share.

Returning to our main example, IVE has a reported active share of 42% against the S&P 500[4].

Hence, we can say that IVE = 100% IVV + 42% X. This also means that 0.14% of the 0.18% fee is associated with our active bets, X. (We calculate this as 0.18% – 0.04% x 100%.)

If we take 0.14% and divide it by 42%, we get the implicit fee that we are paying for our active bets. In this case, 0.333%.

So now we have to ask ourselves, “do we think that a long/short equity portfolio can return at least 0.333%?” We might want to dive more into exactly what that long/short portfolio looks like (i.e. what are the actual active bets being made by IVE versus IVV), but it does not seem so outrageous. It passes the sniff test.

What if IVE were actually 0.5% instead? Now we would say that 0.46% of the 0.5% is going towards our 42% position in X. And, therefore, the implicit amount we’re paying for X is actually 1.09%.

Am I confident that an equity long/short value portfolio can clear a hurdle of 1.09% with consistency? Much less so. Plus, the fee now eats a much more significant part of any active return generated. E.g. If we think the alpha from the pure long/short portfolio is 3%, now 1/3^rd of that is going towards fees.

With this framework in mind, it is no surprise active managers have historically struggled so greatly to beat their benchmarks. Consider that according to Morningstar[5], the dollar-weighted average fee paid to passive indexes was 0.25% in 2000, whereas it was 1% for active funds.

If we assume a very generous 50% active share for those active funds, we can use the same math as before to find that we were, in essence, paying a 2.00% fee for the active bets. That’s a high hurdle for anyone to overcome.

And the closet indexers? Let’s be generous and assume they had an active share of 20% (which, candidly, is probably high if we’re calling them closet indexers). This puts the implied fee at 4%! No wonder they struggled…

Today, the dollar weighted average expense ratio for passive funds is 0.17% and for active funds, it’s 0.75%. To have an implied active fee of less than 1%, active funds at that level will have to have an active share of at least 30%.[6]

Conclusion

As the ETF fee wars rage on, and the fees for standard benchmarks plummeting on a near-daily basis, the only way an active manager can continue to justify a high fee is with an exceptionally high active share.

We would argue that those managers caught in-between – with average fees and average active share – are those most at risk to be disintermediated. Most investors would actually be better off by splitting the exposure into cheaper beta solutions and more expensive, high active share solutions. Bar-belling low fee beta with high active share, higher fee managers may actually be cheaper to incorporate than those found the middle of the road.

The largest problem with this approach, in our minds, is behavioral. High active share should mean high tracking error, which means significant year-to-year deviation from a benchmark. So long as investors still review their portfolios on an itemized basis, this approach runs the risk of introducing greater behavioral foibles than a more moderated – yet ultimately more expensive – approach.

[1] https://blog.thinknewfound.com/2017/10/frustrating-law-active-management/

[2] https://twitter.com/choffstein/status/880207624540749824

[3] Perhaps it is “examples” all the way down.

[4] See https://tools.alphaarchitect.com

[5] https://corporate1.morningstar.com/ResearchLibrary/article/810041/us-fund-fee-study–average-fund-fees-paid-by-investors-continued-to-decline-in-2016/

[6] We are not saying that we need a high active share to predict outperformance (https://www.aqr.com/library/journal-articles/deactivating-active-share). Rather, a higher active share reduces the implicit fee we are paying for the active bets.

Category: Portfolio Construction Page 8 of 10

The New Glide Path

Summary­

Building a New Glidepath

The Results

Conclusion

Factor Investing & The Bets You Didn’t Mean to Make

Summary­­

Value Investing with Countries

The Unintended Bet

Conclusion

Levered ETFs for the Long Run?

Summary­­

Wait, Aren’t Levered ETFs Dangerous?

So, Are They Evil or What?

Levered ETFs in a Portfolio

A Particular Application with Alternatives

Conclusion

Portable Beta: Making the Most of the Returns You’re Already Getting

Summary­­

Efficiency Over Alpha

Portable Beta Theory

Portable Beta in Practice: Risk Cannot Be Destroyed, Only Transformed

Conclusion

It’s Long/Short Portfolios All The Way Down

Summary­­

The Importance of Long/Short Portfolios

Re-Thinking Fees with Long/Short Portfolios

Conclusion

Summary

Summary

Summary

Summary

Summary