The Research Library of Newfound Research

Tag: portable alpha

Is Managed Futures Value-able?

In Return StackingTM: Strategies for Overcoming a Low Return Environment, we advocated for the addition of managed futures to traditionally allocated portfolios.  We argued that managed futures’ low empirical correlation to both equities and bonds and its historically positive average returns makes it an attractive diversifier. More specifically, we recommended implementing managed futures as an overlay to a portfolio to avoid sacrificing exposure to core stocks and bonds.

The luxury of writing research is that we work in a “clean slate” environment.  In the real world, however, investors and allocators must contemplate changes in the context of their existing portfolios.  Investors rarely just hold pure beta exposure, and we must consider, therefore, not only how a managed futures overlay might interact with stocks and bonds, but also how it might interact with existing active tilts.

The most common portfolio tilt we see is towards value stocks (and, often, quality-screened value).  With this in mind, we want to briefly explore whether stacking managed futures remains attractive in the presence of an existing value tilt.

Diversifying Value

If we are already allocated to value, one of our first concerns might be whether an allocation to managed futures actually provides a diversifying return stream.  One of our primary arguments for including managed futures into a traditional stock/bond portfolio is its potential to hedge against inflationary pressures.  However, there are arguments that value stocks do much of the same, acting as “low duration” stocks compared to their growth peers.  For example, in 2022, the Russell 1000 Value outperformed the broader Russell 1000 by 1,145 basis points, offering a significant buoy during the throes of the largest bout of inflation volatility in recent history.

However, broader empirical evidence does not actually support the narrative that value hedges inflation (see, e.g., Baltussen, et al. (2022), Investing in Deflation, Inflation, and Stagflation Regimes) and we can see in Figure 1 that the long-term empirical correlations between managed futures and value is near-zero.

(Note that when we measure value in this piece, we will look at the returns of long-only value strategies minus the returns of broad equities to isolate the impact of the value tilt.  As we recently wrote, a long-only value tilt can be effectively thought as long exposure to the market plus a portfolio that is long the over-weight positions and short the under-weight positions1.  By subtracting the market return from long-only value, we isolate the returns of the active bets the tilt is actually taking.)

Figure 1: Excess Return Correlation

Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.

Correlations, however, do not tell us about the tails.  Therefore, we might also ask, “how have managed futures performed historically conditional upon value being in a drawdown?” As the past decade has shown, underperformance of value-oriented strategies relative to the broad market can make sticking to the strategy equally difficult.

Figure 2 shows the performance of the various value tilts as well as managed futures during periods when the value tilts realized a 10% or greater drawdown2.

Figure 2: Value Relative Drawdowns Greater than 10%

Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.

We can see that while managed futures may not have explicitly hedged the drawdown in value, its performance remained largely independent and accretive to the portfolio as a whole.

To drive the point of independence home, we can calculate the univariate regression coefficients between value implementations and managed futures.  We find that the relationship between the strategies is statistically insignificant in almost all cases. Figure 3 shows the results of such a regression.

Figure 3: Univariate Regression Coefficients

Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. *, **, and *** indicate statistical significance at the 0.05, 0.01, and 0.001 level. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.

But How Much?

As our previous figures demonstrate, managed futures has historically provided a positively diversifying benefit in relation to value; but how can we thoughtfully integrate an overlay into an portfolio that wants to retain an existing value tilt?

To find a robust solution to this question, we can employ simulation techniques.  Specifically, we block bootstrap 100,000 ten-year simulated returns from three-month blocks to find the robust information ratios and MAR ratios (CAGR divided by maximum drawdown) of the value-tilt strategies when paired with managed futures.

Figure 4 shows the information ratio frontier of these portfolios, and Figure 5 shows the MAR ratio frontiers.

Figure 4: Information Ratio Frontier

Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.

Figure 5: MAR Ratio Frontier

Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.

Under both metrics it becomes clear that a 100% tilt to either value or managed futures is not prudent. In fact, the optimal mix, as measured by either the Information Ratio or MAR Ratio, appears to be consistently around the 40/60 mark. Figure 6 shows the blends of value and managed futures that maximizes both metrics.

Figure 6: Max Information and MAR Ratios

Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.

In Figure 7 we plot the backtest of a 40% value / 60% managed futures portfolio for the different value implementations.

Figure 7: 40/60 Portfolios of Long/Short Value and Managed Futures

Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.

These numbers suggest that an investor who currently tilts their equity exposure towards value may be better off by only tilting a portion of their equity towards value and introducing a managed futures overlay onto their portfolio.  For example, if an investor has a 60% stock and 40% bond portfolio and the 60% stock exposure is currently all value, they might consider moving 36% of it into passive equity exposure and introducing a 36% managed futures overlay.

Depending on how averse a client is to tracking error, we can plot how the tracking error changes depending on the degree of portfolio tilt. Figure 8 shows the estimated tracking error when introducing varying allocations to the 40/60 value/managed futures overlay.

Figure 8: Relationship between Value/Managed Futures Tilt and Tracking Error

Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.

For example, if we wanted to implement a tilt to a quality value strategy, but wanted a maximum tracking error of 3%, the portfolio might add an approximate allocation of 46% to the 40/60 value/managed futures overlay.  In other words, 18% of their equity should be put into quality-value stocks and a 28% overlay to managed futures should be introduced.

Using the same example of a 60% equity / 40% bond portfolio as before, the 3% tracking error portfolio would hold 42% in passive equities, 18% in quality-value, 40% in bonds, and 28% in a managed futures overlay.

What About Other Factors?

At this point, it should be of no surprise that these results extend to the other popular equity factors. Figures 8 and 9 show the efficient information ratio and MAR ratio frontiers when we view portfolios tilted towards the Profitability, Momentum, Size, and Investment factors.

Figure 9: Information Ratio Frontier for Profitability, Momentum, Size, and Investment Tilts

Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions. 

Figure 10: MAR Ratio Frontier for Profitability, Momentum, Size, and Investment Tilts

Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.

Figure 11: Max Information and MAR Ratios for Profitability, Momentum, Size, and Investment Tilts

Source: Kenneth French Data Library, BarclayHedge. Calculations by Newfound Research. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.

Once again, a 40/60 split emerges as a surprisingly robust solution, suggesting that managed futures has historically offered a unique, diversifying return to all equity factors.

Conclusion

Our analysis highlights the considerations surrounding the use of managed futures as a complement to a traditional portfolio with a value tilt. While value investing remains justifiably popular in real-world portfolios, our findings indicate that managed futures can offer a diversifying return stream that complements such strategies. The potential for managed futures to act as a hedge against inflationary pressures, while also offering a diversifying exposure during relative value drawdowns, strengthens our advocacy for their inclusion through a return stackingTM framework.

Our examination of the correlation between managed futures and value reveals a near-zero relationship, suggesting that managed futures can provide distinct benefits beyond those offered by a value-oriented approach alone. Moreover, our analysis demonstrates that a more conservative tilt to value, coupled with managed futures, may be a prudent choice for inverse to tracking error. This combination offers the potential to navigate unfavorable market environments and potentially holds more of a portfolio benefit than a singular focus on value.

Appendix A: Index Definitions

Book to Market – Equal-Weighted HiBM Returns for U.S. Equities (Kenneth French Data Library)

Profitability – Equal-Weighted HiOP Returns for U.S. Equities (Kenneth French Data Library)

Momentum – Equal-Weighted Hi PRIOR Returns for U.S. Equities (Kenneth French Data Library)

Size – Equal-Weighted SIZE Lo 30 Returns for U.S. Equities (Kenneth French Data Library)

Investment – Equal-Weighted INV Lo 30 Returns for U.S. Equities (Kenneth French Data Library)

Earnings Yield – Equal-Weighted E/P Hi 10 Returns for U.S. Equities (Kenneth French Data Library)

Cash Flow Yield – Equal-Weighted CF/P Hi 10 Returns for U.S. Equities (Kenneth French Data Library)

Dividend Yield – Equal-Weighted D/P Hi 10 Returns for U.S. Equities (Kenneth French Data Library)

Quality Value – Equal-Weighted blend of BIG HiBM HiOP, ME2 BM4 OP3, ME2 BM3 OP3, and ME2 BM3 OP4 Returns for U.S. Equities (Kenneth French Data Library)

Value Blend – An equal-weighted Returns of Book to Market, Earnings Yield, Cash Flow Yield, and Dividend Yield returns for U.S. Equities (Kenneth French Data Library)

Passive Equities (Market, Mkt) – U.S. total equity market return data from Kenneth French Library.

Managed Futures – BTOP50 Index (BarclayHedge). The BTOP50 Index seeks to replicate the overall composition of the managed futures industry with regard to trading style and overall market exposure. The BTOP50 employs a top-down approach in selecting its constituents. The largest investable trading advisor programs, as measured by assets under management, are selected for inclusion in the BTOP50. In each calendar year the selected trading advisors represent, in aggregate, no less than 50% of the investable assets of the Barclay CTA Universe.

Portfolio Tilts versus Overlays: It’s Long/Short Portfolios All the Way Down

Several years ago, I started using the phrase, “It’s long/short portfolios all the way down.”  I think it’s clever.  Spoiler: it has not caught on.

The point I was trying to make is that the distance between any two portfolios can be measured as a long/short strategy.  This simple point, in my opinion, is a very powerful and flexible mental model for understanding portfolios.

If that sounds like gibberish, consider this practical example: you are a value investor who benchmarks to the S&P 500.  To implement your strategy, you buy the iShares MSCI USA Value ETF (“VLUE”).  If we subtract the weights of holdings in VLUE from the S&P 500, we can identify how much VLUE is over- or underweight any given position.

Figure 1. Relative Weight Differences Between VLUE and S&P 500 for the Top 20 Stocks in the S&P 500 by Weight
 Source: SSGA; iShares.  Calculations by Newfound Research.

Functionally, this is equivalent to saying, “VLUE is equal to the S&P 500 plus a long/short portfolio” where the longs are the overweights and the shorts are the underweights.

This is important for two reasons.  First, it helps us identify our implicit hurdle rate for alpha required to overcome the fee.

If we continue the exercise above for all the holdings of the S&P 500 and VLUE, we find that the longs and shorts both sum up to 86.2%1.  If we normalize the portfolio such that the longs and shorts both add up to 100%, we can say:

VLUE = 100% x S&P 500 + 86.2% x Long/Short

The positions in the long/short capture our active bets while the 86.2% here is our active share.  You may recall articles of years past about whether active share is predictive of alpha.  I believe it is clear, through this decomposition, that it is the active bets that control whether any alpha is generated.  Active share is key, however, in determining whether the strategy can overcome its fee.

For example, the current expense ratio for VLUE is 0.15% and the current expense ratio for the iShares Core S&P 500 ETF (“IVV”) is 0.03%.  Using the formula above, we can say:

0.15% = 0.03% + 86.2% x Fee of Long/Short

Doing some simple arithmetic, we find that the implicit fee of the long/short strategy is 0.139%.  This is the hurdle rate that the long/short portfolio must clear before it adds any excess return.

What if the active share was just 10% (i.e., the fund was a closet benchmarker)?  In that case, the hurdle rate would jump to 1.2%!  While active bets are responsible for generating alpha, the combination of a high fee and a low active share can lead to an unclearable hurdle rate.

The second reason I believe this concept is important is because it demystifies the idea of portfolio overlays.  Through the lens of long/short portfolios all the way down, everything is an overlay.  Buying value stocks?  Equity long/short overlay on broad equity market.  Rebalancing your portfolio?  Multi-asset long/short overlay on top of your prior asset allocation.

Consider the figure below, where I plot the equity curves of two strategies.  In the first, I buy the broad US equity market and overlay a 70% position in the classic Fama-French long/short value factor.2  In the second strategy is simply buying large-cap value stocks.

Figure 2. Equity Market plus Long/Short Value Overlay versus Value Stocks

Source: Kenneth French Data Library.  Calculations by Newfound Research.  Market is the Fama-French Market Factor.  Value Long/Short is the Fama-French HML Factor.  Value Stocks is the Fama-French BIG HiBM. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions. 

We can see how similar these two approaches are.  Buying value stocks is, effectively, buying the market and adding a big overlay of the long/short value factor.

There are some subtle, and important, differences.  For example, in tilting towards value stocks, the implicit short in any given stock is limited to that stock’s weight in the index (as the weight cannot go below zero).  In tilting towards value stocks, the size of the long/short overlay will also vary over time.3

Nevertheless, over the long run, on a log scale, drawn with a large enough crayon, and if we squint, we see a very similar picture.

This is all well and good on paper, but for many leverage-constrained investors, making room for an interesting equity long/short strategy means having to sell some existing exposure, giving the resulting cash to an alternative manager who holds onto it while implementing their strategy.  In the figure below, I plot two equity lines. In the first, we hold 80% in broad U.S. equities, 20% in cash4, and 20% in the classic Fama-French long/short value factor.  In the second, we buy large-cap value stocks.

Figure 3. Selling Stocks to Buy Alternatives Leads to a Beta Drag

Source: Kenneth French Data Library.  Calculations by Newfound Research.  Market is the Fama-French Market Factor.  Value Long/Short is the Fama-French HML Factor.  Value Stocks is the Fama-French BIG HiBM. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.

The terminal wealth results are not even close for two reasons.  First, as we saw in Figure 2, the appropriate overlay level is closer to 70%, not 20%.  Second, to make room for the long/short portfolio, we had to sell broad equity beta.  Which means the portfolio can really be thought of as:

100% U.S. Equity + 20% Long Cash / Short U.S. Equity + 20% Value Long/Short

Once again, it’s long/short portfolios all the way down.  That “long cash / short U.S. equity” component is a big drag over a 100-year period and captures what I like to call the “funding problem.”  As attractive as that value long/short may be, can it overcome the hurdle rate of what we had to sell to make room?

Part of the takeaway here is, “implicit leverage is good and may be hard to beat.”  The other takeaway, however, is, “there may be interesting things to invest in that may become more interesting if we can solve the funding problem.”

What are some of those things?  Ideally, we are adding things to a portfolio that have positive expected returns5 and also diversifying our existing holdings.  For most allocators, that means a portfolio of stocks and bonds.  An easy starting point, then, is to consider when stocks and bonds perform poorly and try to identify things that do well in those environments.

Following the methodology of Ilmanen, Maloney, and Ross (2017)6, we identify growth and inflation regimes using a composite of economic growth, inflation, and surprise factors.  Growth and inflation regimes are then combined to create four combined regimes: Growth Up / Inflation Down, Growth Up / Inflation Up, Growth Down / Inflation Down, and Growth Down / Inflation Up.

By design, each of these combined regimes occurs approximately 25% of the time throughout history.  We find that any given decade, however, can exhibit significant variation from the average.  For example, the 2000s were characterized by the Growth Down environment, whereas the 2010s were characterized by an Inflation Down environment.

Figure 4: Regime Classifications

Source: St. Louis Federal Reserve Economic Data; Federal Reserve of Philadelphia Survey of Professional Forecasters.  See Appendix B for regime definitions.

Using these regimes, we can evaluate how different asset classes, equity factors, and trading strategies have historically performed.  In Figures 5, 6, 7, and 8 we do precisely this, plotting the regime-conditional Sharpe ratios of various potential investments.

Note that due to data availability, each figure may cover a different time period.  The 60/40 portfolio is included in each graph as a reference point for that sub-period.

Figure 5: Sharpe Ratio of Equities, Bonds, and a 60/40 Portfolio in Different Economic Regimes (March 1962 to March 2023)

Source: Kenneth French Data Library; Tiingo; FRED; AQR; Bloomberg.  Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.  See Appendix B for economic regime definitions.

Figure 6: Sharpe Ratios of Equity Long/Shorts in Different Economic Regimes (March 1962 to December 2022)

Source: Kenneth French Data Library; Tiingo; FRED; AQR; Bloomberg.  Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.  See Appendix B for economic regime definitions.

Figure 7: Sharpe Ratios of Hedge Fund Categories in Different Economic Regimes (March 1998 to December 2022)

Source: Kenneth French Data Library; Tiingo; FRED; AQR; Bloomberg; HFRX.  Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.  See Appendix B for economic regime definitions.

Figure 8: Sharpe Ratios of Commodities and Managed Futures in Different Economic Regimes  (March 1985 – December 2022)

Source: Kenneth French Data Library; Tiingo; FRED; AQR; Bloomberg.  Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.  See Appendix B for economic regime definitions.

There are two standout takeaways:

  1. Stocks and bonds don’t do well during Growth Down / Inflation Up periods.7
  2. Other stuff does.

Specifically, we can see that Quality long/short and Managed Futures have historically been robust across regimes and have provided diversification during Growth Down / Inflation Up regimes.  Unfortunately, while the Quality long/short – or, at least, a proxy for it – can be achieved by tilting our long-only equity exposure, the same cannot be said for Managed Futures.

One question we might pose to ourselves is, “given the possible canvas of tilts and overlays, if we wanted to maximize the Sharpe ratio of our portfolio for a given active risk budget, what would we do?”  We can, at the very least, try to answer this question with the benefit of hindsight.

We’ll make a few assumptions:

  • Our strategic portfolio is 60% stocks and 40% bonds.
  • Our equity tilts can only be up to 60% of the portfolio (i.e., replace long-only equity one-for-one).
  • Our overlays can fill up the rest of the portfolio (i.e., we can replace any remaining long-only stock or bond exposure with capital efficient instruments – like futures or swaps – and allocate the available cash to fund the overlay strategy).

Using these rules, we can run an optimization8 maximizing the realized Sharpe ratio subject to a tracking error constraint.  The results are illustrated in Figure 9.  As the active risk budget increases, so does the allocation to tilts and overlays.  To understand the relative proportional exposure to each, normalized weights are presented in Figure 10.

Without emphasizing the specific allocations, the blue band represents the tilts while the orange, grey, green, purple bands represent the different overlay categories (long/short equity, hedge fund strategies, commodities, and managed futures, respectively).

This whole process uses the benefit of hindsight to measure both returns and covariances, so is by no means a prescriptive endeavor.  Nevertheless, I believe the results point in at least one clear direction: at all levels of active risk, the solution calls for a mix of tilts and overlays.

Figure 9: Maximizing the Realized Sharpe Ratio of a 60/40 Portfolio for a Given Active Risk Budget

Source: AQR Data Library; Kenneth French Data Library; HFRX.  Calculations by Newfound Research. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.

Figure 10: Normalized Portfolio Weights

Source: AQR Data Library; Kenneth French Data Library; HFRX.  Calculations by Newfound Research. Performance is backtested and hypothetical.  Performance is gross of all costs (including, but not limited to, advisor fees, manager fees, taxes, and transaction costs) unless explicitly stated otherwise.  Performance assumes the reinvestment of all dividends.  Past performance is not indicative of future results.  See Appendix A for index definitions.

For leverage-constrained allocators (e.g. many financial advisors), overlays have historically remained out of reach.  More flexible institutions were able to implement it through a process that became known as “portable alpha,” originally pioneered by PIMCO in the 1970s.  The implementation, on paper, is fairly simple:

  1. Replace passive beta exposure with a capital efficient derivative (e.g. futures or swaps) to free up capital.
  2. Allocate freed up capital to the desired alpha source.

Figure 11: Portable Alpha Example

The net portfolio construction, in effect, retains the beta and “ports” the alpha on as an overlay.

Historically, this required investors to manage a book of derivatives or hire a separate account manager.  Today, mutual funds and ETFs exist that provide pre-packaged capital efficiency.

Figure 12 demonstrates one such example where a 60/40 allocation is packaged into a capital efficient “90/60” fund, allowing an investor to utilize just 2/3rds of their capital to capture the same exposure.  Figure 13 demonstrates that when this freed up capital is allocated, it effectively “stacks” the exposure9 on top of the original 60/40 portfolio.  We have taken to calling this approach Return StackingTM.

Figure 12: Capital Efficient Funds

For illustrative purposes only.

Figure 13: Return StackingTM

For illustrative purposes only.

The other in figure 13 is where we can implement our alternative investment, effectively creating an overlay.  Ideally this is something that has positive expected returns and low correlation to both stocks and bonds.  We’re partial to managed futures for a variety of reasons, but allocators can pick their own adventure here.

Tilts and overlays are not mutually exclusive: it’s long/short portfolios all the way down.  While overlays remained out of reach for many leverage-constrained investors, new capital efficient mutual funds and ETFs enable their implementation.

 


Appendix A: Index Definitions

U.S. Stocks – U.S. total equity market return data from Kenneth French Library until 5/24/2001 when total returns returns from the Vanguard Total Stock Market ETF (VTI) are used.  Returns after 5/24/2021 are net of VTI’s underlying expense ratio.  Data for VTI provided by Tiingo.

10-Year U.S. Treasuries – The 10-Year U.S. Treasury index is a constant maturity index calculated by assuming that a 10-year bond is purchased at the beginning of every month and sold at the end of that month to purchase a new bond at par at the beginning of the next month. You cannot invest directly in an index, and unmanaged index returns do not reflect any fees, expenses or sales charges. The referenced index is shown for general market comparisons and is not meant to represent any Newfound index or strategy.  Data for 10-year U.S. Treasury yields come from the Federal Reserve of St. Louis economic database (“FRED”).

Value Tilt – BIG HiBM Returns for U.S. Equities (Kenneth French Data Library)

Size Tilt – ME LO 30 Returns for U.S. Equities (Kenneth French Data Library)

Momentum Tilt – BIG HiPRIOR Returns for U.S. Equities (Kenneth French Data Library)

Quality Tilt – 50% BIG LoINV + 50% BIG HiOP Returns for U.S. Equities (Kenneth French Data Library)

Low Beta Tilt – BIG LoBETA Returns for U.S. Equities (Kenneth French Data Library)

Value Long/Short – HML Devil Factor Returns for U.S. Equities (AQR Data Library)

Size Long/Short – SMB Factor Returns for U.S. Equities (Kenneth French Data Library)

Momentum Long/Short – UMD Factor Returns for U.S. Equities (Kenneth French Data Library) 

Quality Long/Short – QMJ Factor Returns for U.S. Equities (AQR Data Library)

Anti-Beta Long/Short – BAB Factor Returns for U.S. Equities (AQR Data Library)

HFRX Equity Long/Short –HFRX Equity Hedge Index (Hedge Fund Research, Inc.)

HFRX Event Driven – HFRX Event Driven Index (Hedge Fund Research, Inc.)

HFRX Macro/CTA – HFRX Macro/CTA Index (Hedge Fund Research, Inc.)

HFRX Relative Value – HFRX Relative Value Arbitrage Index (Hedge Fund Research, Inc.) 

Managed Futures – Time Series Momentum Factor (AQR Data Library). From inception to 2003, a 2% annual management fee and 3% annual estimated transaction cost are applied.  From 2003 to 2013, a 1.5% annual estimated transaction cost is applied.  From inception to 2013, a 20% annual performance fee is applied at the end of each year, so long as the end-of-year NAV exceeds the prior high-water mark.  From 2013 onward a 1.5% annual fee and 0.6% annual estimated transaction cost is applied.

Equal-Weight Commodities – Excess Return of Equal Weight Commodities Portfolio (AQR Data Library)


Appendix B: Regime Classifications

Growth and Inflation are each defined as a composite of two series, which are first normalized to z-scores by subtracting the full-sample historical mean and dividing by the full-sample historical volatility.

“Up” and “Down” regimes are defined as those times when measures are above or below their full sample median.

Growth:

  • Chicago Fed National Activity Index
  • Realized Industrial Production minus prior year Industrial Production forecast from the Survey of Professional Forecasters.

Inflation:

  • Year-over-year CPI change
  • Realized year-over-year CPI minus prior year NGDP forecast from the Survey of Professional Forecasters.

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

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. ReturnAnn. VolatilityMax Drawdown
S&P 5009.2%14.1%55.2%
60/40 Portfolio8.0%7.7%29.8%
Levered 60/40 Portfolio12.5%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 PortfolioTarget PortfolioPortable Beta
U.S. Equities100%66%-34%
U.S. Aggregate Bonds0%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. ReturnAnn. VolatilityMax Drawdown
U.S. Equities10.0%15.7%54.7%
100/50 Portfolio10.7%16.3%51.1%
Dynamic 100/50 Portfolio11.1%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 2/3rds of our capital in the 100/50 portfolio gives us nearly the same notional exposure as a 60/40, effectively freeing up 1/3rd 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)

 

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