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u/otto_delmar

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Shifting into and out of leverage with a simple macro signal
▲ 7 r/ETFs_Europe

Shifting into and out of leverage with a simple macro signal

EDIT: as per the comments from sajornet below, there is a major error in the article, and some of the conclusions drawn here don't have the support I thought they had, At the same time, there is one more aspect to check on as mentioned in my response to sajornet. Readers seeing this post for the first time may want to wait until that's done, I will update here again when that has happened. I apologize for my error.

*****
Hi all, while working on my "DIY hedge fund" portfolio policy, I investigated whether there is a safe way to use leverage. (This post is a substantially updated version of a previous post I made in /LETF. Although I use U.S. ETFs in this, the same logic applies to European investors using European leveraged ETFs/ETNs.)
 
According to various versions of the adventurous hedgefundie's strategy, a balanced portfolio of two or three 3x leveraged ETFs "only" drew down in the vicinity of max 70% over the available simulated history. But what has not happened yet might just happen in future. A couple of months where everything - equities, bonds, gold - drop hard together could push the drawdown into 80% territory. Maybe even worse. I wanted to see if I can add something of value here. Something that goes beyond relying on diversfication alone to keep things on the rails.

Headline result, equities-only: in a test running from May 1977 through June 2026, a macro-economic rule applied to simulated all-world equities produced a CAGR of 14% annual return, compared with 13% for continuous 2x exposure. Maximum drawdown fell from 87% to 60%. The rule changed allocations just 28 times in almost half a century. (I'm personally more interested in all-world equities than US-only but I did also test on SPY.)

TL;DR:

  • The rule holds 2x equities in a “Goldilocks” macro regime and ordinary, unlevered equities otherwise. This rule never moves equities to cash!
  • Across the tests described below, it produced higher Sharpe ratios than both permanent 2x exposure and a 10-month moving-average rule evaluated monthly using the same 1x/2x choices.
  • Returns were broadly competitive with both alternatives. Goldilocks had smaller maximum drawdowns and required significantly fewer allocation changes than the moving-average rule.
  • The signal was developed for another purpose. None of its parameters were chosen to fit leveraged-equity returns.

My portfolio policy has additional risk filters that directly address recessionary bear markets and other shocks, separate from the macro signal modulating leverage. Those other risk filters do move equities to cash and other defensive assets on rare occasions.

Where this came from

The Macro Regime Overlay ("MRO") originally had a much bigger job in my project. I wanted it to adjust the weights of the different sleeves inside the portfolio according to the prevailing macro conditions. So, shift weights among value, momentum, ballast, bitcoin, and other allocations. A sleeve’s percentage of total NAV would rise or fall depending on what the overlay signalled was happening in the economy.

The whole mechanism became far too involved for my taste. Lots of moving parts, lots of trading, not sufficiently impressive gains. So, I abandoned that approach.

Then, later, I wondered if maybe I could give the overlay a smaller job. Might it signal when to use leverage on an equity allocation that I already wanted to hold?

As mentioned, the overlay was never designed with leveraged investing in mind. No observations of leveraged returns went into its design. I didn’t optimize it for its original purpose, and I haven’t optimized it for this one. The thresholds, smoothing and confirmation rules have never changed since inception.

The backtests aren't out-of-sample but there is no curve-fitting either.

What does the overlay actually measure?

The MRO looks at economic activity, inflation and the yield curve. It sorts conditions into Goldilocks, Reflation, Stagflation and Slow Growth, with a further distinction for yield curve stress.

For the leverage decision, only Goldilocks matters. It requires two things:

Economic activity is at or above its recent historical average.

Inflation is neither too cold nor too hot.

Activity is measured using the Chicago Fed National Activity Index, smoothed over three months and compared with its own trailing ten-year history. I use a rolling z-score, with zero as the cutoff.

Inflation is the year-on-year change in core US PCE, also smoothed over three months. Between 1% and 3%, inclusive, is the acceptable band. I use headline CPI as fallback if core PCE is unavailable, with a slightly higher band of 1.4% to 3.4%.

The US yield curve helps distinguish the other regimes, but doesn't veto Goldilocks. So despite the broader overlay’s name and ambitions, the allocation decision here comes down to economic activity and inflation.

The signal is evaluated monthly, using prior-month (or older) observations. Two consecutive monthly signals must agree before the allocation changes, in either direction. So yes, I used US macro signals and applied them to world equities! I also backtested SPY/SSO the same way, for another reference point.

If anyone wants the details for the MRO, just ask, I'll post them in the comments.

What the backtests show

I used simulated VT history from testfol.io. For SPY, I used Yahoo adjusted prices after inception and an S&P 500 history reconstructed with Shiller dividend data beforehand. For each, there are four portfolios: ordinary buy-and-hold, continuous 2x, Goldilocks switching between 1x and 2x, and a 10-month SMA evaluated monthly, using the base asset’s total-return index and also switching between 1x and 2x leverage.

All returns are nominal, in USD, with dividends reinvested. The 2x series are daily-reset simulations throughout, with borrowing costs linked to the federal funds rate and an additional 1% annual implementation cost while levered. (Note that 1% TER is higher than what SSO and WLDU charge today.) The results include those financing and implementation costs, but exclude trading costs and taxes. Signals trade at the following session’s close. There are no contributions or withdrawals.

First, the full period. May 1977 is where the growth signal has accumulated enough history for its ten-year calculation. Maximum drawdowns below are measured daily.

May 1977–June 2026

Asset Strategy CAGR Sharpe Max DD
SPY Buy & hold 1x 12.02% 0.522 -55.19%
SPY Buy & hold 2x 14.86% 0.461 -88.23%
SPY Goldilocks 1x/2x 15.86% 0.598 -57.27%
SPY SMA 10-month 1x/2x 15.45% 0.517 -59.20%
VT Buy & hold 1x 10.86% 0.450 -58.35%
VT Buy & hold 2x 12.87% 0.403 -86.80%
VT Goldilocks 1x/2x 14.01% 0.510 -59.61%
VT SMA 10-month 1x/2x 14.19% 0.473 -67.63%

The permanent 2x portfolios beat ordinary equities on return, but the drawdowns are brutal. Goldilocks produces a higher return than either permanent 2x portfolio, with a worst loss close to ordinary equities. (Remember, we don't shift from equities to cash. We shift from levered equities to unlevered.)

Against the SMA, it's a toss-up for returns alone. But Goldilocks has the higher Sharpe and smaller drawdown in both cases.

And importantly, Goldilocks has less than half the number of trades of the SMA approach .

Full-period allocation changes Goldilocks SMA 10-month
SPY 28 73
VT 28 66

(Each change means selling one holding and buying the other. For VT, that’s 56 individual orders for Goldilocks versus 132 for the SMA, excluding opening and closing the test portfolio.)

Goldilocks held 2x for about 39% of trading days over the full period. The VT SMA held it for about 78%. Getting broadly comparable returns with much less time at 2x is something I find particularly interesting.

What about revised macro data?

There is an obvious objection to a macro backtest. Economic data arrives late and gets revised. Using the final series can give a historical strategy information it couldn’t have had at the time.

The long test above uses revised history. To check this, I also ran a 2001 onward test using archived Chicago Fed releases and ALFRED inflation vintages. For the more recent period, we can reconstruct the signal using archived releases rather than retrospectively revised history. That gives us greater confidence that the inputs reflect what an investor could actually have known at the time. The signal is reconstructed from what was available at each decision date, rather than the final numbers we see now. The first three months default to 1x because the usable CFNAI archive starts later; April is the first month with the required inputs.

Here is that higher-confidence period.

January 2001–June 2026

Asset Strategy CAGR Sharpe Max DD
SPY Buy & hold 1x 8.99% 0.523 -55.19%
SPY Buy & hold 2x 11.27% 0.446 -84.38%
SPY Goldilocks 1x/2x 15.45% 0.696 -55.19%
SPY SMA 10-month 1x/2x 13.15% 0.571 -59.20%
VT Buy & hold 1x 7.97% 0.445 -58.35%
VT Buy & hold 2x 9.10% 0.381 -86.80%
VT Goldilocks 1x/2x 13.43% 0.592 -58.35%
VT SMA 10-month 1x/2x 11.44% 0.494 -67.63%

 The result survives. Goldilocks beats both alternatives on returns and Sharpe for both markets. Its worst drawdown equals ordinary buy-and-hold because it was unlevered throughout the decisive peak-to-trough decline.

Here is the earlier segment on its own, using revised macro history. This helps show whether the result is merely a feature of the more recent market.

May 1977–December 2000

Asset Strategy CAGR Sharpe Max DD
SPY Buy & hold 1x 15.38% 0.521 -32.96%
SPY Buy & hold 2x 18.85% 0.477 -60.56%
SPY Goldilocks 1x/2x 18.56% 0.588 -32.96%
SPY SMA 10-month 1x/2x 17.99% 0.470 -58.95%
VT Buy & hold 1x 14.06% 0.455 -28.62%
VT Buy & hold 2x 17.08% 0.431 -57.37%
VT Goldilocks 1x/2x 16.83% 0.517 -29.04%
VT SMA 10-month 1x/2x 17.24% 0.452 -44.03%

 It's essentially all a toss-up for returns. Goldilocks has substantially smaller drawdowns than the two other strategies using leverage. It also beats the SMA on Sharpe.

Across all six asset/period comparisons, Goldilocks has the highest Sharpe of the four portfolios. It beats the SMA’s return in four of six and has the (significantly) smaller maximum drawdown in all six. (The periods overlap, of course; these aren’t six independent discoveries.)

Other Questions and Objections

The headline comparison raises three reasonable questions: whether the benefit comes simply from using less leverage, whether avoiding the 1987 crash was fortunate timing, and whether a few major bear markets explain most of the result. My thoughts on each of these below.

Does the strategy merely use less leverage?

Goldilocks was levered for about 39% of trading days over the full sample, giving it mean exposure of about 1.391x. Some reduction in drawdown is therefore to be expected simply because its average leverage was lower. A fairer test compares Goldilocks with constant leverage matched separately to its average exposure in each period.

In that comparison, Goldilocks had higher CAGR and Sharpe and smaller maximum drawdown for both assets in all three periods. The results below report Goldilocks first and matched constant exposure second in each pair.

Period Asset Mean exposure CAGR Sharpe Max DD
1977–2000 SPY 1.267x 18.56% / 16.61% 0.588 / 0.504 −32.96% / −41.05%
1977–2000 VT 1.267x 16.83% / 15.07% 0.517 / 0.444 −29.04% / −36.29%
1977–2026 SPY 1.391x 15.86% / 13.56% 0.598 / 0.492 −57.27% / −69.58%
1977–2026 VT 1.391x 14.01% / 12.03% 0.510 / 0.425 −59.61% / −72.67%
2001–2026 SPY 1.472x 15.45% / 10.57% 0.696 / 0.480 −55.19% / −72.02%
2001–2026 VT 1.472x 13.43% / 9.01% 0.592 / 0.409 −58.35% / −75.04%

 These results suggest that the timing of leverage added value beyond simply lowering average exposure.

Was avoiding the 1987 crash just luck?

Maximum drawdown is particularly sensitive to a single extreme event. A strategy that happens to be unlevered during that event can look much safer than one that remains levered. This makes the treatment of Black Monday a meaningful robustness question. One way to stress-test it is to force the strategy to remain at 2x through the crash, even though its actual signal called for 1x, and compare the result with the original backtest.

Forcing Goldilocks to remain levered during the 1987 crash reduced full-period VT CAGR from about 14.0% to 13.5%. Quite the dent over so many years. But this also shows that the reported return advantage was not entirely dependent on avoiding that one crash.

More importantly, Goldilocks was unlevered in 1987 because inflation had risen outside its acceptable band. In other words, arguably, being out of leverage then was not luck at all. The MRO had a reason to be out of leverage that may have been a necessary condition for Black Monday. Inflation was a big part of the narrative that led up to Black Monday. Without it, Black Monday may never have happened. I don't know that for a fact but it is plausible.

The broader objection remains valid: an event resembling 1987 could occur while the macro signal still says Goldilocks. Monthly data, smoothing and a two-month confirmation rule cannot provide reliable warning of every sudden shock. The MRO is a rule for modulating leverage, not a comprehensive crash-protection mechanism. In my wider portfolio policy, diversification into assets with low or no correlation and a standing tail hedge address risks that can arise outside the macro signal’s scope.

Is the advantage concentrated in the dotcom and 2008 bears?

A rule can appear broadly successful while deriving most of its advantage from just a few episodes. To investigate this, I examined the full-period cumulative log-return advantage over constant leverage matched to Goldilocks’ average exposure. Approximately 57% of SPY’s advantage and 52% of VT’s advantage came from outside the dotcom and 2008 bear markets. After excluding October 1987 as well, approximately 44% for SPY and 43% for VT came from all other periods combined.

Those percentages concern the contribution to cumulative log-return advantage. They are not the share of drawdown reduction, and they're not the fraction of years or trades that were successful. They indicate that the return benefit extended beyond the three crash episodes, while also showing that those episodes contributed materially to the overall result.

What happens when both rules are forced to bear the same crash risk?

A further stress test forced both Goldilocks and the moving-average strategy to remain levered through 1987, the dotcom bear market and the 2008 financial crisis. This deliberately overrides the signals in their most consequential periods. It asks how the two approaches compare when their ability to reduce exposure during those episodes is removed.

My follow-up reported full-period VT figures of 19.08% CAGR, 0.728 Sharpe and −44.58% maximum drawdown for MRO, against 20.99%, 0.718 and −44.03% for SMA. On those figures, SMA had the higher return and slightly smaller drawdown, while MRO retained a slightly higher Sharpe.

My preference for MRO rests partly on its economic rationale: it adjusts leverage in response to activity and inflation, whereas the SMA responds to prices. Critically, the MRO can act where price trends have not yet weakened. I can imagine scenarios where that would happen. Because I have other risk filters in my portfolio policy as well that react, in part, to price action, the MRO adds specific value.  

Would I use it?

My conclusion is: the MRO’s Goldilocks signal is the most useful signal I have found so far for deciding when to add leverage. It produces competitive returns, a better Sharpe and less than half the allocation changes required by the SMA.

Over these tested histories, it would have avoided the most catastrophic losses associated with continuously holding 2x exposure. In the full-period tests, the worst drawdowns fell from roughly 87–88% to roughly 57–60%. I would rather face the latter.

So, yeah, I would and will use it.

How I put this together

I used LLMs for the legwork: writing the backtest code, pulling and processing the data, producing the comparisons, etc.. I supplied the original macro rule, the research questions and the portfolio definitions. The final comparisons were standardized to use the same return data, costs and execution assumptions. I read and examined everything several times over and found a few major errors that I had corrected. I also had alternate LLMs double-check. Still, it's possible that there are mistakes in the backtests. I wrote the article myself! This is not "AI-generated". If you see em dashes, that's because Word converted them from regular dashes!

If there is interest, I can upload the data sets and code to GitLab so that anyone can examine and replicate them.

*****

I will be publishing and tracking my DIY hedge fund experiment at www.hedgefol.io.

u/otto_delmar — 11 days ago
▲ 1 r/portfolios

A different way to diversify: Managed Futures

As part of a DIY hedge fund concept I'm working on, I recently wrote a position paper on Managed Futures. Thought I'd share this here for anyone who'd like a systematic introduction to this topic. I've adapted the paper for sort of post. I don't talk to myself like this! Writing articles like this is my way of making sure I'm not being sloppy in my reasoning for my own investing. I hope it's interesting to some of you. Feedback is encouraged and appreciated.

Managed Futures funds hold positions across many different assets, not just equities. Because of this, they have low or negative correlation to equities. This type of fund is fairly new in the ETF form but has longer track record in the mutual funds form. The oldest managed futures ETF launched in 2019.
  
First, a little attention booster so you all don't nod off when we get into the details. Take a look. The red curve is 100% All-World Equities (VT), the blue curve is All-World 60% + Managed Futures 40%, rebalanced once annually. This is the result from a test that runs from 1988 through mid-August 2026. It's already inflation-adjusted. Which one would you rather have? (By the way, this article isn't meant as advocacy for any particular equities index, I use VT as the agnostic proxy for the asset class but you can mentally swap in any other index you prefer, or any combination of equities ETFs.)

https://preview.redd.it/dkp7gu6363lh1.png?width=2473&format=png&auto=webp&s=a703a4dcf43058d60bcc0a58161ffff9bf278434

TL;DR:

  • "VT and chill" can be a reasonable approach, especially for younger investors. However,  it only diversifies across equities. It doesn't diversify across other asset classes. This can be problematic in some scenarios that may - and almost certainly will - happen at some point in your life.
  • Managed Futures make a strong case as a more robust form of ballast (asset diversification) than the government bonds traditionally used in long-term, balanced portfolios.
  • Across the backtests presented here, portfolios using managed futures produced higher real returns and better risk-adjusted results than comparable portfolios using bonds. 
  • The outperformance came outside of crises, while during crises, managed futures performed about the same as bonds.

What is ballast expected to do?

Most investors want the ballast portion of their portfolio to provide a counterweight to equity crashes. They put a portion of their portfolio (typically anywhere between 20% and 50%) in government bonds. When there's an equities crash, their portfolio is relatively stable and resilient because bonds may fall less than equities or even rise, moving in the opposite direction of stocks. This can be psychologically advantageous vs sitting through the full brunt of a 2008 type of horror show. For an older investor who is only one or two decades out from retirement, it can make the difference between "comfortable" and "hurting".

The recovery in 2009 and the long equities bull run that followed was lucky and historically abnormal. It may teach the wrong lesson. The scenario to be concerned about is a crash and then a protracted stagflationary scenario. Imagine seeing your $100k brokerage account crash to $40k, and then not recover for a decade. This sort of experience will be stressful for anyone. Staying the course, continuing to put money into equities - even if that is your most rational bet - may be difficult.

In a balanced portfolio, ballast can cushion the blow from an equities drawdown, and even provide growth when equities don't. Some portion of the funds preserved as ballast can also be converted into equities when those are cheap, setting the foundation for strong future growth. A sporadic rebalancing would achieve this automatically.

But are bonds really the best option for ballast?
  
  When inflation causes the trouble, bonds can fall with stocks. The same can happen when real interest rates rise, government borrowing pushes up the term premium, or markets lose confidence in fiscal and monetary policy. The 2022 joint stock-bond decline provides a recent nominal example. The 1970s provide a longer example of inflation damaging the real returns of both assets.

So what now? 

A different form of ballast

Managed Futures funds spread their investments across several asset classes, including  government bonds, commodities, currencies, and also stocks. Most of these funds use trend-following models that look for sustained price movements. If an asset has been rising, the fund may take a long position and benefit from further gains. If it has been falling, the fund may take a short position and benefit if the decline continues. This gives managed futures a source of return that does not depend on stocks going up. The academic literature on trend-following offers strong evidence across many markets and long samples. It is one of the most thoroughly documented sources of alpha.

Managed Futures funds gain their exposure through futures, which are standardized contracts traded on regulated exchanges. So, rather than holding the actual assets, these funds hold contracts that are tied to the assets and therefore reflect the prices of assets, too. This makes it possible to move between markets and take both long and short positions without buying each asset itself. The fund handles the contracts, collateral and risk controls, while the investor owns an ordinary fund or ETF. The trading inside such funds happens algorithmically, and doesn't normally involve human discretion.

Futures contracts have existed for a long time. The first futures exchange in history was launched for rice contracts in 18th century Japan. The Chicago Board of Trade became the first futures exchange in the West in the 19th century. Futures have since become a pillar of commodities and financial markets.

Managed Futures investing strategies are called "managed" because a professional manager oversees the trading of potentially dozens of different futures contracts and the risks that are involved in that. It would be very difficult for a retail investor to do this by themselves.

Managed Futures became available to institutional investors in the 1980s. In the 2010s, some US mutual funds made them accessible to small retail investors. In the 2020s, several ETFs have become available, primarily in the US, but also in Europe.

As I will document below, Managed Futures produce greater returns than bonds over long time horizons and are a more robust form of diversifcation.
  
 Fair warning though: trend-following strategies can suffer when markets move back and forth without direction. They can enter a sudden crash when new trends establish. Their positions may detect the  change in trend too late, and they may be stuck in a losing position for some time. It is precisely this risk why such funds trade across many different assets at the same time. That reduces the risk of a major fund-wide crash.

While not an iron-clad guarantee for a greatly improved return during an equities crash, the multi-asset, long/short nature of managed futures means that fully participating in an equities crash is far less likely than with simple long equities exposure. In comparison to a straight bond fund, managed futures also have a much broader opportunity set.

Consider the below major economic scenarios and how bonds and managed futures behave in each.

Market Condition Gov't Bonds Managed Futures
Fast recessionary crash Strong if yields fall (they usually do) Unlikely to fall at the same rate as equities and may even rise
Slow equity bear market Strong if yields fall Can adapt as trends form and make money
Inflation and rising rates Typically poor protection Can profit from shorting bonds and from commodity trends
Stagflation Typically poor protection Broad opportunity set
Deflationary recession Strong Can adapt as trends form and make money, including from long bonds positions
Fiscal or monetary credibility shock May fall together with stocks Broad opportunity set

 

OK, but how well have such strategies performed in practice?

What the backtests show

All backtests were performed on testfol.io which provides simulated or reconstructed data for many funds prior to their inception dates. They're based on the index data that underlie the funds, or on roughly equivalent predecessor mutual fund data. Typically, such data series include the fees that the actual ETFs charge. To be clear: every backtest here relies on backfilled data. So these tests are based on reasonable approximations, not on live ETF data.

All backtests in the article start with an initial investment of US$10,000 and have no further contributions; portfolios are rebalanced annually; returns are inflation-adjusted. A 60/40 split between equities and ballast is generally considered a cautious allocation, which is why I go with that.

These backtests are illustrative examples, not authoritative evidence, or a prediction of how these indexes and funds perform in future. The point here is to conceptually understand their effect in concert with equities.

The first test runs from 2000 to mid-August 2026, and uses a simulated history for the US ETF "DBMF" as the managed futures portion. It begins in 2000 because that's when the data series available begins. I don't choose any particular dates here, I just run the test across the entire available history. It includes the dotcom drawdown, the 2008 disaster, and the Covid blurb. It also contains both bond bulls and bears, and managed-futures bulls and bears.

Here's the backtest results from testfol.io, Please remember that this is inflation-adjusted.

Name Ending Value Cumulative Return CAGR Max DD Longest DD Volatility Sharpe
VT 60/ Bonds 40 $25,993 159.93% 3.65% -38.64% 4.69y 11.58% 0.43
VT 60/ DBMF 40 $36,508 265.08% 4.99% -36.37% 3.75y 12.23% 0.51
VT 100 $32,229 222.29% 4.50% -59.02% 6.16y 19.19% 0.36

Reference: https://testfol.io/?s=h0XhhWtftU1

As you can see, the All-World Equities/Managed Futures combo beats not only the All-World/Bonds portfolio but even the 100% All-World Equities one. And look at the maximum drawdown! Look at the Sharpe ratio! And note how bonds provided similar drawdown protection to managed futures but significantly smaller returns!

Onward!

DBMF is my preferred Managed Futures proxy for backtesting purposes. It reverse-engineers the aggregate return stream of the SG CTA Index that tracks twenty different managers. This disperses manager and model luck. However, KMLM - a single-index fund - has the longer simulated history. It's a reasonable substitute for DBMF for testing purposes.
  
 Let's test it over its entire history, all the way from 1988 to current. Again, returns are inflation-adjusted. Since 1988, the 60/40 All-World + KMLM portfolio had the highest real CAGR at ~6%, compared with 4.5% for All-World + Bonds.

This is the longest test in the review, starting in 1988, running to the current date. It's what the image I shared at the beginning of the article shows.

Name Ending Value Cumulative Return CAGR Max DD Longest DD Vol Sharpe
VT 60/ Bonds 40 $63,413 534.13% 4.90% -38.85% 4.87y 10.87% 0.48
VT 60/ KMLM 40 $102,268 922.68% 6.21% -32.18% 3.56y 11.02% 0.58
VT 100 $93,138 831.38% 5.95% -59.02% 6.16y 17.78% 0.40

Reference: https://testfol.io/?s=cnl63cDBWjH

As you can see, over this longer period, too, and swapping one managed futures fund for another, managed futures beat bonds hands-down, and even perform somewhat better than "VT and chill".

This isn't evidence that a equities/managed futures combo will always beat 100% equities. Sequence of returns and timing luck play a big role. But for our purposes here, we shouldn't be interested in the best case scenario for equities. We should be interested in the worst-case and middling scenarios.

Next, let's zoom in on the effect of managed futures during equities crashes.

First, the dotcom drawdown, January 2000 through December 2002.

Name Ending Value Cumulative Return CAGR Max DD Longest DD Vol Sharpe
VT 100 $5,702 -42.98% -17.11% -50.97% 2.77y 19.36% -0.94
VT 60/ Bonds 40 $7,890 -21.10% -7.61% -28.35% 2.77y 11.18% -0.76
VT 60/ DBMF 40 $7,974 -20.26% -7.29% -26.40% 2.77y 10.72% -0.77

Reference: https://testfol.io/?s=bCBtVwkt1n1

Toss-up. DBMF and bonds did about equally well. Equities suffered horrendously.

Now the Lehman shock-induced crash. January through December 2008.

Name Ending Value Cumulative Return CAGR Max DD Longest DD Vol Sharpe
VT 100 $6,016 -39.84% -39.94% -52.56% 1.00y 42.78% -1.00
VT 60/ Bonds 40 $7,715 -22.85% -22.92% -35.20% 0.99y 25.83% -0.93
VT 60/ DBMF 40 $7,914 -20.86% -20.93% -32.18% 0.63y 23.91% -0.91

Reference: https://testfol.io/?s=8gTwoRSKlZ6

Again, same picture and basically a toss-up.

Looking at these two tests and comparing them with the earlier DBMF test, you can see that the equities + managed-futures combo outperforms the equities + bonds combo on returns over long time horizons mostly because of better returns outside crises. 

Some objections

Let me address some objections a critical reader might raise.

First, let's talk about the worst periods for Managed Futures. Since 2000, both KMLM and DBMF had weak periods between 2004 and 2008, between 2009 and 2014, and between 2016 and 2022. KMLM has also shown weakness since 2023. Of course, all of these were periods of strength for equities, so overall portfolio performance was strong, even as Managed Futures slacked off. That's what one would expect. In periods of strong equities performance, returns from trend-following in other asset classes should tend to weaken as more and more capital flows into equities. When equities weaken, as happened in 2022, managed futures should perk up. Historically that has proven to be true. This is also why sporadic rebalancing is required to fully benefit from combining the two.

Next objection – survivorship bias. This is a valid concern when managed-futures performance is inferred from databases of surviving CTA funds. It's a weaker objection here. KMLM’s historical series comes from applying a fixed trend index to futures prices, not from selecting surviving managers. DBMF’s simulated history may inherit selection biases from the SG CTA Index and adds replication-model risk, but its live ETF returns since 2019 are actual investor returns. So, as time goes by, the objection becomes weaker and weaker. The last few years have already been quite instructive.

Next up, eroding returns. The idea is that, as trend-following and managed futures find more widespread adoption, they may see lower returns and longer periods of mediocre performance. Basically, that they become a "crowded trade". So far, this doesn't seem to be an issue. These funds still have relatively small AUM, and I can see no sign that their performance has been eroding. Wider adoption will probably compress returns at the margin, especially for common signals in smaller markets, but it is more likely to reduce trend’s Sharpe ratio than destroy the strategy. Diversifying across two or three different managed futures funds may also help with any erosion.

And finally, high total expense ratio. DBMF's TER, e.g. Is 0.85%. This is high compared with a passive equities ETF due to the more active trading going on in the managed futures fund. The expense comparison should therefore be with other managed futures funds. There are cheaper ones but they achieve this by greatly simplifying their strategies. To me, the expense for DBMF seems justified by the diversification and net returns the strategy delivers. All fund returns in my backtests are already net of TER, although backfilled data should, of course, be taken with a grain of salt. But since DBMF has been live since 2019 so for those seven years, anyone can directly evaluate whether the expense was worth it.

Should managed futures replace bonds in balanced portfolios?

My conclusion is: for the long-term ballast role in a general investment portfolio, Managed Futures is the more robust choice than bonds. Managed Futures cover a greater variety of adverse scenarios, and have more tools at their disposal to react to them.

Nothing is to stop a cautious investor from combining Managed Futures with bonds (and/or gold) in their portfolio.

Another option is to hold managed futures and gold as the standing ballast, then add bonds when some kind of macro-economic rule identifies a disinflationary regime. Such a rule could use inflation expectations, government bond price trends, growth data, and central-bank policy. It may make sense to fund that position by reducing equities rather than dismantling the managed futures sleeve.
  
 Investors today can consider themselves fortunate that they have access to a form of ballast that was largely unavailable even a decade ago.

*****

By the way, I'm working on an "open-source" DIY hedge fund concept for retail investors. I will be publishing and tracking that experiment publicly at www.hedgefol.io. Investors looking for ways to improve returns over static allocations while managing risks actively may find this interesting. It's free of charge! The original position paper that this post is based on was written as part of the hedge fund project.

 

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u/otto_delmar — 2 months ago
▲ 46 r/ETFs_Europe

A different way to diversify: Managed Futures

I got a fairly negative reaction to a comment in this subreddit the other day suggesting Managed Futures as a way to diversify an all-world equities portfolio. Let's see if I can do better with a more detailed explanation.

Since I recently wrote a position paper on Managed Futures for myself, I thought I'd share this here. Writing articles like this is my way of making sure I'm not being sloppy in my reasoning for my own investing. The following is an adapted version of my paper specifically for this subreddit. I hope it's interesting to some of you.

Managed Futures funds hold positions across many different assets, not just equities. Because of this, they have low or negative correlation to equities. This type of fund is fairly new in the ETF form (it has a longer track record in the ordinary investment funds - or mutual funds - form) but has gained significant popularity in the US. In Europe, it's still largely ignored. There is an excellent Managed Futures UCITS ETF available, though, so there is no practical reason why European retail investors couldn't take advantage of this.
 
Let me give you a little attention booster so you don't nod off when we get into the details. Take a look. The red curve is "All-World and Chill", the blue curve is "All-World + Managed Futures", rebalanced once annually. This is the result from a test that runs from 1988 through mid-August 2026. It's already inflation-adjusted. Which one would you rather have?

All-World 60/Managed Futures 40 outperforms All-World 100 over long time horizon

TL;DR:

  • "VWCE and chill" can be a reasonable approach, especially for younger investors. However,  it only diversifies across equities. It doesn't diversify across other asset classes. This can be problematic in some scenarios that may - and almost certainly will - happen at some point in your life.
  • Managed Futures make a strong case as a more robust form of ballast (asset diversification) than the government bonds traditionally used in long-term, balanced portfolios.
  • Across the backtests presented here, portfolios using managed futures produced higher real returns and better risk-adjusted results than comparable portfolios using bonds. 
  • The outperformance came outside of crises, while during crises, managed futures performed about the same as bonds.

What is ballast expected to do?

Most investors want the ballast portion of their portfolio to provide a counterweight to equity crashes. They put a portion of their portfolio (typically anywhere between 20% and 50%) in government bonds. When there's an equities crash, their portfolio is relatively stable and resilient because bonds may fall less than equities or even rise, moving in the opposite direction of stocks. This can be psychologically advantageous vs sitting through the full brunt of a 2008 type of horror show. For an older investor who is only one or two decades out from retirement, it can make the difference between "comfortable" and "hurting".

The recovery in 2009 and the long equities bull run that followed was lucky and historically abnormal. It may teach the wrong lesson. The scenario to be concerned about is a crash and then a protracted stagflationary scenario. Imagine seeing your €100k brokerage account crash to €40k, and then not recover for a decade. This sort of experience will be stressful for anyone. Staying the course, continuing to put money into equities - even if that is your most rational bet - may be difficult.

In a balanced portfolio, ballast can cushion the blow from an equities drawdown, and even provide growth when equities don't. Some portion of the funds preserved as ballast can also be converted into equities when those are cheap, setting the foundation for strong future growth. A sporadic rebalancing would achieve this automatically.

But are bonds really the best option for ballast?
 
 When inflation causes the trouble, bonds can fall with stocks. The same can happen when real interest rates rise, government borrowing pushes up the term premium, or markets lose confidence in fiscal and monetary policy. The 2022 joint stock-bond decline provides a recent nominal example. The 1970s provide a longer example of inflation damaging the real returns of both assets.

So what now?  

A different form of ballast

Managed Futures funds spread their investments across several asset classes, including  government bonds, commodities, currencies, and also stocks. Most of these funds use trend-following models that look for sustained price movements. If an asset has been rising, the fund may take a long position and benefit from further gains. If it has been falling, the fund may take a short position and benefit if the decline continues. This gives managed futures a source of return that does not depend on stocks going up. The academic literature on trend-following offers strong evidence across many markets and long samples. It is one of the most thoroughly documented sources of alpha.

Managed Futures funds gain their exposure through futures, which are standardized contracts traded on regulated exchanges. So, rather than holding the actual assets, these funds hold contracts that are tied to the assets and therefore reflect the prices of assets, too. This makes it possible to move between markets and take both long and short positions without buying each asset itself. The fund handles the contracts, collateral and risk controls, while the investor owns an ordinary fund or ETF. The trading inside such funds happens algorithmically, and doesn't normally involve human discretion.

Futures contracts have existed for a long time. The first futures exchange in history was launched for rice contracts in 18th century Japan. The Chicago Board of Trade became the first futures exchange in the West in the 19th century. Futures have since become a pillar of commodities and financial markets.

Managed Futures investing strategies are called "managed" because a professional manager oversees the trading of potentially dozens of different futures contracts and the risks that are involved in that. It would be very difficult for a retail investor to do this by themselves.

Managed Futures became available to institutional investors in the 1980s. In the 2010s, some US mutual funds made them accessible to small retail investors. In the 2020s, several ETFs have become available, primarily in the US, but also in Europe.

As I will document below, Managed Futures produce greater returns than bonds over long time horizons and are a more robust form of diversifcation.
 
 Fair warning though: trend-following strategies can suffer when markets move back and forth without direction. They can enter a sudden crash when new trends establish. Their positions may detect the  change in trend too late, and they may be stuck in a losing position for some time. It is precisely this risk why such funds trade across many different assets at the same time. That reduces the risk of a major fund-wide crash.

While not an iron-clad guarantee for a greatly improved return during an equities crash, the multi-asset, long/short nature of managed futures means that fully participating in an equities crash is far less likely than with simple long equities exposure. In comparison to a straight bond fund, managed futures also have a much broader opportunity set.

Consider the below major economic scenarios and how bonds and managed futures behave in each.

Market Condition Gov't Bonds Managed Futures
Fast recessionary crash Strong if yields fall (they usually do) Unlikely to fall at the same rate as equities and may even rise
Slow equity bear market Strong if yields fall Can adapt as trends form and make money
Inflation and rising rates Typically poor protection Can profit from shorting bonds and from commodity trends
Stagflation Typically poor protection Broad opportunity set
Deflationary recession Strong Can adapt as trends form and make money, including from long bonds positions
Fiscal or monetary credibility shock May fall together with stocks Broad opportunity set

 OK, but how well do such strategies perform in practice?

What the backtests show

All backtests were performed on testfol.io which provides simulated or reconstructed data for many funds prior to their inception dates. They're based on the index data that underlie the funds, or on roughly equivalent predecessor mutual fund data. Typically, such data series include the fees that the actual ETFs charge. To be clear: every backtest here relies on backfilled data. So these tests are based on reasonable approximations, not on live ETF data.

All backtests in the article start with an initial investment of US$10,000 and have no further contributions; portfolios are rebalanced annually; returns are inflation-adjusted. All backtests are performed with US data series and are on a USD basis, simply because there is far more to work with than with European data. A 60/40 split between equities and ballast is generally considered a cautious allocation, which is why I go with that.

The first test runs from 2000 to mid-August 2026, and uses a simulated history for the US ETF "DBMF" as the managed futures portion. It begins in 2000 because that's when the data series available begins. I don't choose any particular dates here, I just run the test across the entire available history. It includes the dotcom drawdown, the 2008 disaster, and the Covid blurb. It also contains both bond bulls and bears, and managed-futures bulls and bears.

DBMF is also available as a European UCITS ETF: 
 https://www.justetf.com/en/etf-profile.html?isin=LU2951555403#overview

Here's the backtest results from testfol.io, Please remember that this is inflation-adjusted.

Name Ending Value Cumulative Return CAGR Max DD Longest DD Volatility Sharpe
All-World 60/Bonds 40 $25,993 159.93% 3.65% -38.64% 4.69y 11.58% 0.43
All-World 60/DBMF 40 $36,508 265.08% 4.99% -36.37% 3.75y 12.23% 0.51
All-World Only $32,229 222.29% 4.50% -59.02% 6.16y 19.19% 0.36

Reference: https://testfol.io/?s=h0XhhWtftU1

As you can see, the All-World/Managed Futures combo beats not only the All-World/Bonds portfolio but even the 100% All-World one. And look at the maximum drawdown! Look at the Sharpe ratio! And note how bonds provided similar drawdown protection to managed futures but significantly smaller returns!

Onward!

DBMF is my preferred Managed Futures proxy for backtesting purposes. It reverse-engineers the aggregate return stream of the SG CTA Index that tracks twenty different managers. This disperses manager and model luck. However, KMLM - a single-index fund - has the longer simulated history. It's a reasonable substitute for DBMF for testing purposes.
 
Let's test it over its entire history, all the way from 1988 to current. Again, returns are inflation-adjusted. Since 1988, the 60/40 All-World + KMLM portfolio had the highest real CAGR at ~6%, compared with 4.5% for All-World + Bonds.

This is the longest test in the review, starting in 1988, running to the current date. It's what the image I shared at the beginning of the article shows.

Name Ending Value Cumulative Return CAGR Max DD Longest DD Vol Sharpe
All-World 60/Bonds 40 $63,413.26 534.13% 4.90% -38.85% 4.87y 10.87% 0.48
All-World 60/KMLM 40 $102,267.68 922.68% 6.21% -32.18% 3.56y 11.02% 0.58
All-World 100 $93,137.94 831.38% 5.95% -59.02% 6.16y 17.78% 0.40

Reference: https://testfol.io/?s=cnl63cDBWjH

As you can see, over this longer period, too, and swapping one managed futures fund for another, managed futures beat bonds hands-down, and even perform somewhat better than "VWCE and chill".

This isn't evidence that an equities/managed futures combo will always beat 100% equities. Sequence of returns and timing luck play a big role. But for our purposes here, we shouldn't be interested in the best case scenario for All-World equities. We should be interested in the worst-case and middling scenarios. Which is precisely what we got in the two tests conducted so far. 

Next, let's zoom in on the effect of managed futures during equities crashes.

First, the dotcom drawdown, January 2000 through December 2002.

Name Ending Value Cumulative Return CAGR Max DD Longest DD Vol Sharpe
All-World 100 $5,702 -42.98% -17.11% -50.97% 2.77y 19.36% -0.94
All-World 60/Bonds 40 $7,890 -21.10% -7.61% -28.35% 2.77y 11.18% -0.76
All-World 60/DBMF 40 $7,974 -20.26% -7.29% -26.40% 2.77y 10.72% -0.77

Reference: https://testfol.io/?s=bCBtVwkt1n1

Toss-up. DBMF and bonds did about equally well. Equities suffered horrendously.

Now the Lehman shock-induced crash. January through December 2008.

Name Ending Value Cumulative Return CAGR Max DD Longest DD Vol Sharpe
All-World 100 $6,016.22 -39.84% -39.94% -52.56% 1.00y 42.78% -1.00
All-World 60/Bonds 40 $7,714.89 -22.85% -22.92% -35.20% 0.99y 25.83% -0.93
All-World 60/DBMF 40 $7,913.78 -20.86% -20.93% -32.18% 0.63y 23.91% -0.91

Reference: https://testfol.io/?s=8gTwoRSKlZ6

Again, same picture and basically a toss-up.

Looking at these two tests and comparing them with the earlier DBMF test, you can see that the equities + managed-futures combo outperforms the equities + bonds combo on returns over long time horizons mostly because of better returns outside crises. 

*****

I should also briefly touch on the worst periods for Managed Futures. Since 2000, both KMLM and DBMF had weak periods between 2004 and 2008, between 2009 and 2014, and between 2016 and 2022. KMLM has also shown weakness since 2023. Of course, all of these were periods of strength for equities, so overall portfolio performance was excellent, even as Managed Futures slacked off. That's what one would expect. In periods of strong equities performance, returns from trend-following in other asset classes should tend to weaken as more and more capital flows into equities. When equities weaken, as happened in 2022, managed futures should perk up. Historically that has proven to be true.

Should managed futures replace bonds in balanced portfolios?

My conclusion is: for the long-term ballast role in a general investment portfolio, Managed Futures is the more robust choice than bonds. Managed Futures cover a greater variety of adverse scenarios, and have more tools at their disposal to react to them.

Nothing is to stop a cautious investor from combining Managed Futures with bonds (and/or gold) in their portfolio.

Another option is to hold managed futures and gold as the standing ballast, then add bonds when some kind of macro-economic rule identifies a disinflationary regime. Such a rule could use inflation expectations, government bond price trends, growth data, and central-bank policy. It may make sense to fund that position by reducing equities rather than dismantling the managed futures sleeve.
 
 Investors today can consider themselves fortunate that they have access to a form of ballast that was largely unavailable even a decade ago. For European investors, the iMGP DBi Managed Futures fund is an attractive option to consider.

*****

By the way, I'm working on a DIY hedge fund concept for retail investors. I will be publishing and tracking that experiment publicly at www.hedgefol.io. Investors looking for ways to improve returns over static allocations while managing risks actively may find this interesting. It's free of charge! The original position paper that this post is based on was written as part of the hedge fund project.

(This article is a conceptual study, not investment advice, and contains no prediction of outcomes. I have no commercial incentive, such as commissions or affiliate fees, to promote any of the funds used in this article. I hold the iMGP DBi Managed Futures fund in my own portfolio. )

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u/otto_delmar — 2 months ago
▲ 4 r/investing

Bonds vs Managed Futures + Tail Hedge

I've been working on a policy manual for a DIY hedge fund concept. As part of the process, I wrote a position paper on ballast. It's my way to make sure I'm not being sloppy in my reasoning. Thought I'd share, maybe this helps some of you review your own thinking about this.

TL;DR:

  • Managed futures make a strong case as a more robust form of ballast than government bonds for a long-term diversified portfolio.
  • Across the backtests presented here, portfolios using managed futures produced higher real returns and better risk-adjusted results than comparable portfolios using bonds. 
  • The outperformance came outside of crises, while during crises, managed futures performed about the same as bonds.
  • Adding an options-based tail hedge such as CAOS is a more experimental proposition. Its crisis gains may need to be actively harvested before they disappear during the recovery.

Bond funds have been available for a long time, managed futures and tail hedge funds, not so much. They're available now, though, thus the case for a Managed Futures + Tail Hedge combo seems worth investigating.

(By the way, I'm talking about long-term saving and retirement investing here. This isn't about cash needed next year or a bond ladder for known expenses.)

What is ballast expected to do?

Most investors want the ballast portion of their portfolio to provide a counterweight to equity crashes. When there's a crash, their portfolio is relatively stable and resilient. This can be psychologically advantageous vs sitting through a Lehman shock type of horror show. Some - or all - of the funds preserved in bonds can also be converted into equities when they're depressed. That's the explicit rationale. Implicitly, most also want that ballast to produce a positive real return during calm markets. The more, the better.

High-quality government bonds have often been strong partners for equities. This was true for much of the period from the 1980s through 2020. Which was a secular bond bull market, at least for US government bonds, and which is all most everyone investing today remembers.
 
 Look just beyond either bookend of that period, and the picture changes.

When inflation causes the trouble, bonds can fall with stocks. The same can happen when real interest rates rise, government borrowing pushes up the term premium, or markets lose confidence in fiscal and monetary policy. The 2022 joint stock-bond decline provides a recent nominal example. The 1970s provide a longer example of inflation damaging the real returns of both assets.

Consider the below major economic scenarios and how bonds, managed futures and an options-based tail hedge behave in each.

Market condition Government bonds Managed futures Tail Hedge
Fast recessionary crash Strong if yields fall (they usually do) May (or may not) enter with the wrong trend-based positions and need time to adapt Explosive returns
Slow equity bear market Strong if yields fall Can adapt as trends form Helps only if & when stocks fall fast
Inflation and rising rates Typically poor protection Can short bonds and profit from commodity trends Helps only if & when stocks fall fast
Stagflation Typically poor protection Broad opportunity set Helps only if & when stocks fall fast
Deflationary recession Strong Can adapt, but may lag Helps only if & when stocks fall fast
Fiscal or monetary credibility shock May fall together with stocks Broad opportunity set Helps only if & when stocks fall fast

A different form of ballast

Managed Futures funds obtain most of their market exposure through futures and other derivatives rather than by owning the underlying assets. They still hold collateral, usually in Treasury bills or cash-like instruments. Using futures for exposure can lower trading frictions and make it easier to short an asset. This is the most important characteristic of these funds in my quest for the optimal portfolio ballast:  because they can go both long and short across several asset classes, they have a much wider opportunity set than a bond fund. The fact that they don't trade the underlying assets may deter some investors who prefer to hold "real things". My own view is that these futures amount to effective exposure to the underlying assets, and the caveats concerning this type of instrument are acceptable.

Trend-following is the primary source of alpha in Managed Futures (there are others, like mean reversion, carry, etc., but trend is the dominant aspect). A trend-following fund can become short equity indexes during a bear market, short government bonds when yields rise, and long commodities during an inflationary surge. It can also become long bonds during a deflationary recession.

The academic case for trend (aka time-series momentum) has evidence across many markets and long samples. But trend strategies can suffer when markets move back and forth without direction, and they can enter a sudden crash with positions inherited from the prior regime. A fast decline followed by a fast recovery can hurt them in both directions.

While not a guarantee for a greatly improved return during a crash, the multi-asset, long/short nature of managed futures means that fully participating in an equities crash is far less likely than with simple long equities exposure. Some managed futures ETFs, like Simplify's "CTA", don't even include equities in their universe. Nevertheless, such a fund could, with some bad luck also fall drastically during an equities crash, until a new trend regime reshapes its positions.

This initial reaction problem is where tail-hedge ETFs, in particular "CAOS", enter the picture (investors who prefer a tail hedge denominated in currencies other than the USD or tied to an index other than the S&P500, please see the note at the end of this article). 

CAOS holds S&P 500 put options and cash or cash-like instruments. It's designed to produce explosive returns during a fast and severe crash. During calm or sideways markets, or even slow, multi-year declines, it tends to produce a cash-like return. At least, that has so far been the case. Unlike managed futures, it doesn't need to detect a new trend before its protection begins to work. If the S&P 500 falls far and fast enough, the puts gain value through their contractual payoff and the rise in implied volatility that tends to accompany equity crashes.

Because of this, a combination of a managed futures ETF like KMLM, DBMF or CTA with CAOS is interesting. The two funds cover different problems. CAOS is there for the sudden break, when managed futures may still be positioned for yesterday’s market. If the crash turns into a prolonged bear market, managed futures have time to adapt.

Some form of portfolio-level profit-taking or threshold rebalancing is required to capture the full benefit of CAOS. The fund can otherwise give back much of a crisis gain as markets recover - as it did following the 2020 crash.

OK, so does the combination outperform bonds in backtests?

What the backtests show

All backtests were performed on testfol.io which provides simulated or reconstructed data for many funds prior to their inception dates. They're based on the index data that underlie the funds, or on roughly equivalent predecessor mutual fund data. Typically, such data series include the fees that the actual ETFs charge. To be clear: every backtest here relies on backfilled data. So these tests are based on reasonable approximations, not on live ETF data.

Unfortunately, we cannot backtest CAOS before 2013. CAOS itself only launched in 2022. Its mutual fund precursor had its inception in 2013. Simulated data that go back further aren't available. So, we have no direct evidence of how CAOS would have behaved in serious market trouble like the Lehman shock. S&P 500 puts made a bundle during the sharpest part of 2008, so a CAOS-like strategy would have had the right basic setup, and would presumably have produced a positive return in that year. Notably, the mutual fund precursor of CAOS worked as intended in the brief but drastic 2020 Covid shock.

I tested three portfolios with annual rebalancing and inflation-adjusted returns. Initial contribution is always $10k. The first test begins in 2013 and uses a simulated history for the ETF "DBMF" to occupy the managed futures portion. It includes the managed-futures "bear market" between 2013 and 2022, the COVID crash, and the bond bear market that followed the post-Covid inflation surge. 

Here's the backtest results from testfol.io, 2013 to current:  

Name Ending Value Cumulative Return CAGR Max Drawdown Longest Drawdown Volatility Sharpe
VT + BND $16,647.15 66.47% 4.01% -27.03% 4.13y 8.95% 0.59
VT + DBMF $22,947.62 129.48% 6.61% -20.82% 2.06y 10.53% 0.75
VT + DBMF + CAOS $20,670.64 106.71% 5.75% -17.79% 2.59y 9.80% 0.71

Reference: https://testfol.io/?s=g9sLcBV2Thj

As one might expect, since there was only a short equities crash in this period, and since we apply no profit taking to CAOS, the 50/50 VT + DBMF portfolio produced the highest return. Replacing half of DBMF with CAOS reduced the return but also produced the smallest maximum drawdown. Sharpe remained essentially equal with or without CAOS.

So the most important take-away is: managed futures did better than bonds in a period that contained both bond bull and bear periods, as well managed-futures bull and bear periods. 

Next, I tested a longer reconstruction using simulated KMLM data. While simulated DBMF data is my preferred managed-futures proxy for backtesting purposes due to it representing a replicated aggregate of a bunch of different managed futures funds, KMLM - a single-index fund - has the longer simulated history. It's a reasonable substitute. Again, returns are inflation-adjusted. Since 1988, the 50/50 VT + KMLM portfolio had the highest real CAGR at ~6%, compared with 4.5% for VT + BND. Adding SHY to the VT/KMLM portfolio (instead of CAOS) reduced drawdowns somewhat but also cost returns. This is what I would also expect to happen with CAOS and no profit-taking. With a profit-taking rule, the picture would probably change quite a bit.

This is the longest test in the review, starting in 1988, running to the current date.

Name Ending Value Cumulative Return CAGR Max Drawdown Longest Drawdown Volatility Sharpe
VT50 + BND50 $55,805.24 458.05% 4.55% -32.80% 4.09y 9.25% 0.50
VT50 + KMLM50 $99,088.27 890.88% 6.12% -28.67% 3.18y 10.16% 0.61
VT50 + KMLM25 + SHY25 $67,858.11 578.58% 5.08% -25.05% 3.46y 8.87% 0.58

By the way, a portfolio containing VT, KMLM, and BND (not in the table) returned ~5.5% with a smaller drawdown than either of those other two portfolios.

The simulated price series for DBMF starts in January 2000. Here's how the VT/DBMF combo with or without SHY has done since then. Same picture as above. Equities + Managed Futures easily beats Equities + Bonds. And again, with SHY in the mix, we don't get any substantial improvement.

Name Ending Value Cumulative Return CAGR Max Drawdown Longest Drawdown Volatility Sharpe
VT50/BND50 $23,885.42 138.85% 3.33% -32.80% 4.09y 9.82% 0.45
VT50/DBMF50 $35,614.72 256.15% 4.89% -29.55% 3.50y 10.81% 0.56
VT50/DBMF25/SHY25 $29,480.77 194.81% 4.15% -31.40% 3.64y 9.98% 0.52

Next I zoomed in on the effect of managed futures during equities crashes.

First, the dotcom drawdown, January 2000 through December 2002.

Name Ending Value Cumulative Return CAGR Max Drawdown Longest Drawdown Volatility Sharpe
VT100 $5,702.39 -42.98% -17.11% -50.97% 2.77y 19.36% -0.94
VT50/BND50 $8,524.67 -14.75% -5.19% -21.58% 2.77y 9.27% -0.66
VT50/DBMF50 $8,704.35 -12.96% -4.53% -18.27% 2.83y 9.12% -0.60

Toss-up. DBMF and bonds did about equally well.

Now the Lehman shock-induced mega crash. January through December 2008.

Name Ending Value Cumulative Return CAGR Max Drawdown Longest Drawdown Volatility Sharpe
VT100 $6,016.22 -39.84% -39.94% -52.56% 1.00y 42.78% -1.00
VT50/DBMF50 $8,441.92 -15.58% -15.63% -26.48% 0.63y 19.44% -0.84
VT50/BND50 $8,175.07 -18.25% -18.31% -30.24% 0.99y 21.88% -0.87

Again, basically a toss-up. Profit-taking with CAOS would presumably have produced a positive return in that year.

Overall, the equities + managed-futures combo outperforms the equities + bonds combo over time, mostly because of better returns outside crises.

Whether adding CAOS with profit-taking outperforms both over long periods is an open question. For example, while CAOS would have given us a gain in 2008, it may not have done much in the slower dotcom drawdown. Also, between 2009 and 2019, it would only have produced cash-like returns. Bonds may have produced better performance given the bond bull of that period.

Should managed futures + tail hedge replace bonds?

Before I give my conclusion, I should point out that the question asked here puts only managed futures on the spot. It suggests that bonds are the default and the burden of proof lies with managed futures. I see no good reason for this framing, and investors should also ask the reverse question: should bonds replace managed futures + tail hedge?

My conclusion is: for the long-term ballast role in a general investment portfolio - as opposed to some sort of liability-matching role - managed futures, or managed futures in combination with a tail hedge, is the more robust choice than bonds alone. It covers a greater variety of adverse scenarios, and has more tools at its disposal to react to them.

One might choose to forego the tail hedge portion, and either accept the cushioning effect of managed-futures alone as "best we can do", or look for some other, tactical risk management tool to further address equities crashes. (In my hedge-fund like portfolio concept, I do apply such a tool.)

Another option is to maintain managed futures and CAOS as the standing ballast, then add duration when some kind of macro-economic rule identifies a disinflationary regime. Such a rule could use inflation expectations, Treasury price trends, growth data, and central-bank policy. I would fund that position by reducing equities rather than dismantling the managed-futures and tail-risk sleeve.

****

Note for non-USD investors. European investors in particular can access a DBMF equivalent through the iMGP DBi UCITS fund. I have not found a direct UCITS equivalent to CAOS. A smaller allocation to Ellipsis Optimal Solutions Alternative Hedging combined with short-duration euro sovereign bonds may offer a comparable division between tail protection and low-risk collateral. Sizing and return profile would need separate testing.

 

u/otto_delmar — 2 months ago